It’s always exciting to read about advancements and predictions for technological trends. Since I work with analytics, I tend to pay closer attention to what’s coming for the field of data science and analysis. After doing some research, I saw that many new trends involve artificial intelligence and the use of machine learning algorithms. There was one trend that caught my eye, though, and Gartner calls it augmented analytics.
What is Augmented Analytics?
One of Gartner’s definitions:
Augmented analytics is a next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists.
The tools of augmented analytics will be able to automatically go through data, clean it up, identify data patterns and trends, present them as a visualization or in natural-language narratives such as “50% of web traffic is middle-aged women from US”, and then convert these insights into actionable steps without professional supervision.
The CEO of one company focused on developing algorithms, which will help to draw insights from analytics tools, including Google Analytics, compared augmented analytics to the evolution of a car. Nearly everyone in the United States knows how to drive a car despite it having a very intricate design. That complexity was obscured by technological advancement and the drivers just need to know how to press pedals and steer the wheel to make decisions while driving.
Now, the progress has gone further with the advent of smart cars making the task of “driving” unnecessary—we can simply focus on more important things such as efficiently and safely moving from point A to B. Frankly, we’re not as advanced in the data field as in the automobile industry. According to Gartner’s 2017 ITScore assessments, while organizations realize the importance of digital analytics and want to be data-driven, they still predominantly accumulate large amounts of data without driving actionable insights.
The image below shows that only 34% of companies can confidently say they’ve adopted diagnostic analytics and can answer questions such as “Why is one product selling better than another?”, “Why are expenses higher this month?”, “Why did this patient respond better to a particular treatment?”. Even a smaller number of companies are able to fully take on predictive and prescriptive analytics.
Source: Gartner (July 2017)
A Mixture of Pros and Cons
When machine learning tools become powerful enough to prepare data and drive business insights, a question arises: Will it replace the need for businesses to hire analysts and data scientists? I believe that augmented analytics will definitely have a positive effect on small business owners who have no means of getting services of experienced professionals but desire to use data to help them grow, know their customer, and stay on top of competition.
Although, the same business owner who has access to all the great machine generated insights will face the responsibility to choose to apply them to their business or not. There is always a human factor present in the decision making regardless of having amazing tools that automate some aspects of data analysis.
The data scientist’s help will still be needed for the more mature and intricately structured businesses because it’s necessary to implement a correct data model before any analysis can be done by augmented analytics tools. At the same time, business data is becoming more complex and its analysis can take a lot of time. That’s where augmented analytics will step in to provide faster time to insight.
Despite the fact that automation in the data field is inevitable, we will still seek new opportunities to be more innovative and create jobs that never existed before. It’s proven by history from controlling fire to creating a wheel to discovering electricity and so on.
What is your outlook on augmented analytics? Do you think it will replace a need for human analysis?