{"id":21016,"date":"2020-01-09T15:18:27","date_gmt":"2020-01-09T14:18:27","guid":{"rendered":"https:\/\/dataconomy.ru\/?p=21016"},"modified":"2020-01-09T15:18:29","modified_gmt":"2020-01-09T14:18:29","slug":"2020-the-decade-of-intelligent-democratized-data","status":"publish","type":"post","link":"https:\/\/dataconomy.ru\/2020\/01\/09\/2020-the-decade-of-intelligent-democratized-data\/","title":{"rendered":"2020: The Decade of Intelligent, Democratized Data"},"content":{"rendered":"\n

From wild speculation that flying cars will become the norm to robots that will be able to tend to our every need, there is lots of buzz about how AI, Machine Learning, and Deep Learning will change our lives. However, at present, it seems like a far-fetched future. <\/em><\/p>\n\n\n\n

As we enter the 2020s, there will be significant progress in the march towards the democratization of data that will fuel some significant changes. Gartner<\/a> identified democratization as one of its top ten strategic technology trends for the enterprise in 2020 and this shift in ownership of data means that anyone can use the information at any time to make decisions.
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The democratization of data is frequently referred to as citizen access to data. The goal is to remove any barriers to access or understand data.  With the explosion in information generated by the IoT, Machine Learning, AI, coupled with digital transformation, it will result in substantial changes in not only the volume of data but the way we process and use this intelligence.<\/p>\n\n\n\n

Here are  four predictions that we can expect to see in the near future:<\/p>\n\n\n\n

1.<\/strong>  Medical records will be owned by the individual<\/strong><\/h2>\n\n\n\n

Over the last decade, medical records have moved from paper to digital. However, they are still fragmented, with multiple different healthcare providers owning different parts. This has generated a vast array of inefficiencies. As a result, new legislation will come into effect before the end of 2023 that will allow people to own their health records rather than doctors or health insurance companies.  <\/p>\n\n\n\n

This law will enable individuals to control access to their medical records and only share it when they decide. By owning your health golden data record, all of the information will be in one centralized place, allowing those providers that you share this information with to make fully informed decisions that are in your best interest. Individuals will now have the power to determine who can view their health records and this will take the form of a digital twin of your files. When you visit a doctor, you will take this health record with you and check it in with the health provider and when you check out, the provider will be required to delete your digital footprint. <\/p>\n\n\n\n

When you select medication at CVS, for example, the pharmacist will be able to scan your smart device to see what meds you are taking and other health indicators and then advise if the drug you selected is optimal for you. This will shift the way we approach healthcare from a reactive to a personalized preventative philosophy. Google has already started on this path with its project Nightingale initiative with the goal of using data machine learning and AI to suggest changes to individual patents care. By separating the data from the platform, it will also, in turn, fuel a whole new set of healthcare startups driven by predictive analytics that will, in time, change the entire dynamics of the healthcare insurance market. This will usher in a new era of healthcare that will move towards the predictive maintenance of humans, killing the established health insurance industry as we know it. Many of the incumbent healthcare giants will have to rethink their business model completely. However, what form this will take is currently murky. <\/p>\n\n\n\n

2.<\/strong>  Employee analytics will be regulated <\/strong><\/h2>\n\n\n\n

An algorithm learns based on the data provided, so if it’s fed with a biased data set, it will give biased recommendations. This inherent bias in AI will see new legislation introduced to prevent discrimination. The regulation will put the onus on employers to ensure that their algorithms are not prejudiced and that the same ethics that they have in the physical world also apply in the digital realm. As employee analytics determine pay raises, performance bonuses, promotions, and hiring decisions, this legislation will ensure a level playing field for all. As this trend evolves, employees will control their data footprint, and when they leave an organization rather than clearing out their physical workspace, they will take their data footprint with them.<\/p>\n\n\n\n

3.<\/strong> Edge computing: from niche to mainstream<\/strong><\/h2>\n\n\n\n

Edge computing is dramatically changing the way data is stored and processed. The rise of IoT, serverless apps, peer2peer, and the plethora of streaming services will continue to fuel the exponential growth of data. This, coupled with the introduction of 5G, will deliver faster networking<\/a> speed enabling edge computing to process and store data faster to support critical real-time applications like autonomous vehicles and location services. As a result of these changes, by the end of 2021, more data will be processed at the edge than in the cloud. The continued explosive growth in the volume of data coupled with faster networking will drive edge computing systems from niche to mainstream as data will shift from predominantly being processed in the cloud to the edge.<\/p>\n\n\n\n

4.<\/strong>  Machine unlearning will become important<\/strong><\/h2>\n\n\n\n

With the rise in intelligent automation, 2020 will see the rise of machine unlearning. As the volume of data sets continues to grow rapidly, knowing what learning to follow and what to ignore will be another essential aspect of intelligent data. Humans have a well-developed ability to unlearn information; however, machines currently are not good at this and are only able to learn incrementally. Software has to be able to ignore information that prevents it from making optimal decisions rather than repeating the same mistakes. As the decade progresses, machine unlearning where systems unlearn digital assets will become essential in order to develop secure AI-based systems.<\/p>\n\n\n\n

As the democratization of intelligent data becomes a reality, it will ultimately create a desirable, egalitarian end-state where all decisions are data-driven. This shift, however, will change the dynamics of many established industries and make it easier for smaller businesses to compete with large established brands. Organizations must anticipate these changes and rethink how they process and use intelligent data to ensure that they remain relevant in the next decade and beyond.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

From wild speculation that flying cars will become the norm to robots that will be able to tend to our every need, there is lots of buzz about how AI, Machine Learning, and Deep Learning will change our lives. However, at present, it seems like a far-fetched future.  As we enter the 2020s, there will […]<\/p>\n","protected":false},"author":493,"featured_media":21017,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_social_meta":[],"jnews_override_counter":[],"footnotes":""},"categories":[3229,338,2629,408,340,2627,347,3228,332],"tags":[],"coauthors":[6979],"class_list":["post-21016","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-business-intelligence-and-analytics","category-big-data","category-contributors","category-data-science","category-internet-of-things","category-machine-learning","category-tech-trends","category-technology-and-it-industry"],"_links":{"self":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts\/21016","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/users\/493"}],"replies":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/comments?post=21016"}],"version-history":[{"count":0,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts\/21016\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/media\/21017"}],"wp:attachment":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/media?parent=21016"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/categories?post=21016"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/tags?post=21016"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/coauthors?post=21016"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}