The volume of data readily available in today’s digital world is unprecedented. Modern sources of data include web, mobile phones, smart accessories, and countless other connected devices. Meanwhile, algorithms that analyze data from these sources have grown in sophistication, effectiveness, and applicability to real-world use-cases; and the computational power necessary for data processing has become widely accessible. This intersection of increased data availability, analytical capabilities, and processing power has resulted in powerful new analytics tools, creating the potential for market disruption.
Unsurprisingly, the adoption of data analysis has increased across the business community in recent years. According to a survey of middle market CFOs published in early 20201, more than half of respondents said they intended to increase spending on analytics and digital transformation – a higher figure than any other initiative mentioned in the survey. For those companies and others considering expanding their analytics capabilities, the following trends have made analytics adoption more accessible than ever.
- Automating the data pipeline: Nearly every step of the data science pipeline has been or is in the process of becoming automated, allowing data pipelines to run without human effort and with APIs instead facilitating communication between data sources. This automation has led to not only increased data accuracy, but it has also massively increased the amount of data available to companies and allows for real-time analysis of that data.
- Cloud enables big, big data: The volume of big data has grown past the capacity of a personal computer for a typical Fortune 500 company. The same is true for some middle market companies. By outsourcing data needs to cloud computing providers, many companies have been able to leverage significantly greater computing power to run high-level analysis. This has led to an interesting inflection point where computing power is seldom the analytical bottleneck it once was, allowing for the management of big data to be far less costly than the organizational changes needed to deploy it.
- Natural language processing: Advancements in the fields of deep learning and neural networks, using techniques not unlike human pattern recognition, have shown great promise in being able to recognize speech and images. Even at a more rudimentary level, many firms that deploy data analytics have begun to move away from simple descriptive analytics, and advances in cloud computing power have allowed in some cases the deployment of these cutting-edge techniques to create business insights from their data.
Despite the explosion of data science, many firms still believe that that leveraging advanced data is either for large, resource-heavy companies with the ability to finance legacy system overhauls, or nimble start-ups unencumbered by existing infrastructure. The truth is that data science methodologies can help enhance top-line performance, achieve operational excellence, and develop a competitive edge for companies of all sizes, including the middle market.
1Freedman, R. (2020, February 02). 2020 trends: Middle market CFOs ready to spend on growth. Retrieved September 17, 2020.