Biological and intellectual advancement in human beings follows a set pattern, as does implementing data analytics into a business.

Unfortunately, many organisations want to rapidly ascend from having no analytics to having mature predictive analytics. Often, this revolution-based approach fails to realise the importance of data and analytical maturity. Instead, an evolutionary-based approach following the ‘Sit. Crawl. Walk. Run.’ principle will yield better adoption and results.

Organisations are investing heavily in data teams and solutions, but often fail to realise the importance of building a solid data foundation, first and foremost. Heavily investing in data and analytical capabilities will only yield fruitful (and tangible) results if the teams driving the adoption thereof acknowledge the value of incrementally showing the business value.

Incremental value delivery is very important.

Understanding the Analytics Continuum, and how it provides a guideline for incremental value delivery can help guide your organisation to correctly apply the ‘Sit. Crawl. Walk. Run.’ Principle. Ultimately, the main goal is to build the foundation first, before adding your walls, windows and roof.

Step 1: Descriptive Analytics – Observing

The ‘what is happening here?’ step.

In an immature environment this should be step 1 (Sit). The foundational building block of data and analytical maturity emphasises that the first step towards data evolution is to start measuring what is happening – the ‘Understanding’ phase.

Step 2: Diagnostic Analytics – Analysing

The ‘why is it happening?’ step.

Unpacking an issue or opportunity is extremely important, and the data analysis can deliver tangible data-driven insight in step 2 (Crawl). Leverage your data to tell a story, diagnose a problem, and inform the business on insights that will deliver operational intelligence and assist in effective decision making.

Step 3. Predictive Analytics – Signalling

The ‘if I do X, I can expect Y to happen’ step.

The fancy ‘buzz word’ that everyone wants in their organisation. The step towards maturity places the emphasis on exploring hidden patterns in your data that might be too difficult to spot with the human eye (Walk). This is a playground where predictive solutions can leverage Big Data to deliver data science models that predict customer churn, forecasts stocks, or determines the optimal route for logistics.

There should be some rules on the playground though – it is great that your team wants to deliver predictive solutions, however, solutions should solve real business problems and not just be shiny cool toys that add no business value.

Step 4. Prescriptive Analytics – Acting

The ‘if I want to achieve Y, I must do X’ step.

All those observations and predictions are great, but how do we get concrete actions from this? It does not stop at only predictions. With Prescriptive Analytics you will create concrete advice about what should happen, including directly putting it into practice (Run).

Yet none of the steps can be skipped, and neither should they be rushed if you want to collect meaningful data. Map the steps out, build the foundations and move methodically, as each step informs the next. You will start to sit up straight as you transition through the maturity model, and certain aspects or systems which need to be put in place to help you get to the next step will see you learning to crawl, then walk, and then run.

Learn to sit before you crawl, crawl before you walk, walk before you run.