We talk about data analytics today like it’s a new thing. But it’s been around for quite a while. Like everything else, it’s not the term but the timing that different this time. In the 1980’s it was just data by itself with launch of database marketing. Now, you are seeing the combination of manufactured products connected to electronic technology, operated by software connected to the Internet. Or, just software that is interconnected on the Internet. Voila, the creation of massive amounts of data leading to the creation of the data analytics industry. Chief Marketing Officers and other executives of yesteryear blended “data” with other input points like trends, competition, customer research and market strategy to determine the right strategy and tactics to execute a strategic plan. Now, more than ever, executives are beginning to rely almost exclusively on the data itself. That could be good or bad. Time will tell. After all data is just data. You need to figure out ways to turn it into information.
The good news is that this confluence of data and technology is yielding some interesting opportunities for small businesses, startups and individuals. The analyst firm Gartner published a report in October, 2020 titled: Gartner Top 10 Data and Analytics Trends for 2020. After reading the report, here are three trends you should pay attention to in the next year or two.
Become a data analyst/scientist. In the long term, it would probably be unwise to bet against data science as career move, especially when you widen the field to include related positions like research engineers and machine learning engineers. The U.S. Bureau of Labor Statistics sees strong growth for data science jobs skills in its prediction that the data science field will grow about 28% through 2026. Also, as technology improves, companies have been able to increase the sophistication of their data operations and analysis. Increasingly, that means inserting artificial intelligence (AI) capabilities into the business processes of regular companies (i.e. non-tech giants). And that means demand for data scientists (average salary in USA $111,100) and related positions (research scientists and machine learning engineer) will also go up. While the tools are getting better, data scientists looking to accel in the marketplace will still need to have a solid understanding of the basics, including data modeling, relational databases, and basic statistical analysis. Those are critical skills that are likely to survive any future shifts in data science job functions.
Automated decision making with pinch of AI. Last week, my SUV stopped without me touching the brakes. Turns out the new vehicle accident avoidance system had been triggered by several factors to work flawlessly. Welcome to the age of machine learning meets software meets a set of conditions that prompt a decision. Whether it’s a vehicle, a RING doorbell security camera, Google Home, and the list goes on, an industry is rising rapidly. Is it artificial intelligence or software programs responding to set algorithms and criteria? It does not matter. This industry will continue to grow exponentially over the next 20 years. Regardless of the application, artificial intelligence is changing how consumers, operators, and makers interact with devices. And that creates tremendous opportunities for startups in this industry.
Data marketplaces and exchanges. With the recent announcements by Apple, Facebook, Google and others about perhaps restricting first party data (without use permission), it creates a problem for advertisers but an opportunity for data marketplaces and exchanges to rise. Generally, data marketplace is a term used to describe a place (platforms) for buying and selling third-party data. These platforms typically focus on the transactional aspect of buying and selling data, including publishing, licensing, discovering, and distributing. Meanwhile, data exchange is most widely used for technologies that enable the exchange of data without an associated financial transaction. These are targeted at organizations that are unlikely to be selling their data and are instead seeking to extract value by exchanging it. The exchange can either be one-way or mutual to drive joint value propositions. Some data exchanges are very basic, but new offerings are emerging to provide deeper functionality. For example, Johnson and Johnson has a great amount of consumer data. Could they exchange the data with another company that is not a competitor so both could benefit? This industry could flop or grow rapidly as the Wild, Wild West of the data industry continues to explode.
