Data Maturity: When a business is ready to boost profitability out of its data?

Way too many among the managers of small & medium enterprises feel they are too small or they are not yet prepared to benefit from applied data science. Sometimes they are right and sometimes wrong. This is rising the question, when actually a business is ready to generate more profits/savings out of their existing data? How big it should be before consideration of data crunching. If a manager doesn’t dig deep enough into capabilities of applied data science or doesn’t have data analytics expert or at least consultant by their side, it is quite easy to miss significant opportunities for positive change and growth. This blog post is focused on this specific perception.

Let’s focus on businesses taxonomy regarding of their data maturity. They are five (relatively broad) levels of business data utilization as you can see from the graphic bellow.

data maturity stages

Data collection

The absolute minimum for a business is to generate and collect its own data. It is quite common for such data to be as granular as possible and as close to transactional as possible (e.g. sales receipts). Such data could be collected from POS terminals.

Actually, any legal business should be positioned at least into the basic Data Collection segment. This is giving them the ability to answer the question “How much have I sold in the past?” and they may be looking at “How much would I sell tomorrow” type of insights.

Operational Metrics

On top of the Data Collection stage is the lear of businesses tracking some operational metrics (e.g. an ERP system with both current and historic data). They can have a clear view of the current effectiveness of their business.

These businesses usually keep tracking of the operational KPIs like “How much inventory do I have right now in the store/warehouse/DC” and might be looking for the insight into when they should restock or run their manufacturing.

Business Metrics

These metrics track the health of the business and are usually provided by a BI system (Microsoft Power BI, Qlik, Tableau, etc.). At this level, businesses are concerned with efficiency of the business.

Those managers usually know the answers to these questions:

  • How much is my working capital?
  • What is the working capital’s turnover period?
  • How much waste there is?

And are concerned with these questions:

  • What are my missed opportunities?
  • What is going to be my waste tomorrow?
  • How can I influence my business KPIs going forward?

This is where a business is mature enough in terms of data to boost its profitability and growth in relatively timely manner. A lot of the backbone is already done, there are at least 2 years of business data; the ERP system is tracking current performance; BI systems are able to give many different perspectives in terms of business KPIs.


Businesses at this level need to plan the future of the business KPIs that they have defined and are concerned both with ways to further improve their efficiency.

If a business is at this stage, applied data science can certainly help not just the movement to the next one but also generating measurable value. Forecasting is answering questions like:

  • How much a business is going to sell?
  • Which professional is most likely to leave the business?
  • What will consumers will look for a couple of seasons ahead?
  • How to execute logistics at lowest possible cost?

There are so many answers that applied data science can give to a business at the Forecasting stage. It can help not just identify opportunities but also to suggest decision whether to pursue them or not, based on quantifiable data driven evidence.

This is the moment when data analytics can truly empower management and officers to perform better than expected.


The highest level of data maturity of a business is the Automation. A businesses on lower levels already has all the infrastructure they need in terms of data. The Forecasting level is enabling executives to be more prepared for the future whatever it brings. By fine tuning the Forecasting processes, a business will be able to automate processes. The benefits of this is lower response time, less errors, more revenue, unbiased actions and so much more.

Automation on top of accurate forecasting is a growth propeller. Think of it as auto transmission on a modern day car. You don’t do non-essential work because machines are better than you. They are faster, more accurate and more economical.

At A4E we are able to help businesses to boost profitability for wide range of businesses staged at Business Metrics and above.

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