So, you want to compete and become a data-driven organisation, like everyone else in your market? Increasingly, organisational use of analytics has moved beyond process improvement, as analytics maturity increases. The most advanced organisations are now using data and analytics to generate new business models, products and services. In practice, however, becoming data-driven – and particularly generating new value from data – is not as simple as it sounds. Many organisations fail in the process, and waste their investment. But why does this happen?
The importance of data governance
There are a number of things that you need to consider if your company is truly going to compete and generate value from all that beautiful data you have so carefully collected, stored and maintained. I think that one of the most important is data governance. There are three elements that I think many organisations should consider carefully as we move into 2019. They are:
1. Share data tools
More companies now are moving towards a hybrid approach and providing multiple capabilities that are available across different departments. This allows wider access to all data and tools. It makes very little sense to have each department buying its own tools and reinventing the wheel. Improved access to both data and tools will encourage more collaboration and innovation. Just like children get value from playing collaboratively, so adults can learn more by working together. In particular, we tend to come up with more innovative solutions when working cooperatively and sharing ideas. Better collaboration and wider use of data will also ensure that data, once clean, is used enough to justify the investment in its quality.
2. Spread data skills
So often, data skills sit in pockets. Some organisations have a dedicated data science team, or there may be a group in marketing or fraud with particular expertise. But why don’t we think more often about sharing data skills more widely across the organisation? Everyone has unique perspectives on how to carry out tasks, and discussing these perspectives can improve practice across the organisation. Ultimately, “sharing is caring,” and collaboration is key for innovation – and therefore for beating the competition, increasing market share, decreasing cost to serve, decreasing fraud and improving forecasting.
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3. Spread data responsibility
It does not make sense for all the responsibility for data to fall on the shoulders of one team or even, in some cases, just one or two people. I often hear companies talking about their “data science team,” and it always makes me want to ask one important question: Do you really want your key data-driven decisions to be put in the hands of one team? Instead, it makes more sense to share the knowledge and skills and, ultimately, the responsibility for both data and decision making because this means less risk and more governance. More importantly, increased familiarity with data means more and better data-driven creativity and efficiency.
In just one word: Collaboration
It is possible to argue that almost any area of work is important to getting value from analytics. To my mind, however, they all come back to collaboration: sharing skills, tools, data and, ultimately, responsibility. In a modern organisation, we cannot afford to work in silos, or guard our skills against our colleagues. Only by encouraging collaboration and close working can organisations thrive and compete in a data-driven world.