{"id":16989,"date":"2022-01-26T13:30:38","date_gmt":"2022-01-26T12:30:38","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=16989"},"modified":"2023-04-07T10:06:45","modified_gmt":"2023-04-07T08:06:45","slug":"6-mind-bending-trends-in-data-science-for-2022","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/data-science\/6-mind-bending-trends-in-data-science-for-2022\/","title":{"rendered":"6 Mind-Bending Trends in Data Science"},"content":{"rendered":"\t\t\t\t
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\n\t\t\t\t\t\t\tTable Of Contents\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
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  1. 1. Edge Intelligence<\/a>
  2. 2. Observability<\/a>
  3. 3. Customer Analytics<\/a>
  4. 4. Hybrid Cloud Automation<\/a>
  5. 5. Hyperautomation<\/a>
  6. 6. Democratizing AI<\/a>
  7. Onward and Forward<\/a>
  8. Languages for data science<\/a><\/ol>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\n\n\n

    Do you remember all those headlines declaring \u201cData Scientist\u201d as the hottest job of the century<\/a>? <\/strong>Well, that was a decade ago. So, what has happened since then? Let’s take a look at some of the latest data science trends.<\/p>\n\n\n\n

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    1. Google is running a professional certificate course<\/a> in data analytics to bridge the talent gap in the industry.<\/li>\n\n\n\n
    2. Even today, it takes businesses 280 days on average<\/a> days to identify and limit a data breach.<\/li>\n\n\n\n
    3. Natural Language Processing<\/a> has gone mainstream.<\/li>\n<\/ol>\n\n\n\n

      While these might appear to be primary topics of discussion in data science for 2022, they are mere offshoots of undercurrents with much more impact. From security to efficiency to making data science more accessible<\/strong>, here are the most mind-bending data science trends<\/a> you should know about in 2022.<\/p>\n\n\n\n

      1. Edge Intelligence<\/strong><\/h2>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      Cloud computing has been touted as the future of scalable AI. However, training and deploying AI models in the cloud has its wide range of challenges:<\/p>\n\n\n\n

      Wide Area Networks (WANs): <\/strong>While the data is stored and processed in the cloud, the transmission process requires WAN. The legacy infrastructure can often create cost barriers at entry and scale.<\/p>\n\n\n\n

      Latency Issues<\/strong>: Full accessibility in the cloud depends on the precise functioning of every touchpoint across the network. However, maintaining this level of performance for extended periods is impossible<\/strong>; hence, there are latency or downtime issues even with cloud systems.<\/p>\n\n\n\n

      Privacy Challenges<\/strong>: Even with a long history of data stored and processed in the cloud, despite enterprise-grade security, there will be cybersecurity risks. As a matter of fact, by 2025, as much as 80%<\/a> of companies with suboptimal data governance practices will not be able to scale their digital business.<\/p>\n\n\n\n

      Imagine a system where the data is processed and used exactly where it is collected. This can be an IoT device or an endpoint device that is interacting directly with the user who is generating the data. By simply shifting the responsibility of collecting, processing, and analyzing the data at the \u201cedge\u201d instead of the cloud<\/strong>, three major challenges \u2013 suboptimal economics, latency issues, and privacy challenges can be solved. This is what edge computing is all about<\/a>.<\/p>\n\n\n\n

      Applications such as CCTV monitoring and live video streaming are executed to their full capacity at the device level instead of data being sent back to the cloud or a data center to be processed and analyzed.<\/p>\n\n\n\n

      Developers and companies choose between different combinations of cloud and edge to train the AI models, process data, and deliver insights. As AI systems become more efficient at scale<\/strong>, edge data collection, processing, and analysis becomes more accessible.<\/p>\n\n\n\n

      A layer above edge computing is the new area of research and application called edge intelligence. If we are storing and accessing data at the edge, we should analyze it with AI models at the edge, right? While it is still relatively unexplored territory, the answer seems to be Yes.<\/p>\n\n\n\n

      2. Observability<\/strong><\/h2>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      If you intuitively understand metrics, tracking and monitoring, you can graduate to the idea of observability<\/a>, which revolves around complex systems where self-adaptive elements and asymmetric consequences (chaotic behavior) are common.<\/p>\n\n\n\n

      When you proactively collect, visualize, and apply intelligence to metrics and logs within this complex system, you can understand its behavior better<\/strong>. Therefore, a simple way of defining observability is understanding an IT system by observing the work it does.<\/p>\n\n\n\n

      Now, try to observe the modern software development lifecycle with Kubernetes, distributed development teams, and continuous integration and continuous delivery (CI\/CD). It becomes very easy to lose grip on all the moving parts and establish the cause of the errors.<\/p>\n\n\n\n

      Monitoring is somewhat diagnostic because you understand specific indicators as the causal factors for failure. Hence, as far as you can correlate these factors across a timeline, you can diagnose the issue at hand before it scales<\/strong>. Observability goes beyond this idea and includes metrics, events, logs, and traces to deliver a more comprehensive picture.<\/p>\n\n\n\n

      In terms of process, observability depends on telemetry data collection and absolute visibility across the topography of critical assets in the network.<\/p>\n\n\n\n

      Moreover, the data collected must be backed by metadata (data about data, or attributes of data) that helps establish the proper context for further analysis. Companies that can use metadata are projected to increase their data team productivity by as much as 20%.<\/strong> With such contextual intelligence available, it would be easier to automate a substantial amount of IT operations and workflows intelligently.<\/p>\n\n\n\n

      3. Customer Analytics<\/strong><\/h2>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      Data has been at the core of several successful products and companies<\/a>. But, with more and more granular data available on user actions and behavior online, the playing field has been leveled for small businesses and startups to leverage accessible insights at par with enterprises.<\/p>\n\n\n\n

      Customer analytics can be a broad term used to assess user behavior on a platform. Since technology products are easier to track, they lend themselves to many service-oriented applications.<\/p>\n\n\n\n

      The most popular and universal use today is in automation-focused CRM tools<\/a> featuring web- or app-based chatbots that utilize deep learning models. These bots aim to understand the context of support tickets or platform-specific messages and recommend an appropriate course of action to customers<\/strong>. The system also learns from the outcome of each customer interaction and builds a knowledge base for marketing, sales, and customer service.<\/p>\n\n\n\n

      When implemented at scale, customer analytics can help in:<\/p>\n\n\n\n