Data Preparation Need Not Be Cumbersome or Time Consuming

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Click to learn more about author Kartik Patel.

Data drives decisions and, in today’s competitive business environment, the kind of fact-based decisions achieved through advanced analytics are mandatory. These data-driven decisions ensure that a business does not make a misstep or have to guess at forecasts, plans, and the root cause of problems.  

But, with so much data in the average enterprise, it can be difficult to integrate and prepare data from all available sources so that data analysts and business users can accurately analyze data and make informed decisions.  

Data lakes and information oceans can be intimidating and overwhelming. After all, what good is all that data if you can’t find the needle in the haystack? As organizations struggle to provide adequate resources, IT staff, and data scientists to clean, scrub, and prepare data, business users and management teams demand data democratization and the kind of access to data that is required if the organization is to move forward quickly and with confidence.  

No enterprise has unlimited resources or an unlimited budget to hire staff to be at the ready to answer all data preparation and advanced analytical requests.

So, it is essential that the business find a solution that is cost-effective and one that business users can easily learn and use to perform augmented analyticsassisted predictive modelingsmart data visualization, and self-serve data preparation without losing time and waiting for a skilled professional to help.

Gartner predicts that data and analytics organizations that provide agile, curated internal and external data sets for a range of content authors will realize twice the business benefits as those that do not.

With augmented data preparation features, business users with average skills can perform data preparation and transform, shape, reduce, combine, explore, clean, sample, and aggregate data without the need for SQL skills, ETL, or other programming languages.

Rather than using complex data extraction, transformation, and loading (ETL) tools, solutions that offer ETL for business users allow them to prepare data for analytics and quickly move data into the analytics system.

These sophisticated, intuitive tools let business users compile and prepare data for use in analytics to test hypotheses, visualize data, and create and share reports with other users. Machine learning capability provides guidance to determine the best techniques and the best-fit transformations for the data business users want to analyze, allowing for a better understanding of data.

This approach is the most cost-effective, productive way to gather and prepare data for advanced analytics without wasting time or resources. Every analysis is unique, and every data set requires a slightly different approach. If the business can provide easy-to-use tools, business users, data scientists, IT, and managers can engage in the kind of analysis that will reap benefits and allow for confident decisions and productive users.

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