Combining data culture, tech advances to meet data demands

Traditional, manual data integration processes can't meet today's data demands − but modern, automated technology can.

Change data capture (CDC) is one foundational technology that is utilised to modernise the data environment. CDC continuously identifies and captures incremental changes to data and data structures from a source (or multiple sources) and replicates them to a target (or multiple targets), where the data can then be transformed and delivered to analytics applications. This sounds like a technical challenge for technical people, right?

The key is, however, to identify where faster data will translate into profit for the business by highlighting use cases that exemplify true competitive service advantages that matter to stakeholders whether they are clients or suppliers.

The knowledge of that business reality more often sits in the non-technical business space. That is why data culture and technology need to work hand-in-hand to bring transformation change.

Use cases are present in every organisation and can include customer experience, credit risk, insurance approval, etc. The company needs to establish if it has brought together a multidisciplinary team to perform the thought exercises necessary to identify the creative use cases of real-time data in the value chain.

Data culture is not a culture on its own, but rather an element of the larger culture in the organisation.

Why is this an element of data culture? Because technology will help to bring data to the business and embed it into workflows − business frontline workers or executives will make data-driven decisions in the moment without having to go to an analytical system for the insight.

Of course, the technology and business intelligence environments are constantly evolving. We know culture is a product of environment. So, it is important not to get stuck in a moment but rather to evolve with the technology available and bring business along this journey of discovery and technology evolution.

Data and technology make it possible but ultimately, people make it happen! Why do I say this? Well, it goes back to the question of why are we doing all this data stuff?

The response to that is we do this for improved action due to data-driven decision-making. If that defines the end goal, let's see where value is realised in the value chain, remembering that it is all really a question of value.

Many people are involved in converting raw data into improved action. All the way back from system owners, data transfer and transformation, insights, analytics and decision-making − this is called the data value chain.

But the question is: where is the value unlocked? The answer to that is only at the end of the chain when an improved decision is made due to the output of the data value chain and that requires the ability of the decision-maker to read, work with, analyse and communicate with that data.

This is where it is challenging to build a data culture, because while data-literate and -fluent people come in the first part of the data value chain, it is only in the last mile of the process that business value is unlocked, and typically this end of it is associated with low data literacy skills.

So often we spend a lot of time and effort getting to insight, but we fail to unlock value because data culture has not filtered through to the business decision-makers. That is why technology is so important, but people are more important when it comes to culture.

So, what do we do to extend data culture to the ends of the organisation?

The first thing to do is to understand that this begins with recognition of the current state of the data culture. Individuals make up a community and culture, and companies need to foster a culture of data literacy for all − from the top down and the bottom up. In other words, throughout the value chain.

The crux of the crisis is that we are dealing with a data-illiterate world where only 24% business of decision-makers, 32% of C-suite and generally 21% of 16- to 24-year-olds are deemed to be data-literate.

All three components of data, technology and people need to be present and work in harmony for data strategy to succeed and data culture to flourish. But remember, it is a journey, not a project with a start and end date. Data needs to be woven into the existing company culture.

To quote the multi-attributed adage: “If you want to go fast, move alone; if you want to go far − travel together.” Find partners for the journey to help with the various aspects of it.

In conclusion, data culture is not a culture on its own, but rather an element of the larger culture in the organisation.

The task is not to change the culture, but rather to influence it by weaving a strand of data into the tapestry that is organisational culture. Do this by understanding the current culture, aligning data strategy with organisational strategy, identifying the critical elements necessary to achieve success by providing the right mix of data, technology and enabled people to ensure success.

So, if you’re still asking why companies should change to a data-driven culture, in a nutshell:

• Greater collaboration.
• Improved competitiveness.
• Refined strategies.
• Better ability to identify cause and effect.
• Improved outcomes.