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2021: Intelligent Data Management Will Enable the Future of Your Business
The EU’s GDPR has a major impact on the data privacy ecosystem. The regulation is an essential step to strengthen individuals’ / Business fundamental rights in the digital era we are living in. After two years of the introduction of the GDPR, the following question still arises: What will 2021 bring in terms of data management and data protection? According to Gartner, by 2023, 65% of the world’s population will have its personal data covered under some kind of modern privacy regulations.
It’s predicted that the technology for the preparation, control and administration of data will become much more efficient so that data is available more quickly and reliably. With the focus on foundational components of data integration, data governance, and data preparation the effectiveness of big data projects can be improved. With the right technology, data management can also drive enormous business value and support digital transformation. It’ll certainly help organizations to better manage the availability, usability, integrity, and security of their enterprise data.
Data has evolved over the years and will continue to evolve. Today’s organizations are data-centric; they accumulate enormous amounts of information in many different formats. Those who are unprepared to deal with the amount of data will be left behind compared to those ready to welcome all business opportunities that big data has to offer. Here below are 5 main areas that play a huge role in the good preparation of data management.
- Data orchestration
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A frequently used term in the sales and marketing domain for whom data has a high priority as their data is the foundation of just about everything, they do. Simply put, data orchestration is the automation of data-driven processes that includes data preparation, making decisions based on that data, and taking actions based on those decisions. Data and API integration and data movement need to grow together to support all kinds of DataOps (data operations) methods. It’s a process that often spans across many different systems, departments, and types of data. It also requires a combination of different technologies that ensure a central data flow. This is the only way to orchestrate data-related activities – across different locations, on-premise or in the cloud.
- Data discovery
In this process, relevant data insights are uncovering and transferred to the business users who need them. A comprehensive directory for searching, making available, saving, and interpreting data and other objects is becoming more and more important. Advanced Analytics enables the automation of mundane data management tasks and frees up resources to actually generate added value from the data. With the right use of data discovery tools, even the non-IT staff can easily access complex data sets and draw out the information they need. This process of knowledge discovery can be performed by anyone, without the technical know-how that was required in the past.
- Data preparation
“Data preparation is one of the most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms, Rita Sallam – Research Vice-President at Gartner.” However Artificial intelligence (AI) has solved this problem by creating the basis for advanced data transformation and by enabling automatic cleansing and consolidation of data. This enables users without any prior technical knowledge to use data.
- Model management
Model Management technologies help organizations consistently and safely in developing, validating, delivering, and monitoring models that create a competitive advantage. The focus is to put the central control of all models in a single application instead of the separate management of individual models. In view of the fact that many analytical models never go into production or quickly become obsolete (model decay), it is important that companies can quickly and easily register new models, adapt, track, evaluate, publish, regulate and document them. Previously, model management referred just to monitoring production models, but it’s beyond that. Models drive new breakthroughs and operational improvements for businesses. According to a McKinsey study, organizations that leveraged models extensively showed a 7.5% profit margin advantage over their peers, whereas those that did not use models had a 2.5% profit margin deficit compared to their peers.
- Data governance
“A data governance plan, supported by effective technology, is a driving force to help document the basis for lawful processing.” Data protection laws require companies to have data governance programs that provide “data privacy by default” and define policies, roles, and responsibilities for the access, management, security, and use of personal data. If they do not proactively advance standards and programs, they not only run the risk of contradicting legal requirements but they could also lose the trust of their partners/customers. With the use of advanced analytics and artificial intelligence in decision-making, they are therefore even more challenged to bring transparency to the algorithms.
Sources:
- Gartner Says By 2023, 65% of the World’s Population Will Have Its Personal Data Covered Under Modern Privacy Regulations
- Artificial Intelligence The Next Digital Frontier?
- Data Governance for GDPR Compliance: Principles, Processes, and Practices
- Intelligent Data Governance For A Trusted And Business-Ready Data Lake