The explosion of data is an unavoidable facet of today’s business landscape. Domo recently released its fourth annual installment of its Data Never Sleeps research for 2016, illustrating the amount of data that is being generated in one minute on a variety of different platforms and channels. The astounding rate in which data has been growing shows no indication of slowing down, anticipating a digital universe saturated in nearly 44 trillion gigabytes of data by the year 2020. With data being produced in an increasingly unprecedented rate, companies are scrambling to set data management practices in place to circumvent the difficulties of being overwhelmed and eventually bogged down by the deluge of information that should be informing their decisions. There are a plethora of challenges with calibrating and enacting an effective data management strategy, and according to Experian’s 2016 Global Data Management Benchmark Report, a significant amount of these issues are internal.
Most businesses strive for more data-driven insights, a feat that is rendered more difficult by the collection and maintenance of inaccurate data. Experian reports that 23% of customer data is believed to be inaccurate. While over half of the companies surveyed in this report attribute these errors to human error, a lack of internal manual processes, inadequate data strategies, and inadequacies in relevant technologies are also known culprits in the perpetuation of inaccurate data. While the reason for the erroneous input of data is still largely attributed to human oversight, it is the blatant lack of technological knowledge and ability that is barring many companies from leveraging their data, bringing us to our next point.
Data Quality Challenges
Inescapable and highly important, the sheer volume of information being generated by the second warrants a need for organizations to improve data culture. Research shows that businesses face challenges in acquiring the knowledge, skills, and human resources to manage data properly. This is reflective of organizations of all sizes and resources, not just large companies, as a baffling 94% of surveyed businesses admit to having experienced internal challenges when trying to improve data quality.
Experian’s data sophistication curve identifies four different levels of data management sophistication based on the people, processes, and technology associated with the data: unaware, reactive, proactive, and optimized. While the ultimate goal is ascending to the optimized level of performance, 24% of the polled businesses categorize their data management strategies as proactive, while the majority (42%) admits to merely reaching the reactive level of data management sophistication. The reactive approach is inefficient in many ways, a prominent one being the data management difficulties, both internal and external, espoused by waiting until specific issues with data crops up before addressing and fixing them as opposed to detecting and resolving such problems in a timely manner.
The most deleterious disadvantage of failing to address these pressing issues as they are detected is the careless neglect of invaluable business insight that is concealed in the mass of available data. Data management systems that have not optimized their operations will not be able to process data to produce relevant information in a timely manner. The lack of machine learning mechanisms within these sub-optimal systems will hinder businesses in their knowledge discovery process, barring organizations from making data-driven decisions in real time.
Denisse Perez, Content Marketing Analyst for True Interaction, contributed to this post.