{"id":42915,"date":"2021-03-31T16:45:10","date_gmt":"2021-03-31T13:45:10","guid":{"rendered":"http:\/\/datalabsua.com\/ua\/?p=42915"},"modified":"2024-05-24T16:50:35","modified_gmt":"2024-05-24T13:50:35","slug":"10-big-data-trends-that-accelerates-business-development","status":"publish","type":"post","link":"https:\/\/datalabsua.com\/en\/10-big-data-trends-that-accelerates-business-development\/","title":{"rendered":"10 Big Data trends that accelerates business development"},"content":{"rendered":"

Technological advance and achievements instigated huge amount of data appearing. Many of us even don\u2019t suppose that we\u2019re producers of them. Every search request generates data and as a result we produce data in a few days more than in a decade in history. Received data has not to be just stored, it has power and effect if it is manipulated. Data is corporate assets that are used by different organizations to improve operative business. The reduction to practice of artificial intelligence (AI), machine learning (ML), IoT and other technologies improved the quality of data-driven business decisions. And it is not a hyperbolic affirmation \u00abdata makes business smarter\u00bb.<\/p>\n

Let\u2019s get a view of 10 big data trends that accelerates business development:<\/strong><\/p>\n

1. Accessible AI<\/strong><\/em><\/p>\n

Big data that company has can generates value if it is processed using advanced technologies. For effective analyzing companies use: AI, ML and neural networks to forecast. However, to minimize technical readblocks there is cloud sources trend that essentially simplify data access. The hybrid cloud is capable to provide with more flexibility and possibilities to deploy data by moving processes between private and public clouds.<\/p>\n

2. Continuous intelligence<\/strong><\/em><\/p>\n

Gartner explains continuous intelligence as a design pattern in which real-time analytics is integrated into business activity, processing current and historical data to prescribe actions in response to business events. Continuous intelligence use technologies like event stream processing, business rule management, ML, optimization and advanced analytics.<\/p>\n

3. Advanced Analytics<\/em><\/strong><\/p>\n

Advanced Analytics is a part of data science. It uses ML and AI to improve analytics across all data life cycle (from the preparatiom method to analyzing). This technology promotes development of business flexibility, rapidly and credible information gaining, data sharing, and cutting time for information extracting and understanding. More detailed information in the entry \u00abMore analytics \u2013 more possibilities\u00bb.<\/a><\/p>\n

4. DataOps and self-service analytics<\/strong><\/em><\/p>\n

DataOps is a newish term, but it already is in favor in IT world. DataOps is a technology mix of continuous integration designed to afford actual data for every process member rapidly and fluently. With the help of this companies have a possibility to rise speed and improve data management quality.<\/p>\n

Self-service analytics is a kind of analytics whereby users have an opportunity to make data requests and generate reports by themselves. Self-service analytics implementation allows to receive quick and exact result, simplify information sourcing all across chain.<\/p>\n

5. Data-backed tools<\/em><\/strong><\/p>\n

Currently data for business processes is generated from all sides. The essential accelerator of this became IoT devices influence. Therefore, it instigated problems appearing. The fact of the matter is that data passes a long way to the centralized source.\u00a0 But technologies allowed to avoid crisis. The edge computing conception allows to hold data in the local storage device near the IoT device for better data management.<\/p>\n

6. Smart chat-bots<\/strong><\/em><\/p>\n

Chat-bots became an indispensable contact source between the business and consumers. With the help of this tool companies process customers\u2019 requests and establish more personalized cooperation with them herewith reduce a real staff necessity. Chat-bots are based on big data as a connection source needs large data sets to work in the personalized format. Big data is the main source of the information transfer to chat-bots.<\/p>\n

7. Intelligent security<\/strong><\/em><\/p>\n

One of the biggest business problems is a security threat. Using big data in a corporate security strategy it is possible to get essential profit. As big data contains all information concerning previous cyberattack attempts, phishing attacks, ransomware etc. it is possible to forecast, prevent and cushion an impact of future attempts.<\/p>\n

8. Big data as a service (BDaaS)<\/strong><\/em><\/p>\n

BDaaS is a suite of big data analyzing methods and cloud computing platforms with the help of which it is possible to manage big data in cloud and provide their access at any time and for every user. Also, BDaaS promotes cost and time saving to deploy big data projects.<\/p>\n

9. Dark data<\/strong><\/em><\/p>\n

Dark data is a part of big data that stays in the background. It is collected in consequence of specific network operations that aren\u2019t covered by analytics. However, such data can have more value as one can imagine. Also, dark data can create a business security threat. That\u2019s why it\u2019s necessary to recover it or use correctly.<\/p>\n

10. Cloud usage<\/strong><\/em><\/p>\n

Companies show a high interest in cloud technologies. This may be due to the fact that they able to change management methods of information and technologies business resources, providing their efficiency, security, flexibility, safety, automation, accessibility and optimization.<\/p>\n","protected":false},"excerpt":{"rendered":"

Technological advance and achievements instigated huge amount of data appearing. Many of us even don\u2019t suppose that we\u2019re producers of them. Every search request generates data and as a result we produce data in a few days more than in a decade in history. Received data has not to be just stored, it has power and effect if it is manipulated. Data is corporate assets that are used by different organizations to improve operative business. The reduction to practice of artificial intelligence (AI), machine learning (ML), IoT and other technologies improved the quality of data-driven business decisions. And it is not a hyperbolic affirmation \u00abdata makes business smarter\u00bb.<\/p>\n","protected":false},"author":2,"featured_media":44994,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[90,24],"class_list":["post-42915","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-bigdata","tag-business-intelligence"],"_links":{"self":[{"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/posts\/42915","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/comments?post=42915"}],"version-history":[{"count":6,"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/posts\/42915\/revisions"}],"predecessor-version":[{"id":42922,"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/posts\/42915\/revisions\/42922"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/media\/44994"}],"wp:attachment":[{"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/media?parent=42915"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/categories?post=42915"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datalabsua.com\/en\/wp-json\/wp\/v2\/tags?post=42915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}