As Gartner describes, augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyse data in analytics and BI platforms.
Applying augmented analytics in business means “they can get more value of their data, if they train their ML well,” commented Nico Fischbach, CTO at Forcepoint.
A new era of analytics
Elif Tutuk, AVP of Innovation and Design at Qlik, explained that analytics technology has moved on significantly since the first-generation passive systems, which restricted users to static data reports and quickly became obsolete.
She said: “The most effective augmented analytics combines the best aspects of machine intelligence and human creativity and experience to help users get faster responses and improved productivity, along with targeted insights to help make better decisions.”
Tutuk uses the example of a sales leader, who can use search-based analytics to evaluate performance for individual sales reps.
“The leader uses natural language processing to type a question into their analytics platform. Data science and AI immediately go to work analysing both structured and unstructured data against the search terms to display the most relevant results, including visual representations. The user can then explore interpretations of that data that were previously unavailable to help them to make the best business decisions.”
Augmented analytics makes data analytics accessible to everyone and “businesses who invest in [these] tools, in easy-to-consume visual representation and data narratives, are the ones who will propel data literacy at all levels and boost business success,” added Tutuk.
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The changing role of the data scientist
Alexis Fournier, director of AI Strategy at Dataiku, agrees that for modern business, the introduction of augmented analytics allows for greatly accelerated, and automated, AI modelling.
However, he suggests that with augmented analytics comes the changing role and definition of the data scientist.
“Implementing augmented analytics in a business will not make its data scientists obsolete, but it will democratise data, making it more accessible with the wider workforce. With this comes new business needs and responsibilities that involve educating all staff as data citizens. Introducing augmented analytics doesn’t mean setting up shiny new data tools and leaving staff to it — proper training and understanding is needed to ensure that staff are able to connect any data-related work to the business questions they are wanting to answer.”
“Despite the hope that augmented analytics will eventually bring total automation, businesses are already best leveraging its benefits for the faster and more accurate creation of machine learning products. In turn, this paves way for better business processes, accurate operations and the faster development of new products” — Fournier
Fournier continues: “The introduction of augmented analytics also means that transparency is more important than ever. Closing the disconnect between staff, data tools and business goals involves having clear conversations on how analytics and other related AI technologies are expected to change the workforce, business processes, and ways of working.
“With the right knowledge and understanding in place, all staff can successfully guide their way through data-led technologies like automated machine learning to create models, draw comparisons and improve business operationalisation. Ultimately, it will be this transparency around augmented analytics that will bring data science and business intelligence practises together, enabling new and centralised access to once disparate sources of business insight.”
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Move beyond historic data
Rupert Morrison, CEO and founder at orgvue, warns that businesses utilising analytics need to move beyond historic data, “which assumes the past determines what’s ahead.”
Instead, he advises business leaders to take into account far more than just historical data in their decision making and organisational planning process.
He said: “Businesses should also not be blinded by the promise of better business performance delivered by AI and machine learning. Of course, these new and smart technologies have their place but business leaders must make sure that the data they have is actionable. Forging a future based on historical data is folly. These technologies simply can’t predict what comes next. Instead, organisations need to take control and model different scenarios for the future. They need to be looking beyond historic people data and understand the work of the future and the workforce they need to achieve it.
“The uncertainty and confusion of Covid-19 makes this even more vital. Businesses can’t hope to have an actionable plan for whatever comes next based on a wealth of analysis centred on historical data. Those that take the best of AI & machine learning and consider it alongside continuous analysis, modelling, planning and execution, will be the ones that ultimately lead the way.”