How many times have you heard business leaders claim that ‘data is the lifeblood of our business,' or ‘we’re a data-driven organization’? They certainly have the right idea but, unless you are a digital-native newcomer, then it’s probably more wishful thinking than reality.
While it’s still early days for most digital transformation programs, we’ve come far enough to see how inaccurate data can hold up progress. Whether it’s the CIO’s network inventory or the Marketing director’s CRM files, you can’t give your users or customers a great experience if you’re working with flawed, unreliable data.
To become a genuinely data-driven business is not something that can be achieved without a significant shift in focus and practice and mindset. It means accepting that data will become your primary source of business value (not your products and services). It takes leadership commitment, a complete overhaul of current data management practices, and an enterprise-wide cultural change - a workplace where everyone treats data as something valuable and precious and takes responsibility for getting it right. Paradoxically, building a successful data-driven digital business is very much about people and human behavior.
The Rise of the Chief Data Officer
Smart businesses are recognizing that successful digital transformation needs dedicated leadership to focus on data. Digitally-born companies have been very good at this from the beginning, but for established organizations, it is much harder to change the mindset shift.
More than a decade after some visionaries said "data is the new oil," there’s a trend for creating senior roles to take responsibility for data. Large corporations are appointing chief data officers or similar senior positions – to support the board’s digital transformation plans. One of the chief data officer’s priorities will be to ask what data the organization needs and why. They can then begin to simplify the data model and discard what’s not adding value. The lean management tool “value stream mapping” can be useful here, helping to identify problems and separate emotions from facts.
Fixing Legacy Data
Inaccurate historical data is a problem for all established businesses. There are myriad reasons for data errors and omissions, but most often, a human being is behind the mistake. The contact center agent hears ‘13’ instead of ‘30’ so the delivery goes to the wrong address. A product in the field has been upgraded but not appropriately documented, so the engineer turns up with the wrong parts. Information gets passed from system to system where data fields don’t quite match. Corporate restructuring brings together incompatible data sets.
Fixing complex legacy data will probably require a formal program of manual intervention to clean up everything. However, once it’s fixed, you need to keep it that way. Applying the ‘clean room’ principle, a concept borrowed from the semiconductor manufacturing industry, is a useful strategy. Cleanroom thinking characterizes an environment where there is a set way of doing things so that you avoid even the smallest amount of contamination. A clean room data environment makes data more real because you can more easily understand what you’ve got and what you do with it.
Keeping the room clean requires the introduction of standards, rules of recording that prompt the human being to record data in a specific way, and rules of access that limit the risk of contaminating data. For example, languages have different protocols for say, personal titles, and addresses, so it's essential to have an agreed format to record such information. In a large organization with lots of people, you cannot synchronize by word of mouth. Standardization is the solution.
Obsessed with Data Quality
A data-driven business develops a culture which empowers its employees to be accountable, to take ownership of data. It should be unthinkable to record or handle inaccurate or inadequate data.
A customer has given you his street address but not the number? Don’t proceed. Go back and clarify. It should be acceptable, even expected, that (like Toyota employees on the production line) an employee can press the ‘stop’ button if they suspect a quality issue with data.
Introducing processes and tools that support everyday data handling and long-term change make the culture sustainable. The aim should be to establish a culture of continuous improvement: Whoever spots an issue needs to raise their hand and flag it. A data governance forum open to all can help prioritize and solve problems. Data becomes the most crucial thing in the organization, and people become obsessed with data quality.
We need to replicate the same approach as we do for corporate security, with robust processes in place supported by a culture where everyone is responsible for making the best decision to protect the organization and the integrity of its data.
Change Must Happen
Data is at the heart of the customer experience, but you can't deliver an exceptional experience if your data is in any way defective.
Most current systems of data management are not fit for purpose for the digital economy. Those of us in established businesses must grasp the nettle and overhaul our approach to data, adapting methodologies and processes from other industries to accelerate progress.
Above all, we should put people in the center of our data management strategies. When we give them the tools and authority to be accountable, to challenge and champion data, only then will be on the way to building a genuinely data-driven business.
Horia Selegean, data governance director, BT wrote this article.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.