How to Clear Up Backlogged Data Requests

Democratizing Data Analysis

Mike OrtmannFebruary 20, 2019

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At every organizational level, across every line of business, and in every operational area, people understand the power of data and the potential in analytics to drive competitive advantage. Every executive is eager to put any analytics tools to the test to make things faster, better, stronger and to understand past performance and predict the future. Everyone wants the results, but to date, few could produce them.

That’s why most IT departments are inundated with data requests. When a select few resources are the only ones capable of preparing data for business users, these requests can pile up, and sometimes they must be put on the back-burner due to competing responsibilities and shifting priorities. IT becomes frustrated from the plethora of tasks already on their plate and new data requests amassing every day.

Technical staff often becomes inundated with weeks’ worth of data requests across departments and the continuous arrival of new ones. Adding to mounting workload is the long and arduous process of completing a single data request. Oftentimes, by the time IT completes a request and the user receives the results, the information is outdated and no longer valid.

Relying only on IT to prepare data is neither a sustainable solution nor an efficient one, given today’s competitive demands. Businesses require a new approach to data prep and analytics. To gain a real business advantage, businesses need a sustainable and streamlined approach to eliminate backlogged data requests and automate the data prep process.

Building a Modern Data Prep Process

 Data prep can no longer fall solely on IT’s shoulders. Businesses need new tools and strategies to remove time-consuming manual data prep, increase efficiencies and improve processes and procedures. By modernizing tools for data preparation and analytics with self-service options, businesses can speed up the process, become more flexible and enable business users to take an active role in data analysis. This can eliminate backlogs and allow organizations to better deploy valuable IT resources.

Creating a data-driven business in the digital age requires an integrated data intelligence platform that brings together data governance, data quality and analytics.  The solution should enable users to rapidly build data-rich and analytically complex applications to improve productivity and dramatically lower overall costs. To empower business users, it should feature an intuitive design with drag and drop interfaces, so less technically adept users can access virtually any data source and easily acquire and parse that data in a fraction of the time of traditional tools. Users may then visually collaborate as they extract, prepare, and analyze data from unrelated sources and create a comprehensive source for analysis.

The solution should also automate tasks like data blending and cleansing, as users profile, aggregate, correlate, and transform selected data. Collaborative feedback loops ensure that business users can quickly and easily communicate with IT when data questions do arise. These self-service capabilities put the power of advanced analytics directly into the hands of those who need it most, delivering speed to insights.

The Benefits of Self-Service Data Prep and Analytics

 With a modern data preparation process and solution suite, businesses can significantly reduce the data prep time, work more efficiently and eliminate backlogged data requests. The results speak for themselves.

For example, the IT department at a large consumer bank was receiving 20-30 data requests every day. On average, each request took 3-6 weeks to complete, due to the time needed to develop requirements, gain approvals and allocate costly, highly technical resources for each request. When requests were finally completed, the data was already outdated, generating new requests and questions from business users, in a vicious cycle.

The bank implemented an automated process for logging data requests and an agile process for provisioning results, and stored data extracts in libraries with reusable components for business users. As a result, data requests no longer required recoding or development work, empowering business users to easily acquire and leverage data for advanced analytics in hours, not weeks.

 The solution automated the data preparation process, reducing completion time from 20-30 business days to overnight—giving business users timely, relevant data in a fraction of the time. Within 18 months, the efficiency of automation also freed up $500,000 in resources for other projects.

With an agile approach to data prep and analytics, businesses can minimize their data prep time to uncover additional insights, save money, become more productive and boost growth. That’s what data management is all about.

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