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Data Preparation: What it is, and How to Do it Right

Authors Photo Precisely Editor | September 26, 2022

“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.”

Data preparation: who’s doing it, how quickly, and how well?

The term “data preparation” itself has evolved as fast as the tools for the task, and those who continue to use outdated analytics tools that rely on technical resources will fall further and further behind the competition. Organizations who aren’t making their data work for them will simply cease to exist.

Traditionally, data preparation involved technical tools that required coding expertise and other specialized skills that could only be performed by a select few: think 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.

Financial Shared Services and How to Avoid the Inefficiency Trap

Data preparation tools

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. Here’s a look at some of those tools and why they simply aren’t up to par anymore:

  • Spreadsheet applications are unreliable, unscalable, and generate frequent errors
  • 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

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Agile data preparation, on the other hand, solves many of your biggest issues with these legacy data preparation tools and allows more people to engage in data analysis through a self-service model. This model:

  • Delivers speed with flexible data handling – virtually eliminating pre-planning and data modeling time
  • Offers flexibility when deployment time is critical, taking the place of traditional ETL tools. A library of interfaces and adapters that gives users rapid access to data from virtually any data source
  • Provides rapid prototyping of data flows before integrating them into traditional ETL environments.
  • Empowers business users by enabling them to address new questions as they arise – eliminating the need to involve development staff that adds to expenses and slows down the process
  • Enables on-the-fly improvements to data quality and adjustments to business logic, through a single, visual workflow interface. Users can easily manipulate data, blending data from different sources to create custom analytics that yield highly accurate results.

With these increased efficiencies, your team will have stronger analytics, better results, and more time to focus on moving the business forward.

Are you looking for more information about data prep and self-service analytics? Read our white paper Advancing Self-Service Analytics and learn how to make intelligent business decisions quickly.