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How Royal Bank of Scotland Optimized Analytics with Data Wrangling

August 17, 2017

Accelerating Analytics: A Constant Struggle for Data-driven Organizations

Organizations that efficiently utilize data to drive decision-making and operational efficiency outperform their competitors. In an Economist Intelligence Unit survey, 76 percent of executives from top-performing companies cited data collection as very important or essential, compared with only 42 percent from low-performers. Data-centric firms are able to anticipate customer needs and innovative next-gen products or services more quickly. That same study found that firms rating themselves ahead of their peers in their use of data are three times more likely to rate themselves as substantially ahead in financial performance. It clearly pays to be data-centric.

However, leveraging new data or new combinations of data to is no easy task. Organizations are challenged with merging diverse data from multiple different sources, in different formats, from siloed departments managed at different security hierarchies or with different methods for tracking data lineage. These factors make doing any sort of new, exploratory analysis extremely difficult and time-consuming.

In addition to internal inconsistencies, companies may be pulling data sets from 3rd party sources, such as demographics or weather data, or use public data from census or sources which bring their own unique formatting structures.

Information pulled from disparate sources is not the only hurdle facing data-centric firms. They must integrateor wranglethis dissimilar data faster than the competition. What’s more, data has a very short shelf life and insights are only as valuable as they are fresh. Given that data preparation can take up to 80% of the time allotted for data analysis, organizations are seeking to shorten this aspect of the process, so that they can make data actionable in business decisions while it’s still valuable.

RBS Challenge: Faster Analysis for Tough Data
Nearly every customer comes to Trifacta seeking faster, more intuitive data preparation while also maintaining data lineage and security. Royal Bank of Scotland (RBS) came to us seeking a more efficient way to prepare and analyze their customer support chat data. As their customer service moved to the web, prior analysis methods of Excel spreadsheets and SAS scripts couldn’t effectively prepare the immense volume and unstructured nature of web chat data.

Using these traditional tools, RBS was only able to analyze 1% of this chat data, and the end-to-end process took more than a month to complete. RBS estimated that this inefficient process cost the bank millions of pounds in unidentified complaint trends, lost cross- and upsell-opportunities, and costly customer churn due to a generally inability to personalize service.

How RBS Optimized Analytics with Trifacta
Trifacta worked with RBS to create a solution based on Cloudera & Trifacta to more effectively wrangle and analyze their web-chat data. By deploying Trifacta, RBS was able to quickly explore and prepare 100 percent of complex customer web data for the first time. Analysis was accelerated from conception to delivery by 90%. Insights led them to better identify and meet customer needs, substantially increasing their Net Promoter Score and saving the firm an estimated £3-4 million annually. Watch Trifacta’s Connor Carreras discuss this game-changing collaboration with Dan Jermyn of RBS.