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Interactive reports, rich visualizations and embedded capabilities are no longer enough in the analytics and business intelligence world. At the very least, they're not enough to transform a business for broader adoption of advanced BI tooling. Many folks will continue using their spreadsheet tool of choice because they're comfortable with it. However, pressure is being placed on data teams to make BI easier to implement, manage and consume. That's why marketing buzzwords like self-service, data literacy and data storytelling are all the rage in pushing modern BI platform adoption.
To make analytics and BI easier for both experts and nonexperts alike, augmented analytics and decision intelligence (DI) are taking the analytics world by storm. In fact, research from Enterprise Strategy Group (ESG) showed that of all the capabilities available within BI platforms, augmented analytics is seeing the greatest new net investment -- with expected adoption to grow by 88% over the next year.
What is augmented analytics and where is it being applied? Augmented analytics is the idea of infusing intelligence into the analytics lifecycle via automation, machine learning and natural language processing (NLP). The goal is to enhance data analytics by optimizing processes and workflows, improving data sharing and collaboration, empowering stakeholders and broadening adoption of analytics and BI tools. Some of those key areas include the following:
- Data discovery. This is achieved by automating the search for and availability of relevant internal and external data in minutes so data sets can better answer predictive questions.
- Data preparation. Augmented analytics can blend data, find relationships and recommend best actions for cleaning, enriching and manipulating that data.
- Recommendations. These are based on predictive user intent and behaviors to enable context-aware and highly relevant insights by looking at data in different ways.
- Conversational analytics. Stakeholders can uncover insights by interacting with systems using natural language. Results range from suggested data sets and additional potential questions to relevant charts, graphs and dashboards.
Pairing augmented analytics with traditional or even modern BI may not be enough, though. Traditional BI tools are rooted in historical analyses of data subsets. The generated dashboards, while interactive, are mostly static. Plus, one dashboard may not be enough, so business analysts are forced to jump between multiple dashboards. The ideal modern business must be data-centric, agile and able to work in real time.
As BI platforms continue to position their modernization efforts by incorporating augmented analytics capabilities and working toward delivering real-time predictive insight, DI platforms are founded on those capabilities. They operate on all available data in real time to deliver predictive insight, incorporate business context and deliver dynamic recommendations via NLP to stakeholders. They promise to maintain a consistent single version of truth while eliminating decision latency and human biases.
So, are those differentiation points between traditional BI and DI enough to move the needle for prospects and customers? Is DI viewed as next-generation, modernized BI? Has self-service delivered on its promise? Has adoption broadened within businesses to a point where data literacy is improving? The answer to these questions is probably yes for the most mature organizations that have been prioritizing data and analytics for years, but not for those in the early stages of their BI modernization journeys. Over the next several months, I'll be hearing from enterprise-grade data teams on the uptake of augmented analytics capabilities to better identify pitfalls, common areas causing delays, areas of success, the added value when done right, and whether or not the differences between BI and DI vendors are easily understood by customers.
ESG is a division of TechTarget.