Today, we speak of data analytics as if it were a novel concept. However, it has been around for quite some time. As with everything else, it is not the term that is different this time, but the timing. In the 1980s, with the advent of database marketing, it was all about data. You are now witnessing the marriage of manufactured products and electronic technology, all controlled by software connected to the Internet. Alternatively, it is simply software that is connected via the Internet. As a result of the massive amounts of data generated, the data analytics industry was born. In the past, Chief Marketing Officers and other executives combined "data" with other inputs such as trends, competition, customer research, and market strategy to determine the appropriate method and tactics for executing a strategic plan. Executives are now more than ever relying almost entirely on the data itself. This could be a good or a bad thing. The future will reveal. After all, data is simply that: data. You must determine how to convert it to information.
The good news is that this convergence of data and technology has created some exciting opportunities for small businesses, startups, and individuals. Gartner, a research firm, published Gartner's Top 10 Data and Analytics Trends for 2020 in October 2020. Following your reading of the report, here are three trends to watch over the next year or two.
Develop your skills as a data analyst/scientist. In the long run, it's probably unwise to dismiss data science as a career path, even more so when related positions such as research engineers and machine learning engineers are included. The US Bureau of Labor Statistics predicts that the data science field will grow at a rate of about 28% through 2026. Additionally, as technology advances, businesses have increased the sophistication of their data operations and analysis. This increasingly entails incorporating artificial intelligence (AI) capabilities into the business processes of established companies (i.e., non-tech giants). This increases demand for data scientists (average salary in the United States of America: $111,100) and related positions (research scientists and machine learning engineers). While the tools are improving, data scientists seeking to advance in the market will need a firm grasp of the fundamentals, including data modeling, relational databases, and basic statistical analysis. These are critical skills that are likely to be retained in future job function shifts in data science.
Automated decision-making with a dash of artificial intelligence. My SUV came to a complete stop last week without my touching the brakes. As it turns out, the new vehicle accident avoidance system was triggered by several factors to function flawlessly. We have entered an era in which machine learning meets software meets a set of circumstances that necessitates a decision. Whether it's a vehicle, a RING doorbell security camera, or Google Home, the industry is rapidly growing. Is it artificial intelligence, or are it computer programs that follow predefined algorithms and criteria? It makes no difference. Over the next two decades, this industry will continue to grow at an exponential rate. Whichever application, artificial intelligence is transforming how consumers, operators, and manufacturers interact with devices. This results in enormous opportunities for startups in this industry.
Markets and exchanges for data. With recent announcements by Apple, Facebook, and Google regarding the possibility of restricting first-party data (without user consent), this presents a challenge for advertisers but an opportunity for data marketplaces and exchanges to grow. In general, the term "data marketplace" refers to a location (platforms) for the purchase and sale of third-party data. These platforms typically focus on the transactional aspects of data acquisition and distribution, such as publishing, licensing, discovery, and distribution. Meanwhile, the term "data exchange" is most frequently used to refer to technologies that enable data exchange without involving a financial transaction. These are intended for organizations that are unlikely to sell their data and instead seek to extract value through the business. The exchange can be one-way or mutual in nature to facilitate the development of joint value propositions. While some data exchanges are pretty simple, new offerings are emerging that offer enhanced functionality. Johnson and Johnson, for example, has a wealth of consumer data. Could they share the data with a non-competitor so that both parties benefit? This industry may fail or proliferate as the data industry's Wild; Wild West continues to explode.
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