Probably we all should take a hint. The digital industry has bombed during the pandemic with all transactions moving online. The chart below highlights how a minute is spent online.
But is all of this data used well? Is there a data strategy behind? Have organisations taken steps to ensure they are utilising and following due protocols. That’s what we aim to explore. So hang in there, dear reader, as we explore ahead.
Data has transformed in the last decades from being a side-effect of a product, to being one of the most valuable assets in a business. Yet, data strategy is not being done right.
Data strategy (DS) is crucial for any business, and more than ever important for firms setting foot in the digital world. The below definition of data strategy highlights a very important word – ‘living’.
A data strategy is a living document that defines how the business is setting itself up, both from a technical and organisational perspective, to proactively manage and leverage data towards the long term business strategy.
Most often, top executives make a strategy based on the insights of the time they are in and their possible future prospects. This may not necessarily be a bad idea but they should NOT set it in stone and keep the following points in mind.
- DS is not a doc that takes months to develop and is kept aside to be given to new recruiters when they join. It needs to be revised and updated as the business evolves.
- It should contain both the engineering and infrastructure approach, as well as how you plan to structure the organization and teams to make the most of your data.
- It needs to be well integrated with the rest of the business and should possibly be the north star with all other plans emerging or linking back from it. It should NOT exist in isolation.
And it’s not just us saying that DS is important. Even the EU has acknowledged the importance of data in the future, outlining their own data strategy where they aim to create a single data marketplace to level the playing field in competing against the big tech companies that own vast amounts of valuable data.
So how do you build a data strategy?
Now, this depends on what stage of the customer acquisition journey you are on. Eg: a newly launched startup would like to acquire users rapidly while keeping in mind retention and engagements are high. Here, the data team must focus on building the right foundation to collect and analyze the necessary data to make rapid iterations on the product if necessary.
Next would be to identify your data use cases so that one can focus on the ‘Data Requirements’, ‘Key Project Resources’, and ‘Technology’. It is useful to think of these areas for all data use cases together, so you can spot overlapping requirements across projects and better coordinate resources.
We should also aim to look at ‘Data Architecture’ and ‘Data Governance’. Data Architecture is important because as a business scale so does the amount of data. At an early stage, firms can still switch from one architecture to another but it may not be feasible after a while. Eg: Slack spent 3 years (from 2017 to 2020) migrating their data architecture from active-active clusters to a tool called Vitess. Hence, be mindful of your data requirements and be agile and keep iterating towards product-market fit.
Lastly, do keep a lookout of data governance as they are required by law under regulations such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).
So as you build your organization, do remember that data collection can be expensive in terms of time and money. A well-thought-out data strategy helps to zoom in on which data will unlock the most value and hopefully kick off the data flywheel.
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