Some time ago, we asked the question, “Do you speak data governance?” because everyone needed governance, but no one seemed to have the same definition for it. Fast forward to 2019, and now we’re talking about the evolution of data preparation. Here, the quandary isn’t whether everyone understands data preparation, it’s who’s doing it, how quickly, and how well. The term “data preparation” has evolved as fast as the tools for the task, and those who continue to use outdated analytics tools that rely on technical resources will fall further and further behind the competition. And organizations who aren’t making their data work for them will cease to exist.
Data preparation is the process of gathering, combining, cleansing, structuring and organizing data so it can be analyzed as part of data visualization, analytics and machine learning applications. Sound complicated? Traditionally, it was, with technical tools that required coding expertise and other specialized skills that could only be performed by a select few. These were typically data scientists, working within the confines of IT departments. Enterprise access to data and analysis went through them. They fielded all requests, preparing and analyzing data and providing results to users. While not a perfect solution, it worked. Requests came in, reports and results went out.
Spreadsheet applications remained the analysis tool of the masses, with spreadsheet wranglers doing some amazing things and others making costly mistakes, with all of them limited by a lack of flexibility, scalability, and repeatability. Meanwhile, BI tools proliferated, further teasing business users with the possibilities of detailed data reporting and visualization. With so much potential, requests proliferated, and backlogs soon followed. IT was understandably overwhelmed with user requests, taking weeks and often months to fulfill them.
To appease irritated users, IT departments in some organizations liberated data and opened access to company datasets to allow “self-service” data use. Predictably, this generated a new tsunami of siloed data, reports with contradictory information, and widespread mistrust of data’s source and accuracy.
As these issues proliferated, business leadership teams across the world began to see the success data-driven organizations could derive as big companies like Amazon, Facebook and Google leveraged data analytics for profitability, innovation and growth. Organizations had long used data analysis for myriad one-off business operations and strategic purposes, but now it seemed everyone wanted to capitalize on the analytic possibilities.
The fact is, the democratization of data is well underway. As business users are increasingly empowered with the knowledge to leverage data, they are demanding a stake in how data is managed and deployed. Because the traditional tools of data preparation are notoriously slow, there’s a desperate need for advanced, powerful and flexible tools that handle big data of all types. That’s why self-service data analytics is the next great thing.
Historically, data preparation has been a time-consuming, inefficient process for a wide range of reasons. IT bottlenecks and extensive manual processes have meant data preparation takes, and wastes a tremendous amount of time. Data pros struggle to locate and access data, integration is labored, and cleansing can be torturous.
The tools are part of the problem as well. Traditional tools for data preparation are great at so many things, but are now being asked to do things their developers never envisioned. Spreadsheet applications generate frequent errors, and are unreliable and unscalable tools in data preparation. Traditional ETL solutions are cumbersome, requiring schema-based data flows and “waterfall” development cycles. Traditional relational database models can have costly overhead and be highly rigid.
Agile data preparation, on the other hand, solves many of the biggest issues with these legacy data preparation tools and allows more people to engage in data analysis through a self-service model. It delivers speed with flexible data handling, virtually eliminating pre-planning and data modeling time. When deployment time is critical, it can deliver flexibility and take the place of traditional ETL tools, and also provide rapid prototyping of data flows before integrating them into traditional ETL environments.
To empower business users, the self-service interface allows them to address new questions as they arise, eliminating the need to involve development staff to add to expenses and slow down the process. A flexible solution offers a library of interfaces and adapters to give users rapid access to data from virtually any data source, and a single, visual workflow interface enables on-the-fly improvements to data quality and adjustments to business logic. Users can easily manipulate data, blending data from different sources to create custom analytics that yield highly accurate results.
To learn more about an integrated data preparation solution that can agilely serve your analytics needs, download the data sheet below.
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