What is Self-Serve Data Preparation and How Can It Support Business Users?

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Click to learn more about author Kartik Patel.

Self-Serve Data Preparation is the next generation of Business Analytics and Business Intelligence. Self-Serve Data Preparation makes Advanced Data Discovery accessible to team members and business users no matter their skills or technical knowledge.

What is Self-Serve Data Preparation?

In the past, preparing data for analysis was a time-consuming process, a task that was relegated to the IT team and involved complex tasks like Data Extraction, Transformation and Loading (ETL), access to Data Warehouses and Data Marts and lots of complicated massaging and manipulation of data across other data sources. Today’s organization does not have the time or the money to provide IT resources or to satisfy the day-to-day data inquiry requirements of its ever-changing organization and its business market.

Enter Self-Serve Data Preparation, a set of sophisticated tools, designed for ease-of-use and access by business users in a self-serve environment. Self-Serve Data Prep provides business users with powerful capabilities to explore, manipulate and merge new data sources – all without the assistance of IT staff.

Self-Service Data Preparation solutions make it possible for business users to access data, integrated from multiple sources and to prepare that data using drag and drop features and a simple, intuitive interface. Users can perform Data Preparation, test theories and hypotheses, and prototype to test price points, analyze changes in consumer buying behavior, anticipate changes in the competitive landscape and otherwise leverage data for analytical purposes, all without the assistance of IT or analysts.

Self-Serve Data Preparation solutions provide tools that are flexible so the user is not restricted to dashboards or interfaces that are designed by someone else. The user can use the power of Self-Serve Data Preparation to compile and prepare data, test hypotheses, visualize and share data, drill-down, and drill-through data using selected data elements to prepare for and execute analysis.

How Can Self-Serve Data Preparation Support Business Users?

The best way to support business users is to give them tools that are flexible enough to be personalized to their needs, their role and the issues of the day. No matter how hard IT teams and analysts try, they cannot anticipate every possible requirement and the new, rapidly changing business landscape of today makes it impossible for business users to know what they will need tomorrow.

A May 24, 2016 Gartner report, entitled Embrace Self-Service Data Preparation Tools for Agility, but Govern to Avoid Data Chaos, offers the prediction that, “By 2017, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis.”

As the market shifts and more and more organizations adopt these crucial tools, it must be noted that, those businesses that do not take advantage of self-serve data preparation are likely to fall behind the competition and the market.

With Self-Serve Data Preparation tools business users can gain insight into buying behavior, analyze supply chain issues, identify new markets, locations and opportunities and anticipate resource and training needs using selected data to easily illustrate results and uncover patterns and trends.

With Self-Serve Data Prep, business users can avoid the delays and out-of-date, inaccurate reports that sometimes result from incomplete requests or overworked IT teams. The business users gets access to data from numerous data sources and can easily select data, execute searches and analyze data to make timely, accurate decisions. The organization achieves impressive ROI and TCO, and better business results, and the business user is empowered to get the job done right.

Supporting business users with powerful tools that are meaningful to their role and goals is critical to every organization. Self-Serve Data Preparation takes the complexity out of the data prep and analytical process and results in better Data Discovery.

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