From Data Mess to Data Mesh: How to Optimize Business Intelligence

Digitization as a trend means the world is now generating more data than ever before. In fact, if you uploaded the entire global datasphere to DVD and stacked them all together you could circle the earth 222 times—and that was pre-pandemic.

Unfortunately, current data governance practices are quickly becoming a toxic environment that is hindering rather than fueling business growth. Whether in the cloud or on-premise, businesses’ data warehouses built for storage are often too full to manage effectively. As a result, IT teams have resorted to dumping in data lakes as a quick fix, further complicating the situation with additional bottlenecks.

The key to being able to leverage data for growth is fast and easy access. Yet this isn’t possible when businesses are reliant on IT teams to sieve through the cesspool of analytical and operational data needed for effective decision making.

So, what can be done to improve the situation? 

From data mess to data mesh 

If you haven’t already, be prepared to hear more about data mesh. The data mesh is a transformative approach to business intelligence (BI) that redistributes business architecture for improved access to data assets. For C-suite and senior executives looking to stay ahead of the curve, understanding what data mesh is will be vital for remaining competitive. 

Data mesh is more an approach than a technical product: It’s the cultural shift needed to solve the data mess problem. From monolith to microservices, implementing a data mesh approach is about enabling teams and reducing reliance on IT departments to make data usable. Instead, it encourages an outcome-focused and cross-functional approach to data governance that encourages teams to make and manage their own data products. 

For instance, a retail company could establish data teams in marketing, sales, operations, and logistics. These teams would be responsible for generating, managing, and consuming their own data products, thus promoting data ownership and encouraging collaboration.

By implementing data mesh, the marketing team could create a data product that tracks customer behavior and preferences. The sales team could access this data to identify potential upselling opportunities and target customers more effectively. At the same time, the operations team could analyze the data to optimize inventory management and the logistics team could use it to enhance shipping efficiency. 

This goal-centric and more collaborative approach leads to improved data governance and quicker decision-making. Instead of information silos and bottlenecks, data mesh enables greater collaboration between teams for overall better performance across the organization. It removes the IT middlemen. 

Fail faster, innovate harder

Successfully installing a data mesh means teams have instant access to the analytics they need to create a minimum viable product (MVP), adding value and driving growth. 

Through redistributing digital architectures, BI is optimized with real-time access to the data needed to innovate and scale operations. As such, one of data mesh’s main benefits is the unlocking of data assets, meaning teams can iterate MVPs with more speed to gain a coveted market advantage. 

With a data mesh architecture, companies are free from the confines of a monolithic data warehouse and lake structure that only expert IT teams access. Data mesh enables a decentralized paradigm and modularity within business, creating room for scale and allowing teams to innovate with minimal support from IT departments. 

IT teams become facilitators rather than overlords of new BI opportunities such as externalizing microservices for the market, modularly implemeting artificial intelligence (AI), and connecting teams with cross-functional data assets. 

Implementing a new approach 

Unfortunately, roughly 70% of digital transformations fail—mostly because of employee resistance. In the case of data mesh, it’s having employees on board and ready to embrace the change that makes the difference. 

One of the biggest obstacles to successful implementation is overseeing the pivot of ownership from omniscient data teams to a cross-functional approach. Rather than hold the lock and keys, IT departments as facilitators need to install the processes and frameworks that enable teams to innovate and increases the robustness of their operations. 

Identifying key employees as early adopters is one way to drive this change. As the human face of digital transformation, these individuals are vital in providing the complimentary soft skills needed to convince the wider pool of employees that improved BI is possible. In addition, championing early adopters can help establish the correct goals and governance for the company’s data mesh, improving efficiency as well as security by restricting who has access to what data. 

By embracing data mesh, companies can unlock data assets for teams to take control of their operations and analytics to innovate faster. With greater access to real-time data, IT departments become facilitators for teams to take responsibility for their own data products. While good data governance is well recognized for its importance to BI, it’s companies that integrate the value of data-driven decision-making into every team and function that will gain the competitive advantage.

The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "a mesh of data"