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According to Splunk, although 81% of business leaders feel that data is highly valuable to their organization, 47% of them are sure that they cannot keep up with the rate at which data production is increasing. This is mostly because over half of the data of an organization remains untapped. And the data that is being tapped into is not always used to its full potential.


Several organizations don’t recognize this as an issue, and of those who do, very few are doing something about it. Tell me, does your CFO only look at half of your spending? No! Then why would you look at only half your data to make most of your decisions?




Analytics is no longer confined to a specific team doing a specific job. Everyone who makes decisions – no matter how small they are – needs to have access to all the data and analytics they need, as well as the resources to analyze them for optimal company performance. 


However, implementing analysis techniques without having a mechanism to assess it is like sending your child to school but not giving them any tests. They could have been calculating the area of the rectangle wrong this whole time!


You need a grading system to measure not only the correctness but also the efficiency and scope of their learning. And this is exactly what an Analytics Maturity Assessment tool does for your organization.


What is analytics maturity?


You can probably tell from the phrase that maturity represents the effectiveness with which an organization utilizes its technology/tools, people, processes, and various strategies to manage and analyze data to make informed business decisions.


Having short-term goals are very important to achieving any long-term goal. When you are on the path toward making your company completely data-driven, it’s easy to lose your way midway. A few nudges here and there in the right direction, coming from precise assessments of your progress can do wonders. It will save you time, effort, and money.


These nudges come from analytics maturity models. They describe the progress of a company through the various stages of data analysis capability.  You find what stage your organization is at and the steps that you need to take to reach the next step.


It is necessary that the outcomes of your data analytics strategies are measured against industry-standard parameters so that any existing gaps can be filled. You will then find out what stage you’re at with respect to each of those parameters.


What are the stages?


Here is a generic description of the stages according to industry standards:


Stage 1: Lack of analytics – A company is here if there are no analytical processes at all.


Stage 2: Descriptive analytics – At this stage, companies look at past data to gather information about what has happened.


Stage 3: Diagnostic analytics – Here, one looks for patterns in the data to find the reason behind something that happened.


Stage 4: Predictive analytics – This stage involves forecasting the future, using sophisticated technologies on huge data sets.


Stage 5: Prescriptive analytics – The last stage, where the company uses the data to come up with a plan to sway the forecasted result to what is desired, using insights and optimization techniques.


Check out the Stages of Analytics Maturity according to Alteryx (a model adapted from Competing on Analytics, Davenport and Harris, International Institute of Analytics):


Stages of Analytics Maturity.png


Let’s dig a little deeper


To understand how analytics maturity is measured, let’s look at the different parameters that assessment tools commonly used to determine the stage a company is in. 


Data infrastructure: where is your data being stored?


Is it distributed across locations isolated from each other, like SaaS applications, raw files, and so on? That’s less mature.


Or is it stored in a centralized manner, with cloud platforms that receive data from more than just your applications, like Amazon Data Exchange or Snowflake? Way more mature.


For example, a company that is still improving its data infrastructure is in the descriptive analytics stage, while a company with an ML pipeline with automated workflow is in the predictive stage.


Access to Data: From where can they see it?


Do your employees constantly import or export their own data to analyze them? Less mature.


Are there data pipelines with multiple data integration approaches in place which can move the data to a common location that can be accessed by all? Mature.


Do you also have a data visualization mechanism responding to dynamic requirements that eliminates the need for users to sift through and pick the data they need? A lot more mature.


Data Modeling: Is it easy to understand?


Okay, you know the drill. No data models, huge and boring datasets with mostly tables, which not everybody can draw conclusions, are far from mature.


A user-friendly interface with logical modeling of the data that highlights key point indicators and makes the data understandable to anyone who comes across it is mature.


Utilization of Data: How is it fetched?


Does your organization allow only some employees to use code and SQL to query data while the others have no access to it? Not mature.


What should you do to be more mature, then? Provide data as a service, allowing users to use whatever tools they wish to analyze the data. Embed analytics within apps and operations so that they can be used even outside the analytics dashboard.


Data Insights: How does it impact decisions?


Using data from the past to make decisions is good, but it’s less mature than using it to forecast future results. Imagine what a manufacturing company could do for its inventory with accurately predicted future sales!


But this is easier said than done.


Each organization is different in its own way, with unique assets and liabilities. What we just saw are five different stages in four broad categories. Still, each of them has many sub-parameters that you must consider in different weights – such as data compliance understanding, business goals alignment, the effectiveness of the target operating model, and so on. An assessment tool can study all aspects of your data analytics journey and give you a detailed Analysis Maturity report. 


Get set... go!


Gone are the days when analytics gave you a competitive advantage. It is now an absolute necessity to survive on the playing field because everybody else is doing it. And oh, they’re doing it fast.


Speed is important but in the right direction. Is your organization going the right way with its data?


Maturity Assessment Tool.png


Alteryx can help you with a free analytics maturity assessment tool available online. If you want to have a conversation about this and your analytics strategy – you know who to call (not Ghostbusters, but me)!