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Augmented analytics will make us all data scientists

(Image credit: Image Credit: Sergey Nivens / Shutterstock)

Data overload isn’t a new problem. Every business recognises that producing fast insight is vital to guide efficient operations; and this means tackling the dual challenges of huge data stores, with 2.5 quintillion bytes of data created daily, and increasingly strained resources.

The problem is defining how to do so. Many have turned to automation — driving a 270 per cent spike in artificial intelligence (AI) adoption over the last four years. However, knowledge of how machines can be harnessed effectively is limited; with 43 per cent of organisations citing a lack of AI strategy as a key usage challenge.

Enter augmented analytics: the advanced AI solution that is making smart data integration, management and implementation possible for all, not just data science professionals.

Augmented analytics: 20-second rundown

First coined by Gartner, augmented analytics (AA) is a tool that empowers ‘citizen’ data scientists — those without in-depth expertise — to rapidly generate and harness valuable insight. In short, it democratises data evaluation. Typically, it involves harnessing intelligent technologies, machine learning (ML) and natural language processing (NLP), to automate data handling at every stage, from initial filtering and blending to accurate assessment.

A bonus for the entire business

The most obvious benefit of augmented analytics is its accessibility. By minimising human participation, it opens up assessment to a wider range of internal talent. As data-driven decision-making becomes crucial in every business department, it will be important for all employees to achieve better data mastery; and AA tools will play a major part in improving their abilities. It’s no coincidence that as machine-led data management rises — with 40 per cent of analysis set to be automated by 2020 — the number of citizen data scientists is also due to increase, growing five times faster than data professionals through the next 12 months.

Additionally, this heavy tech focus will considerably alleviate pressure on resources. Not only can companies save millions in workforce budgets that would have gone to expensive data science specialists, but they will also free up time once spent trawling through data for more productive use. Plus, streamlined analysis will make it easier to quickly share data across the organisation; ensuring teams have the up-to-date insight needed to adapt their efforts and strategies for optimal results — whether that’s adjusting product distribution in line with current market conditions or tailoring marketing messages for maximum real time impact.

The key functionality of augmented analytics

AA software has significant potential to revolutionise the way companies create and use business intelligence. But before companies can realise it, they need a clear understanding of how it works at a practical level, alongside its core applications.

With that in mind, it’s worth exploring the three main pillars of AA functionality:

1.            An organised foundation

For most organisations, the constant flood of data has resulted in one central issue: silos. While they appreciate the need to gain a holistic view of company performance, operations and customers: inefficient data storage makes it hard to meet this goal. Often, information is split between many tools — including advertising, owned media, and transaction systems. Even when imbued with AI, the value of such tech is severely limited if each component is isolated and incompatible: using differing formats and data languages.

This is where AA comes in. Companies can enter massive quantities of raw data — covering each department and customer touchpoint — and set AA software to work on coordination. Not only do intelligent algorithms instantly and continuously cleanse, filter and group data into unified sets, but they also lay the foundations for precise analysis; all while persistently removing any inaccurate information, bias, and discrepancies.

2. Everything is illuminated  

Data assessment is, of course, the major strength of AA. Once they have restored order to data chaos, tools can conduct evaluation in just a few hours or minutes: analysing data across a kaleidoscope of dimensions, and using ML and NLP to spot trends, hidden patterns, and anomalies. As well as saving analysts from running 1,000 analyses for a single report, this also avoids the blind spots that can be created by shortcuts — such as trying to reduce time and work by only measuring dimensions deemed essential. Handing over the reigns to machines means all bases are covered, and companies gain a complete understanding of the factors that may be driving or hindering success. What’s more, with the right platform, they can also take a closer look at any findings that show signs of leading to deeper insight.

3.  Harnessing data value

Finally, AA ensures data is put to good use. For many businesses, tracking insight activation is equally as difficult as initial analysis; requiring complex measurement to establish whether data-inspired changes are effective. Using AA tech, companies can also automate this part of the process: with ongoing analysis logging steps taken after analysis, and their impact on organisational profitability. As well as calculating exact returns driven by data based action, they can also identify where tweaks should be made to minimise waste and bolster results.

Simply put, AA is the future for any business looking to make the most of their data, in the easiest and most effective way. So far, analytical success has called for specific — and highly costly — capabilities and an array of specialist tools. Thanks to AA, we can all harness smart AI tools with high data processing and assessment capacity to take control of growing data stacks, and their full value. In other words, we can all be a step closer to proficient data science citizens.

Alexander Igelsböck, CEO and co-founder, Adverity