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At Alteryx, we’re constantly impressed with the amazing things that you do with our software. The Alteryx Analytics Excellence Awards recognize and celebrate your best Alteryx success stories.

The submission period for the 2017 Alteryx Analytics Excellence Awards has ended. A HUGE Thank You to all who participated this year! We wish you the best of luck!

  • To browse qualified entries, click here.
  • Winners will be announced at Inspire 2017 in Las Vegas, June 7th.

For full rules and details, click here.


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Honeywell_logo.pngAuthor: Joseph Majewski - Commercial Finance Director

Team Members: Niki Yang, Larry Scates, Dan Trimble, Richard Haas, Wendy Edsall

Company: Honeywell Aerospace

Business Partner: Sherri Benzelock - VP Business Analytics

 

Awards Category: Best Use of Alteryx Server for Analytics Deployment

 

Alteryx has automated previous manual processes driving over 2000 hrs annual productivity to the Finance community. Alteryx server consolidates quarterly outlook commentary data from over 40 sources every 30 mins daily and publishes to Tableau server as a *.tde data source. Alteryx also loads extracted Essbase files to Tableau server nightly which are joined together for enhanced Tableau analytics providing insight to our quarterly forecast. 

 

Awards Category: Best Value Driven with Alteryx

 

Alteryx helped drive over 2000 annual hours of productivity and enabled over 250 users to migrate to Tableau based self- service analytics. Consumers are now better prepared for weekly Revenue outlook meetings as they have access to the data almost real-time versus waiting for Finance to prepare reports for the weekly forecast meetings. The new analytics developed in Tableau are now being referenced live in weekly meetings with business Presidents. 

 

Describe the problem you needed to solve

Significant manual effort was being done to compile business comments and associate them with the financial data on a management review dashboard in PowerPoint. Meetings were ineffective because only limited static views could be generated. The data presented was information overload and difficult to digest. There was no dynamic selection views so questions typically needed to be captured then answered and communicated after the meeting. Historical versions were saved in separate data sources and were not readily available for the reviews.

 

Alteryx flow to update the Comments which publishes the comments data source to both our Aero-Development and our Aero Certified-Production sitesAlteryx flow to update the Comments which publishes the comments data source to both our Aero-Development and our Aero Certified-Production sites

Describe the working solution

Under the new Alteryx solution, the compilation of weekly commentary files has been automated. Every week over 40 comment files are saved to a shared drive in *.csv format. Since submissions occur from all global regions, files are submitted at different times each week and are now consolidated more frequently (every 30 mins) allowing users across the globe to access the most updated information. The process also manages current week and historical weeks’ comments. We are also using Alteryx to cleanse the data removing all the error values and to correct business team data mapping issues. Alteryx also loads extracted Essbase files to Tableau server nightly which are joined together for enhanced Tableau analytics providing insight to our quarterly forecast.

 

Alteryx flow that brings in the new Essbase data (“SRO_Extract_Current.txt” and updates/appends/replaces the scenarios in the “SRO_Extract_History.txt” Data source file used by TableauAlteryx flow that brings in the new Essbase data (“SRO_Extract_Current.txt” and updates/appends/replaces the scenarios in the “SRO_Extract_History.txt” Data source file used by Tableau

Describe the benefits you have achieved

Utilizing Alteryx has resulted in initial savings of 2000+ hours per year by cleansing, compiling, and blending business commentary with financial data. Analysts are no longer manually compiling, copying, and pasting comments onto quarterly outlook presentations. Weekly review meetings became more effective as over 250 business leaders and analysts are better informed prior to as opposed to seeing the data in the meeting for the first time. This is allowing more efficient business meetings helping HON achieve its revenue growth targets.

The Alteryx dataset allows Honeywell Aerospace to slice and dice all information dynamically using Tableau which was not possible before. Questions are easily answered in the meetings because the data with variance, opportunity, and risk commentary is available prior to the meeting. Ad hoc questions are now researched live in meetings through our enhanced visualizations. Tableau’s dynamic reporting eliminated the need for Excel and PowerPoint reports thus reducing meeting preparation times as well. Business teams are now able to focus more on analytics than the data/presentation preparation. The solution we developed are making our business processes more contemporary, enabling business users quicker access in a format that is easier to consume on desktop and mobile devices ensuring they have the necessary information to make the right business decision.

 

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Visual of Tableau Analytic with associated commentsVisual of Tableau Analytic with associated comments

Catalyst logo.pngAuthor: Jason Claunch - President

Company: Catalyst

Business Partner: Slalom Consulting - Sean Hayward & Marek Koenig

 

Awards Category: Best Use of Alteryx for Spatial Analytics

 

The developed solution used many of the Spatial Analytics components available within Alteryx:

  • Trade Area – have user select target area to analyze
  • Spatial Match – combine multiple geospatial objects,
  • Intersection – cut objects from each other to create subject area
  • Grid tool – sub-divide the trade blocks to determine complete coverage of trade ring
  • Distance – use drivetime calculation to score and rank retailers in the vicinity

Describe the problem you needed to solve

Retail site analysis is a key part of our business and was taking up too much time with repetitive tasks that could have been easily automated.

 

Describe the working solution

To support selection of best-fit operators, Catalyst partnered with Slalom Consulting to develop a tool to identify potential uses to target for outreach and recruitment. Previously, we would have to manually build demographic profiles using tools like qGIS, ESRI, and others, but found the process to be cumbersome and quite repetitive. Demographic data was acquired at the trade bloc level, which was too granular for identify target locations and would not mesh well with the retail data.

 

Alteryx and its spatial capabilities was used in a few ways:

 

1) Minimize our retail data selection from the entire US to a selected state using the Spatial Match tool.catalyst1b.png

 

2) Create a demographic profile for each retail location that consisted of data points such as median income, population, daytime employees, and others. The data was aggregated around a 3 mile radius of the specific retail location with an Alteryx Macro composed of a Trade Area, Grid Tool, Spatial Match, and Summarization tool.

 

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3) Using a Map Input, the user selected an area to profile and candidate retailers were output for further review.

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4) After selecting specific retailers to do an in-depth analysis on, Alteryx would score all possible locations by distance (Drivetime Analysis) and by score (proprietary weighting of various demographic attributes). The profiled results were then used to build a client presentation; the automated profiling tool saved us countless hours and allowed us to deliver more detailed analysis for our clients.

 

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Describe the benefits you have achieved

Using Alteryx was a massive time saver, the tool that we built took a process that normally required at least 8 hours of manual work down to merely a few minutes. This has directly benefited our bottom line by allowing us to focus on more key tasks in our client outreach and recruitment. A return-on-investment was immediately realized after we were able to close a deal with a major client using our new process.

Author: Cesar Robles, Sales Intelligence Manager 

Company: Bavaria S.A.

 

Awards Category: Best Business ROI

 

Describe the problem you needed to solve

In September 30th 2015, a gossip widespread through whatsapp reduces our Pony Malta sales to 40% of normal levels. The social networks’ gossip that impacts a brand destroys brand equity and creates distrust in our customers. Our company executes a 1st stage plan that helps to spread the gossip in the first weeks to more customers increasing the crisis. In Colombia no brand had suffered an attack like this before.

 

Describe the working solution

The Alteryx solution was develop to design and decision tree that define which customers has an relevant impact in sales volume in 5 groups that allows define differentiated protocols to recover our sales in a healthy way. These 5 groups were:

 

Citizens: Actual Customers without any impact related to social network crisis.
Refugees: Customers that reduce significantly (<50%) his rate of sales related to social network crisis.
Deportees: Customers that didn’t bought our brand related to social network crisis.
Pilgrims: Customers with doubts about our products related to social network crisis.
Aliens: New customers without any impact related to social network crisis.

 

Our gap in crisis was 180k PoS (Point of Sales) impacting 92 KHl (Kilo-hecto-liters)

 

This workflow runs monthly and uses multiple sources of information in SQL server related to Customer properties and historic sales levels. We report some results in Excel and Java applications to define our performance in recovery actions. Actually we are migrating to in database process to optimize the algorithm performance and use Tableau to manage our visualization process.

