In a digital-first world, it is essential for raw data to quickly turn into knowledge, where enterprise intelligence creates new opportunities across brand new competitive landscapes. Knowledge workers must have full access to the databases and also be empowered with data literacy to self-service their data that enabling the right decisions at the right time. In a time of profound change, there’s a modern way to manage and share data.
Enter data democratization, where there’s universal access to data for both data scientists and business users, where duplication of data is significantly reduced. The imperative is simply to make data accessible across all data teams for powerful collaboration and trusted data-driven decisions.
Data practitioners have always taken the burden of not only managing data but getting it to the place where it is most needed. Teams work diligently to create value in their own domains, such as inventory, sales, and customer behaviors. But these “knowledge silos” often isolate true collaboration, and most of all prevent the best use of massive amounts of data. Data management efficiency is paramount as data scales exponentially but putting data to best use happens when data driven initiatives augment cross-collaboration. Data teams need to work together.
In addition, data team leaders always look to balance the needs of the immediate core business versus the most efficient data architectures. However, these competing needs can clash and the challenges that teams face include:
A common scenario that’s prevalent across the enterprise, and one that stifles data democratization, is where an agile manifest puts higher emphasis to a working product and a lower emphasis to data documentation. In an attempt to save time, development teams put low priority to proper data architecture and design of data solutions; instead, they generate physical data structures from the application code itself. Some see this as working backwards to make the data fit the business needs and application. This can be prevalent with machine learning, where data is selected to fit the learning outcome more exactly.
Although the data structures do support the code and can quickly be put into production, there are also important risks: the data integrity and technical data debt, as well as total cost of ownership far outweigh the time savings. These risks include non-recognized data integrity errors which result when unknown erroneous data is used to make important business decisions.
The result can be seen in poor outcomes, usually at the decision-maker level, or even worse, lower quality data sent to machine learning algorithms. Again, the democratization of data, namely using the same data sets among all teams, can set the right direction for the modern enterprise. What’s more, training of new data sets is quickly evolving as all forms of data and APIs in large volumes are changing the way we learn from trusted data.
Data as a commodity is old thinking. So, as organizations move away from the notion that data is just a static asset, awareness for the need of data-driven insights is essential. In fact, to embark on this new idea that data is more of a product – actually a living, dynamic data set – creates a mindset of collective accountability and responsibility for both the data scientist and business user.
There’s a cultural data shift today where needs occur at all layers within an organization, top to bottom and across. Data democratization is a concept that has been around for a while but putting it into practice is another thing altogether. Successful data leaders always take a pragmatic approach, so when they talk about data democratization, they see it as essential because of many factors such as:
To break the barriers between the IT and business teams, enterprises are making serious efforts into data literacy – the ability to communicate data in context. That means understanding data sources and constructs, analytical methods and techniques, and the ability to describe use cases, applications, and value. Both sides are using the same data in a new context of collaboration.
In addition, streamlined tools are becoming a vital part of strategic investment to ensure the right data is available to right people at the right time. Breaking down knowledge silos has become an urgent mission, where data solutions are not black boxes; rather a new mindset where technology teams design and review artifacts, solution diagrams, data models, canonicals, and data transformation specifications – deeper than what’s currently being developed with their non-technical partners. This must precede any coding initiative.
What’s next. Modern data literacy must now be the common language for the enterprise. For the truest data democratization, data leaders, scientists, engineers, and business users can now embrace the modern way of sharing data, and this means using a combination of both data and application driven strategies.
Rama Ryali serves as Vice President of Product Evangelism and Strategy at RightData, a thought leader for modern data. Throughout his career, dedicated to data management and implementation, Rama served as CTO and has directed many multi-million-dollar data management initiatives. email@example.com
RightData is a trusted total software company that empowers end-to-end capabilities for modern data, analytics, and machine learning using modern data lakehouse and data mesh frameworks. The combination of Dextrus software for data integration and the RDt for data quality and observability provides a comprehensive DataOps approach. With a commitment to a no-code approach and a user-friendly user interface, RightData increases speed to market and provides significant cost savings to its customers. www.getrightdata.com