In 2021, every company should have data strategy!
Your data strategy should guide all your efforts to become a data-driven organization.
With the surge of many new or updated analytics technologies and platforms (GA4, CDPs, GTM Server Side, Databricks, Oribi, Stream Analytics, Mode, Cloud Analytics, etc.), I wanted to give you a quick reminder that your data strategy should guide all your efforts to become a data-driven organization. The size of your organization should not act as a barrier to launching a data strategy. The crucial point is not relying on intuition alone to make business decisions.
So, why is a data strategy still important (and even critical!) in 2021 for a performance-driven company?
- With the COVID-19 crisis, many organizations are accelerating their digital and business transformation endeavours to become more agile and nimble in their day-to-day operations. While customer satisfaction and cost reduction are important drivers behind these efforts, the data is an important pillar of any business transformation. (You can’t improve what you can’t measure!)
- According to a survey by Gartner, many CFOs and CDOs are focusing on turning data into valuable business assets (Gartner: Top Priorities for IT: Leadership Vision 2021, Data and Analytics Leaders). A data strategy is a necessary tool to achieve this goal.
- For CMOs, justifying digital marketing expenses will be increasingly difficult in a new world where digital media attribution will become a challenge (if you haven’t yet, get familiar with the cookie apocalypse coming our way). The importance of building a data strategy that takes into account the critical nature of this new reality in terms of performance measurement.
A data strategy is not:
- A list of analytics tools that you are planning to buy next year;
- The number of new analytics employees you will hire next year;
- A wish list of data sources you need for a vanity dashboard.
In simple terms, a data strategy is:
- A plan to acquire the data you need in order to make better decisions and improve your business performance;
- A strategy to bring value to your data.
“Bringing value” can mean a lot of things depending on your discipline:
- Finding insights to improve customer experience;
- Finding insights to optimize marketing spending for better ROI;
- Finding insights to guide your strategy (competition, market, benchmarks);
- Finding insights to improve product adoption.
What are the key steps to follow when building a data strategy?
- Select the business area where you want to make some improvements (marketing, product, customer service, operations, finance, sales, etc.) and make sure you understand its inner workings. You need to find a real business need or challenge that can be resolved with data.
- Set clear business outcomes. Without clear business outcomes, you should not be embarking upon a data strategy. It should help improve something – something that is measurable. For example: improving customer satisfaction by X%, improving customer retention by X%, improving customer value by X%, reducing the cost of an operation by X%, etc. A target should be set to be able to decide later on if the data strategy has helped improve the chosen KPIs.
- Assess your current state. Knowing your maturity level in terms of data and availability of data sources is critical. It will enable you to size up the task ahead, and build your strategy around a set of manageable modules and deliverables.
- Build your strategy for the future. You need to decide how to source your missing data while maintaining a strong foundation in data governance (validity, accuracy, ownership, accessibility, privacy and regulations, etc.). Think about what technology is needed to collect, clean, store, and transform your data, as well as which experts you’ll need to turn data into business insights and actions. (That’s where the real value lies, after all.) Spend time assessing your risks and blind spots.
- Launch a Proof of Concept (POC). Before scaling your data initiative, you’ll need to build a POC to validate all your assumptions and measure the desired outcomes at a micro level.
- Measure and Iterate. Refining your data strategy is a never ending story.