 

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Figure 1. Decision Tree description

 

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Figure 2. 1st Quarter Deportees results

 

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Figure 3. 1st Quarter Refugees results

 

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Figure 4. 1st Quarter Citizens results

 

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Figure 5. Numerical Distribution Initial and End State

 

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Figure 6. Blending Workflow

 

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Figure 7. Decision Tree workflow

 

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Figure 8. Hierarchy and Priority workflow

 

Describe the benefits you have achieved

The project defines a new way to customer segmentation in our company. We use the same algorithm to define not only crisis contingence, also we used to brand expansion and price control process including geographical variables and external info of our providers (Nielsen, YanHass, Millward Brown).

 

The solution had not been implemented before Alteryx. An estimated time saving show us that initial state needs 2 or 3 weeks to develop compared with 4 or 5 days that we used in Alteryx (We just used it 1 month ago in the firs solution). Right now our response time is less than 2 days in similar solutions.

 

In Business terms, we achieve to recover 100k PoS (approximately 25% of all Colombia Market) and increase our sales in 75% of normal levels in the first 3 months. In August 2016, we recover our normal levels of sales with the trade marketing actions focused support by Alteryx workflow.

Author: Kristin Scholer (@kscholer), Insight Manager In-2C-14px.png
Company: Ansira
Awards Category: Most Time Saved

 

Ansira, an industry-leading marketing agency in integrated real-time customer engagement, activates big data through advanced analytics, advertising technology, programmatic media and personalized customer experiences. Ansira leverages superior marketing intelligence to build deeper, more effective relationships with consumers and the retail channel partners that engage them on the local level. Marketing intelligence is infused across all disciplines and executed through digital, direct, social, mobile, media and creative execution, marketing automation, co-op and trade promotion. 

 

Describe the problem you needed to solve

As a data-driven advertising agency, Ansira is constantly profiling customer behavior for a variety of clients in industries such as quick service restaurants, automotive brands and large retailers. Ansira’s Analytics team heavily utilizes media and consumer research that comes from the MRI Survey of the American Consumer to create Customer Behavior Reports. This large survey provides a vast database of demographics, psychographics, media opinions and shopper behavior that give insights into the actions and behaviors of the U.S. consumer. These insights help Ansira better understand consumers for new business perspectives as well as develop strategies for existing clients.


The challenge the Analytics team faced was that these rich insights were not easy to format, interpret or analyze. The data is accessed through an online portal and exported into an Excel format that does not make the data easy to manipulate. Depending on the project requirements, it could take an analyst 4-8 hours to format the data, identify survey responses that are statistically significant, build out a report to display all the information and write up a full summary. This is not cost effective and it became clear that a better way to transform this data was needed if Ansira wanted to utilize it on a regular basis.

 

Describe the working solution

After using Alteryx to format unfriendly Excel output for many projects, it was clear to the Analytics team that Alteryx could also be a solution for speeding up the Customer Behavior Report process. In about two days, one team member was able to create an Alteryx workflow that did all of the Excel formatting in just three seconds (this was generally taking over an hour to do manually).

 

Then Alteryx was taken one step further as formula tools were integrated to identify which behaviors were statistically significant for an analysis (this was taking 1-2 hours to work through manually). Next, the process was simplified one more time by incorporating the reporting tools to create a full report of all the data needed in the form of a PDF. The report even included color coding to easily identify statistically significant behaviors. Not only did this create a beautiful report in seconds but made key behaviors easy to identify, thus taking the analysis and summary process from 2-3 hours down to 15-30 minutes.

 

Describe the benefits you have achieved

The process that was created in Alteryx has allowed the Ansira Analytics team to offer Customer Behavior Reports to New Business and Strategy departments that can be turned around in a day instead of a week. If a full analysis is not needed, the Analytics team can turn around just the PDF data report in as little as 15 minutes (see picture below). This allows Ansira to gain additional direction on who is the consumer they are targeting, which can be instrumental in creating a new campaign.

 

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To make this process even easier, the Analytics team has created a request form (see picture below) that anyone at Ansira can use to identify the behaviors they are interested in seeing for their client. Once the request form is received by the Analytics team, they can do a quick data pull from the MRI online portal, update the Alteryx workflow and have a full report created in under an hour.

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Ansira recently had a consumer packaged goods client where the Strategy team needed to learn more about the difference in behavior patterns between Millennials and Non-Millennials who purchased 16 specific products. The Analytics team was able to pull data from MRI on these 16 products, run it through the Customer Behavior Report workflow and create 16 individual reports for each product comparing Millennials and Non-Millennials purchase behaviors in less than 4 hours. Without Alteryx, this would have taken a single analyst almost a full week to complete and likely would have never even been a possibility due to budget and capacity constraints.

 

Creating these Consumer Behavior Reports have become a regular occurrence with two to three requests coming into the Analytics department each week. With the help of Alteryx, these reports have become a large asset to Ansira as they provide very impactful information without a lot of effort.

Author: Kunpeng Zhang - Sr Quality Control Analyst & Lam Truong

Company: Southwest Airlines Co.

 

Awards Category: Best Value Driven with Alteryx

 

This project aimed to figure out our station level workload and data sampling plan. Our team, Quality Control, conducts monthly audits at a fixed number per month, which varies by station type (heavy station, intermediate station, and line station). Each station handles different types and volumes of work. Assigning the right amount of audit work that aligns with work load capacity provided a better way to collect the work samples we needed to evaluate each stations performance.

 

To make the audit quantity decision, the analysis involves combining work load, work schedule, staffing, and performance score data. The data required to complete this project exists in totally different environments, which makes it difficult to combine and mix. This is where Alteryx jumped in and helped tremendously to solve the problem. Previously, our analysis was arbitrarily set to review 4, 8, and 12 year histories. However, as time went by, the workload began to vary greatly for each station every month. The workflow we created enabled us to increase data collection without interfering with each stations work capacity.

 

Prior to Alteryx, this process had been handled by Excel, Access, Oracle PL developer and Python, which altogether made it difficult to implement and manage. After adopting Alteryx, we are able to focus on the algorithm and data analysis, as opposed to manually building connections between disparate data sources.

 

Describe the problem you needed to solve 

Scattered data sources, slow calculation speed, multiple tools and platforms.

  

Describe the working solution

Alteryx performs three basic jobs for this project:

  • Data connector
  • Model builder
  • Task runner

Alteryx consumes data from a cloud platform, an enterprise data warehouse, and department reports, processes and mixes them via model, then exports the results back to the cloud platform (From: Text files, Quickbase, Teradata, SqlServer, and Excel, TO: QuickBase)   

 

Describe the benefits you have achieved

I can quickly test an idea or a model using Alteryx, which saves me around 70% of the time I would have spent coding calculations on massive amounts of data. It also enables and tolerates different data sources from other departments, which are typically out of my control.  This allows me to focus on the analyses rather than spending time connecting to the data.

SummarySummary

 

 Wizard Work Load By Task CardsWizard Work Load By Task Cards

 

Work Load From SBX by HoursWork Load From SBX by Hours

 

Staffing From QuickBaseStaffing From QuickBase

 

Compliance Score from ExcelCompliance Score from Excel

 

Write Back To QuickBaseWrite Back To QuickBase

84.51_logo.pngAuthor: Michael Carrico (@mcarrico) - Senior Analyst

Team Members: Justin Conley, William Storey, Jeff Bevan

Company: 84.51°

 

Awards Category: Best Value Driven with Alteryx

Following our POC with Alteryx, analysts began to socialize the automation benefits and workflows that not only saved an estimated 6,300 hours across 15 projects (realized and projected hours), but that also align with our company mission of making people’s lives easier.  Further detail pertaining to the value driven by Alteryx can be seen in the Benefit section below.

 

Awards Category: From Zero to Hero

Using the workflow outlined in the Business Challenge section below as well as other exploratory work in our POC, we were able to see the potential of Alteryx to solve challenges within our analytical organization. As a result of these POC projects, we have launched a series of initiatives outlined in the Solution section below to empower employees across 84.51° to realize the benefits offered by Alteryx.

