For many years I was a bit of a ‘logic evangelist’; helping leaders and senior decision makers utilise data to make more informed decisions. As business intelligence [BI] gained ground, software developers were helping to drive more logic into decision making. At the time, decision making was rather blind, and often informal process, guided by concrete strategic plans.

Industrial Age Approach Outdated

This was a very industrial-age approach, where time was not a major factor and lengthy discussions could be held to nut through a challenge or opportunity. In many instances, the loudest, or most senior, voice won.

As businesses have been pulled into the digital age, there are those that have gone willingly, being pulled along with the wave. However, there are many who staunchly believed that ‘the old tried and true’ ways would continue to see them through. These businesses are now having to garner forces to push them into the new realm.

As digital advances are forever speeding up operational processes, the flow on effect was to speed up economies. Social media has made every business a communications company. Digital has driven market feedback cycles down from months to hours, and product development cycles down from years to weeks. The old management and leadership styles of the past no longer cut it. Most leaders know this, yet there are still too many holding onto old ways of working and thinking.

The power base of industrial-based companies is eroding. We are moving from economies controlled by the few, to ecologies powered by the many. This means that ‘playbooks’ beyond just decision making are being rewritten. One of the most significant changes already being felt by many businesses is that IT infrastructure is no longer a business advantage. It is only trusted data, that provides leaders with competitive insights, that insight required to underpin strategic execution. Yet not any data will do. Only thick data will provide you with truly humanised insight. More on that later.

Facilitating data-driven decision making, meant I was not only working on the business side of improving performance, but also on the IT side, specifically in data architecture strategy and management.

Data Architecture Strategy

Data architecture design has typically followed a very technical approach, with logical relationships and system database design providing the parameters for data architecture strategy. However, as the business is finally taking ownership of its data, both the business and IT need to work more closely together – and that means a new way to communicate. Data is that bridge. Data provides a common set of business terms, defined by attributes clearly understood [and agreed] by all stakeholders. Since it is the business that both owns and consumes data, the way it is defined needs to be meaningful to them.

This meant a move beyond primary keys and logical links and looking at data more from a conceptual view. Conceptual data maps have more meaning to business users.

As most companies are working their way through data governance, data quality management, and master data management, they are gaining more insight into how much value data adds to the organisation, and how significant data quality is in various areas. From this understanding, a broader perspective is being gained of where and how data can deliver benefit, both to strategic and operational performance.

It is from this conceptual perspective that we must also define our data architecture. But is this enough?

Thickening Up Data

As data quality program start making progress, decision makers are building trust in their data, and from this trust comes the willingness to let go of the ego attached to ‘gut feeling’. However, we must take care not to let the pendulum swing too far. There are limits to pure data-driven decision making. It lacks #theHumanFactor. Just like in defining data, we cannot focus on raw logic alone.

It is the human factor that considers morality, ethics, culture, purpose and the future of society. Adding these human elements into data ‘thickens’ it – adding contextual life to otherwise thin raw logic. Thick data requires an empathetic understanding of human behaviour.   Empathy is one area that many leaders have not been developed in, which is why emotional intelligence [EQ] is a major program for Insight Mastery. Studies have shown that ego is a major block to CEO performance. Without empathy, we are simply a poor version of a machine. Raw data provides us with the ‘what, how, where and when’. But only by thickening the data can we understand the ‘why’.

Making Data AI Ready

As the emerging power of machine learning is driving maturity of AI to unprecedented levels, we are faced with a myriad of decisions as to where and how AI will play a role, both at work and in our personal lives. Whilst this discussion will swell over the next few years, there is one underlying capability that will not change – that will be needed regardless of the decision. Trusted, Consumable Thick Data. So, this is the very place we need to be starting with in transforming any business, of any size, from the industrial to the digital information age. It is the common factor underlying any operation, and any strategy. It is what will provide businesses with both flexibility and insight – critical factors in the sustainability of any business today.

Thick data matters most.