The Analytics Continuum – Data Driven Decisions & Actions

Big Data, data, Data analytics, Descriptive Analytics, Predictive Analytics, Prescriptive Analytics

This blog is a shared effort between Gertjan Hendriks, Progress Corticon Consultant at Caesar Experts & Rick Bouter Innovation Consultant at Caesar Experts. This blog originally appeared on the Caesar Experts blog

What to do, what to do

What can my organisation do with its data? That is a question that might have gone through your head as manager or decision-maker. Because many businesses have this question, the market is currently being flooded by applications, technologies and methods. That is only normal because the answer to the question “What can I do with my data?” is universal for your organisation. For many organisations, this competition will be their last.

“How do we utilise our Big Data optimally?”

So much data, so little time

A variety of data is available in and around your business: financial data, customer data, transaction data, process data, etc. Then there’s also the difference in structured and unstructured data and so on.

What isn’t helping is the unawareness and questions around this theme. Because:

  1. How do I reach this data?
  2. Who is responsible for this data?
  3. How do I create a data lake from all these loose silos?

The shift from man to machine

Changes follow faster and faster, also in the area of data analysis. Gartner showed this movement beautifully in her “Analytics continuum”. In this image, Gartner shows when the maturity of her data analytics grows, the input of humans becomes nought or disappears completely.

Source: Business analytics from basics to value

It is great to see the trend in the data analysis, but now back to the original question: “How do we utilise our Big Data optimally?”

From Observing and Signaling to Acting

Step 1. Descriptive – Observing
Through Descriptive Analytics you, as a company, can make your data insightful and determine what happened the previous period. That looks like the regular Business Intelligence, which might already be used in your company but in this case, the source data is more diverse than your current data warehouse.

Step 2. Predictive – Signaling

If you go a step further, you can predict and present the previous observations through Predictive Analytics, so you can see which insights you have now and/or signal what could happen. A little Machine Learning can often lead to surprising new insights and results for this.

Step 3. Prescriptive – Acting

All those observations and predictions are great, but how do we get concrete actions from this? It does not stop at only predictions. We would preferably act on those signals directly. With Prescriptive Analytics we will create a concrete advice about what should happen, including directly putting it into practice.

 Right when we have observed facts, we can start making concrete decisions. Alternatively, having them made. That goes further that automatically igniting your barbecue when the weather is hot or the opening of an umbrella when it observes the first raindrops.

This blog is a shared effort between Gertjan Hendriks, Progress Corticon Consultant at Caesar Experts & Innovation Consultant at Caesar Experts.