 

Describe the problem you needed to solve

The original use case for Alteryx within our organization was for geospatial analysis by a small team within our larger analytics organization. After great success in the geospatial space, this team setup a proof of concept for a select number of members of our insights and engineering organizations to investigate the value of the software outside of its geospatial capabilities. One of the projects in our pipeline required automating a PowerPoint deliverable that pulled from a large number of sources located in multiple technologies (i.e. CSV, Excel, SQL Server, Oracle). The team decided to evaluate Alteryx to tackle this automation task, and the result is an ever-evolving analytic app that leverages a wide array of Alteryx tools as well as custom development called within the workflow. Through this combination, we were able to not only glue together many disparate data sources, but to create a solution that achieved end-to-end automation of a PowerPoint deck in ten minutes, which would previously have taken days for our client leads to complete. In the process of making this Frankenstein workflow, the team gained exposure to analytic apps, batch and iterative macros, and most importantly, the Run Command tool, which enabled us to call a custom framework developed in-house to populate templated PowerPoint decks with category-specific data without any manual intervention. In the end, it is estimated that this single workflow saves approximately 450 hours annually of repetitive, mindless work; more importantly, though, it exposed our wider analytical organization to all of the power and possibilities that Alteryx offered. Many of the lessons learned in this first workflow would go on to guide future development opportunities and automation that have resulted since.

 

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Describe the working solution

After understanding the power of the tools included in Alteryx as well as the ability to extend them using packaged macros and the Run Command tool, we have continued to find projects well-suited for Alteryx automation. Dreaded data refreshes and endless email exchanges have evolved into scheduled workflows and analytic apps, and our analysts have taken notice. Recognizing this increased interest, the members of our POC began to lay the groundwork for equipping our entire insights organization with the capacity to perform analysis and automation within Alteryx.

 

Our "A-Team" has since been formed to create Alteryx trainings, standards, a macro library, learning groups, reference guides, video tips, and template workflows to continue spreading the Alteryx love across not only our analytical organization, but our client line and IT groups as well. By supplementing the existing abilities of Alteryx with these company-specific resources, Alteryx has now become a standard starting point for many of our automation tasks, and the success stories that are being shared in company-wide meetings have generated enough buzz that our user base has more than doubled in the last 6 months. In addition to automation, our larger user base is continuing to push the software in other directions such as incorporating new data sources, modeling, and building on the geospatial success that started Alteryx in our organization.  

 

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Describe the benefits you have achieved

Alteryx has enabled our analysts to think differently about the way that they approach projects. Before Alteryx, several different technologies may have been required to execute various phases of projects. For example, a typical analyst execution plan may have included SQL to pull data from the warehouse, the analyst's programming language of choice to transform/analyze/model that data, and then into Excel/Tableau/PowerPoint for telling the story. This fragmented technology stack caused for inefficiencies in data movement from start to finish, as well as an analytical plan that was not set up for repeatability. Alteryx has facilitated the consolidation of each of these steps into a single workflow that can quickly be flipped from a custom insights project to an analytic app that can either be made available for our client associates to order themselves without analyst intervention or setup on a scheduler to allow periodic refreshes of deliverables.

 

Members of our insights organization have created analytic apps and workflows that have either saved or are projected to save the organization an estimated 6,300 hours across 15 projects. To put that in perspective, that is over 8.5 months of savings or approximately 3 FTEs over just 15 projects. Many of these hours are recurring so we will continue to realize these savings as these workflows are sustained and enhanced. Furthermore, these automation tasks are not only saving time, but they are enabling members of our analytic and client organizations to work more efficiently now that tasks which previously took days are reduced to hours, and those that took hours are reduced to minutes. Through our training, standardization, and enablement initiatives, we are hoping to scale these benefits from 15 projects spread across 7 analysts to countless projects spread across 150 analysts.  

Amway logo.jpgAuthor: Adam Rant (@Rant) - Business Systems Analyst

Team Members: Tom Madden, Jordan Howell, Brian Conrad, Megan Lilley & Sankar Mishra

Company: Amway

Business Partner: Marquee Crew (@MarqueeCrew)

 

Awards Category: From Zero to Hero

 

Global Procurement at Amway began its journey with Alteryx by purchasing 2-licenses in October of 2015. We generated instant value from this tool, and knew there was so much untapped potential. A few short months after our initial purchase, we started hosting internal Alteryx enablement sessions to spread the word throughout our organization. It wasn’t long before we were up to 10 licenses. As our scope continued to expand, IT got involved and pursued a mini-trial that included Server for the remainder of 2016. In 2017 we purchased the full year pilot to drive even greater user adoption. Today, we have over 30 users and growing!


As our user base and experience with Alteryx grows, we have evolved from diverse data blending to language translation and normalization. We are moving from old legacy tools like Access and Excel, into the modern world of Analytics with Tableau and Alteryx. We are structuring data that we once thought to be impossible, and even built out applications to search online E-bay and now Amazon. We are pushing into the world of predictive analytics. Models are being developed and geospatial tool-sets are being examined. Most of these were pipe dreams before we were introduced to Alteryx. Now we are making these things come to life and blazing a trail for Analytics at Amway.

 

Describe the problem you needed to solve

We found Alteryx through Tableau. It started with simple Data blending and Tableau data automation, but grew from there.

  • Automating Manual Scorecards/metrics
  • Translation Macro using Google Translate
  • E-Bay Web Scrap
  • Amazon Web Scrap
  • Commodity Predictive Modeling
  • Spatial

 

Describe the working solution

We are using a wide range of data sources including excel, access, SQL Server (In DB tools), Oracle (In DB tools), SharePoint, Google Sheets, E-bay, and Amazon. Most of our data sets are published directly to Tableau Server. We have Server up and running to automate most of our Tableau Dashboards. Alteryx Gallery for deploying Apps is our next project to tackle. 

 

Describe the benefits you have achieved

Alteryx is the engine that is driving our team to new levels. We are automating all of our scorecards from a data perspective. We are able to provide daily insights on the health of our supply chain verses monthly reporting. Here are a few of the major projects we accomplished inside of Alteryx.

 

  1. Automated over 20 data processes, eliminating over 350 hours of data prep, and saving over $80,000 annually.
    • These savings are simply based on time savings. Factor into it the ability to run these workflows daily and deliver insights to our users and this is very conservative.
    • These workflows are now reusable processing engines that we can continue to enhance and build off of.
  2. Using Alteryx we are able to automate the translation of data. We operate in over 100 countries. Using Alteryx we developed a workflow that can go through our data and translate it automatically using Google translate. We plan on deploying this on our Server as an App for others to leverage.
  3. Jordan Howell eliminated a custom Access Database that cost us $3 Million dollars to build. This process eliminates 40 hours a month in manual data preparation due to the database, and will save us $24,000 a year. In 3 weeks he was able to recreate the database in Alteryx.
  4. E-bay & Amazon web scrapping to effectively audit Amway products that are being sold on these sites. Before Alteryx we manually accomplished this, and we would only get 1-10% of the total products on the sites (We had trouble answering questions at a Macro level about how many products). After Alteryx we can do this in seconds and have 100% of the products. Allowing users to focus on delivering insights from the data and not having to pull it!

 

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Author: Michael Barone, Data Scientist
Company: Paychex Inc

Awards Category: Best Use of Predictive

 

Describe the problem you needed to solve

Each month, we run two-dozen predictive models on our client base (600,000 clients). These models include various up-sell, cross-sell, retention, and credit risk models. For each model, we generally group clients into various buckets that identify how likely they are to buy a product/leave us/default on payment, etc. Getting these results into the hands of the end-users who will then make decisions is an arduous task, as there are many different end-users, and each end-user can have specific criteria they are focused on (clients in a certain zone, clients with a certain number of employees, clients in a certain industry, etc.).


Describe the working solution

I have a prototype app deployed via Alteryx Server that allows the end-user to “self-service” their modeling and client criteria needs. This is not in Production as of yet, but potentially provides great accessibility to the end-user without the need of a “go-between” (my department) to filter and distribute massive client lists.

