The What and Why of Augmented Analytics
With the extensive amount of data available in contemporary business, you’d think most companies have mastered data analytics. The truth is, many businesses have trouble leveraging their data to make valuable decisions. As a result, companies often misinterpret or completely bypass critical areas within the organization that require attention. By digging deeper into the data, enterprises can identify opportunities within companies that have the potential to increase efficiencies at all levels.
Fortunately, augmented analytics can help businesses understand the value of their data by producing actionable insights. What does that mean for your business precisely? In this post, we’ll define augmented analytics and explain why augmented analytics are essential for your business.
What is Augmented Analytics?
Augmented analytics are considered the future of data analytics by industry experts around the world. Having coined the term in their 2017 report, Gartner describes augmented analytics as “an approach that automates insights using machine learning (ML) and natural-language generation.” In other words, augmented analytics are tools that leverage ML and Artificial Intelligence (AI) systems to transform the way analytics information is developed, consumed, and shared. These tools assist and accelerate data storytelling for analysts, data scientists, executives, and beyond.
Why is Augmented Analytics Important?
For businesses, augmented analytics can facilitate the technical steps required for turning raw data to insights. Augmented analytics can collect data, clean the data, conduct the analysis, and create action plans for executives or marketers automatically, with hardly any supervision. As a result, enterprises of all shapes and sizes can benefit from augmented analytics in many ways. We’ll list the top 4 reasons you’ll want to implement augmented analytics into your overall strategy as soon as possible.
Why Your Business Needs Augmented Analytics:
1. Shortage of Data Scientists
According to the McKinsey Global Institute, the U.S. economy is projected to have as little as roughly 250,000 data scientists by 2024. This number accounts for both entry-level data scientists currently working in the field and those enrolled in education programs. Therefore, businesses struggling to recruit data scientists should consider augmented analytics. With advanced ML and AI algorithms, augmented analytics eases the dependence on data scientists and makes analytics accessible to other decision-makers in the organization who can take ownership.
2. More Data to Be Analyzed
As companies increase their usage of automated technology, the amount of information to collect and analyze is higher than ever before. By adopting augmented analytics, humans can use machine intelligence to accelerate the analytical process on a much larger scale.
3. Reduces Human Error and Limitations:
There is a lack of overall business expert knowledge in data science. As a result, executives must work very closely with data scientists to ensure the analytical results make sense with the overall business strategy and goals. Plus, data scientists spend over 80% of their time preparing data according to a 2016 survey by CrowdFlower. That leaves little time for them to thoroughly interpret data, develop actionable insights, and solve complex business problems.
Augmented analytics can immensely reduce the problems associated with data analytics and data science. However, it will require time, resources, and the combined efforts of many individuals within your company. By automating the process of data science, your organization will reap the benefits of augmented analytics and innovative technology.
Don’t wait to begin your digital transformation. Contact the industry experts at Katalyst Technologies today to get started!