A major goal of modern business intelligence (BI) is to deliver data insights stakeholders within an enterprise need to make timely, informed decisions. This hinges on connecting the right decision-makers with the most relevant insights at an opportune time. How well businesses accomplish this goal can determine the value they derive from their data strategy and the return on investment they net as a result of their investment in BI software.
The ever-growing volume of data available to companies, then, presents both an opportunity and a challenge. On one hand, there’s more insight available to employees spanning across more data sources than ever before.
The flip side to this opportunity is the fact there’s so much data it would likely require thousands of hours to mine a fraction of the insights lurking within. Moreover, even if employees routinely pull ad hoc data insights, many remain undiscovered just the same. In fact, nearly three-fourths of enterprise data (60 to 73 percent) may be going unused in terms of analytics.
There is promising news for enterprises looking to maximize the usefulness of their data though. Artificial intelligence (AI) and machine learning analytics improve BI by addressing these challenges and powering — while improving upon — a foundation of self-service search analytics.
Machine Learning in AI Analytics: A Brief Overview
Getting an AI algorithm to act a certain way used to require training them. Machine learning adds the ability for algorithms to learn without manual input from data scientists; rather, by providing “data and desired response,” this tech learns and implements the rules on its own during data mining.
Here’s how the Chief Data Evangelist of ThoughtSpot describes machine learning analytics in a BI context: Instead of building the answers manually in advance, it trains the data/platform to produce them for users.
As the focus shifts from what has happened in the past performance-wise to what is actively happening as close to real-time as possible, machine learning is becoming even more crucial. As ZDNet notes, users tend to want to spend the least amount of time they can creating and interpreting reports, dashboards, and data visualization models, while still driving the best business outcomes possible. Empowering stakeholders with access to AI and ML-driven analytics fuels those useful insights at a speed and in a format conducive to decision-making. Employees can then offer feedback as they go about the relevancy of the insights, which in turn helps train the ML algorithm for next time.
Possible Use Cases for Machine Learning in Data Analytics
We’ve discussed how machine learning can optimize data mining and insight detection over time without needing to be manually taught how to do so. Here are some ways in which employees can harness this ML and AI-powered BI to drive tangible business outcomes:
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Increase operational efficiency
Machine learning can play a crucial role in using data to refine processes across an org, from finance to customer service and more.
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Deep dive into customer relationships
AI and ML can help companies understand customers granularly using data from social media, cross-channel behavior, reviews, and more. These types of insights help customer service teams act quickly to repair or strengthen relationships.
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Free up analysts for more valuable projects
When AI and ML take over the “grunt work,” analysts can work on higher-level projects that deliver more value.
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Predict the future based on the past
AI and ML analytics can crunch huge volumes of historic data to improve forecasting accuracy.
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Spot anomalies as they pop up
Rather than having to discover anomalies down the line, analytics-driven by AI and ML can flag them promptly for immediate response.
As you can see, machine learning analytics brings many advantages to BI — namely the ability to mine more insights so stakeholders can act upon them when it matters most.
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