The antagonist in a horror film aimed at small businessmen wouldn’t be a machete-wielding monster or a nightmare with knives for hands. It would be a clock with simply the words “It’s later than you think” on it instead of any numbers. Even though it’s hard to imagine needing additional time when working 80-hour weeks, most entrepreneurs do. You don’t have time for busy work or to stay current on the technological developments that will rule the next 10 years. Fortunately, one trend—augmented analytics—can assist you in resolving both issues.
According to Gartner, a popular analytical software, business intelligence software purchases will be “dominantly driven” by enhanced analytics capabilities by 2020.
Already, your rivals are seeking software with enhanced analytics capabilities. Thankfully, you can as well. You’ll gain time back by cutting down on busy work if you invest in business intelligence solutions with enhanced analytics capabilities immediately. Additionally, you’ll be putting money into the market-disrupting technology of the upcoming decade.
In this article, we are going to discuss 3 ways in which augmented analytics can help your business.
Table of Contents
What is Augmented Analytics? Why is Augmented Analytics Superior to Traditional Analytics?
Machine learning (ML) algorithms are used to enhance business intelligence (BI) systems. The enhanced analytics’ ML algorithms are effective at automating routine jobs (much like ML algorithms in any field).
In addition, ML algorithms are more adept at identifying patterns and comprehending spoken language than programming languages like SQL, Java, or Ruby on Rails. Data preparation and other repetitive chores take up a significant portion of most analysts’ days. Thus BI solutions with enhanced analytics are a better option. They free up your analysts to really execute the analysis by handling the tedious, repetitive job.
Analytics and data-driven thinking might potentially be applied by more people than only your data analysts with the help of BI tools with augmented analytics. Natural language query (NQL), a feature of augmented analytics, enables users to pose questions in standard English. In other words, the BI tool is less like a cumbersome, typical BI application and more like a search engine.
Understanding Augmented Analytics and its Benefits to the Business
1. Enhanced Analytics Eliminates Busywork
Although it may sound corny, augmented analytics actually hastens the arrival of your future. According to research by the Data Warehousing Institute, between 41 and 80 percent of respondents’ time is spent on data preparation. Analysts might spend up to 1,669 hours per year getting your data ready for usage (based on the average of 2,087 hours worked a year). That much time was spent.
What does such planning entail? In large part, it involves correcting minor mistakes (for example, “Montana” is spelled out 50\% of the time and “MT” 50\% of the time).
The tedious task of manual data preparation is reduced through augmented data preparation. Augmented analytics will automatically change all the “MT”s in your spreadsheets to “Montana” rather than taking you 70 days to do it. Your analysts will obtain the information they require and reach the insights in less than half the time, resulting in increased thinking time and decreased brain-draining busywork.
2. Using Enhanced Analytics, you may ask questions more quickly.
When it comes to busywork that takes a lot of time, consider which is quicker:
“What’s the typical price of this item?” as text.
The identical query entered in SQL appears like this:
You can benefit from the first choice by using augmented analytics with natural language query (NLQ). Lacking it? You’ll be forced to study SQL.
The ability to ask your computer questions in plain English is known as natural language querying (NLQ). Your company can save time with NLQ in two ways:
Using basic English while posing a question makes it simpler and quicker.
Analytics are easier for the typical business user to access, which frees up time for your analytics personnel.
Employees in the line of business are unlikely to learn SQL. A SQL-based system will likely scare them away as a result, and your ideal of hiring workers who are data-driven won’t come true.
However, NLQ-based software is much simpler to understand. Your staff members can learn if they know how to use a search engine.
3. Augmented Analytics suggests the Best Course.
Not only does augmented analytics comprehend inquiries in simple English. The answers may also be explained in straightforward English.
Natural language generation (NLG), the same technology that underlies NLQ, is responsible for this capability. The NLG algorithms that comprehend your inquiries in plain English may also provide detailed responses in terms you can comprehend (these responses are frequently referred to as “narratives”).
NLG features are available in several cutting-edge business intelligence systems, and they allow you to discover and present insights from your data in a narrative style.
Conclusion
Augmented Analytics refers to the fusion of emerging technologies rather than something new. Artificial intelligence, natural language processing, and machine learning are a few examples. These technologies enable the delivery of business insights incredibly quickly and with a higher degree of efficiency.
The Era of Data was a time in the past. We should now refer to our time as the Era of Big Data. The amount of data has increased dramatically in recent years. We all leave a huge digital footprint behind us without even realizing it. Our daily use of websites, social media networks, electronic gadgets, and payment platforms all capture and keep operational data. The size, complexity, and rate of growth of data sets have increased to the point where conventional business intelligence tools are no longer able to handle them. We are now seeing the rise of Augmented Analytics, the future of business intelligence tools, and the best you can do for your business is to get on board.
