Innovation needs great data management
There are so many new technologies on the market for businesses to harness their data. Many of them include artificial intelligence, machine learning, and robotic process automation. All of them need quality data to deliver results and insights.
Data is instrumental to innovation in all areas of the business from creating efficiencies in supply chain management to delivering effectiveness in marketing. The industry defines the characteristics of great data as:
- Clean – accurate, de-duplicated, timely, and complete data that matches pre-determined standards
- Safe – data that’s compliant and secure and only the right people have access to it
- Connected – data that reflects the whole truth consistently
Quality data that conforms to these characteristics delivers a solid platform for analytics, as it is:
- Trusted – gives your decision makers confidence in the facts they are basing their decision on
- Timely – gets insights in time to take action
- Accessible – data is in a business-ready state, in a business context, and ready for business use
Consider the immense innovations in customer experience over the past few years. Great data has helped transform personalisation-at-scale to such an extent that consumers expect every interaction with a business to be predictive and automated. They expect the “Netflix effect” for everyday tasks such as placing an order, checking a reservation, or researching a purchase. They also expect businesses to know them, to remember their buying history, to have a record of customer support requests, and so on.
These unified, personalised experiences must be built on a foundation of tightly governed data. But data is often in many places, captured when its significance and value were not recognised, captured in silos throughout the company, captured in different formats or in different applications, all of which have slightly different versions and various levels of duplication without a clear or single source of truth. That’s where the importance of data management becomes clear.
So why isn’t data management and analytics the number one priority in every enterprise today? It’s because data has yet to be seen as a strategic asset and, in many cases, the ownership for data management at a business level remains unclear, falling somewhere between the business and the IT technology function.
Identifying data ownership
While the IT function is responsible for building and maintaining systems, platforms, developing pre-specified applications and algorithms, it is often not close enough to the business strategy and objectives, the risk management considerations, or the threats posed from potential disruptors to leverage an organisation’s data asset effectively. This presents challenges around the complexity and multiplicity of systems, the sheer scale of data, and the legacy thinking that data belongs to and is the responsibility of the technology team. An organisation’s leadership team can help identify the right business unit owners to work alongside IT to contextualise the data in a business context. In this way, the business can fully exploit effective data management and analytics.
Governance methodologies provide confidence in the data quality. And from good quality data comes insights, from insights comes action, and from action comes tangible, measurable business benefits. However, value does not come from simply accumulating more and more data, value is only delivered by using that data to help make better, faster decisions to give you a competitive advantage and protect your organisation from new entrants to your market.
The journey to best-in-class data management will vary from organisation to organisation depending on the scale, complexity of operations, and the markets they serve. A typical journey will take anywhere from six months to a couple of years but is usually iterative and ongoing.
Executive sponsorship is key to success
While the best algorithms (Artificial Intelligence (AI) or Machine Learning (ML)) can work wonders, they can’t speak for themselves in the boardroom. They will only deliver meaningful insights with strong data management, governance, and the help of human imagination, experience, and creativity.
Client Solutions has helped many organisations on their data management and analytics journeys over the last 26 years. Some of our key learnings include:
- Throwing money at data management won’t make it a success.
- Data initiatives require collaboration between IT teams and business teams.
- Executive championship of data initiatives is crucial. The C-suite must sponsor the data strategy from the top down and make the necessary hires to prioritise data as a strategic asset.
- Partnerships are key. Talk to industry experts who have solved data problems in the past and know what pitfalls to watch for in data management and governance, learn from experience and insight.
Read Part 1 of this Article here