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Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

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What Is Augmented Analytics? A Simple Explanation For Anyone

2 July 2021

Although the term augmented analytics was just coined in 2017 by Gartner, it’s rapidly becoming an essential part of the future for all organisations. In this article, I look at what augmented analytics means and how it can be used to empower all organisations, regardless of size and resources, to be data-driven.

Data is useless to an organisation unless the organisation can extract meaningful insights from it. The inability to glean insights from data has many businesses, especially small- and medium-sized organisations, struggling to figure out how to use data effectively. Without data scientists on staff or available to interpret data and turn the intel into solid business activity, the benefits of data could remain unlocked. This situation will change with augmented analytics.

What is augmented analytics?

Research firm Gartner coined the term augmented analytics in its 2017 Hype Cycle for Emerging Technologies report and claimed it would be the “future of data analytics.” Augmented analytics describes the process where data is automatically taken from raw data sources, scrubbed and analysed in an unbiased manner, and communicated in a report using natural language processing that humans can understand. Thanks to machine learning, augmented analytics looks for patterns in the data or discovers other valuable insights without the involvement of data scientists. This analysis can then be shared with human team members. Since the reporting is easy to understand for non-technical people, individuals in an organisation don’t need to wait for an intermediary tech professional to interpret what the machine saw in the data.

To illustrate, let’s consider what needs to transpire before you can create meaning from data. First, you might gather a data point that indicates your revenue is down 20% year over year. There are more questions that need to be answered before you know what this truly means. Is it because your marketing isn’t effective? Are others in the industry experiencing the same? Is it your product or sales personnel? So, just knowing a data point about declining revenue doesn’t make that information valuable to your organisation.

A deeper dive to uncover what this truly means is necessary. This deeper dive requires that you look at information from multiple resources, clean up the data and analyse it, figure out the insights from the data and then communicate to the organisation about what needs to happen to improve revenue. Before augmented analytics, businesses needed to hire data scientists or analysts to make sense of the data, and this was only possible for some organisations. Even if an organisation had money to invest in hiring a data scientist, one might not be available—the McKinsey Global Institute predicts that there will be a shortage of approximately 250,000 data scientists by 2024 in the United States alone. Plus, a significant portion of what the data scientists needed to do was gather data from various sources, label it and clean it up—repetitive work that artificial intelligence could do quite easily and error-free. Gartner predicts that the advances in augmented intelligence could mean that by 2020, 40 per cent of data science tasks will be automated.

Besides, all data scientists don’t have the business acumen to determine the business action that needs to be taken from the data insights. So, an executive with that business sense would need to work very closely with the data scientist to take the data insights and transform it into business action. Taking this time to work with tech professionals to first understand the data and then figure out what action to take was often not possible for many execs.

With the addition of augmented analytics, businesses don’t need to hire a data scientist to interpret the data. Augmented analytics democratises data and enables all businesses, no matter their size, to extract meaningful insight from its data sources. Augmented analytics has made it easier for all businesses to become data-driven.

While there are software tools on the market to assist organisations with visualising and communicating the analysis completed by data scientists to decision-makers in your organisation, most of these tools aren’t analysing the data. Augmented analytics is able to do it all.

Data scientists will still be in high demand even when more organisations adopt augmented analytics. As the name suggests, this artificial intelligence application augments human efforts by taking over repetitive tasks of data collection and preparation freeing up time for data scientists to take on more strategic and creative tasks such as asking better business questions, finding innovative data sources, and strategically interpreting insights.

Benefits of augmented analytics

There are many benefits of augmented analytics for an organisation, including:

  • IT and data professionals are freed up to focus on strategic matters and special projects.
  • Since machines can efficiently analyse myriad data sources and combinations augmented analytics allows for more in-depth data analysis.
  • Augmented analytics simplifies data analysis making it easier to get to actionable insights.
  • More individuals in an organisation become data focused and empowered by data since it becomes a part of their everyday activity and not just reserved for the data professionals.

Augmented analytics is a way for organisations to handle the complexity and scale of data they are inundated with daily by helping to prepare, manage, analyse and report on data so that business decisions can be made using the insights the data provides.

Augmented analytics promises that it will give more people in the organisation access to analytics and insights from data. We refer to this as data democratisation. In my work, helping companies improve how they use data and analytics, I see culture and skills as the major roadblocks. Data literacy is going to be important for everyone in a data-driven organisation, and companies need to invest in skills and awareness initiatives, alongside technical solutions, if they want their augmented analytics investments to deliver value.

Data Strategy Book | Bernard Marr

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