Handling hordes of complex data is not only time-consuming but data scientists find it hard to understand and analyze within a short amount of time. Which is why augmented analytics will change and disrupt business intelligence for the better. Powered by machine learning and natural-language generation, augmented analytics is an approach that plans to automate data preparation, insight discovery and sharing for a broad range of business users, operational workers, and citizen data scientists. And by doing so, data scientists will get more time to focus on specialized problems such as embedding enterprise-grade models into applications.
Why Augmented Analytics?
Internet is always expanding and so is the volume of data to process and analyze. Businesses now expect to receive insights faster and they certainly don’t have extra time to make critical decisions. Adoption of augmented analytics using machine learning and natural-language generation has helped curb this problem; not only is it faster but it lacks the bias and incomplete conclusions that are present in the manual approaches.
In an age where a digital business has reached an inflection point, the use of augmented analysis is expected to be crucial for delivering unbiased decisions and impartial contextual awareness. It will change how users consume and interact with data and act on insights. Happening largely in response to disruptive innovations by startups such as IBM, augmented analytics has already made its way into modern business intelligence, data science, and machine learning platforms.
Here are some statistics from Gartner’s research report on augmented analytics —
- By 2020, the number of users of modern business intelligence and analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate — and deliver twice the business value — of those that are not.
- By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms.
- By 2020, 50% of analytical queries will be generated via search, natural-language processing or voice, or will be automatically generated.
- By 2020, organizations that offer users access to a curated catalog of internal and external data will derive twice as much business value from analytics investments as those that do not.
What is Natural-language Generation?
Simply put, natural-language generation is a computer process that translates pre-defined data into plain texts or language. And it’s turning out to be a successful tool for converting massive amounts of business data into understandable, intelligent and actionable insights. For instance, a platform would be fed a structured amount of data to work on within a set of parameters and rules. This could include reports, paragraphs or emails which, after translating would seem as if they were written by a human. The objective of this is to optimize decisions and actions in organizations and to give the end user a seamless experience. And speaking of end users, augmented analytics will also be a key feature of conversational analytics.
What does that mean?
It will enable both businesses and people to explore data, generate queries, receive and act on insights in natural language (voice or text) over mobile devices or personal assistants (Google Home and Alexa).
NLP or natural-language processing, on the other hand, enables machines to analyze the imperfect way of how humans talk or write. This also plays an important role in the overall functionality of natural-language generation.
When combined, machine learning and natural-language generation automate the process of data analysis to help users receive insights into their data. And it takes only a matter of seconds compared to the hours of labor that data analyst/ data scientists take to finish their tasks.
How Will Augmented Analytics Impact the Entire Analytics Workflow?
Gartner’s research report also suggested that augmented analytics capabilities will rapidly achieve mainstream adoption as a key feature of self-service data preparations, modern BI and analytics and data science platforms.
Below is an example from a case study done by Gartner to find out how augmented analytics is working out for organizations:-
By using Paxata, which is an augmented data integration and management service, a multinational banking and financial services company was able to curb the length of regulatory compliance and the anti-money-laundering process by 95%.
To avoid any regulatory risks and penalties and to protect its reputation, the bank monitors money-laundering activities and tracks transactions made by PEPs. All the established processes were manual using Microsoft Excel and script-based with a staff of over 70 analysts.
The company was able to fix this by
- Replacing the processes to harmonize data from internal transactions from across the bank’s network, ATMs, tellers, deposits, withdrawals, accounts and credit cards that were across 43 countries.
- It also took care of data that was sourced externally; these included money transfers and PEP data sources.
- It improved data quality and PEP monitoring from 25% matching to 77%.
- This reduced the processes from 21 days to 1 day.
- Due to the user workflow being easy to use, these data preparation and cleaning processes are now carried out by business staff, rather than the IT staff.
One of the amazing things about machine learning is that it can evaluate data much faster and easier compared to a human. And as a result of that, people can focus more on the subjective aspects of data analysis such as setting business strategy which is much more reliable than the results of automated analysis.
As for NLG, it’s proving to be a huge time saver for businesses. The company, Associated Press is able to produce 4,400 quarterly business recaps using Wordsmith. Instead of having a journalist sift through the information that a public company publishes about its corporate earnings, the software can seamlessly scan and derive insights from each release; translating them into plainly written narratives.
The inception of augmented analytics marks the next wave of disruption in the data and analytics market as more and more BI vendors and businesses have switched to it. From startups to big established companies, adoption of augmented analytics can prove to be very profitable in many different ways such as a deeper understanding of insights, better use of resources, getting faster results and deriving actionable insights to inform your business strategies.