Data analytics and digital transformation

Top tip: To better understand the context surrounding what Max is talking about in this video, it is recommended that you read the text first and then watch the video afterwards.

So far in this course, we’ve introduced some of the key data analytics terminologies and processes that we need to know. Now, it’s time to find out how we can apply what we’ve learned so far, particularly as decision-makers. We’ll do this by starting a series of videos in which you’ll hear from some of the leading experts in the field.

Because one of the biggest benefits of data analytics is that it enables everyone to be a better-informed decision-maker, wherever they might sit within their organization or whatever stage they may be at in their careers.

Even though a lot of the major decisions around the topics covered in this course start with leadership and management, remember that a data strategy is driven and supported by everyone in the organization. To make better informed decisions and ensure that it becomes the new norm to think about data analytics throughout the organization, all employees need a level of understanding and data literacy.

Data analytics, data strategy and digital transformation

We’ve previously discussed the different types of data analytics and what questions they can help us answer about our organization, but as a reminder, they are:

• Descriptive analytics, which tells us what has happened by taking basic information and adding context, transforming it into hindsight
• Diagnostic analytics, which tells us why it happened by taking the hindsight and context, and adding correlations to create insight
• Predictive analytics, which tells us what might happen next by applying Machine Learning (ML), and human decision-making processes to this insight so that it becomes foresight
• Prescriptive analytics, using Artificial Intelligence (AI), provides suggested courses of action based on the hindsight, insight, and foresight that we’ve gained. Data has transformed from basic information all the way through to amplified intelligence

What happened? > Hindsight, Diagnostic Analytics > Why did it happen? > Insight. Looking into the future: Predictive Analytics > What is likely to happen next? > Foresight, Prescriptive Analytics > Based on modeling, what could we do as a result? > Amplified intelligence”>

Deciding which type of data analytics to use will depend on which of them is going to be most useful for the organization and its goals. This is determined by the data strategy.

A data strategy should also include planning for hiring or upskilling/reskilling our data teams, and helping the business-focused teams adapt to the changes that need to happen as the organization becomes data-led. Ultimately, a data strategy is a business strategy.

Part of this strategy is remembering that data is a tool used to help us arrive at a solution. It isn’t the solution itself. We also need to remember that data is reliant on human input, and therefore human error still plays a part, and we must be mindful of this.

The first clip from our experts comes from Max Métral, senior analytics manager at Formula 1, and illustrates both these points. As with many subjects, when it comes to data analytics, you might notice that there’s some variance when it comes to terminology. We’ve found that there are always alternative viewpoints when we provide a definition of a term or concept. In the case of this particular course, we’ve used the terms data-driven and data-led, and pointed out they are two separate things.

In Max’s experience, being data-led (or as he calls it, data informed) allows people to use their expertise and intuition in conjunction with the power of data analytics. This is a key distinction from being strictly data-driven. It allows us to make decisions that are holistically informed by the data analysis and business insights, rather than solely trusting the data.