Recent years have been marked by unprecedented events that have profoundly changed the world scenario.

The economic recession and world conflicts continue to have a strong influence on the market and companies around the world are looking to make the smartest investments.

Moreover, the continuous push on innovation makes it necessary to understand which are the best strategic choices that can support in the achievement of economic objectives.

Data Intelligence professionals must equip themselves with Data Quality tools and consider the following 5 priority trends:

1. The primacy of digital language over human language

NLP, natural language processing, is a sub-branch of linguistics, computer science, and artificial intelligence that deals with the interaction between computers and human language. For some time now studies and research have been underway in this field, just think of the customer support Chat Bots on many sites or mobile apps and the famous ChatGpt.

The popularity of these technologies is expected to grow by about 18% over the next two years and evolve rapidly. There are several new models in development that will have implications for how we take information and how it is interpreted. The goal is not only to find the data you are looking for but to get even those you had not thought of.

In addition, according to recent research, 80% of the data a company has is obsolete and unstructured and it is very complicated to derive useful information. With a natural language understanding, NLP systems can read, understand and extract data from any type of document quickly, automatically and effectively.

2. Investments in derivative data for unexpected events

The events of recent years have highlighted the importance of investing time and resources in forecasting and risk management. Unfortunately, prior to the pandemic there was no information available on similar situations to prepare for such a particular crisis. It is in this context that synthetic data can bridge the gap.

Research suggests that models trained with synthetic data can be more accurate than others, eliminating many privacy and copyright issues. Derived information can be used for multiple needs and allows you to plan various scenarios to better manage any future problems.

3. Smart Data Governance

Investments in Data Analytics activities have seen a sharp increase in the last two years, in fact 93% of companies have said they want to continue to increase the budget in this field.

The rapid evolution of privacy regulations, distribution, diversity and data dynamics hinder the ability of companies to achieve better business performance. This phenomenon becomes particularly challenging in a complex and dynamic economic-social scenario, as Data Governance becomes more complicated to manage.

The goal is to create a system that improves access, real-time movement and the advanced transformation of data between sources and business systems so as to fully exploit the power of this strategic resource.

4. Artificial Intelligence for Business Intelligence activities

Currently, many companies are in a situation of lack of skills and Artificial Intelligence and Machine Learning can be valuable supports to automate some of the Data Preparation tasks.

Introducing AI in information management can save time and resources for data preparation; it is estimated that currently less than 20% of the time is devoted to data analysis, while just over 80% is used to identify, preparing and managing sound and appropriate information.

With smart tools, Data Talent can focus on Data Analytics, generating new knowledge and helping Decision Makers make more informed choices.

5. Real-time data to manage sales

As repeatedly pointed out in this article, the events of recent years have greatly affected market changes and supply chains have been deeply compromised. Anyone who has bought a car or a simple new pc knows that waiting times can be very long.

Having Real Time Data to predict future situations in order to react quickly has become one of the most important aspects for an enterprise. It is expected that by 2027 60% of the technological expenditure for the acquisition and handling of data will concern the enabling of simulation, optimization and recommendation capabilities in real time.

Real Time Analytics represent one of the main sources of competitive advantage. In fact, speed is one of the three V(s) that make up the definition of Big Data and it is a feature that is finally becoming concrete in all companies.


At Drive2Data, we believe that having a quality Data Collection Method is essential to making the right business decisions.

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