If data sits at the heart of the digital workplace, then managing data and enabling organizations to get best insights out of the data they have is also key. As the amount of data entering the enterprise explodes, technologies to manage these swollen data silos are also being developed at an impressive rate.
“To innovate their way beyond a post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, research vice president at Gartner, of a recent study the company carried out into these emerging technologies and trends.
In field of data management — the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively — there have also been major developments, one of which Gartner identified as augmented data management. Gartner explains that augmented data management (ADM) uses machine learning and artificial intelligence techniques to optimize and improve operations. It also converts metadata so it can be used in auditing, lineage and reporting to powering dynamic systems. ADM products can examine large samples of operational data, including actual queries, performance data and schemas.
Using the existing usage and workload data, an augmented engine can tune operations and optimize configuration, security and performance. Before doing this, however, enterprises need to know how ADM fits into the enterprise.
As organizations are hit with more, complex data, users are struggling to identify important actionable insights, Tapan Patel, senior manager for product marketing at Cary, NC-based SAS, said. Data scientists and data engineers proportionally spend more time manually accessing, preparing and managing data. To improve efficiencies, avoid mistakes, and speed up availability of data for analytics or AI, data management tools are ideal subjects for automation.
To help data engineers and data scientists, augmented data management employs machine learning algorithms that automatically detect and analyze data usage to blend, find data relationships, and recommend best actions to take for cleaning, enriching and manipulating data. These algorithms not only automate mundane activities, but also find regularities in data to the point that the algorithms learn and gain skills.
“With mundane data management tasks automated and learning from experience, users can make data ready for model building quickly and easily, freeing up analytics and IT staff to work on more innovative and value-added opportunities,” he said.
Over the past decade, there has been a shift in focus from merely managing data to managing information. Enterprises now realize that a confluence of trends, including increased regulations and litigation, sensitivity toward data privacy, and accelerated technology development, has resulted in the need to manage and control all data under its roof and convert them to insights. If information is the corporate gold mine, Kon Leong, CEO and co-founder of Milpitas, Calif.-based ZL Technologies, said, then ADM is the mining equipment.
