It will take extensive amounts of time and costs, but the outcome will be worth the effort, argues this expert.
In today’s competitive technology world, having a coherent data strategy —characterized by a clear vision of how to gather, manage and maintain data—provides an invaluable edge. Good data management gives businesses agility, better connectedness to their consumers and better operational efficiency.
However, leveraging data requires careful thought, planning and strategic execution. This translates into three key steps every company must take to be successful: Craft a specific strategic vision; build a data collection and governance strategy; and bring the strategy to life through well-executed data use cases.
Setting a strategic vision
Despite appreciating the importance of data, many companies still struggle to develop a data strategy that complements their business vision. This is often the result of lack of clarity around the key needs the business is hoping data will solve, and the optimal outcome it hopes to achieve from the data analytics effort.
Identifying these two goal posts is essentially the starting point and end point of a data strategy: it provides the direction to come up with an actionable plan.
It is worth noting that there can be multiple and often shifting reasons for a company’s data approach. For example, in a normal year in e-commerce, data analytics may be used to drive customer acquisition and better logistics. In uncertain times, analyzing changing customer behavior may be more important. A company’s data strategy should include this overarching holistic vision of its use.
Building flexibility into the data vision is important so that analytics can pivot as markets change. It also maximizes the upfront effort it takes to get solid data collection, governance and analytical capabilities in place.
Building the collection and management strategy
Once the strategic vision is determined, companies need to move into defining the data they need, how they will organize it, and the data analytics capabilities that will be layered on top to best use the information.
The primary challenge in this step is the largely unstructured nature of business data. Accelerated by the advancements in technology, the large amounts of information have to be tracked, but a majority is still unused for analytics. Much of the data lies in disparate systems and in formats that cannot be easily used. Furthermore, poor data quality and the inability to access the necessary data in a timely manner, are also significant obstacles.
Pulling the information together, cleaning it and ensuring that the final data sets are of usable quality takes a significant amount of time and it is not a trivial exercise, although it is one that reaps great benefits when executed correctly.
As companies prepare the inputs into their data strategy, they need to simultaneously build the machinery that will operate around their data. These tools can range from predictive analytics using AI, to visualization software that makes it easy to discern meaningful trends and patterns.
This machinery needs to be cybersecure by design and managed with sound governance measures. Companies should focus on establishing principles that clearly set the responsibilities, accessibility and accountability measures that will be used to protect the information. This protection is an obligation they owe their customers.
As companies work on data governance, they will be forced to examine trade-offs that have far-reaching implications on the business. For instance, to encourage extended dataset usage, companies may want to ensure that the latter is readily and easily accessible. However, this may also come at the expense of greater security and privacy risks. Similarly, prioritizing speed in data collection can sometimes lead to lower data quality. Management will need to evaluate these tradeoffs and work to best resolve the inherent tensions.
Furthermore, the rise of consumer privacy-related concerns requires companies to ensure they treat their consumers with data decency. This means consumers should be offered the option to assess, amend and remove their data. This transparency is critical to building consumer trust and consent in how data is used and collected.
Bringing data to life
Once data is made useable, technical capabilities are defined and deployed; and governance structures are institutionalized; companies must integrate the use of data analytics into their day-to-day operations. For organizations trying to create more data-driven outcomes, not connecting data analytics well to decision-making processes is one of the biggest pitfalls.
This can be addressed by creating compelling data use cases that will motivate employees to acquire the necessary skills, and understanding the data culture they need, to have more optimized use of data. For staff, having regular, data-driven dialogs with decision makers is also a powerful change-management tool to ensure everyone is aligned.
The process of using data for analysis should ideally be a virtuous cycle. When companies achieve small wins—spurred by the insights derived from the newly implemented data system—they should use these to encourage continuous evolution in their data analytics and management systems.
This effort can include identifying more complex business needs to which data can be applied; collecting new data points; and streamlining the data architecture.
It is understandable that even with the innumerable benefits, some companies may still be hesitant to adopt a data strategy due to the extensive amount of time and costs involved. However, they should rest assured that the outcome is worth the effort.
A successful data strategy—defined at the start, executed thoughtfully and refined over time—will pay rich dividends that far outweigh the initial investment: by helping to drive the businesses forward in every situation.
