The digital transformation wave is growing huge across the whole society and all industries, boosted by the data tsunami created by all of us. Using analytics to find actionable insights in all this data for business success is not new anymore but the digital transformation has put the spotlight on the topic.

Many new reports and surveys have been published to describe where we are today and how to accelerate the Data Driven Business Model. A new report from EY and Forbes Insight, Data & Advanced Analytics “High stakes, High rewards”, based on a survey of over 1,500 executives from around the world, sheds new light on the current situation and points at some key areas to think about when moving forward.

Organizations using analytics most effectively have moved on from the original ways of using their data. These organizations—the most mature in their use of data analytics—are no longer merely improving their existing processes. Instead, they are using the insights generated from data to radically alter their product or services, and completely change how they view and interact with their customers. They are, in fact, building new business models that are giving them a real advantage against their competitors.

This is paying off both in revenue terms and operating margins. A massive >60% of the advanced analytics users saw revenue growths of more than 15%, with increased operating margin compared with just 13% of the less advanced analytic users reached growth. Analytics maturity has a direct effect on the bottom line and it also affects risk profile, with 60% of the top group reporting improvements in that, against only 8% of bottom group.

How to become data driven - information flows and problem areas

The report identifies five points at which problems may arise in moving to become more data-driven. It suggests that these five points can be likened to synapses in a brain, because they represent the movement between steps in the process. The five points are:

1. Competitive differentiation. The point between defining a market and developing a strategy to address that market. To move between these two steps requires an organization to identify how it can distinguish itself from its competitors and best serve the market.
2. Operating model.This is the step between strategy and initiatives, where the organization needs to work out what exactly it is going to do to deliver its strategy, and provide an effective way of operating.
3. Initiative design. To move from initiatives to analytics production requires an organization to define the products and activities that will be necessary to achieve its desired business outcomes.
4. Intervention design. This stage comes between analytics production and analytics consumption, and translates all the goals, modelling and methodology into actual insights that can inform action.
5. Measurement and Learning. Moving from analytics consumption to evaluate outcomes requires an organization to quantify and learn from its analytical activity, and feed that into business outcomes. A feedback loop helps to ensure continuous learning.

Drawing parallels between the process of using analytics to become data-driven and a brain, however, is slightly misleading. There is a difference between synapses in a brain, and an organization. A brain is very complex but communications move naturally and rapidly across synapses, whereas organizations must work hard to ensure a smooth flow of information. In practice, this means that simply throwing money at analytics does not necessarily result in improvements. Organizations need to find suitable ways to manage the information flow and ensure that they get the maximum benefit from any investment in analytics.

Being data-driven is about giving decision makers the power to explore data and make predictions. Help your business stakeholders with tips from this collection.

 

Three steps to get benefits from analytics investments

There are three key steps to gaining benefits from investments in analytics.

First, organizational leaders must be aware of the risks and opportunities associated with each problem point, particularly for their organization. By understanding the issues and their organization’s capacity, they will be better able to develop a clear plan to improve capability.

The second step is to look at the human issues. The survey results show clearly that analytics investments are most likely to break down over problems around relationships and communication, the areas which are common to many organizations. Collaboration between business units and analysts is essential as in any change initiative; you cannot overlook the importance of people in this.

Finally, organizations need to apply best practice in each area. Benchmarking against the best at each point will enable organizations to learn from the best, and apply the learning in their own part of the operations.

A familiar model for improvement

If this sounds familiar, it’s probably because it is. Analytics may have a huge impact on organizational success, but the processes need to become embedded in the organization to reach full potential. To implement a data driven business model is a change which involves people, and that work should not be underestimated. As such, a clear-eyed look at the organization is required, a focus on getting people to work together effectively, and an emphasis on continuous and ongoing learning, including by benchmarking against other successful organizations.

As always change needs support through the whole organization starting at the top and lead by actions and not just words - It’s not rocket science, although it may be as hard for some.

Have your say

This report review is part of our preparation for a discussion on data-driven business models at Analytics Experience 2017 in Amsterdam that my colleague Matthieu Joosten and I will be leading.