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Data Science Maturity


The above chart displays the journey to enterprise intelligence and how data science fills the gap. Looking at the chart you will notice 4 axis, namely "Profitability", "Competitive Edge and Market Share", "Risk of doing Business" and "Enterprise Intelligence". As you move along the path profitability increases, competitive edge and market share grows, your risk of doing business reduces and and ultimately your level of intelligence about your own business grows.

On the maturity curve you can measure yourself in terms of where you are and the next steps in the process.


This is the initial step to enterprise intelligence. This is is start-up phase where you do not have any systems, people or processes in place and there is no control over the current operations. In this step you will need to plan, design and develop strategic objectives you want to achieve.
In order to start managing your business you will need a system, people and processes to work together to gain control over current operations. Full control is probably the most important objective in the journey to enterprise intelligence. Without it, none of the other objectives will be realised. Here the key is to align systems, people and processes to work in harmony.
After you have implemented "Full Control", you will be able to draw standard reports from your system. These reports, will normally quantify past results, but the information will not be live and there will be a lag in the view you have on the business. The information delay is normally caused by manual capturing of information after the actual event occurred. In example: The previous day's production, will only be captured today.
Here the objective is to automate the flow of data as close to a live environment as possible. If you have up to date information ready available, your decision making will improve and less time will be spent on finding latest facts. Answer simple questions like "What was yesterday's sales?" or "How much did we produce this morning?" shouldn't be difficult questions to answer.
Business intelligence is a nice visual tool to access historical information. Using a BI platform will enhance your knowledge of past events. Answering questions like "What was our sales this year compared to last year?" or "How did our cost to revenue ratio change over the last 6 months?" is simple and easy with BI software.
The function of analytics is to predict the future. Analytics will specifically look at past results in an effort to predict future patterns. Simulations and scenario analysis are aspects of analytics regularly used in business. Questions like "If we remove the top 10 worst performing products, and replace them with higher velocity SKUs, what will the impact be on revenue and profitability?" or "Given the current demand cycle for our product, in what sequence should we produce our products? can be answered with analytics.
The interpretation of analytics is the single most important aspect of making quality decisions. Interpretation relates directly to the push and pull levers affecting the results you are analysing. Understanding these variables and their influence on the outcome will greatly enhance your ability to make quality decisions.
Insight the that "Aha!" moment when you realise the impact of your analysis and what you can do to change or influence the business. Insight is gained when you know what to do with the information. If you can't put it into action, you can't benefit from the analysis. Putting your results into action will directly influence the performance of your business.
Results are achieved when you physically execute the insight you have achieved in the previous. In this step it is important to diligently execute the plan, measure your performance along way and finally assess impact on your business when you are finished.