 

Step 1: ETL

  • I have an app that runs every month after our main company data sources have been refreshed:

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This results in several YXDBs that are used in the models. Not all YXDBs are used in all models. This creates a central repository for all YXDBs, from which each specific model can pull in what is needed.

  • We also make use of Calgary databases as well, for our really large data sets (billions of records).

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Once all the YXDBs and CYDBs are created, we then run our models. Here is just one of our 24 models:

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  • Our Data Scientists like to write raw R-code, so the R tool used before the final Output Tool at the bottom contains their code:

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The individual model scores are stored in CYDB format, to make the app run fast (since the end-user will be querying against millions and millions of records). Client information is also stored in this format, for this same reason.

 

Step 2: App

  • Since the end-user will be making selections from a tree, we have to create the codes for the various trees and their branches. I want them to be able to pick through two trees – one for the model(s) they want, and one for the client attributes they want. For this app, they must choose a model, or no results will be returned. They DO NOT have to choose client attributes. If no attribute is chosen, then the entire client base will be returned. This presents a challenge in key-building, since normally an app that utilizes trees only returns values for keys that are selected. The solution is to attach keys to each client record for each attribute. My module to build the keys in such a way as I described is here (and there will be 12 different attributes from which the user can choose):

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  • Here is what the client database looks like once the keys are created and appended:

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  • The model keys do not have to be as complex a build as client keys, because the user is notified that if they don’t make a model selection, then no data will be returned:

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  • Once the key tables are properly made, we design the app. For the model selection, there is only one key (since there is only one variable, namely, the model). This is on the far right hand side. This makes use of the very powerful and fast Calgary join (joining the key from the pick-list to the key in the model table). For the client table, since there are 12 attributes/keys, we need 12 Calgary joins. Again, this is why we put the database into Calgary format. At the very end, we simply join the clients returned to the model selected:

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Step 3: Gallery

  • Using our private server behind our own firewall, we set up a Gallery and Studio for our apps:

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  • The app can now be run, and the results can be downloaded by the end-user to CSV (I even put a link to an “at-a-glance” guide to all our models):

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  • The user can select the model(s) they want, and the scores they want:

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And then they can select the various client criteria:

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Once done running (takes anywhere between 10 – 30 seconds), they can download their results to CSV:

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Describe the benefits you have achieved

Not having to send out two dozen lists to the end-users, and the end users not having to wait for me to send them (can get them on their own).  More efficient and streamlined giving them a self-service tool.

Ford logo.pngAuthor: Luojiao Shen (@Beta) – Business Analyst

Team Members: Dan Totten, Celia Ortiz, David Harris, Matt Shanku, Mario Beasley, Rong Jing

Company: Ford Motor Company

 

 

Awards Category: From Zero to Hero

 

Our Ford IT Analytics Architecture Team started to work on an Enterprise Technology Refresh Program in Feb 2016. The objective was to simplify the technology footprint and the tech renewal process across the Enterprise in support of our IT Strategic Initiatives.


We use Alteryx to blend dozens of data sources in order to prepare the data for analysis and visualizations. Our team was trained within the company and able to use Alteryx in a week. We use Alteryx to visualize the current state of technology and application portfolios in order to gain insights. Additionally, we perform data analysis for resource prioritization and planning of Tech Refresh activities.

 

Describe the problem you needed to solve

Initial Business problem: In order to visualize the Technology Footprint, it was necessary to pull data from multiple data sources across our large, complex IT organization. Technology is moving faster than ever before. Ford IT needs to stay ahead of this rapid pace in order to satisfy its customers with premium information based services and exceptional mobility products. Additional use case: Technologies of servers, applications, appliances, database components, hosting landing zones, etc. need to align with rapid delivery of capabilities to the business

 

Describe the working solution

Our inputs came in every imaginable format. Dozens of repeatable workflows and macros were created to blend, ingest and process data to drive sound decision making. InDB tools were utilized for Hadoop Big Data transformation and output to visualizations in Tableau and QlikView.

 

Describe the benefits you have achieved

Our data blending processes are now automated using Alteryx macros to generate multiple reports and visualizations. Alteryx enabled self-service and object-based trouble shooting, data preparation and blending.

 

The user friendly interface helps us to rapidly modify or create new workflows.

  • Time saving – processing time is reduced by 80%
  • Reduction of errors – is experienced due to the process now being automated
  • Business customer satisfaction has increased due to improved efficiency, quality and rapid delivery of actionable metrics

 

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Author: Wayne Franklin, Student Experience Evaluation Officer

Company: Charles Darwin University

 

Awards Category: Most Time Saved

 

The time saved mainly effects my workload; this in turn allows me to work on other projects for the department which helps the overall organisation. Being a smaller organisation our resources are limited so any time saved makes a significant difference to our overall output. Using Alteryx has quite often saved days of manual work and it significantly reduces the risk of errors.

 

Describe the problem you needed to solve 

The issue we faced was how best to amalgamate multiple data tables into three new unique excel files to be used by a 3rd party survey tool - Blue eXplorance. While this doesn't sound too difficult, it becomes very time consuming when there are thousands of rows of data in each data source. Being a smaller educational institutional I am at present the only person that works with all the setting up, running and reporting of all the surveys within the university; spending a day or two stuck on setting up one survey can have a detrimental effect on other projects.

 

Describe the working solution

The old way of doing things: Download each of the data sources which included the full student and unit information for a given semester. This was followed by a series of pivot tables, copy pasting, creating new fields, making things more meaningful (i.e. change 'M' to Male - not much but supervisors like it better that way). Once all that was done I would eventually end up with clean unit, student and relationship files that are set up to be used for 3rd party survey software. This doesn't sound like much but is quite time consuming. I got pretty good at excel formulas which helped cut the time down a little, but still took a day messing around in excel to get the final product. The new way to do things: click run on the Alteryx app I made, wait a minute, done! The app I created allows me to select the files to upload and where to save the output files at the end.

 

Describe the benefits you have achieved

What started as a solid day or two work is now reduced to a minute wait time as the Alteryx app is running. This frees me up to continue work on other projects I am working on and be a more productive member of our team.

Author: Katie Snyder, Marketing Analyst

Company: SIGMA Marketing Insights

 

Awards Category: Most Time Saved

 

We've taken a wholly manual process that took 2 hours per campaign and required a database developer, to a process that takes five minutes per campaign, and can be done by an account coordinator. This frees our database developers to work on other projects, and drastically reduces time from data receipt to report generation.

 

Describe the problem you needed to solve 

We process activity files for hundreds of email campaigns for one client alone. The files come in from a number of different external vendors, are never in the same format with the same field names, and never include consistent activity types (bounces or opt-outs might be missing from one campaign, but present in another). We needed an easy, user-friendly way for these files to be loaded in a consistent manner. We also needed to add some campaign ID fields that the end user wouldn't necessarily know - they would only know the campaign name.

 

Describe the working solution

Using interface tools, we created an analytic app that allowed maximum flexibility in this file processing. Using a database query and interface tools, Alteryx displays a list of campaign names that the end user selects. The accompanying campaign ID fields are passed downstream. For each activity type (sent, delivered, bounce, etc), the end user selects a file, and then a drop down will display the names of all fields in the file, allowing the user to designate which field is email, which is ID, etc. Because we don't receive each type of activity every time, detours are placed to allow the analytic app user to check a box indicating a file is not present, and the workflow runs without requiring that data source.

 

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All in all, up to six separate Excel or CSV files are combined together with information already existing in a database, and a production table is created to store the information. The app also generates a QC report that includes counts, campaign information, and row samples that is sent to the account manager. This increases accountability and oversight, and ensures all members of the team are kept informed of campaign processing.

