Big data was once a responsibility reserved for data analysts and technical experts, but we’re entering an era where everyone is, in some ways, required to use data in their own roles.
Business intelligence software that allows employees to access valuable insights without SQL knowledge or other IT skills is already emerging, and soon, some element of data collection, organization or management may be a part of every department. Marketing and customer service can use it to better understand your customers. R&D can use it to make better products. Management and HR can use it to improve performance.
So why is this change happening, and what will the business world look like once it manifests?
Why the Change Is Happening
There are several push and pull factors driving this change:
- Technological sophistication. Data analytic technology is constantly growing more sophisticated. In some ways, this allows it to be more independent; software platforms can handle complicated background calculations, while still presenting an approachable user interface to manipulate variables and enter new data. The better this technology gets, the easier it will be for non-experts to use on a daily basis.
- Increased demand for data analysis. Companies are hungry for data. They’ve seen the power of big data number crunching, and they want more—in some cases, that means pulling in more data, and in others, it means collecting data for a more diverse array of applications. Either way, demand for data analytics is increasing faster than the available supply of dedicated experts.
- Increased data supply. Data is also becoming more plentiful. There are hundreds of ways to pull and organize data from your customers, especially now that personal devices are being used on a near-constant basis by almost every demographic in the country.
- High costs for expertise. Data scientists and analysts make a lot of money. Businesses that want the benefits of data analytics may not be willing or able to provide them with a competitive salary. Accordingly, they have to turn to non-experts to tackle some of the lower-level responsibilities.
Predictions for the Future
So what could this change look like once it fully matures?
- A decline in data analyst positions. Though demand for data analysts and scientists will likely still grow for the next few years, the middle to distant time horizon may see a reversal of that trend. Instead, companies will look for all their incoming candidates to have light experience and skills related to data management. This doesn’t mean that analyst positions will disappear, but demand will see a marked decrease.
- Consolidation to Chief Data Officers (CDOs). The true data experts are going to bear much more responsibility for creating data tools, setting high-level goals, sourcing software platforms and guiding the organization to data success. Accordingly, today’s data analyst and scientist positions may be split; low-level responsibilities will trickle down to other positions, while high-level responsibilities are grouped and consolidated to a single, overarching position—the Chief Data Officer (CDO).
- WYSIWYG-style software. Website building used to be a responsibility exclusively reserved for those with skills and experience in design and development, but WYSIWYG (what you see is what you get) editors completely revolutionized the scene, giving everyday users an intuitive interface they could use to accomplish the same goals. We’ll soon see the emergence of data analysis platforms capable of making the same jump.
- Interdepartmental training on data management. Companies will be responsible for training their employees, of all levels, on the fundamental best practices of data management. This will require a significant investment for company training programs, but will ultimately allow those companies to more closely integrate data management into every existing role.
- Data subtypes will emerge. The ubiquity of data will lead to an emergence of distinct data subtypes, including “fast data,” which can be gathered and crunched quickly in response to real-time events, and “actionable data,” which will drive high-level insights that guide the company’s future. Different roles within the company will be responsible for a different collection of data subtypes.
There’s a distinct possibility that machine learning and automation will guide the future of data analytics; after all, automation is extremely cost-efficient and less likely to make mistakes. However, for the near future, even the best predictive analytics platforms will still need a human mind to tackle high-level analysis. Look for major changes in almost all human roles within your enterprise to come.
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