Post by account_disabled on Jan 22, 2024 10:08:07 GMT 5.5
Businesses are increasingly immersed in working with data. Big data and machine learning are the talk of the town, and companies are feverishly collecting all the information they can about their production, marketing, finances and customers. Often without a clear understanding of what to do with all this. This ocean of data hides important discoveries and valuable business ideas in its depths. But in order to get to them and not “drown”, you need the right tools - means of rapid classification, assessment, and integration of information. Such tools are rapidly developing in IT today; they are united by the term DataOps. Electronics giant IBM defines DataOps as the coordination of “people, processes and technology to deliver reliable, high-quality data.” In practice, this means for a company the ability to quickly combine data from different sources, process it using machine learning tools and turn it into knowledge useful for the business. Why invest in DataOps Why invest in DataOps All over the world, DataOps is no longer perceived as just another fashionable IT trend, but as a promising field for investment.
Wells Fargo Asset Management estimates that global investments in Big data solutions and business transformation using AI could reach $1.8 trillion in 2021. Data from 451 Research indicates that 80% of companies already using DataOps find it useful. The essence of DataOps is the application of proven Agile approaches to working with data. They are designed to simplify a number of tasks as much as possible: Integration – combining disparate databases into one. It can be very difficult to connect fundamentally different storage facilities and data sources. Validation – Con B2B Email List tinuous verification of data to ensure decisions are made based on accurate information. In some areas (medicine, finance), checking and double-checking data is critically important. Structuring is the organization of an array of data, taking into account their origin, connections and chronology of changes. Structured data is no longer a “closet” with numbers and facts, but potentially useful information. Interpretation – It is important to recognize whether the information received has business value. To do this, it is important to take into account its entire context. The goal of DataOps specialists is to put work with data “on the conveyor belt” and provide the business with an uninterrupted flow of accurate and valuable information. In what areas is DataOps indispensable? In what areas is DataOps indispensable? e-Commerce .
Data research helps an online store predict consumer behavior, build a pricing policy and form an assortment. The agility of DataOps is indispensable when it comes to responding to market shocks and capitalizing on trends. Real estate . The prospects for DataOps in the Real Estate sector are enormous - it is possible to provide the client with the most detailed and flexible catalog of objects, calculate the cost of unfinished housing for years to come, and predict risks for buyers, tenants and developers. Logistics . Correct work with data will help not only find weaknesses in supply chains, but also predict them. The risks of human errors are minimized. Pharmacology and healthcare . The development of new drugs increasingly depends on the ability to conduct large-scale studies and quickly obtain reliable data on their results. The use of machine learning in medicine has already become a reality – it allows for more accurate diagnoses. Finance . DataOps is indispensable when you need to establish analytics of clients’ financial behavior, predict risks and create new banking products. The success of a fintech startup depends entirely on how it works with data. Difficulties in Implementing DataOps Flexible work with data provides undeniable advantages. But these benefits are best weighed against the typical challenges that arise when implementing DataOps practices. Isolation of departments Teams of data specialists are trying to overcome the isolation of individual divisions and departments of the company, but are faced with system inertia, technological obstacles, and even conscious resistance. It is important to plan database integration well, involve employees in the process and receive feedback.
Wells Fargo Asset Management estimates that global investments in Big data solutions and business transformation using AI could reach $1.8 trillion in 2021. Data from 451 Research indicates that 80% of companies already using DataOps find it useful. The essence of DataOps is the application of proven Agile approaches to working with data. They are designed to simplify a number of tasks as much as possible: Integration – combining disparate databases into one. It can be very difficult to connect fundamentally different storage facilities and data sources. Validation – Con B2B Email List tinuous verification of data to ensure decisions are made based on accurate information. In some areas (medicine, finance), checking and double-checking data is critically important. Structuring is the organization of an array of data, taking into account their origin, connections and chronology of changes. Structured data is no longer a “closet” with numbers and facts, but potentially useful information. Interpretation – It is important to recognize whether the information received has business value. To do this, it is important to take into account its entire context. The goal of DataOps specialists is to put work with data “on the conveyor belt” and provide the business with an uninterrupted flow of accurate and valuable information. In what areas is DataOps indispensable? In what areas is DataOps indispensable? e-Commerce .
Data research helps an online store predict consumer behavior, build a pricing policy and form an assortment. The agility of DataOps is indispensable when it comes to responding to market shocks and capitalizing on trends. Real estate . The prospects for DataOps in the Real Estate sector are enormous - it is possible to provide the client with the most detailed and flexible catalog of objects, calculate the cost of unfinished housing for years to come, and predict risks for buyers, tenants and developers. Logistics . Correct work with data will help not only find weaknesses in supply chains, but also predict them. The risks of human errors are minimized. Pharmacology and healthcare . The development of new drugs increasingly depends on the ability to conduct large-scale studies and quickly obtain reliable data on their results. The use of machine learning in medicine has already become a reality – it allows for more accurate diagnoses. Finance . DataOps is indispensable when you need to establish analytics of clients’ financial behavior, predict risks and create new banking products. The success of a fintech startup depends entirely on how it works with data. Difficulties in Implementing DataOps Flexible work with data provides undeniable advantages. But these benefits are best weighed against the typical challenges that arise when implementing DataOps practices. Isolation of departments Teams of data specialists are trying to overcome the isolation of individual divisions and departments of the company, but are faced with system inertia, technological obstacles, and even conscious resistance. It is important to plan database integration well, involve employees in the process and receive feedback.