 

Process Opt Out File - With Detour:

 

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Join All Files, Suppress Duplicates, Insert to Tables:

 

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Generate QC Report:

 

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Workflow Overview:

 

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QC Report Example:

 

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Describe the benefits you have achieved

In making this process quicker and easier to access, we save almost two hours of database developer time per campaign, which accounts for at least 100 hours over the course of the year. The app can be used by account support staff who don't have coding knowledge or even account staff of different accounts without any client specific knowledge, also saving resources. Furthermore, the app can be easily adapted for other clients, increasing time savings across our organization. Our developers are able to spend time doing far more complex work rather than routine coding, and because the process is automated, saves any potential rework time that would occur from coding mistakes. And the client is thrilled because it takes us less time to generate campaign reporting.

Author: Kiran Ramakrishnan

 

Awards Category: Most Time Saved 

 

Through automating processes we received a lot of management attention and a desire to create more automated and on-demand analysis, dashboards and reports.

 

Another area where we have benefited significantly is training and process consistency. No more are we reliant on training new resources on learning the systems and process or critically affected by sudden departure of a team member.

 

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Describe the problem you needed to solve 

We are a semiconductor company located in the Silicon Valley. We are in business for more than 30 years with 45 locations globally and about 5000 employees. We are in business to solve our customers' challenges. We are a leader in driving innovations in particular for Microcontrollers. The company focuses on markets embedded processing, security, wireless, and touch technologies. In Automotive we provide solutions beyond touch such Remote keyless or networking. Our emphasize is IoT applications. We see a potential in the Internet of Things market combining our products especially MCUs, Security and Wireless Technologies.

 

In this industry, planning is essential as the market is very dynamic and volatile but manufacturing cycles are long. Most electronic applications have comparatively short product life cycles and sharp production ramp cycles. Ignoring these ramps could result in over/under capacity. For a semiconductor company it is key to clearly understand these dynamics and take appropriate actions within an acceptable time.

 

To forecast and make appropriate predictions, organizations need critical information such as actual forecast, billing, backlog and bookings. Based on this information Sales, BUs and Finance are able to build models. As End of Life parts convert immediately into revenue we need to treat them separately. Typically semiconductors sales is based on sales commission. Sales commissions are calculated on product category and type. Therefore each line item needs to be matched to a salesperson by product life cycle. In public companies this is done on a quarterly basis and regular updates increase an organization's confidence to achieve set goals. As electronic companies are demanding more and more security levels to data access, consolidated dataset needs to be protected to ensure compliance with customer agreements. Large organizations also require data security to ensure data is only accessible on a need-to-know basis.

 

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Historically, people from these different groups manually created, cleansed and merged data and information into various files and sources to achieve insight. It is common to use different environments such as Oracle DBs, SAP, ModelN, SharePoint, Salesforce, Excel, and Access. This is extremely time consuming and requires a huge manual effort. Usually data consistency between different sources is not guaranteed and requires additional cleansing and manipulation. As every person/group has also their own way to gathering and consolidating this information it typically leads to different results and layout as it is hard for someone outside the group to clearly understand the other person's approach. These reports are regularly necessary a necessity and need to be complied on a weekly/daily basis on the refresh frequencies.  We also want to get independent of resources to update dashboards on demand. Current process makes the reporting heavily reliant on human resources.

 

Describe the working solution

In Alteryx we found the solution to our problem. Alteryx was utilized to join data sources of in different data formats and environments gathered from different departments including Sales, Finance, Operations/Supply Chain, and Human Resources.

 

  • The Sales department provides Forecast in an Excel worksheet. As the worksheet is being accessed and edited by more than 500 individuals, data inconsistency between fields (such as time dimension) is an ongoing issue and data architecture needs to be re-organized and consolidated.
  • The Finance department provides Billings in the format of Oracle Hyperion, where there are data inconsistencies between Billings and Backlog & Bookings due to system differences. Billings need to be merged with Backlog & Bookings to identify EOL parts for commissions and forecast are identified.
  • The Operations/Supply Chain department provides Backlog & Bookings through SAP, which also has data inconsistencies between Backlog & Bookings and Billings due to system differences. Backlog and Bookings need to be merged with Billings, and EOL parts for commission and forecast are identified.
  • The HR department provides Organization Hierarchy through SAP HANA, in order to apply a row level security on the dashboards later on.

 

To resolve the issues, all relevant data is structured and follows the overall defined data architecture described in Alteryx. First, Alteryx pulls relevant data from various sources and stores it in a shared drive/folder. Then, Alteryx runs its algorithms based on our definitions. A special script was developed to publish and trigger a refresh of the dashboard with the latest data on a daily basis. Finally, a notification via email is sent to all the users (more than 500) with a hyperlink, once the refreshed data is published.

 

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Describe the benefits you have achieved

Prior to the Alteryx implementation, a lot of time was spent downloading, storing, and consolidating the files, which resulted in multiple unexpected errors which were hard to identify. The accuracy and confidence level of the manually created dashboard was not very high, due to the unexpected human errors. Very often, the dashboards required so much preparation that by the time they were published they were already outdated.

 

Through the Alteryx approach, we have now eliminated manual intervention and reduced the effort to prepare and publish/distribute the reports to less than 1% compared to previous approach. In addition, through this streamlined approach we have stimulated collaboration on a global basis.

 

Departments such as IT, Finance, Sales are able to work much tighter together as they are seeing results within an extremely short period of time.

The other advantage of this solution is that it is now broadly being used throughout the organization from the CEO to analysts based on the defined security model.

 

Running_Time.pngHow much time has your organization saved by using Alteryx workflows?

It used to take us one week to create and develop the workflow. The biggest challenge we faced was to determine the individual steps and the responsible person as various resources and departments were required to contribute.

 

Through Alteryx workflow we are able to save more than 15 hours per week in data merging alone and at the same time we are now able to publish the reports/analysis on a daily basis. Through Alteryx we are now saving over 75h from various departments to run the process from end-to-end on a daily basis.

 

What has this time savings allowed you to do?

Through automating the process we received a lot of management attention and a desire to create more automated and on-demand dashboards and reports.

 

Another area where we have benefited significantly is training and process consistency. No more are we reliant on training new resources on learning the systems and process or critically affected by sudden departure of a team member.

Author: Andrew Kim, Analyst (@andrewkim80916)

 

Awards Category: Name Your Own - Scaling Your Career with Alteryx

 

Describe the problem you needed to solve 

Deciding on a tool to invest your time in is a problem everyone faces in their career. Learning to blend the tools given to us in college versus what the professional world is actually using are starkly different. I have quickly discovered to have a career that has both the opportunity to start from a company from scratch and the flexibility to work in a Fortune 100 environment requires the knowledge of assets that can scale without a significant investment of time or money.  My background is in Marketing and Finance with most of my work experience in small to midsize companies where every person is required to be/do more for the company to survive.

 

Describe the working solution

I set out to find these tools 3 years ago with the understanding that information drives a business, which lead me to Gartner report. I went through trials of a dozen different options and even had contracted assistance from a developer of one of the software options. Alteryx quickly became my option of choice which greatly contributed to my  previous company's growth from $250k in annual revenue online to $12 million in 2 years. The ability to access multiple data source types, leverage Amazon MWS data and use historical competitive landscape information allowed us to create the perfect dashboards in Tableau to analyze current inventory and buying opportunities that were previously inconceivable.  I was able to save 10,000 labor hours a day in discovering new products. Prior to Alteryx being purchased the average buyer's assistant could run 200 Amazon listings per 8 hour day. After Alteryx we were retrieving over 250,000 listings per run multiple times a day (The math: 250,000/25 listings per hour=10,000 hours per run). The primary customer in this scenario were the buyers for the company. By taking all of the data processed through Alteryx and providing them with Tableau dashboards to conveniently view current and historical product information compared to the previous Excel models we were able to maximize inventory turnover and margins.

 

Describe the benefits you have achieved

Alteryx knowledge allowed me to advance to my current company and position in a Fortune 50 company where I am a Data Analyst/Programmer. I now work heavily with survey data and again Alteryx has proven an indispensable asset even with the change in scale. Its versatility has allowed all of my skills to transfer from operational data to qualitative without skipping a beat. I find Alteryx is an asset that has only increased my passion for data and I am eager to see how I can continue to scale my career with it.

