In this age of analytics, it’s easy to forget that the efficacy of advanced data analysis technologies remains contingent on the data itself. For a business to be truly data-driven, it must first implement data quality management measures to ensure data integrity, because as the saying goes, “cleanliness is next to godliness.”
In this post we examine the areas of business that will most benefit most from improvements in data quality.
In the world of digital marketing, false or inaccurate data sets in the form of demographics can have a hugely detrimental effect on a digital ad campaign performance. Since the major paid ad vendors, like Google Ads, LinkedIn, and Facebook charge per click, advertising to audiences built around bad data, is a wasteful use of spend. In fact, according to a recent study conducted by Forrester, 37% of marketers waste spend as a result of poor data quality, which is why 62% of marketing professionals consider improving data quality to be the biggest priority for their campaign management.
The proper collection, management, and analysis of operational data can have a major impact on R&D performance. GlaxoSmithKline’s (GSK) R&D organization, for instance, is using large-scale analytics to help reach its goal of reducing the average drug development timeline from 8-20 years to 2 years. Companies like GSK, who have adopted advanced analytic processes in their R&D organizations remain early adopters, with only 25% of R&D executives claiming their organization makes “wide use of advanced-analytics techniques in R&D,” according to a recent McKinsey survey. One of greatest barriers R&D teams face in implementing advanced analytics techniques is data quality. According to Imran Haque, VP of Data Science in Recursion Pharmaceutical’s R&D organization, "90+% of effort in real-world machine learning projects will end up focused on 'mundane' data cleaning and data management, not 'exciting' models and algorithms work.“ For this reason, R&D organizations with effective data governance measures in place will have a major competitive advantage when adopting these big data technologies.
In the SaaS world, the ability to delight your customers, and improve customer satisfaction often hinges on your access to good Business Intelligence (BI) data. For example, a routine renewal call could quickly devolve into a cancellation request if a customer hasn’t been using their software license for months. If, however, the account manager is already attuned to their customer’s poor usage metrics, they could instead use the call as an opportunity to position free product training and help rescue the account. While the insights afforded by BI data can be a major asset to managing a customer relationship, things can go horribly awry if that data is incomplete or inaccurate. One infamous example of bad quality data fueling a horrible customer experience was Youtube’s search autocomplete algorithm, which kept suggesting conspiracy theories to users instead of relevant, personalized content.
Within the consumer package goods (CPG) industry, big data, and predictive analytics has proven to be a boon for operational efficiency. It has allowed producers to optimize inventory turns, to precisely forecast demand across their geos, and better recognize changes in consumer behavior. The reap of the benefits of analytics, however, CPG industries cannot rely on the technologies alone, they require good data. In fact, one of the key causes of Kentucky Fried Chicken’s (KFC) supply chain fiasco in the UK last year, was bad data, as KFC’s supply chain management technology failed to detect an inventory stock-out issue, and did not issue early warning signals.