4 key elements of a data-driven company

In the blog article What is data, and why is it valuable we discussed how data is valuable because it can be transformed into insights and information about nearly anything. But becoming the data-driven company that manages to embrace the use of fact-based information in everything from decision-making to backing up new ideas requires more  than just the data itself. 

Making better marketing decisions relies on our ability to process data, but we can only do this successfully by working within organizations that encourage this culture.

Since BI Builders was established in 2011, our objective has remained helping organizations becoming data-driven. We have identified how this journey is never about technology alone, but about how data, technology, culture, well-defined processes and responsibilities are combined, managed and facilitated. 

Here are four essentials of a data-driven company. 

1. Data, compliance and the management of it

The data you collect will not be useful until you process it to become insight for a specific organizational use case and purpose. Data-driven organizations collect relevant data, ensure its quality and make it available for the organization to use. 

The General Data Protection Regulation (GDPR) was introduced to protect the end users by regulating the amount of information that businesses retrieve from them. Understanding what information your organization has, where it is stored and how it is used is key to becoming or remaining compliant. 

Metadata is data about data. It can be used to simplify the process of finding relevant data, understand how it is structured, and lower the threshold for reuse in other data-driven initiatives. Data-driven organizations use metadata to get a holistic view of their data platform. It provides an understanding of where data is coming from and used to ensure governance and control. 

2. Technology (scalability, interaction and automation)

A key challenge for many organizations entering the data-driven journey is choosing technology. There is a huge range of technology components available, each serving its specific purpose in the data platform value chain of transforming data to insight. Data-driven organizations select technology components that interact collectively, serve their purpose well, and scale as the organizational needs grow. Data comes in a variety of sizes, structures and volumes. The choice of technology should reflect the needs with the flexibility to change as you go. 

Collection, transformation and distribution of data requires coding. Lots of coding. This code constitutes the data streams that become the data platform itself. The challenge is that manual coding is time-consuming, resource intensive and can easily become incorrect. In addition, it creates personal dependencies, and it makes maintenance and further development more difficult.

To minimize this risk, data-driven companies use automation to reduce repetitive tasks, ensure quality and accelerate speed. Automation software will build, maintain and produce the code using the same underlying structures and processes that would be used if done manually. 

The effect is, among other things, reduced development time and costs, faster time-to-market, reduced risk and increased quality.

3. Culture (transparency, responsibility and well-defined processes)

A data-driven culture starts at the top. Companies with strong data-driven cultures tend to have top managers who set an expectation that decisions must be anchored in data, and that employees are trusted to use data in their daily decision-making. 

As organizations aim at using data as a strategic asset, getting decision makers to fully trust their data remains a key challenge. Trust requires easily accessible data definitions and complete end-to-end transparency in where data is coming from and which business rules have been applied in the process. 

These are key elements of efficient data governance, and tools and processes supporting them must be generally available for the data users. A data catalog can mitigate this challenge and provide a searchable overview as required by decision makers to trust their data.

4. Ensure value

Make proofs of concept simple and robust, not fancy and brittle. In analytics, promising ideas greatly outnumber practical ones. Often, the difference doesn't become clear until companies try to put proofs of concept into production.

A better approach is to engineer proofs of concept where a core part of the concept is its viability in production. One good way is to start building something that is industrial grade but trivially simple, and later ratchet up the level of sophistication.

A data-driven company starts at the C-level

As discussed in a previous blog article, the transition toward a data-driven company poses many technical, organizational and cultural challenges. Data alone is not enough. Crafting a data-driven culture begins with the approval and advocacy of top executives. 

The importance of data and strategic advantage of embracing a data-driven culture must be communicated across the organization. To make employees believe in the culture you’re trying to foster, your leadership team must lead through example.