DSC_0035.JPGAuthor: Erik Miller (@erik_miller), Sr Systems Engineer - Cyber Security Analytics

 

Awards Category: Most Time Saved

 

Describe the problem you needed to solve

My team's story starts from the ground level of analytics: no tools, no resources, no defined data sources. But our Information Security team had an idea: to be able to report out on all of Western Union's Agent Locations (think Kroger grocery stores, mom & pop shops, etc) and the risk they posed by not having certain security measures implemented - look at every PC/terminal they have to determine their individual risks (2.4 million when we started), their fraud history, their transaction limits, etc, etc. and risk-rate every one of those 500,000+ Locations. We completed a proof of concept and realized it was completely unsustainable, requiring over 100+ hours every month to be able to produce, what outwardly looked like, a simple report. We took that process and built it out in Alteryx. And with just a little over 2.5 hours with the tool, we took a process which dominated my time and turned it into a 5 ½ minute layout of time. What's more, we've turned this POC project and turned it into a full-fledged program and department, focused on risk analytics surrounding employee & contractor resource usage (malicious or uneducated insiders), customer web analytics (looking for hackers), and further Agent analytics.

 

Beyond our humble beginnings, there's the constant threat of data breaches, fraud, and malicious insiders in the Information Security world - it's the reality of the work we do. Having the ability to build out an strategic analytics program has been a huge step in the right direction in our industry and company & not an area which many other companies have been able to focus on, which also sets us ahead of the curve.

 

Describe the working solution

We are using Alteryx to assess several data sources - HR data sets for active/terminated employees & contractors, clickstream data from our digital assets and websites, security data from our Netezza system, fraud data, log files from our various security platforms, user behavior data from our UBA (User Behavior Analytics) system, Identity and Access Management attributes/entitlements, system infection logs, installed applications, etc., etc. As I've said in other talks, we don't have a data lake, we have an ocean.

 

We are currently exporting our data to Tableau tde files, Hadoop, and MySQL databases. In addition, we have started looking/experimenting with our Alteryx Server implementation (which I support for our company).

 

Describe the benefits you have achieved

Overall time savings is nearing 150 hours a month, so a massive savings and an ability for our team to stay incredibly lean - no additional FTEs needed to keep taking on more and more data and challenges. We've also been able to give visibility to the security implementations for all of our 500,000+ worldwide locations - something which we didn't have visibility to prior to now, and which helps us drive the business to implement security features where needed - based on logic, numbers, and fraud data, not feelings.

 

We also are able to provide insights into our user base - how are our employees using our assets, what are they doing that's lowering our security posture, how are they getting infected. We're providing insights which can help our company become more secure.

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How much time has your organization saved by using Alteryx workflows?

What has this time savings allowed you to do?

With just our first workflow, we saved over 100 hours per month - so over a full FTE of time has been taken off of my plate. Alter
yx has allowed us to now only save time each month, but keep our team incredibly lean (we only have three people, and that's all we need to churn through massive amounts of security & fraud data each month).

 

So what has this time saving allowed us to do? Many, many things.

 

First, I was promoted to Sr. Systems Engineer - Cyber Security Analytics. With that change in title, also came the opportunity to build out a strategic-focused Information Security Analytics team, focused on looking at all security data throughout the company and identifying areas where we can improve our security program and posture.

 

Second, It's allowed me time to work with other departments to build out their analytics programs and help them learn to use the Alteryx tools in their respective areas.

 

Third, it's allowed my team to work on new, expanding projects with great ease.

Author: Michael Peterman, CEO In-2CRev-28px-R.png

Company: VeraData

 

Awards Category: Best 'Alteryx for Good' Story

 

We provide deep analytics services for hundreds of clients.  Of particular interest is the NCCS (National Childrens Cancer Society).  This prestigious and highly respected organization has been doing more for the families of children with cancer since 1987 - yep, for almost 30 years.  We are honored to be serving them as a client.

 

Describe the problem you needed to solve 

NCCS, like every other large charity in America, sends out direct mail fundraising solicitations to support these families.  Like any other business has to spend money to acquire new customers, non-profit organizations spend money to acquire donors.  They were faced with a year over year trend of increasing donor acquisition costs and increasing costs to reactivate lapsed donors.   This was coupled with a concern was that there was a shrinking universe of potential donors who were willing to support their efforts.

 

Describe the working solution

Enter VeraData. Our initial engagement with NCCS was to build a donor acquisition model to reduce their costs to acquire donors, which subsequently reduces the cycle time to break-even on the investment in new donors. Concurrently, we developed a lapsed reactivation model that used tons of external, outside information to select from their audience of former donors the individuals most likely to donate again, therefore increasing the universe of marketable names while maintaining the cost to reactivate. Lastly, our third component was to uncover an expanded universe of individuals who had the propensity to support the NCCS. This meant identifying new data sets and determining which individuals would be profitable to pursue.

 

There were several methodologies deployed to achieve these goals. Our analytic team settled on a series of support vector machine models solving for response rate, donation amount, package and channel preferences, etc. All of the information in our arsenal was called upon to contribute to the final suite of algorithms used to identify the best audience. Using Alteryx, R, Tableau and our internal machine learning infrastructure, we were able to combine decades worth of client side data with decades worth of external data and output a blended master analytic database that accounted for full promotional and transactional history with all corresponding composite data on the individuals. This symphony achieved all of the goals, and then some.

 

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Describe the benefits you have achieved

The client experienced a 24% reduction in their cost to acquire a donor, they were able to reactivate a much larger than anticipated volume of lapsed donors (some were inactive for over 15 years) and they discovered an entirely new set of list sources that are delivering a cost to acquire in line with their budget requirements. Mission accomplished.

 

Since that point, we have broadened the scope of our engagement and are solving for other things such as digital fundraising, mid-level and major donors. Wouldn't have been possible to do with the same speed and precision had we not been using Alteryx.

QralGroupLogo.jpgAuthor: Ryan Bruskiewicz (@rbruskiewicz) - Management Consultant

Company: Qral Group

 

Awards Category: Best Use of Alteryx for Spatial Analytics

 

I am using spatial analytics in Alteryx, in combination with healthcare utilization data for drugs and procedures published by Centers for Medicare & Medicaid Services (CMS) and shape files on data.gov (ZCTA, US primary roads) to optimize geographic territory alignment for sales representatives in the life sciences industry.

 

The process leverages several spatial analytics tools in Alteryx, including Distance/Driving Distance, Find Nearest, Create Points, SpatialObjCombine in Summarize Tool, Spatial Match, and Location Optimization Macros. The Alignment Optimization workflow outputs data files for visual mapping, analysis and summary reporting in Tableau, and outputs files to a tool called TerritoryMapper for manual refinement of territory zip code boundaries.

 

Describe the problem you needed to solve

The Initial Business Problem

The business problem solved is optimization of sales force territory alignments. The objective is to create territories that are balanced in terms of workload and sales potential and consider geographic constraints and travel time. The approach developed in Alteryx efficiently optimizes geographic alignments in a matter of minutes without costly purchases of third party data sources:

  • Traditional approaches to this business problem in the life sciences typically require purchase of third party healthcare drug/procedures utilization data for specific therapeutics areas or markets that represent a significant investment. Leveraging data published by CMS, we were able build a completely flexible model that can optimize territory alignment design for any market basket (i.e. any combination of drugs or healthcare procedures) without purchasing additional data.
  • Traditional approaches also typically require weeks or months to complete (as opposed to minutes or hours with our approach in Alteryx). The approach eliminates time spent purchasing/acquiring data, loading data, preparing/summarizing data, loading data into an alignment tool, manually defining and refining territory boundaries, and summarizing alignment results – these steps are fully-automated with our Alteryx workflow.

Additional use cases solved
Leveraging the alignment optimization workflow as an initial platform, solutions to address other related business problems have been developed and integrated into the workflow:

  • Market sizing & value concentration curve
  • Physician segmentation by patient volume & specialty
  • Sales force sizing to determine optimal # of sales representatives for a given targeting strategy
  • Territory-level call plans to physicians/accounts

Together, this set of solutions provides a suite of tools to automate and optimize field sales force deployment.

