Data-driven organizations rely on three strategies to be successful: an enterprise data platform, the right operating model, and a company-wide data culture.
In an era where every company wants to be a data-driven business, enterprises across industries leverage data to streamline operations, increase efficiency, and create better customer experiences. Successful data-driven organizations rely on three strategies to accomplish these goals: an enterprise data platform, the right operating model, and a company-wide data culture.
1) Build an Enterprise Data Platform
Building an enterprise data platform (EDP) with “truth” in data is key to unlocking data’s value. Many companies started with fragmented data with ownership across distributed groups and successfully moved to a single centralized governed EDP. Broadly speaking, the clear and winning model is “data at the center, analytics at the edge.”
Successful data-driven organizations are intentional about how data is managed, governed, controlled, used, and owned. Even for truly data-driven companies, day-to-day management, and governance over data quality is a continuing journey. Unfortunately, the focus on outcome data often means organizations often overlook the criticality of the input data.
Many companies are eager to start using artificial intelligence and machine learning (AI/ML) to run predictive analytics. Without an enterprise data platform, that journey cannot even get started. Whether leveraging predictive maintenance in machines or medicine compliance, it’s the data that makes the AI recommendations possible. Without the right data foundation, any AI/ML initiative is dead on arrival.
An Enterprise Data Platform can also act as a single source of truth. Data can tell whatever story you want to hear, leading to a proliferation of reports and the resulting loss in usefulness. As data and its usage proliferate across the corporation, business leaders increasingly seek a single source of truth. Successful data-driven organizations have significantly reduced report creation busy-work and instead are investing in quality and singularity of data and making the certified data available to the businesses.
See also: Everyone Wants to be Data-Driven, but Few Want to do the Driving
2) Design the Right Operating Model
Successful data-driven organizations are moving to an operating model that combines a central data organization with analytics embedded into the businesses. Data and analytics teams are becoming more strategic partners to the business, and they are clearly moving away from being order takers to being partners and leaders.
Leaders agree that the data organization is not about technology alone. It’s also about people and process, often requiring a top-down mandated approach to data and data-driven decisions. Many great success stories exist around new CEOs or divisional heads completely reorienting decision-making, thereby setting the foundation of data and operating models that deliver larger results beyond day-to-day decision support.
One area of the operating model to monitor is the proliferation of roles across the company. It feels like the “data-something manager” is becoming the most popular role in recruiting. Rectify any over-swing of the pendulum. A best practice is exercising two concepts – thinning the teams out and driving clarity of roles. To the latter, streamlining three interactive but distinct role types across the corporation – business analyst, data analyst, and data scientist – and deliberately removing any hybrid roles is a tried and tested approach.
Finally, a long-term framework around the operating model is critical. Take care to purposefully orient around three sources of value. The first is running the business and decision support – for instance, driving revenue-neutral cost savings. The second is delivering higher value to clients – for instance, achieving equitable medical care. Lastly is monetizing data by creating new business models – for instance, FedEx providing internal data that can validate addresses in client databases for fraud protection. The key to not being short-sighted with data is developing a strategic framework with intentionality around the long arc of value.
See also: Empower Decision Makers: Going from Data-Driven To Data-Agile
3) Build a data-driven culture
The culture within and across the business is vital – it must understand and see data as a new asset class. There is often a vacuum in leadership around data-driven cultures, and the best CIOs and CDOs play a huge and visible role in helping businesses understand how to use data to achieve better outcomes.
One critical distinction that has come to light between data-efficient organizations and data-driven organizations, and the corresponding role of the senior technology executive, is the focus on asking the questions the business does not know to ask. The other critical success factor is to narrow the scope and focus on developing and driving initiatives that will have an impact at scale and within a reasonable timeframe.
But along with the data democratization we want to see, several new issues emerge, especially around curiosity, accountability, ownership, and change management – often bundled together as “data literacy.”
A final word
A grounded three-part framework can provide the foundation to accelerate corporate data literacy programs. The first piece is introducing new tools that are increasingly designed for businesspeople instead of just data scientists. The second part is driving agile programs across the company, demonstrating the journey from ideation to visualization to outcomes. Ultimately, the third piece is affecting mindset in making data a first-class citizen. The best practice is to drive mindset top-down, not bottom-up, starting with the chief executive’s office.
