AUGMENTED ANALYTICS: THE FUTURE OF DATA & ANALYTICS
We live an era of data. Not just data, Big Data: data sets have become so huge, complex and fast moving, that traditional business intelligence solutions just can’t handle them. They all either fall in getting the data, dealing with the data, preparing the data, or just understanding the data. Data is everywhere and more of its being produced all the time. Netflix, Google, Facebook, Amazon and Spotify crunch immense amount of users data and mix it with your own unique profile to surface new content and product. Hospitals, governments and charities use augmented analytics to find new ways to administer services and help more people. But first we have to know what actually Augmented Analytics is.
What is Augmented Analytics?
According to Gartner, in their October 2018 research “Augmented Analytics Is the Future of Data and Analytics”. Gartner defines augmented analytics as “an approach that automates insights using machine learning and natural language generation,” before stating it “makes wave of disruption in the data and the analytics market”. Augmented analytics uses machine learning /artificial intelligence techniques to automate data preparation, insight discovery and sharing. It also automates data science and machine learning model development, management and deployment. Jen Underwood says that “augmented analytics uses machine-learning automation to supplement human intelligence across the entire analytics life-cycle”. Bill Su, writing on medium, says that augmented analytics automates insight generation through the use of advanced machine-learning and artificial intelligence algorithms.
How is Augmented Analytics Applied?
Augmented analytics is a young developing technology, but solutions are emerging. As an example, at ThoughtSpot, a full-stack AI and search-driven data analytic platform that certainly would be classified as augmented analytics. Using ThoughtSpot, any employee can search the platform with a question like they would in Google. Answers are instantly generated from the company’s entire database through interactive visualizations like charts, graphs and tables. There’s no need to put a report request into the data team. No need to wait for an answer for two weeks. ThoughtSpot is continually learning and customizing its findings depending on each user, companies get the advantage of an all-in-one analytic platform that fit each specific department, team and employee’s preference.Augmented analytics is still in its early growth phase, but companies can realize its benefits now by leveraging the simple, smart, and enterprise-grade capabilities of ThoughtSpot.
Benefits from the right insights-
Augmented analytics platforms will also have a stronger social component. All tech users are familiar with social networks, where sharing images and stories and tagging friends and family creates a richer, shared experience. Organizations will use a similar social network effect in augmented analytics platforms. When dashboards and visualizations are created and insights discovered, users will share them, tag others within the organizations , add notes and stories, and begin building a larger narrative and fitting that data into the business mission, all within the platform. Instead of creating dashboards and reports and waiting to present them in a meeting, letting precious time slip away and slowing decision making, this instant notification interweaving of many different users efforts, augmented analytics system become a productivity tool. think of an augmented analytics platform as an always immersive system that takes people from questions to insights to decisions within a persistent environment, across departments, teams, devices, and locations. This type of simplified sharing may help boost adoption across teams, especially among on technical users or teams that don’t perform analysis frequently but could still benefit from the right insight at the right time.
From Big Data To Smart Data: Augmented Analytics Role-
Augmenting the traditional BI process with AI is happening, but it’s not the only change coming to how analytical platforms handle huge amounts of data. Data Cognition Engines will change the world of Big Data and analytics forever, giving users unprecedented abilities to work with these immense data sets. First off, they open the door for interactive data exploration with millisecond response times, only querying the more expensive, slower Big Data system directly when very precise detail is required. They also compress terabytes of data into a model that occupies less than 5 megabytes for each terabyte. Once in place, the tiny DNNs require no access to the underlying data, eliminating the need for storage, processing power, and bandwidth. Advancements in data processing tools and adoption of next generation technologies such as Augmented Analytics automated data insights from Big Data are expected to drive the smart Data market towards $31.5 billion by 2022. Companies such as Datameer, Xcalar, Incorta and Bottlenose are already focusing on developing end to end smart data analytics solutions to obtain valuable insights from Big Data. Turning Big Data to Smart data :Emerging opportunities, highlights key market developments, technologies used to convert big data to smart data, government programs.
How Should Companies Proceed?
Whether companies should adopt augmented analytics is not the question – the question is when to start, and how. The market is flooded with different tools for the different steps discussed above, and there is no one tool that addresses all stages or fits all companies. The best way for businesses to proceed is to work with a consulting partner that has broad experience in analytics and in emerging augmented analytics tool sets and methodologies to create a custom journey. There is no cookie-cutter approach; it is the combination of people, processes and technology that need to come together to create the appropriate path forward.
Augmented analytics is still an evolving field. At this point, most companies are not adopting augmented analytics for the entire end-to-end process but are starting with one small piece. In the next few years, I expect that that will change, and organizations will be using augmented analytics for the entire data analytics life cycle. For now, it’s important to know the significant benefits augmented analytics provide: speed, democratization and broad adoption. With these capabilities, enterprises are better equipped to anticipate customer needs, improve business processes and prepare themselves for competitive success in the future.