A solid data strategy is critical for business success in today's data-driven world. Yet, many companies struggle to make their data strategies work, leading to missed opportunities, wasted resources, and stagnated growth. What's going wrong, and how can CEOs fix the problem?
This article explores the common pitfalls of data strategies and provides practical steps for CEOs to align their teams, set clear priorities, and ensure their data strategy delivers real business impact.
Before discussing why data strategies fail, it’s crucial to distinguish between data vision, data strategy, and data products. Each of these serves different roles and requires different leadership approaches.
The data vision is the long-term aspiration for how data will be leveraged within the company, and it serves as the North Star guiding the organization's data initiatives.
It should outline:
Your data vision should inspire both the data team and the business stakeholders. It’s a strategic narrative that paints a picture of where the organization is headed in terms of data capabilities over the next 3-5 years.
Key metrics to measure its success include:
While the data vision is aspirational, the data strategy is operational and action-driven. It defines the specific steps needed to achieve the vision. This includes:
The data strategy links business goals to measurable data-driven outcomes and ensures accountability.
Metrics to track for success are:
Data products are the tangible outputs of the data strategy. These include:
To foster a product-oriented approach to data, treat data products like any other product—defined by lifecycle management, user feedback, and iterations.
Success is measured by:
CEOs need to steer and align these three elements:
One of the most significant challenges arises from a disconnect between executives and data teams. While data professionals focus on practical solutions—gathering actionable data and implementing measurable KPIs—executives often take a broader, more abstract approach. This gap creates confusion, with management discussing visions or high-level concepts while data teams are asking, "What exactly do we need to measure and how?"
CEOs must play a crucial role in bridging this gap by fostering open and focused discussions between both groups. It's essential to shift conversations away from abstract visions or technological tools and towards specific, actionable KPIs that align with the company's immediate objectives.
Example: At a rapidly growing e-commerce company, the CEO implemented weekly "data alignment" meetings. These sessions brought together executives and data team leads to discuss specific, measurable goals for the quarter, such as "Increase customer retention rate by 5% through personalized product recommendations."
Many companies get lost in endless debates about the latest technologies or lofty data principles, which creates confusion and delays. Executives often want to discuss the future of their data landscape five years from now or the technical architecture that will support it. But without clear, immediate objectives, these discussions lack direction.
Instead of focusing on tools and abstract principles, CEOs should center the conversation around clear KPIs that drive business goals. What does the company need to achieve right now? How will we measure our success in customer acquisition, retention, or product growth?
Example: A SaaS startup shifted its focus from debating cloud platforms to defining key metrics like "Reduce customer churn by 2% month-over-month" and "Increase feature adoption rate by 10% for new users." This clarity allowed the data team to choose appropriate tools and methods to achieve these specific goals.
The Problem
Often, there is no clear ownership of KPIs, leaving data teams without direction. When a KPI moves—whether up or down—teams are unsure who is responsible or what actions need to be taken.
Every KPI must have a single, accountable owner who can manage and report on its performance. This person should know exactly what needs to happen when a KPI changes. CEOs must ensure this ownership is clear and enforced, assigning specific roles to ensure nothing falls through the cracks.
Example: A B2B software company implemented a "KPI Ownership Matrix," assigning each core metric to a specific department head. The head of customer success, for instance, became responsible for the "Net Promoter Score" KPI, with clear action plans for both positive and negative movements.
It's easy for companies to fall into the trap of collecting data for the sake of it, without understanding the real value. The result is a bloated data warehouse filled with unused information, a lot of DATA NOISE, wasting resources, and complicating decision-making.
CEOs should push for a strong focus on Data ROI and Data Utilization:
Example: A retail chain implemented a quarterly "Data Audit" process. They found that 60% of their collected customer data hadn't been accessed in over a year. By focusing on the most utilized and high-ROI data points, they streamlined their data collection, reduced storage costs by 30%, and improved the speed of their analytics processes.
While CTOs often play a crucial role in data management, assigning data strategy solely to them can limit its effectiveness. By placing data strategy under the direct supervision of executives who are deeply invested in business outcomes, such as the CEO, CFO, CPO, or CMO, organizations can ensure that data initiatives are aligned with strategic goals and drive tangible value.
Instead, the responsibility should lie with those closest to the business outcomes—such as the Chief Product Officer (CPO) or Chief Marketing Officer (CMO)—who are the primary consumers of data, and I dare say even under the CEO's direct supervision.
Example: A media company shifted its data strategy ownership from the CTO to the CEO, who worked closely with the CMO and CPO on defining what matters for him and how he will measure their function's success. This led to more business-aligned data initiatives, which can result in improved content engagement metrics.
Companies often treat data as an afterthought—something to be collected and stored without a clear plan for how it will be used. This leads to a reactive approach rather than a proactive strategy.
CEOs must advocate for a product mentality in data strategy. Data should be treated like a product—built, iterated on, and continuously improved.
Example: A fintech startup adopted a "Data Product Management" approach, assigning product managers to key data initiatives. They implemented sprint cycles for data projects, regular user feedback sessions, and iterative improvements. This approach led to increased adoption of data-driven tools across the organization.
List key KPIs for tracking performance and link them to business outcomes.
Define the potential monetary impact and alignment with strategic objectives.
Hold bi-weekly sessions where executives and data teams discuss specific KPIs and progress toward business objectives.
Create a matrix that assigns ownership of each KPI to a department leader, with clear accountability.
Review which data is being used and its ROI. Eliminate irrelevant data to reduce noise and optimize resources.
As a CEO, your role in shaping and driving data strategy is crucial. By understanding the distinctions between data vision, strategy, and products, and by taking an active role in their development, you can transform your organization's approach to data.
Remember:
By embracing these principles and taking the actionable steps outlined in this article, you can lead your company to success in the data-driven future. Start with the 60-minute exercise to draft your data strategy framework, and build from there. The journey to effective data leadership starts with you.
You can find the 60 minutes exercise in here.