Is Augmented Analytics the new future of data?

Post Author - TYDS MANZIL MEHTA

Why Data Analytics still remains the biggest challenge to almost all enterprises? 

        At this point, almost everyone in the world agrees that data is good for your business, and has potential to drastically increase your traction in revenue if done properly. But the challenge is that data analytics is not the easiest task to do. In fact, data on its own is completely useless, unless there is some actionable insight that can be driven out of that particular set of data.

        For example, your data shows that your sales have declined 10% in the last six months, maybe because of the industry trend, or because the market is changing or is it because one of your advertising channels is not doing well and your targeting is not correct. So to reveal these trends and analysis you will need to go deep into your web analytics, google analytics, and e-commerce data.

How will augmented analytics help us?

        

        Data is not exactly a Bittman pill which you can just swallow and forget about it. This is the problem that augmented analytics solves. Augmented analytics allows the user, who's looking at a particular problem, to set a simple natural language, where it can be a verbal or chat-based communication and reveal insights to get the answers from the data that has been provided, so that their data doesn't really need to go through manual visualization. Analysis, to be able to provide the answers you need, actually needs a very data savvy person - a data scientist, who understands what data is revealing. What Augmented analytics really does is relieves a company’s dependency from data scientists by automating insight generation using advanced machine learning algorithms and artificial intelligence. That's how you can get natural language outputs through chat based or voice based commands. So, you should be excited about using augmented analytics because now it will be available to everyone; you don't need to be a data scientist to understand what your data is revealing to you. 

        So, I’m going to demystify augmented analytics for you. In the past couple of years there have been so many technology terms that were dropped on us- like machine learning, neural networks, cognitive computing and blockchain, which happens to be a little bit hazy from all sides for us. So, augmented analytics is one of them and Gartner explains it by saying that it is an approach to analytics that automates insights using machine learning and natural language processing and it's said to disrupt the analytics market as it exists today.

 

Augmented analytics is the use of statistical and linguistic technologies to improve data management and performance, right from data analysis to data sharing and business intelligence. It is somehow connected to the ability to transform big data into smaller, more usable, datasets. However, in this case, the main focus of augmented analytics stays in its assistive role, where technology does not replace humans, but supports them, enhancing our interpretation capabilities. Data analytics software, along with augmented analytics make use of machine learning and NLP to understand and interact with data as humans would do but on a large scale. The analysis process often starts with data collection from public or private sources.

        

        You can think of the web or a private database. After data is gathered, it needs to be prepared and analyzed in order to extract insights that should then be shared with the organization, along with an action plan to do something with these learnings. All these tasks are usually performed by data scientists, who spend 80% of their time on collection and preparation of this data, and just the remaining 20% on finding insights. The goal of augmented analytics is to automate the processes of data collection and data preparation in order to save data scientists 80% of the time.

        However, the real, ultimate goal of augmented analytics is to completely replace the data science teams with AI, taking care of the entire analysis process from data collection to business recommendations to decision makers. To make it very clear, you could imagine asking the augmented analytics tool to find online reviews about one of your products and tell you what you should improve to sell more of it, having, then, the machine responding to you with a clear textual answer and of course some compelling charts.

Conclusion

 

All in all, augmented analytics is the analytic version of human + artificial intelligence. It also has a good potential for BYO AI, which is to bring your own artificial intelligence framework into reality. Opportunities with vendors are creating more tools using artificial intelligence in order to help data integration, data visualizations and data expression. Augmented analytics is definitely going to be a hot topic in the coming years.