The world of digital transformation has got a new and a very topical entry: data strategy. With eight out of the ten of the largest organisations in the world stating that they make money from their data, companies big and small are turning their attentions to the business value of their own data and assessing how to turn it into a credible revenue driver.
So what is a data strategy?
Data strategy starts with outcomes. What are you trying to achieve and what does success look like? Understanding your end game is the starting point. An analysis of the day-to-day pain points you face will help you to identify the focus areas of your data strategy. For example, are you always questioning the accuracy of data in reports, rather than focusing and acting on the reports themselves? Do you always seem to have multiple versions of the truth?
In our experience, the two biggest issues facing organisations today are the quest for quality data and the never ending and sometimes unsolvable puzzle of systems integration. As an organisation grows, it collects data from all over the place and then adds systems to collect and manage the resulting data puddle it creates. This brews ever more complex problems.
Understanding your pain points as well as the processes and systems through which your data currently flows to the end user will tell you about the quality of data that needs to be fixed at the root. The exhilarating ride of data strategy starts at the bottom of the mountain with the quest for quality. The desired outcome is to achieve data purity and accuracy.
The context of your organisation will define the most appropriate data strategy. For example, if you have high quality, well-managed and governed data, your data strategy will focus on generating insights, analytics and data science.
This is why, as a start point for data strategy, we create a digital maturity model for our clients. This gives you a clear understanding of where you are in your digital transformation journey and which areas of your business your data strategy should concentrate on and prioritise.
The actionable element within your data strategy is equally important. While the main deliverable should be a roadmap and a plan of action to drive outcomes and to fix processes, tech, data and people along the way; it also needs to demonstrate business value in the short term to gain stakeholder buy-in for longer term investment.
So; what actionable recommendations should a data strategy cover?
- A benchmark on your organisation’s maturity with data (the penny drop moment)
- A plan on how to convert the data swamp you have built into a well governed data set
- Recommendations on solving the ‘I am at crossroads with my system’ puzzle
- A way to chart your journey from MI (Management Information) to BI (Business Intelligence) to Analytics to Machine learning
- A clear picture of the data team of today and future for you
- A way to keep regulators at bay with good data governance
- A roadmap of automating your processes
Tip – When choosing a partner to write a data strategy, look out for their commercial and leadership experience; not just their data expertise. Otherwise you are in danger of getting a set of tactical technical recommendations rather than a plan to achieve your outcomes.
- Start with outcomes and work backwards. Don’t fall into the trap of starting with data and technology.
- The first question to ask is: ‘What outcomes are you looking to deliver?’ rather than ‘What technology you use?’
- Fix strategic issues rather than tactics. Remember you are writing a strategy and not a set of tactics.
- Ensure the data strategy proves value along the way.
- Choose a data partner with strong commercial and leadership experience. Strategy is NOT a set of tactics.