Covering the essentials of business intelligence, explore the features & functions for an overview.
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The need to stay competitive is driving businesses to invest deeply in digital transformation, seeking ways to obtain insights that go beyond static reports and dashboards. Augmented analytics is a class of tools that use machine learning and artificial intelligence to reveal patterns that may have remained hidden from conventional methods of data discovery. This new technology uses automated data preparation and discovery to transform how analytics content is developed and consumed.
Over three decades we’ve seen analytics rapidly evolve through a data warehouse era that was designed for IT professionals to the business intelligence (BI) era, which empowered business users.
The augmented analytics era is aimed at an even broader base of users who don’t have data science skills, but need to analyze ever-increasing volumes and types of data.
In many cases, it’s an effort far larger than a human team can manage. Augmented analytics applies machine learning and artificial intelligence to analytics to expose insights based on context (like what the user has accessed in the past and what their role is within the business.) In addition to this, augmented analytics uses natural language processing that enables users to query the data using human language instead of SQL or other languages so that non-technical users are empowered to ask questions of their data.
Augmented analytics can’t automate the decision-making, however. It provides a streamlined way of accessing, processing and analyzing data, but it’s still up to the user to take those insights and find potential applications for them within the business.
Despite the advancement and availability of ML-enabled technologies like augmented analytics, operational reports and dashboards will continue to hold a valuable place in business for a long time. This technology shows promise for streamlining and accelerating data analysis, but it’s only one piece of a very large puzzle.
And it comes with an important caveat: the rush to adopt augmented analytics can put analysts and data scientists in the background. This can leave businesses vulnerable to the hazards of allowing an algorithm to analyze business data without human critical thinking.
Instead of a full overhaul of their BI platform, organizations should consider incorporating augmented analytics into their existing analytics toolsets, to enhance their analytics activities while still relying on human thought when it comes to applying business strategy.
As the volume and complexity of business data continue to grow, augmented analytics will be a valuable tool to improve productivity and enhance data democracy in the enterprise.
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