Augmented data management and the future of data management

Augmented data management and the future of data management
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A carefully planned and precisely designed data management is essential for any enterprise aiming to maximise the impact of their analytics initiatives and move towards data and AI-driven business. It’s the backbone of any data and advanced analytics initiatives. But as we’ve seen, the data ingestion and preparation stages of the data pipeline may be extremely labour-intensive for data scientists and data engineers, destroying their efficiency and productivity. As seen, augmented analytics has significantly assisted throughout the entire process from data collection to providing insights and recommendations to inform business decisions. And as augmented analytics is becoming a more standard model among companies, it’s giving a spur to a related discipline of augmented data management which is revolutionising the information management landscape, presents Analytics Insight.

Augmented data management and the future of data management
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The future is in automation

In response to the need for more efficiency in the data management process, vendors are adding ML capabilities and AI engines to automate manual tasks and allowing less technical users to be more autonomous when using data, indicates Gartner. While at the same time, more technical users can be focused on high-impact tasks.

Augmented data management leverages AI and machine learning to impact all aspects of the enterprise data management disciplines, for example, information quality and integration, metadata management, master data management, and database management frameworks, and make them “self-arranging and self-tuning,” describes Gartner.

Infusing the disruptive technologies of machine learning and artificial intelligence elevates data management and brings serious advantages to data preparation and insight discovery.

Augmented data management is in its early stages and is going to grow more pervasive. As Gartner predicts, “through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management.”

The impact of augmented data management and challenges to be solved

Augmented data management presents many benefits for organisations and capabilities. Automating the time-consuming tasks will also enable more accurate, faster and scalable decision-making. Backed up by automation, companies can avail themselves of more accurate anomaly detection and correction.

Utilising augmented data management helps secure high-quality data for real-time analytics which translates into instant business decisions. Leveraging ML and AI capabilities to render data management tasks self-configuring and self-tuning empowers the business to break away from traditional data management and analytics. In a traditional data analytics process, business is dependent on the data team for traversing the organisation hierarchy to scout the right data, cleans it, model it, analyse it and generating insights. Augmented data management enables companies to harness data through cross-department collaboration, accomplish various tasks and make proactive decisions within their departments. 

Faster utilisation of data and realising its value helps break down the data silos, and as a result, decrease costs of business operations. An added benefit of augmented data management is its capacity to convert metadata so it can be used in auditing, lineage and reporting to powering dynamic systems. ADM solutions can analyse large samples of operational data, including actual queries, performance data and schemas. 

As we already said, it presents first-line support for data scientists and data engineers to improve efficiencies, avoid mistakes, and speed up the availability of data. By employing machine learning algorithms, ADM automatically detects and analyse data usage to blend, find data relationships, and recommend best actions to take for cleaning, enriching and manipulating data, describes CMS Wire. These algorithms also find regularities in data to the point that they can learn and gain skills.

Augmented data management and the future of data management
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But the impact of augmented data management goes beyond just data management tasks. ADM and augmented analytics are the core of the digital workplace. The latest shift in the workplace due to the COVID-19 crisis and the need for more quality customer experience heightened by economic pressure, introduced intelligent automation into the workplace, argues CMS Wire. An Augmented Workforce presents a workplace where business works alongside artificial intelligence to drive better business outcomes.

Nevertheless, augmented data management requires human intervention and creates a mutually-beneficial interaction where humans, AI and ML complement one another’s gaps. Automating database performance, tuning, and optimisation may reduce the need for entry-level database administrator positions, but it won’t limit the requirement for human skill and contribution for data management, affirms Analytics Insight. While AI and ML present smart recommendations, it’s people’s role to settle in on the final decision.

How does the future of augmented data management look like?

Introducing augmented management in the company should be done with the end goal to automate the process of data circulation and dispel the complexities relating to information, claims Analytics Insight, which should be the goal of every company.

As for what we can expect from ADM and advanced analytics in upcoming years, experts predict that it will embody systems that organisations can build themselves and that span the spectrum from fully automated to fully manual processes, adapting the level of automation to the particulars of a given use case, relates CMS Wire. Use cases can leverage complete (or nearly complete) automation, which entails feeding a dataset and a target to an automated pipeline and get back cleaned data with engineered features, together with the best performing model on top.

This automation of machine learning projects is the essence of the idea of “enterprise AI” and it would allow for greatly accelerated AI modelling while ensuring that a person remains in the loop when needed. By this, it will solve one of the biggest roadblocks to enterprise AI – data management –  which is essential to enabling the organisation to leverage data from the bottom up, democratising data use across teams and roles, relates CMS Wire.

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