Gartner recently published its report, “Top 10 Data and Analytics Technology Trends That Will Change Your Business.” As the authors of the report note: “These data and analytics technology trends will have significant disruptive potential over the next three to five years.” Augmented data analytics emerges as one of the key trends highlighted in the report. This article is intended to help you gain a better understanding of this important trend in data analytics.
What Is Augmented Data Analytics?
The term “Augmented Data Analytics” refers to the use of Machine Learning (ML) as well as Natural Language Processing (NLP) in the enhancement of data analytics. The enhancements speed up and automate previously manual tasks related to data collection, organization and preparation for the analytics process.
Analytics software platforms are usually able to integrate augmented analytics tools for the purpose of handling large data sets, saving significant time in the process. ML and NLP enable augmented analytics tools to understand and interact with data organically. On their own, they can notice unusual trends that might be valuable to the business.
Advantages of Augmented Data Analytics
Speed and accuracy of analytical processes are two of the main advantages of augmented data analytics. Manual processes are slow and prone to bias. In any serious analytics effort, it’s necessary to gather data from disparate sources. These include basic structured data (e.g. databases), unstructured data like files and social media posts, web analytics, public data streams like stock market indices and so forth. This takes time.
What also takes a lot of time is the preparation of all this data for analysis. The data must be organized and refined before anyone can tee up the analytics tools to discover meaningful insights. Reporting the findings is time consuming as well.
Manual processes in the analytics workflow are inherently slow. Correctly implementing these processes, with today’s tools, requires a data scientist. Data scientists are quite difficult to find. They’re also highly paid and generally carry significant workloads. So, in addition to the sluggish pace of manual data analytics steps, you will almost inevitably face delays as your project makes its way onto the data scientist’s schedule.
Augmented analytics helps solve this problem by automating many data collection and preparation tasks. This speeds things up and allows the data scientist to spend her precious time on analysis, not prep work. Augmentation also reduces errors. And, perhaps more importantly, the use of augmented data analytics minimizes the potential for bias in the overall process.
Even with the best of intentions, people often unknowingly introduce bias into their data models and analytical hypotheses. As Gartner noted in its report, “A fundamental component of all of these activities is that hypotheses about relationships in data must be known in advance. Using current approaches, it is not possible for users to explore every possible combination and pattern, let alone determine whether their findings are the most relevant, significant and actionable out of all possible options.”
2019 Market Guide for Data and Analytics by Gartner
Why Is Augmented Data Analytics a Necessity Going Forward?
Augmented data analytics is a necessity for any organization that’s doing, or planning to do, data analytics going forward. For some, it may not even be a choice. The augmentations will simply be part of the toolset. The time and expense of current analytics sets a significant threshold for projects due to the significant investment required. Augmented analytics both time and expense and lower the threshold, allowing businesses to drive more value with analytics. Data-driven businesses are pressing every possible advantage to stay ahead. Augmented analytics can provide a competitive edge in the marketplace.
Inefficiency leads to unnecessary spending on analytics projects. Significant savings can be found by automating processes currently being manually conducted by highly-paid data scientists in your organization. You might not even be able to find or afford this hard-to-find skill set at all. For small-to-midsized companies, the idea of hiring full-time data scientists is not realistic. Augmented analytics allows these firms to work with consultancies on a part time basis to leverage analytics for their business’ benefit.
In industry after industry, the leaders are leveraging advanced data analytics to achieve competitive advantage. This might involve using data to make stronger, more sustainable connections with customers, cut costs, identify new opportunities or make better decisions about future moves. Competitively, moving on customer data analytics has become table stakes in many industries.
Financial services companies seeking better customer retention can analyze data about account balances, risk tolerances, demographics, market trends and so forth to determine the best kinds of services and offers to extend to customers. The firms with the most insightful analysis will be in the best competitive position.
Impact on Data Analytics Planning
It makes sense to include augmented data analytics into any planning you’re doing for the next phase of your analytics program. Gartner has two specific recommendations in this regard. They write, “Explore opportunities to complement existing data and analytics initiatives by piloting augmented analytics for high-value business problems currently requiring time-consuming, manual analysis.” Start with a pilot to learn how augmented analytics work.
They also recommend, “Build trust in machine-assisted models by fostering collaboration between expert and citizen data scientists to back-test and prove value.” This is sound advice. People are the users of analytics tools and the beneficiaries of their output. It’s critical that everyone involved in the process trust the models offered through augmentation.
Clarity Insights can help you understand how augmented data analytics can positively affect your data analytics work. Contact us today to learn more about how we work with augmented data analytics solutions.
Written by Patrick McDonald
Chief Architect - Advanced Analytics, Clarity Insights
Topics: Customer Analytics