 

Drivers and applications
These solutions are used on projects to support Business Development and/or Commercial Operations teams within life sciences companies. The alignment optimization workflow has also been generalized to enable Sales Operations teams in any industry to design sales territories by taking an individual company’s customer target list and demand/sales history as an input.

 

Internally, Qral Group has leveraged this tool to create territory alignments for many combinations of sales force size and therapeutic area. With this broad set of alignment scenarios, we can quantify how much of territory alignment is effectively “objective” due to population distribution vs. variable for specific therapeutic areas due to regional differences in disease incidence and prevalence. We found that there is typically an 80-85% overlap of territory alignment, regardless of therapeutic area!

 

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Describe the working solution

The working solution integrates ~12 GB of data representing ~12 billion healthcare claims from the following data sources:

  • Flat files (.csv)
    • Healthcare provider universe
    • Medicare provider utilization & payment data for inpatient procedures, outpatient procedures, Part D prescribers, and Part B services
    • Demographic data for population by age, gender by ZIP Code
  • Shape files (.shp)
    • S. primary roads geodatabase
    • Cartographic boundary shape data for ZIP Code Tabulation Areas (ZCTA)

The alignment optimization tool consists of two repeatable workflows.

  • The first workflow integrates the data sources described above into a database that can be consumed by the alignment optimization algorithm, and is only run when raw data sources need to be updated (typically twice per year)
  • The second workflow connects to an excel-based user input form and leverages several custom-built Alteryx macros to execute the geographic territory alignment optimization. This workflow is run on-demand to create the territory alignment output.

The excel user input form allows a user to specify:

  • Drugs, services, and provider types to be considered markers for sales representative workload
  • Planned workload/call frequency by provider segment
  • Target workload range (min/max) for each territory for workload balancing optimization
  • Number of sales representatives, number of first-line sales managers, and number of second-line sales managers

The optimization workflow also includes macros to accomplish certain complex operations, batch processes, iterative processes, and optimizations, such as:

  • Clustering algorithm macro using native R-based clustering tool to identify territory workload centers
  • Adjustment of territory alignment to consider geographic constraints, such as US primary roads, driving time, and state boundaries
  • Batch macro to split heavy geographies into equal territories
  • Location optimization macro to optimally rebalance workload across neighboring territories by reassigning ZIP codes
  • Iterative macro to define locations for 1st line and 2nd line managers, and determine sales force hierarchy (assignment of reps to districts and regions)

The ZIP-Territory alignment and sales force hierarchy is output to Tableau to visualize the geographic alignment and report on summary statistics (e.g. number of customers, sales, workload) for each territory and span of control for managers.

 

Describe the benefits you have achieved

Alteryx has had a significant impact on our consulting project work through time savings and cost reduction.  For this geographic territory alignment use case, specifically:

  • Time savings: Reduced time needed to complete analysis from 1-2 months to 1 day (savings across each analysis step from data acquisition, loading, preparation, alignment optimization, and results visualization)
  • Cost savings: For clients who do not already own prescriber-level data, reduced cost by eliminating need for a costly one-time data purchase from third party vendors to complete territory alignment analysis.

At Qral Group, we have realized many benefits of leveraging Alteryx Designer for other use cases as well.  We regularly analyze large volumes of healthcare claims data.  We can use Alteryx to process hundreds of millions and billions of records without heavy investment in infrastructure.  This capability allows to better understand treatment pathways and patient journey, patient utilization, referral networks, physician and patient segmentation, payer cost impacts, and patient identification for rare diseases.

Author: Manju Devadas (@manju_devadas) - CEO

Team Members: Vijay Bondale, Salil Amonkar

Business Partner: Pluto7

Client: Cisco

 

Awards Category: Best Value Driven with Alteryx

 

  • Cisco Virtual Sales organization is responsible for nearly $5 Billion plus in revenue. One of the key challenges they faced was figuring out which bookings to associate with opportunities. There was a lack of mapping data, which would have helped achieve this visibility directly.
  • With Alteryx we did data science work and built fuzzy logic to match data based on geography, customer hierarchy and other factors.
  • The visibility provided was humanly difficult to achieve otherwise.

 

Describe the problem you needed to solve

Bookings to opportunity matching was missing hence the sales leadership had a tough time attributing the sales efforts to the results. 

 

Describe the working solution

SAP Hana, Excel. Department has Alteryx Server. Workflows with data science logic for bookings to sales pipeline matching for business insights. We exported the data to Tableau. The goal here is to drive higher sales effectiveness.

 

Describe the benefits you have achieved

a. Sales leadership can now drive higher productivity and results from their sales work force.
b. Potentially increase the revenue for Cisco Virtual Sales.

Author: Shelley Browning, Data Analyst

Company: Intermountain Healthcare

 

Awards Category: Most Time Saved

  

Describe the problem you needed to solve 

Intermountain Healthcare is a not-for-profit health system based in Salt Lake City, Utah, with 22 hospitals, a broad range of clinics and services, about 1,400 employed primary care and secondary care physicians at more than 185 clinics in the Intermountain Medical Group, and health insurance plans from SelectHealth. The entire system has over 30,000 employees. This project was proposed and completed by members of the Enterprise HR Employee Analytics team who provide analytic services to the various entities within the organization.

 

The initial goal was to create a data product utilizing data visualization software. The Workforce Optimization Dashboard and Scorecard is to be used throughout the organization by employees with direct reports. The dashboard provides a view of over 100 human resource metrics on activities related to attracting, engaging, and retaining employees at all levels of the organization. Some of the features in the dashboard include: drilldown to various levels of the organization, key performance indicators (KPI) to show change, options for various time periods, benchmark comparison with third party data, and links to additional resources such as detail reports. Prior to completion of this project, the data was available to limited users in at least 14 different reports and dashboards making it difficult and time consuming to get a complete view of workforce metrics.

 

During initial design and prototyping it was discovered that in order to meet the design requirements and maintain performance within the final visualization it would be necessary for all the data to be in a single data set. The data for human resources is stored in 17 different tables in an Oracle data warehouse. The benchmark data is provided by a third party. At the time of development the visualization software did not support UNION or UNION ALL in the custom SQL function. During development the iterative process of writing SQL, creating an extract file, and creating and modifying calculations in the visualization was very laborious. Much iteration was necessary to determine the correct format of data for the visualization.

 

Other challenges occurred, such as when it was discovered that the visualization software does not support dynamic field formatting. The data values are reported in formats of percent, currency, decimal and numeric all within the same data column. While the dashboard was in final review it was determined that a summary of the KPI indicators would be another useful visualization on the dashboard. The KPI indicators, red and green arrows, were using table calculations. It is not possible to create additional calculations based on the results of table calculations in the visualization software. The business users also requested another cross tabular view of the same data showing multiple time periods.

 

Describe the working solution

Alteryx was instrumental in the designing and development of the visualization for the workforce dashboard. Without Alteryx the time to complete this project would have easily doubled. By using Alteryx, a single analyst was able to iterate through design and development of both the data set and the dashboard.

 

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The final dashboard includes both tabular and graphic visualizations all displayed from the same data set. The Alteryx workflow uses 19 individual Input Data tools to retrieve data from the 17 tables in Oracle and unions this data into the single data set. Excel spreadsheets are the source for joining the third party benchmark data to the existing data. The extract is output from Alteryx directly to a Tableau Server. By utilizing a single set of data, filtering and rendering in visualization are very performant on 11 million rows of data. (Development included testing data sets of over 100 million rows with acceptable but slower performance. The project was scaled back until such a time as Alteryx Server is available for use.)

 

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Describe the benefits you have achieved

The initial reason for using Alteryx was the ability to perform a UNION ALL on the 19 input queries. By selecting the option to cache queries, output directly to tde files, and work iteratively to determine the best format for the data in order to meet design requirements and provide for the best performance for filtering and rendering in the visualization, months of development time was saved. The 19 data inputs contain over 7000 lines of SQL code combined. Storing this code in Alteryx provides for improved reproducibility and documentation. During the later stages of the project it was fairly straight forward to use the various tools in Alteryx to transform the data to support the additional request for a cross tab view and also to recreate the table calculations to mimic the calculations the visualization. Without Alteryx it would have taken a significant amount of time to recreate these calculations in SQL and re-write the initial input queries.

