Augmented Analytics - The Next Evolution of Enterprise Analytics
According to Forrester Research, only 0.5% of all data is currently being analyzed and used. Besides, organizations use just 12% of enterprise data. Organizations seem to be underutilizing technologies to interpret the available data.
The situation is only likely to get worse if projections of data volume growth are anything to go by. The rapid growth of IoT-connected devices will generate nearly 80 Zeta Bytes of data.
As data volumes explode, traditional Business Intelligence (BI) and Analytics tools may come up short.
Enter Augmented Analytics, a term introduced by Gartner. Gartner calls Augmented Analytics "the next wave of disruption in the data and analytics market that leaders should plan to adopt.”
So, is Augmented Analytics the future of Enterprise Analytics?
What is Augmented Analytics?
According to Gartner, augmented analytics is an “approach that automates insights using machine learning and natural-language generation.” With augmented analytics, Artificial Intelligence (AI) and machine learning technologies can prepare data and generate insights for data analytics and BI. Simply put, Augmented analytics is the use of AI and machine learning in BI.
The Augmented Analytics approach emphasizes automating the analysis process, which was previously possible only in "specialized" machine learning and data science projects.
Augmented analytics includes techniques such as:
- Augmented data preparation includes the use of algorithms for data enrichment through data transformation.
- Automated analytics (or automated business monitoring) automates the manual data discovery process and accelerates the speed of extracting insights.
- Natural language generation (NLG) and processing (NLP) can simplify the understanding of complex information derived from rich data insights.
The global market for Augmented analytics is projected to reach $30 billion by 2027. But is Augmented analytics better than “traditional” analytics or BI? Here’s why that may be so:
- Machine learning tools are continuously working in the background to self-learn and improve their results.
- Augmented analytics provides faster access to valuable business insights to provide real-time recommendations.
- Augmented analytics locates hidden data patterns and deviations, thus improving the organization’s predictive capabilities.
How is Augmented analytics used across industry domains?
Augmented Analytics – Use cases
Here are some real-world use cases of Augmented analytics:
- Financial services
The financial services industry has already deployed AI technologies in the form of chatbots and growth models. Right from 2016, JP Morgan Chase has been adopting Augmented analytics to develop predictive algorithms. These algorithms are useful in identifying potential customers for equity.
Besides creating targeted promotional campaigns, Augmented analytics has improved customer satisfaction and reduced operational costs. With the growth in fraudulent activities, Augmented analytics help in detecting and preventing financial frauds and money laundering activities.
- Healthcare
Besides financial services, healthcare companies use Augmented analytics to analyze patient data and improve medical care. Patient readmission is a major quality and cost concern for hospitals around the globe. AI-driven analytics can reduce patient readmissions. A recent study in a Wisconsin-based hospital found that Augmented analytics is effective at reducing patient readmissions.
Doctors are also using Augmented analytics to identify their patient’s health graphs and make better treatment-related decisions.
- Sales & Marketing
Among the best use cases, sales leaders can use augmented analytics to explore the latest trends in sales deals and quotas. Similarly, sales and business executives are utilizing visualizations (enabled by augmented analytics) to explore live business data during sales meetings and customer pitches.
Further, marketing agencies can measure the effectiveness of their advertising campaigns and discover hidden data variables (that previously were not uncovered).
Besides industry trends, augmented analytics can leverage customer data to boost the returns from their marketing campaigns. Even marginal changes in promotional campaigns can go a long way in marketers improving their bottom-line revenues.
- Manufacturing
In the IoT age, manufacturers are collecting and analyzing data from sensors. Augmented analytics can automatically analyze large volumes of manufacturing data and detect any production issues. Similarly, supply chain companies use augmented analytics to analyze slow shipment deliveries in selected regions.
Beverage manufacturers like Coca-Cola use AI and Big Data tools to drive decisions around their brand, products, and supply chains.
Among other use cases, augmented analytics can predict weather patterns and crop yields, as well as recommend how to improve them.
Next, let us look at how Augmented analytics offers the best value proposition for business enterprises.
Augmented Analytics – Value Proposition
At its core, augmented analytics automates the manual task of data collection and organization. Effectively, any augmented analytics tool should be able to apply machine learning techniques to high-volume datasets and generate actionable insights.
Companies often struggle to refine their business data. 73% of business leaders concluded that data is “not creating the expected influence.” Augmented analytics accelerates data analytics and management, thus empowering high-level insights.
One such value proposition is in ETL (Extract, Transform, Load) processes, which can be highly time and resource-intensive in extracting actionable insights. Typically, data scientists spend 75% of their productive time in ETL. Through automation, augmented analytics can help data scientists save a lot of time.
With traditional BI tools, companies relied on manual processes as well as human support. Augmented analytics advances the case for automating IT systems to perform tasks with improved accuracy and fewer errors.
According to Forbes, data scientists spend 80% of their time preparing and cleaning data. Augmented analytics can significantly improve the speed of delivering high-quality data using AI and machine learning technologies. Without human supervision and intervention, augmented analytics can enhance objectivity and reduce human bias. It can effectively reduce manual data management tasks by 45%.
Conclusion
In a data-dominated business environment, augmented analytics can be the "difference" between business success and failure. By leveraging both AI and machine learning, augmented analytics is poised to be the "future of enterprise analytics."
At Novigo Solutions, our Data Science & Analytics services enable you to identify business strategies. With our technologies, you can make smart data-backed decisions that can build a competitive edge in the market.
If you are interested in partnering with us, contact us today.