The concept of big data analytics has taken the world over by storm in recent years. The ability to provide accurate business insights based on past data to promote steady growth has not been viewed as an opportunity to pass on. Today, approximately 97.2% of companies in the United States attempt to use data to their advantage; while the global ‘big data’ industry continues to grow, crossing the $200 billion mark this year. Nevertheless, it is said that up to 63% of companies fail to obtain useful insights from the petabytes of data available to them. Despite the business analytics sector booming on paper, just one-third of companies that foray into the big data space profit from it.
This lack of profitability can be attributed to poor revenue generation or a dearth in business growth, but these are reasons to explain shortcomings in the use of the data, which originate from faults within the data stack. In 2017, the Harvard Business Review published an article that stated, “only 3% of companies’ data meets basic quality standards”. The author of the same article – Thomas C. Redman – took to LinkedIn to focus the corporate spotlight on the fact that three years later, in 2020, that statistic hasn’t changed a bit. Even though metadata management is at the forefront of corporate functions across most sectors and industries, the actual data being used has not experienced an improvement in quality. Such data remains unfit-for-use, providing incorrect business insights and thereby promoting poor business decisions that lead to managers losing out on their investments.
When data analytics is a primary function of a company, the volume, types, and general quality of data drives business decisions on an employee level. Employees make business decisions based on the kind of data available to them, and it just so happens that only 14% of companies make their data accessible across the span of their organization. These companies often find themselves in disadvantageous positions with regards to their investment – return ratio into data analytics and insights. Coupled with IT budget constraints, companies that fail to make their data available to all staff face losses in their bid to gain insights and profits from the data stack at hand.
Moreover, the drawbacks of poor data quality are not limited to indirect effects caused by the involvement of factors other than the data itself. Yes that’s right, poor data quality costs money. Companies are forced to make sizeable financial investments into the clean-up of their data stack, which are not always fruitful.. Businesses in the USA experience between $9.7 million and $14.2 million in average losses due to lack of basic data quality standards in their data environment.
Every business and corporate individual has not seen the profitability of data analytics just yet. Despite the promise of business insights provided by analytics tools, only 26 to 27% of companies can say that they have established a data-driven culture among their ranks. To make matters worse, a quarter of companies in the data analytics space claim to have no single source of veracity with regards to their centralized data stack. In the face of persistent costs and losses associated with data analytics, it becomes difficult for some companies to adopt a data-driven outlook on an organizational level, but a survey conducted by Sigma suggests that 71% of individuals in a corporate workspace would like to improve their data management and analytics skills to promote profitability. This shows that data analytics is viewed as the future in the minds of employees, despite any failures experienced until now in the analytics space.
However, the data literacy levels are not nearly where they need to be to stimulate the right kind of business growth. In addition to the fact that only 3% of employees have the ability to locate data they require; companies analyze only 12% of their data on an organizational level. This goes to show that while there is potential in the big data analytics space, current success can be attributed to individual skillsets and coincidences. The world is certainly far away from attaining the kind of data literacy levels required to fully benefit from the insights that exabytes of data have to offer.
Achieving data governance is regarded as one of the most difficult yet profitable tasks When it comes to the data stack, achieving governance over it involves the classification, mapping, and cataloging of data based on various processes like data profiling. Global IDs uses profiling to bring immediate transparency in identifying outliers in the data environment, which are then reported to data stewards for further evaluation. In order to maintain a high level of data quality in the future, Global IDs enables enterprises to place data quality controls on any future data assets, placing the data environment under regimented quality regulations that enable users to have more trust in their data.
At Global IDs, we believe that the foundation for gainful analytics and compliance is suitable data quality standards. Data science and machine learning tools are capable of inferring valuable information from any data environment that has a consistency of high-quality data. Additionally, high quality data makes achieving regulatory compliance significantly easier, preparing your data for the future while ensuring usefulness in the present.