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Recently Alteryx and some of our customers (including McLaren) were featured on the BBC News website. If you haven’t seen it, check it out here.  This made me think about my journey and how I think I became a Citizen Data Scientist many years ago and I potentially didn’t know it.


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 Image Source: Gartner


I graduated as a Computer Science graduate, and luckily for me I knew I had a job lined up in consultancy before graduating.  [Side note: In the new way of working and since the global pandemic kicked in, it has been harder for graduates to do this. However, check out how Alteryx is helping you to get trained, enabled and upskilled to line up that opportunity in your career via our SparkED Program.]


I began work for clients in data warehousing projects, so I was exposed to data quite early in my career.  It was during my time in Investment Banking, where I saw something quite interesting as someone working in IT.  My internal clients/customers were the financial controllers who were tasked to produce reports for the regulators, on a daily, weekly, monthly, quarterly and annual basis.  Financial analysts wanted access to more and more data, and decisions were driven off this data.  They faced the traditional demise of us in IT handling data requests, generating a spreadsheet or compiling the data in a report.  The financial controllers would get the answers to their initial questions, but that’s all they would get.  The regulators were becoming more inquisitive in the data that was presented to them, and they’d ask subsequent questions after they analyze the data.



 Image Source: Data Science Central


Guess what?


Investment Banks started to develop their own tools and interfaces whereby, we in IT, would get the data from the trade systems as soon as possible into the bespoke tools and allow the financial controllers to run their own calculations and thus reports. 


This was a revolution because the financial controllers would get access to their data quicker and ask me more questions about it. 


As opposed to huffing and puffing with the thought of “gosh Rishi where am I going to get the answers from….” I’d revert to the tool and data that I had access to answer them and was able to produce reports and dashboards that they could access themselves going forward if the same questions arise again. 


Despite the obvious potential that these new tools offered, some financial controllers were reluctant to make the switch.  This was especially true for those who became heavily reliant on spreadsheets, especially in finance. These individuals know Excel and they had mastered it. They would take my data and paste it into a spreadsheet to analyze further, and I knew it was going to take time and energy for them to learn the tools we had for them.  It was my job to walk the users away from their spreadsheet fixations and towards a new analytics toolset with self-service dashboards.  Individuals will always be more comfortable with the familiar, but I knew I needed to make this change worth their while.  I showed them how these new dashboards allowed them to view and utilize data that, at one point, was difficult to get.  Once the reluctant individuals recognized this, not only did they embrace the dashboards, but became evangelists for everyone else in finance.


Gartner defines a Citizen Data Scientist as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.  Citizen data scientists are “power users” who can perform both simple and moderately sophisticated analytical tasks that would previously had required more expertise.


I remember reading this definition and looked back at my career, realizing…. “Wait Rishi you have been doing that since 2004 but you just didn’t know it! So did this mean that I was a Citizen Data Scientist back then?” … I wasn’t so sure at the time.


My relationship with Alteryx soon formed, when I later joined a consulting firm.  I had never heard of the company before, but my now ex-colleague Shaan Mistry strolled down to our offices in a flamboyant t-shirt, to train my team and I up for 2 weeks.  At the time, I kept relating the platform back to my days in banking when we spent years developing to serve a purpose for our end users in finance and compared this to Alteryx (because this did exactly the same!).  I embraced Alteryx (not just because of Shaan’s t-shirt, but because I could see that there are still many organizations out there who were experiencing the same issues my financial controllers were, back in 2010).


I was working for an investment analyst who would typically want me to extract relevant financial data, anything from revenue and earnings per share, to operating margins, from a commercially available financial research platform such as Bloomberg.  So I had this request …. luckily this bank already had analytical platforms embedded within the organization along with Alteryx, so I used these to get a suitably sized data set and worked with the investment analyst on it.  We had a process, where we’d award scores of 1 to good investments and 0 to not so good.


Our findings would eventually be given to the data scientists in our team, but data preparation was a tedious and time-consuming task for the data scientists and for some up to 80% of their workload. The investment analyst and I decided to resort to the platforms we had access to and were able to manipulate and prepare the data in a repeatable way.  These platforms gave us actionable insight on how to best correct the errors in our data, for example with missing values for ‘share pay-out ratios’.  Both of us had knowledge of why this was the case and were able to automatically fill in all missing pay-out ratios with zero.  To save time on similar activities in the future we saved the data preparation tasks in a workflow, for auditing and reuse purposes by our colleagues.


At this stage, it was possible to view a visualization of how promising the investment targets were distributed on the various captured financial data points, such as ‘cash flow’.  It was a strong possibility that no clear pattern would be evident.  However, the user led data preparation activities provided the investment analyst with a unique opportunity to visualize and manipulate the raw data prior to working with the data scientist to implement and deploy models within the same platform. 


This turned the tables, allowing the analyst and data scientist to spend 80% on the advanced stuff and 20% of their time on the preparation, etc.

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I now work for Alteryx, and I look back at the 15+ years of my career so far.  As I do this, I realize I was a smart employee who was not specifically trained in math or statistics, but I did and do have insightful perspectives on the business problems for which I hope to apply solutions to for myself, the company I work for and for my customers.  I was being groomed to become an individual who would develop and administer models based on predictive or prescriptive analytics, with me having access to those wizards and templates developed for specific kinds of business analysis and to interpret the results for the benefit of other line of business users. 


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 Image Source: Data Science Foundation


The title of Citizen Data Scientist was best suited for me because of my love of data but also patience, good communication, and consultative skills.  I was and am able to translate between the business problems and the technology tools that can be used to solve them.


Have you gone through the journey to become a Citizen Data Scientist as I had, but haven’t realized it yet? Reach out to me and let’s explore your journey with or without Alteryx and I’d love to partner up with you to help you with your use cases and business problems today.