How does a data strategy fit into your digital strategy?

A digital strategy is the starting point of every digital transformation. Here, the use of data is an essential component. If your data is in order, it will help you to improve your customer approach and your business processes. It not only guides the development and use of new technology, but sometimes even the use of new business models. But how do you ensure that your data actually contributes to innovation, growth and more efficient operations? This article is the first in a series of three in which you can read all you need to know about activating data i.e. unlocking, understanding and using data – along with the insights it provides. We not only look at the strategic and technical aspects of data activation, but also at the mindset and culture required to extract as much value as possible from data. In this article, we examine the usefulness of a data strategy.

Digitalization produces data. Mountains of data. The question is: how can you use this data to create more value for your organization? The computing power and storage capacity of computers and other digital devices have increased so much since computers first appeared that it almost beggars belief. The number of innovations these advances have made possible is staggering. For instance, connectivity, sensors, cloud computing and data analysis. Perhaps even more impressive is the personal development we have undergone as human beings and the way in which we deal with all things digital.

From data to value

All this data opens up opportunities for gaining insight and increasingly allows you to do so in advanced ways. With business intelligence we could already use data to look back on what had happened and analyze it. Using advanced data analytics, we can discover why it happened and predict when something will happen. Thanks to data science, we can even let something happen on purpose in order to discover how we should respond if it occurs in reality.

If organizations use all these innovative new technologies, they can increase their productivity in great strides. Organizations can use information and analysis to guide them and make predictions, leading to better decision-making. However, activating this data is a prerequisite. If you don’t use the insights and convert them into actions, they will ultimately be of no use to you.

Over the years, you can see that curious organizations often end up with a winning hand. Thanks to their curiosity, they have a deeper understanding of the market and they know their customers better. Moreover, they have a firmer grip on production and costs, and that is ultimately reflected in the top and bottom line.

Data strategy – where do you start?

You start with awareness in your organization of the need to change, to improve and to become data driven – and curiosity about what all of this means. Next, you take the following steps.

1. Determine needs and opportunities. What is the business need – and the strategic need – for becoming data driven? This translates into opportunities for data-driven growth and improvement.
2. Identify sources. What distinctive and valuable data do you need to realize these opportunities?
3. Unlock data. This involves data from both internal and external sources. Make sure this data is accessible where it is needed.
4. Create insights. Once the data is accessible, ensure it delivers the insights you desire.
5. Implement. You take the solution into production.
6. Activate data. Finally, you ensure that the changes become part of the daily routines of the people in your organization. It’s very important that you take the organization along with you by building and sharing knowledge.

This is an iterative process, based on a foundation of technology, infrastructure, organization and people.

More data driven, better performance

Why do data-driven organizations actually perform so much better? Most importantly, it’s because they’re able to improve efficiency and realize long-term growth. They are able to act faster on all fronts, with a sharper focus. Moreover, they know all the ins and outs of their products and services, and they know what their customers want. They acquire this knowledge by continuously requesting feedback. By talking to customers, trying things out and processing the results. This also means that they dare to fail. If something doesn’t turn out as desired, it’s used as feedback for doing things a little differently next time. This requires two things: the discipline to gather and organize feedback (e.g. through frequent testing) and the courage to fail. For some people, the latter can still be a bit nerve-wracking. Finally, data-driven organizations are more agile, which enables them to implement changes more quickly.

For instance, suppose you’re implementing a data strategy for a mortgage brokerage. In that case, you make sure that the mortgage brokerage knows everything that the law permits about its customers. In this way, you enable the mortgage brokerage to support its customers optimally when they experience life events, like moving in together, relocating or a death in the family.

The following drivers make the difference here.

• Customer satisfaction. We know that data helps enterprises optimize the service they provide to customers. One example is the recommendations Netflix makes. Another is virtual fitting rooms that allow customers to try on clothes with the help of augmented reality.
• Efficiency. Higher processing speeds lead to higher quality and lower costs. An example is a fraud detection system where a model analyzes customer behavior and detects suspicious or anomalous actions. Detecting and preventing downtime in production also helps to increase efficiency.
• Sales and profits. If you use a model that predicts what your customers will demand or will do, it will lead to higher sales and profits. For example, optimizing the price of a vacation home, predicting what items you can best offer in a sale and when, and how you can optimally arrange your store.

How do you make it concrete?

A business strategy is about markets, customers, products and services. In your data strategy, you should clearly state how it supports the business strategy. You do so by incorporating the following elements into it:

• Data vision statement. What do you want to achieve with your data in terms of advancing the business strategy? For example: learn more about customers, serve customers better at specific points in the customer journey or be data driven in x years to create better products.
• Business cases. What are the first steps and business cases to leverage data? Are you using business intelligence? Or AI instead? Or first a data management case to get the quality right?
• Guiding principles. Determine what your source data is, how it should be maintained, what quality requirements you set for it, and who is allowed to access it and modify it.
• Long-term goals. Within what time frame do you want to achieve something? For example, becoming data driven in three to five years is entirely possible if everyone puts their shoulder to the wheel.
• Short-term goals. First, to better support your customers, you address the quality of your data. To do this, you need an organization that is committed to improving data. In addition, people (or more people) in the organization need to learn how to analyze data and to understand how the technology works.
• Organization and roles. This element makes your data strategy complete and concrete and comes in the form of a roadmap. You can read more about it in the next article in this series.

Conclusion

When it comes to digital strategy, curiosity is the key word. Not only the initiators, but everyone involved should be curious and provide input. A sense of urgency is also important. To achieve that, look at a business unit where things could be better and where data could help. From there, it’s easier to come up with a business case, develop a vision and get started.
Don’t forget that in this process, it’s always a good idea to think big and start small. So start from a suitable customer journey, production process or product.

In the next article in this series, you can read about why a roadmap is important to ensure your data strategy will lead to success and how to create such a roadmap.