According to the Market Research Report, by Fact.MR, the demand of companies for digital transformation is expected to reach around US$2.3 trillion by 2032. As a result, the data market is valued exponentially. As research by Forrester consultancy points out, Data Driven companies grow 30% a year, representing a great investment.
A Data Driven company is one that uses a structured database, with concrete information for decision making. To achieve this goal, it relies on Business Intelligence tools that use a large amount of data from alternative sources, quickly, safely and efficiently.
Profit comes from monetizing this data, calculated based on how much the company can earn from it, compared to the costs and investments needed to structure an operation, develop software and operate it. For the efficiency of these processes, it is recommended that the data scientists spend more time analyzing the quality of the data and the effects of the actions taken. already the business leaders should help the data scientists to analyze the data more broadly, questioning the context in a comprehensive way.
One of the main tools for this analysis is Artificial Intelligence, with Machine Learning (ML), Deep Learning (DL) and robotic process automation. Briefly, DL is an ML technique that uses models of human functioning, powering AI applications.
Analytics and Artificial Intelligence technologies can further increase the revenue of Data Driven companies, as they identify potential business, improve understanding and better serve customers, in addition to attracting them with engagement. At the same time, they reduce operating costs, as they automate tasks and improve the quality of services without the need to increase staff. These pillars, formed by the trio Analytics, AI and Database, form a support tripod in companies and strongly contribute to exponential growth in the digital world we live in today.
Many tools were developed, taking advantage of these strengths of Data Driven, together with the other pillars mentioned, and can be used for forecasting, customer segmentation, targeted marketing and churn forecast, for example, among other diverse operational functions. One of the great advantages is the efficiency impact on paid searches, for example. You define the desired results and manage to get the right message, to the right audience, at the right time. This is a competitive differentiator: many competitors already adopt Analytics and AI, while others that do not adopt will exit the market.
It is worth remembering that data security does not prevent its monetization, but must be given due importance and comply with current legislation. If the information obtained is very sensitive, use it for statistical purposes (insights) only and do not disclose the data itself. However, single data, without alternatives and from a single source are more valued, as they allow new insights and differentiate your data from the others. Today it is very common to accumulate and work with own data together with third-party data. But beware, that too much data may not be of much use either. Always consider value and risk.
Caio Cunha is President of WSI Master Brasil, co-founder of WSI Consultoria and member of the Global WSI Internet Consultancy Advisory Board. With more than 25 years of experience in the technology industry, he has reached high-level executive positions in large multinational companies such as PWC (with IBM and Unisys clients), SAP and Hitachi Data Systems, in Brazil and abroad.
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