Why Is the Shift to Data-Driven Working So Difficult in Practice?

Why Is the Shift to Data-Driven Working So Difficult in Practice?

Data-driven working: who isn’t talking about it? Everywhere I go in a professional setting, it’s a hot topic. It has been for years. Yet, I still see very few organizations where this ambition has been successfully implemented. Data is the new gold — but only if you know where to find it. It simply cannot be the case that all this potential value goes untapped. In practice, however, developing a solid data strategy, implementing it, and maintaining it is far from easy.

Organizations invest in data teams, data projects, tools, platforms, and technologies. Armies of data engineers, analysts, and scientists are hired to create sophisticated dashboards. And yet, too often, these efforts deliver disappointingly little — while consuming a great deal of time and money.

This blog explores why things so often go wrong and what’s needed to make a data strategy truly work.

Why Is It So Difficult?

Today, no one questions the importance of data. It’s a topic in boardrooms too — although recognizing its importance and acting accordingly are two very different things. The general belief is: “We have vast amounts of data, so let’s figure out how to extract value from it.” Most of this data resides in systems, on SharePoint, in Dropbox, or “somewhere in the cloud.” This sounds like IT territory — and it is — so the solution seems obvious: launch an IT project and get started. Data is gathered in a data lake, and people try to extract its (often vaguely defined) value.

Of course, no one approaches this blindly. Thought is given to critical data-related topics like reliability, security, compliance, and quality. Governance rules are drawn up and documented. These are necessary steps and part of data management and governance. So far, so good. Or is it?

Data Belongs to the Business

Data reflects your processes, your customers, your services, and your products. It enters your organization somewhere and flows through it. (This is why we prefer to use the analogy of water rather than gold — trust me, when water becomes scarce, it turns to gold.) Along the way, data is processed, split, enriched, and given added value. In doing so, it touches nearly everything your organization does.

But who owns all this data? Does Sales own sales data? Does Marketing own marketing data? Who do you call when the data you rely on isn’t right? Many employees only understand their small part of the process and have no oversight of the whole. Data affects multiple departments, yet no one truly feels responsible. Or everyone feels a little responsible. Which is worse?

It’s striking how differently we treat data compared to other company assets. No one doubts the need to develop employees, maintain buildings, or properly manage machinery. For staff, facilities, and assets, we have processes, agreements, and budgets. But who is responsible for data?

A Culture Shift Is Required

Adopting data-driven working is not something you do lightly. It requires a cultural shift and a significant organizational change. This demands different leadership from management and different behaviors from employees.

In practice, it is far from easy to develop a good data strategy that fits your organization. It’s a major investment of time, money, and people. But because the expected (or hoped-for) results often fail to materialize, many data projects quickly lose momentum and become nothing more than paperwork gathering dust. That’s a wasted investment. Worse, without data-driven capabilities, you might find your organization no longer competitive.

So, What Does Work?

The shift to data-driven working must begin with a clear and deliberate decision at the executive level: data is vitally important; it is a true asset, just like your people, machinery, and buildings. Once this conviction is firmly held and expressed, it follows that certain things need to be properly arranged. A fitting data strategy, governance, and data management must then be given the necessary attention.

Starting data-driven initiatives as an IT project misses the crucial starting point: what the organization aims to achieve and how data can help accomplish those goals. Without linking data to the broader business strategy, data remains a cost item. Investments in data management will seem pointless if no one considers how costs relate to value.

 Setting up data governance and management does not automatically create value. However, it is a prerequisite for extracting value from your data. This involves a double goal-means hierarchy: managing data only makes sense if you understand the potential value of data as an asset and how it contributes to your organizational strategy.

Assign roles and responsibilities clearly (governance) and ensure that data ownership is not just an extra title slapped onto a manager’s job description. Data-driven leadership must precede data-driven working. Data ownership doesn’t necessarily have to align with your org chart. It may be worth exploring alternative ownership models better suited to how data flows through your organization.

Assess Your Data Maturity

How mature is your organization, and what does that say about your data maturity? Organizations often overestimate their own maturity in any area, including data. If you want to set realistic, achievable goals for your data strategy, it’s strongly recommended to conduct a data maturity assessment first. You need to know where you are before deciding where to go. Frameworks like DAMA offer maturity models, and there are several practical alternatives available.

Once you have established a data strategy tailored to your organization and everyone understands the value of data and their own roles and responsibilities, translate that strategy into small, manageable steps to implement data management and governance. Not all data in your organization is equally important. Start where data matters most, where people are knowledgeable and motivated, and put your strategy into practice there. Think big but start small — and learn by doing.

It Will Get Easier (and More Fun)

Developing a good data strategy can seem daunting because data permeates every part of your organization. However, with clear prerequisites and well-chosen priorities, it’s entirely achievable. Data-driven working requires realism, committed leadership, organization-wide collaboration, and a sharp focus on business objectives. That’s what it’s all about. A data strategy is a means, never an end in itself. Avoid starting with technology and prevent it from becoming an IT party — because it never is. The only question that really matters is: how can data help achieve our organizational goals?

In Short:

• Ensure genuine commitment to the value of data at the management level
• Treat data as a highly valuable asset
• Start with business objectives — don’t let it become an IT project
• Know your starting point before you begin
• Pay close attention to the cultural change required
  

Want to know where your organization stands? Need help developing your data strategy?
Strategy Alliance is here to help!