 

Our customers are now able to view their Workforce Optimization metrics in a single location. They can now visualize a scenario in which their premium pay has been increasing the last few pay periods and see that this may be attributed to higher turnover rates with longer times to fill for open positions, all within a single visualization. With just a few clicks our leaders can compare their workforce optimization metrics with other hospitals in our organization or against national benchmarks.  Reporting this combination of metrics had not been attempted prior to this time and would not have been possible at this cost without the use of Alteryx.

 

Costs saving are estimated at $25,000 to-date with additional savings expected in future development and enhancements.

Author: Mandy Luo, Chief Actuary and Head of Data Analytics

Company: ReMark International

 

Awards Category: Best Use of Predictive

As a trained Statistician, I understand why "70% data, 30% model" is not an exaggeration. Therefore, before applying any regression models, I always make sure that input data are fully reviewed and understood. I use various data preparation tools to explore, filter, select, sample or join up data sources. I also utilize the data investigation tools to conduct or validate any statistical evaluation. Next, I would usually choose 3-5 predictive modeling candidates depending on the modeling objective and data size. I often include one machine learning methods in order to at least benchmark other models. After the modeling candidates finish running, I would select the best model based on both art (whether the coefficients look reasonable based on my understanding of the data and business) and science (statistical criteria's like the goodness of fit, P-value and cumulative lift etc.).  I am also often using the render function for model presentation and scoring/sorting  function for model validation and application.

 

Describe the problem you needed to solve 

ReMark is not only an early adopter in predictive modeling for life insurance, but also a true action taker on customer centricity by focusing on customer lifetime analytics (instead of focusing on 'buying' only). In this context, we need to 'join up' our predictive models on customer response, conversion and lapse in order to understand the most powerful predictors that drive customer activities across pre and post sales cycle. We believe the industry understand that it is insufficient to only focus on any single customer activity, but is still exploring how this can be improved through modeling and analytics, which is where we can add value.

 

Describe the working solution

Our working solution goes with the following steps:

  1. Match over one year post sales tracking data back to sales payment data and marketing data (all de-personalized)
  2. Build 3 predictive models: sale(whether the purchase is agreed or not), conversion (whether the first premium bill is paid or not), 1 year persistency (whether lapse happened at month 13 or not).
  3. Compare model results by key customer segments and profiles
  4. Expert to visualization tool (e.g. Tableau) to present results
  5. Model use test: scoring overlay and optimization strategy

 

Describe the benefits you have achieved

  • We could create customer segments' not just based on tendency to 'buy', but also tendency to 'pay' and 'stay'.
  • We could further demonstrate ReMark's analytics and modeling capabilities covering the whole customer lifetime value chain without missing out

Quantum Spatial Logo Light Blue Q Gray Type on White.jpgAuthor: Pamela Rooney (@prooney) - Business Analyst

Team Members: HeatherAnn Bromell, Amar Shabbir, Dusty Evely, Julio Ramirez

Company: Quantum Spatial, Inc.

Business Partner: Slalom Consulting, Matt Ewalt

 

Awards Category: Best Value Driven with Alteryx

 

Our ERP is great for a lot of things, but it’s not great for tracking historical data. The Enterprise Systems team was charged with creating a way to track project Estimates-at-Complete (EAC) on a week to week basis so that Finance could understand variances in EAC and know where to look if a project EAC varies greatly from its baseline EAC.

 

By automating a weekly pull of project data, not only were we able to greatly impact Finance’s ability to track EACs, but we were also able to create a suite of reports for PMs using Tableau so that they can more easily get in front of large variances in EAC and be proactive in reining in project costs. Being able to accurately track the progress of EAC over the life of projects both helps keep EACs in check, and provides a trail of breadcrumbs to follow in case a project does “go off the rails.”

 

Awards Category: From Zero to Hero

 

A year ago, QSI had no formal BI system in place and the Enterprise Systems team had never heard of Alteryx. Slalom Consulting was engaged for BI advice and they demonstrated how Alteryx could provide value both as a tool for everyday workflows that can be automated to stakeholders and also as a builder of BI resources itself.

 

We were able to start delivering value to the Finance team with smaller workflows almost immediately, but our EAC Snapshot database (described below) has been the “game-changer” that everyone hoped it would be when we had thought it up over a year ago.

 

Additionally, since our ERP and CRM don’t “talk to” each other, we’ve started to bridge gaps in the project life cycle data collection and created the ability to smoothly report on projects from opportunity stage to project closeout.

 

Describe the problem you needed to solve

For years, QSI has been hampered by the inability of our ERP to hold historical data as a “snapshot” of where projects were in their execution at a particular point in time. Project EACs (Estimates at Complete or EAC = Job to Date or JTD + Estimate to Complete or ETC) were visible, but only as of “right now.” Senior leadership wanted the Enterprise Systems team to figure out a way to show EAC variance over time – that is, did the project perform better or worse as time progressed?

 

We were contemplating ways to do this when were introduced to Alteryx by our BI consultant from Slalom Consulting. We immediately saw potential for this to be the answer to our historical data problems. Using Alteryx, we could create a workflow that would take a “snapshot” of all project data each week and use that to compare project data week to week.

 

Since setting up this workflow, stakeholders in Production as well as Finance and Data Acquisition have found immense value in the data being captured each week. In addition to this, pieces of the EAC Snapshot workflow have been used as the basis of a number of reports with completely separate final outputs.

 

Describe the working solutionThis is the entire SQL table creation workflow.This is the entire SQL table creation workflow.

For the EAC Snapshot (and most other workflows), QSI pulls data from the ERP SQL tables and transforms those data in a number of ways.  Figuring out the SQL tables was half the battle with this workflow.  Inputs include:

 

Baseline (all joined in the workflow down to the task and resource level)

  • Consultants (all subcontractors being used on a project)
  • Expenses (all expenses from those working on the project)
  • Labor (all labor for a project)
  • Units (all acquisition assets used on a project – aircraft and sensors)

Job to Date (all joined in the workflow down to the task and resource level)

  • Consultants
  • Expenses
  • Labor
  • Units

Estimate to Complete (all joined in the workflow down to the task and resource level)

  • Consultants
  • Expenses
  • Labor
  • Units

Along the way, qualitative data such as project manager, account manager, and subvertical are added in addition to data from several custom data keys that are stored in Google Sheets. Date filters are used to show the data “as of the end of the previous week” so that we can easily define fiscal periods and make sure all data are appropriately accounted for.

 

qsi 2.png

 The workflow is run and the output table is written to our SQL server in early hours of Thursday mornings for two reasons: on Thursdays, we can be sure that all timesheets and units have been submitted for the previous week, and so as not to hit the database too hard during working hours.

 

Following the scheduled run of the workflow, a second workflow is run to retrieve the data from the EAC Snapshot database and convert it into a .tde so that Tableau can easily digest it. The EAC is then compared to the project baseline for variance in gross margin. There is also a trend version which shows changes week to week. Several other calculations give insight to how a project has progressed over time.

 

Describe the benefits you have achieved

For the EAC Snapshot (and most other workflows), QSI pulls data from the Deltek Vision SQL tables and transforms those data in a number of ways. Figuring out the SQL tables was half the battle with this workflow.

 

If the Enterprise Systems team were attempting to collect these data each week without Alteryx, it would take many, many hours per week. Maybe 40? Thank goodness we never had to try it. PMs save time because they no longer have to try to cobble together several reports to get some of the information they need. Also, they no longer have to try to root around in hard-to-read ERP reports to get to their bottom line.

 

Considering that none of us is a DBA, constructing this database without Alteryx would be scary at best and impossible at worst. The success of this endeavor has Enterprise Systems considering ways that Alteryx could help with client deliverables in our production environment.