Belgian Impact Report

4 min read

Leading the way in the data economy

"Building a data culture is not just an option, it is business-critical. Literally 100 percent of data and analytics investments depend on having a data culture.”

Sudaman Mohanchandralal, Chief Data Officer at Allianz Benelux

The data economy is a strategic priority for Deloitte and its clients. We believe that data will determine the future of countries, companies, and citizens. To help organisations fully grasp the rapidly increasing amount of available data, we are working together with clients, including Allianz, focusing on three key domains: data value, data management, and data literacy. Our goal is to empower companies to thrive in the data economy by helping them to unlock and accelerate the potential value to be generated from analytics.

Why changing habits is necessary to build a data culture

The main challenge faced in trying to unlock the real value of data and analytics is not related to tools and technology, but to culture and people. Without a data literate workforce and a culture of data-driven decision-making, the adoption of, and potential value to be generated from analytics investments will be limited.

A data culture essentially refers to the way an organisation and its workforce makes decisions. Companies that have been able to forge a data culture are those in which data-driven decision-making has become part of their DNA, a habit common to most people.

Fortunately, investments in data literacy and data culture are increasingly found at the strategic agenda of organisations. Still, many companies struggle in designing and prioritising interventions that can increase the readiness, comfort and ability of the entire workforce to embrace data-driven decision-making and contribute to data value generation.

Time is required to create, reinforce and establish data habits

Building a data culture is essentially about creating and sustaining new habits around the way decisions are being made. Organisational change often fails as a result of the failure to change human habits. Cultural programmes are often based on the false assumption that moving people toward data-driven decision-making is simply a matter of getting the messages right.

However, research shows that when people fail to adopt data or analytical insights to drive decision-making, the problem often lies not in a lack of information or even a lack of intention to use the insights. People simply continue or revert to existing habits.This is where insights from behavioural science, the rapidly growing scientific study of our behaviour and decision-making, comes in.

Over the last couple of years, this discipline has taught us how to create an effective strategy for building new and lasting habits. Most of it relies on a select number of principles: making desired behaviours easy to execute, installing some form of rewards and redesigning rules and ways of working.

“Data culture is like the water you swim in. You don’t really notice it, but it carries your full weight.”

Kasper Peters, Consulting Leader for Financial Services

The Deloitte difference

Using simple principles from behavioural science can be extremely effective when designing analytical models and interventions. For instance, a client realised that its people did not adopt analytics solutions throughout their work. Deloitte designed user-centric, intuitive analytical models, solutions and insights that helped employees to improve and augment key decisions, and aligned performance management goals, rewards and incentives to encourage data-driven decision-making.

Similarly, creating a culture of data-driven decision-making in an organisation may require a new function with a clear interaction and governance model. Think of it this way: you can change driving habits only so much by changing the rules of the road; to alter traffic patterns in a major way you must redesign your transportation system. Deloitte has assisted numerous clients in establishing smooth collaboration mechanisms between various stakeholders, enabled through a highly impactful operating and interaction model and transparent implementation commitments. Rather than just focusing heavily on structure, high impact analytics operating models prioritise governance, clarifying the way decisions are being made and implemented in pursuit of its objectives.

Our approaches are consistently rooted in behavioural science, which inspires our teams when creating smart (behavioural) change interventions toward broadly adopted data-driven decision-making. We also practice what we preach and use data-driven insights to steer the change journey. More specially, we use assessments and measurements to understand hot spots for example, skill gaps, readiness issues, personalise interventions, and monitor progress. Similarly, the use of clear KPIs helps our clients to define and track success indicators linked to value generation with data and analytics for customers and employees. In the same line, they help to raise accountability, celebrate successes and course-correct where needed.

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