The 5 ingredients of a successful data strategy

Many organizations continue to struggle with data problems, mostly because a long-term vision is lacking on how to treat data as a corporate asset. By focusing on point solutions limited to tackling individual problems and ongoing data issues, organizations miss opportunities to develop data initiatives that profoundly impact their business. It takes nothing less but a comprehensive data strategy to achieve that. But what exactly is needed to develop and implement a successful long-term data strategy? Let’s look at five ingredients you can’t do without.

Think possibilities

Most frequently, the development of a data strategy is driven by current demands and needs. For instance, financial institutions need to comply with the BCBS 239 regulation to ensure data lineage best practices. Or internal stakeholders want to see specific questions answered on topics such as customer profitability, customer loyalty, or the effectiveness of marketing campaigns.

And that’s fine to start with, because this keeps everybody focused on solving today’s data issues. But then, this rather minimalist approach is also one of the most common pitfalls when developing a data strategy. That is because people fail to look at the possibilities that would come within reach by broadening their field of view. If, for example, data lineage has been identified and documented to comply with legal requirements, how could we use those data to empower new applications and increase ROI? Which are the most pressing questions our business stakeholders are struggling with today, and how should we leverage existing data to create more impact on the business?

Therefore, thinking possibilities and responding proactively to tomorrow’s business demands are crucial in developing a future-proof data strategy and building a roadmap to implement your plan.

Strive for impact

But even with a compelling roadmap in place, data teams assigned to individual data projects should continue to look beyond the actual project implementation, never losing sight of the broader picture. As each data project's ultimate goal is to create a meaningful impact on your business, project teams need to reflect critically on what it takes to achieve that goal.

To put it bluntly, your teams should never be driven by the challenge to develop a new algorithm to predict, for example, customer churn. Instead, they should focus on what is needed to minimize customer churn. That includes dealing with questions such as

• what is required to ensure that our internal stakeholders can meaningfully use customer churn predictions?
• how to measure whether we can reduce customer churn thanks to the prediction model?
• and what do we need to do if that is not the case?

All of this demands an open mind and a critical approach, making sure to thoroughly test project outcomes, experiment with alternative solutions, and implement the changes that have proven to empower your business stakeholders.

Break through silos

Looking into the ingredients of a successful data strategy is not just a matter of identifying possibilities and goals. It is also strongly linked to how data teams operate within your organization. Even if Data Governance teams, Business Intelligence teams, and Data Science teams use the same type of data, the fact that they tend to work in silos potentially leads to massive inefficiencies.

What, for example, if your Business Intelligence teams and Data Science teams were to use different definitions of what a client is? How would that impact the dashboards and algorithms they are developing to support your business stakeholders? While such a scenario may seem far-fetched, it is what often happens in reality. Working in silos not just duplicates (or even triples) the effort to define business terms that should be shared by the entire organization, but is also a recipe for creating confusion. Imagine BI dashboards providing insights in customer sales are based on different criteria compared to prediction models aimed at reducing customer churn. How would that make it easier to keep your sales and marketing aligned?

These kinds of issues do not just lead to inefficiencies. They may also inspire a lack of trust in the data themselves and may even lead to wrong business decisions. So rather than leaving things up to individual teams, a sound data strategy should focus on breaking down silos and creating a common business glossary. Such a centralized inventory of terms, used by your entire business community and agreed upon by everyone who uses them, is indispensable in deploying data initiatives that create economies at scale.

Map out where you stand today

All of this underlines the importance of developing a data strategy that brings all stakeholders together around a common understanding of what data stand for. But as such a data strategy is being developed, it remains crucial to map out where your organization stands today in terms of architecture, processes, and technology. Your current data maturity determines which path to take, how fast to go, and how to coach all stakeholders along the road.

The biggest challenge, however, is to get all people on board. Surely, there are quite a few technological hurdles to be taken. But even if you have made significant hardware and software investments to deal with those technological challenges, the key question remains whether your people have the knowledge, the expertise, and the motivation to use data technologies in the best possible way. A critical evaluation of where your entire organization stands in terms of data literacy must also assess people’s current data knowledge level and include a plan to develop the competencies needed to implement a future-proof data strategy.

Get the right talent on board

In addition to coaching current team members as part of implementing a data strategy, organizations also need to get new talent on board to build teams with the right mix of skills. That starts, of course, with an analysis of the skills that are needed. Which of these skills can we find among current team members? For which skills do we have to insource new people, and how will we do this? Which skills and competencies do we need to have in our permanent staff, for example? For which skills should we temporarily employ freelancers? And where do we need consultants to help us guide change processes throughout the organization?

That is a complex puzzle that many organizations struggle with today. The difficulty is not so much in finding technically skilled people but in attracting people who combine technological skills with a feeling for creating business impact. But the real challenge lies in having a strategy and a plan to build the team that takes you where you want to be in three to five years from now, outlining a career path for the people who will develop your organization’s value proposition for the future.

How can we help?

We’ve helped organizations in various markets implement Data Governance, Business Intelligence, and Data Science projects, working closely with all stakeholders to create buy-in throughout their company and supporting them to realize a future-proof data strategy.

If you struggle with identifying the right data strategy for your organization, don’t hesitate to reach out to us. We’re here to help you.