Augmented analytics tools are coming, but how will they impact enterprises? Could they bring about citizen data scientists and plug the data skills gap?
For many modern businesses, becoming data-driven is a key priority. With more businesses looking to utilise the data they collect, it’s no wonder that the business intelligence (BI) market has seen massive growth in the past decade.
But while BI tools have been the go-to for enterprises, the fact remains, many data initiatives fail to reach fruition.
What it takes to derive and communicate data-insights that can guide business objectives is simply immense. Although many BI vendors in the market have made noteworthy innovations around becoming more dynamic and easy-to-use, arguably, their insights have been too static and lack interaction or drill-down capabilities.
Furthermore, enterprises still depend on IT-professionals to implement much of the solutions currently on the market. As such, there’s now a growing view that these manual reports and dashboards are no longer enough.
Augmented analytics will impact BI
But a new paradigm has emerged: augmented analytics. At the heart of this trend is the use of AI and machine learning to augment human efforts to analyse data.
The term “augmented analytics” was coined by Gartner who considers it as a disrupter in the data and analytics market and says it will transform how analytics content in developed, consumed and shared.
According to Gartner, it will advance rapidly to mainstream adoption, as a key feature of data preparation, data management, modern analytics, business process management, process mining and data science platforms.
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According to Gartner, despite augmented analytics being a relatively new development in data science, by 2020, it will be a major selling point for BI solutions.
“Thanks to the strong processing capabilities enabled by cloud technologies combined with the large volumes of data now available, it means it’s now possible to train and execute algorithms at the large scale necessary to finally utilise AI and ML models,” explained Rita Sallam, VP Analyst, Gartner.
She added: “Right now, augmented analytic tools on the market are best used on proven algorithms and not for specialised, newer approaches where the expert data scientists have not been involved.”
Use-cases to look forward to
“Augmented analytics automates and speeds up the specialist, onerous and error-prone grunt work on the back end so that there’s virtually no lag between asking a data question and gaining real insight,” explained Doug Bordonaro, chief data evangelist at ThoughtSpot. “So, for example, instead of having an analyst produce a report that shows that mobile contracts in Leeds are down 5% over last quarter, augmented analytics lets you ask the question yourself, can tell you why it happened, and even suggest how to address that decline.”
In other words, augmented analytics is looking to assist employees with no data expertise, who might not even know what they’re looking for, proactively spot trends and outliers in massive data sets.
Bordonaro added: “When a user logs into an augmented analytics system, they will get recommended data insights that are relevant to their role, preferences and history.”
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Imagine a regional sales manager for a mobile phone retailer, for example, wanted to drill down into a recommended insight about regional contract sales. She might follow up with a natural language question like: “show me the breakdown of handsets models associated with those contracts’. Through chatbot or voice, the user can have a ‘conversation’ with the system until she gets exactly what she needs. No advance requirements are needed.
With traditional BI platforms, business leaders have to rely on data experts who know all about SQL, table joins and metadata, but less about the business.
“Augmented analytics systems overcome this by letting business people get data insights directly by asking questions using natural language instead of having to ask experts to prepare custom reports that are may come too late to be of any use and usually won’t be used again,” added Bordonaro. “This frees up data experts to work on larger data science projects, custom development and other more valuable activity.”
Data scientists will still be needed
According to Gartner, advances in augmented intelligence mean that by 2020, 40% of data science tasks will be automated.
“Making data science products easier for citizen data scientists to use will increase vendors’ reach across the enterprise as well as help overcome the skills gap,” said Alexander Linden, research vice president at Gartner. “The key to simplicity is the automation of tasks that are repetitive, manually intensive and don’t require deep data science expertise.”
But will this really plug the skills gap? According to Bordonaro, it’s complicated.
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“I would argue that data literacy skills take on even greater importance when companies are using augmented analytics tools,” he said. “Previously, due to low BI adoption levels, most workers relied on gut instinct and guesswork to set key performance indicators and make other decisions.
“This meant people didn’t even realise that data literacy was ‘a thing’. Now that augmented analytics is providing widespread access to data and insights, people increasingly want to gain more competency in applying data insights to improve their own performance and communicate more effectively. We are seeing companies that support this and foster data-literate cultures outperforming their peers.”
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