The Future of Augmented Analytics: Putting the ‘Why’ Back Into Your Data

There are many articles that will tell you that this or that is the future. By now, we have all grown accustomed to buzzword bingo with big data, artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) all getting their time in the limelight. However, we’re here to tell you that augmented analytics is not “another trend” but is, in fact, going to change business intelligence for the better. Due to the explosion of data and the development of new techniques and tools, we will soon be looking at our gigabytes, terabytes, and even petabytes of information in an entirely new and automated manner. At Altair, we call this augmented analytics.

Augmented analytics offers a perhaps-utopian view of the future, where data, science, and AI are blended into a trusted, instantly accessible resource. But, is the future really that far away? Today, augmented analytics is gaining steam, transitioning from the industry’s next big thing to the industry’s must-have tool. Rather than consider what this can deliver for us tomorrow, let’s think about how we can get these next-generation insights into the hands of our employees today.

The movement towards augmented analytics started in the 1960s when Douglas Engelbart realized that beyond merely performing calculations, computers could actually be used to augment the capabilities of the human mind. In the ensuing decades, descriptive analytics hit the mainstream, when users started querying their historical information, building static reports and visualizing this in an easy-to-use graphical interface or dashboard. However, the problem with this was it was too retrospective. We wanted to look into the future, not worry about the past.

Along came predictive analytics. ML and AI promised to change the world with complex coding algorithms. However, this type of data science is an elitist game with scant resources; McKinsey predicting that there will be a shortage of approximately 250,000 data scientists by 2024 in the United States alone. Rather than looking at decision trees, we wanted to look at strategy trees and start to align the numbers we were seeing with concrete business actions and KPIs. What data scientists, and the wider organizations really need, is context. If the data says one thing, then we need to know ‘what’ and ‘why.’

After all these iterations, we have finally arrived at augmented analytics – data’s next frontier. Our friends at Gartner have called this, “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 analyses data.”

How can this bring value to my organization?

Firstly, as hard to believe as it may be, data acquisition and data preparation continues to be a very manual, error-prone and bias-ridden process. We have all heard the well-quoted statistic that a data scientist spends 80% of their time manually preparing data. Thankfully, augmented analytics will have AI components that will radically simplify and accelerate the preparation, cleansing, and standardization of your data allowing you to focus all your energy into the all-important analysis.

Moreover, data exploration, feature-engineering, and feature-selection needs to be an instantaneous and visible process. We don’t need to solely rely on complex coding or scripting any longer. Augmented analytics will allow you to eyeball your data like never before, gaining precious insights at the speed of sight. Going one step further, colleagues will also be able to like, share, and comment on their team-members’ data-pipelines transforming workplaces into the newest social network. Move over Facebook!

Data democratization is a key element to augmented analytics. In 2020, organizational structure is flatter than ever before with decision-making and idea-generation coming from all corners of an enterprise. Therefore, we need to knock down the walls of our offices and ensure everybody in an enterprise can access these analytical tools. These models need to be code-free while also being code-friendly to ensure they are powerful enough for data scientists, accessible enough for data analysts and visual enough for senior executives.

Finally, the three V’s of big data are widely spoken about – volume, velocity, and variety. It goes without saying that an augmented analytical approach will see organizations no longer struggle but, in fact, thrive from more data coming from more data sources (structured, semi-structured and unstructured) at never-before-seen, real-time speeds. This new-generation of models will scale enterprise-wide and future-proof an organization for what’s to come.

What does Gartner say?

In its 2019 white paper: ‘Augmented Analytics is the Future of Analytics,’ Gartner promotes five primary recommendations for organizations looking to adopt these tools:

  • Pilot and Validate – identify a pilot program and test
  • Update Roles and Invest in Data Literacy – educate and train your teams
  • Scale across the Enterprise – rollout and educate business leaders
  • Mitigate Expert Pushback and User Misinterpretation – Set expectation and be honest about what this can and cannot do
  • Assess Vendors – Identify service providers that can demonstrate a command of augmented analytics

What does the future of augmented analytics really look like?

Ultimately, augmented analytics has made it easier for all businesses to become data-driven and for all employees to stay human. No longer will we be scratching our heads trying to think what our data means. Our data will soon feed us the insights, in record time, allowing organizations to couple the ‘what’ with the ‘why.’

Want to learn more? View this on-demand webinar titled, “The Future of Augmented Analytics” to take a deep dive into the world of augmented analytics and what it means for the modern business.