· 4 min read
Invest in Data Strategy at the Right Level
Invest wisely in data strategy by assessing your current data maturity and prioritizing what truly matters.
By: Kursat Hosel
Striking the Perfect Balance
In today’s data-driven world, the temptation to either underinvest or overinvest in data strategy is strong. On the one hand, some organizations treat their data strategy as an afterthought—a checkbox to tick. On the other, you find companies pouring vast resources into data initiatives without a clear path to ROI. Both approaches can lead to problems. The sweet spot lies somewhere in the middle, where you invest at the right level for your business needs.
The key is balance. By assessing where your organization sits on the data maturity curve, you can ensure that your data strategy is appropriately scaled to provide sustainable, long-term value. The goal isn’t to throw money at data for the sake of it. It’s to invest thoughtfully in areas that matter most to your business objectives—today and in the future.
Why Underinvestment Falls Short
Underinvesting in data strategy may seem like a cost-saving measure, but it often leads to inefficiencies and missed opportunities. A lack of adequate resources can result in poor data governance, unstandardized processes, and teams relying on manual or outdated methods for analyzing and managing data. In the long run, this neglect can stifle innovation and prevent your business from making informed decisions.
More importantly, underinvesting leaves data quality to chance. Without proper investment, you risk inconsistent, incomplete, or unreliable data. And while some level of imperfect data is tolerable in specific use cases (more on that later), consistently poor data can impact decision-making and erode trust in your data systems.
The Problem with Overinvestment
At the opposite end of the spectrum, overinvesting in data strategy can be equally dangerous. This often happens when organizations get caught up in the latest trends or technologies without considering their specific needs or data maturity. Sophisticated platforms, tools, or consultants can quickly eat up budgets, especially if there’s no clear roadmap for deriving value from those investments.
Pouring too much into data infrastructure, analytics, or governance without a solid plan for ROI can lead to a waste of resources. You may have the flashiest tools, but they can become costly distractions if they’re not solving real business problems or aligned with your organizational goals.
Invest with Your Data Maturity in Mind
The answer lies in understanding where your organization currently stands on the data maturity curve. Companies just beginning their data journey don’t need the same level of investment as those with fully established data operations. Are you just starting to centralize your data, or are you already running complex machine-learning models across departments? Your investment should match your maturity, scaling up as you progress.
For example, a company in the early stages of its data strategy might prioritize basic data governance and data collection processes. It doesn’t need advanced AI models just yet, but it does need a solid foundation. Conversely, a mature organization may need to invest in predictive analytics, automation, or data integration across complex systems to stay competitive.
Data Quality: Invest at the Right Level
One crucial element of any data strategy is data quality, but not every use case requires the highest level of data perfection. In some scenarios, such as exploratory data analysis or internal reporting, you can tolerate less-than-perfect data without significant consequences. However, for critical business operations, customer-facing platforms, or compliance reporting, data quality must be a top priority.
This is where the concept of investing at the right level becomes especially important. You don’t need to aim for flawless data in every situation—sometimes “good enough” is truly good enough. The key is understanding the business context and determining where it’s worth investing in top-tier data quality and where you can afford some flexibility.
For instance, if you’re running an experiment to test customer preferences, the data doesn’t need to be flawless—it just needs to be directionally accurate. On the other hand, if you’re managing financial data or handling regulatory reporting, your data quality must be airtight. Knowing when to aim for perfect data versus acceptable data will help you allocate resources effectively and avoid overinvestment in areas where it’s not necessary.
Sustainable, Long-Term Value
Ultimately, the goal of any data strategy is to deliver value—both now and in the future. By investing at the right level, you can build a sustainable strategy that grows with your business. This means continuously assessing your data needs, reevaluating your maturity, and adjusting investments accordingly. Rather than chasing short-term gains or becoming bogged down by unnecessary complexity, a balanced approach ensures that your data strategy evolves as your business does.
In the end, your investment should be driven by your organization’s specific goals, not by the latest trends or the fear of falling behind. Keep your eye on what matters: unlocking value from data, ensuring quality where it counts, and creating a strategy that’s built to last.