Blog | Banking and Financial Services

How AI Revolutionizes Business Intelligence

APRIL 07, 2020

The objective of a revolution is a better construct, a new norm, or a complete change. A revolution transforms outdated into optimal.  

The business intelligence revolution currently underway places today’s organizations at the precipice of a fundamental shift—a change in the way they understand their business. The leader of the coup? Artificial intelligence (AI).

AI is upheaving the way businesses garner insights and utilize data by embedding prescriptive and predictive analytics within traditional business intelligence (BI) tools. 

Alone, BI is a set of tools and solutions designed to analyze and describe past and current performance. Business leaders use BI’s data visualization and analytical capabilities to understand “what was” and “what is.” These insights inform future actions based on a view of the current business context.

AI only improves access to this data, empowering leaders to build an even more holistic picture of their organization. In that sense, AI proves the importance of BI tools for forward-thinking organizations. 

But a revolution is radical, complete, and fundamental. While AI has already begun to create change, the next wave of business intelligence software—imbued with machine learning and advanced analytics—will be nothing short of revolutionary. Let’s unpack the wave of change impacting business intelligence and explore where the revolution goes from here.

What Is Business Intelligence? 

It’s now common knowledge that data drives smarter decision-making and greater profitability, but the value of big data was not realized overnight. In fact, modern BI platforms are largely responsible for the explosion in data usage throughout the enterprise. Broadly speaking, BI platforms, dashboards, and solutions enable quicker access and analysis of an organization’s data. These tools and methods are broken down into the following categories:

  • Business intelligence dashboards offer quick, easy-to-digest big data summaries.

  • Visualization tools represent datasets in a variety of visual ways, including heat maps, charts, and scatter plots. Along with dashboards, visualization platforms are the most common BI tools.

  • Reporting allows teams to automate manual tracking of various activities, including sales and delivery operations and marketing campaigns. Reporting is a core component of BI and may also be represented within a business intelligence dashboard. It is also the accessible summary of data intelligence that is used by business leaders to inform key decisions.

  • Data mining falls under the BI umbrella and involves collecting relevant information and insights. In fact, data mining actually results in business intelligence. That’s because the act of data mining culminates the information used to draw business insights.

  • Extract-transfer-load (ETL) tools extract raw data from one or more sources, transform it, prepare it for reporting and analysis, and store it in a data lake or data warehouse.

  • Online analytical processing (OLAP) is the technology behind many BI applications and is an approach to data analysis that allows users to analyze data from multiple perspectives.

Ultimately, BI’s value proposition is its ability to democratize data usage and provide data insights to non-technical employees and managers—and to do so quickly and at scale. Chipotle, for example, used the BI tool, Tableau, to consolidate disparate store data sources and obtain a unified view of their various restaurants. With easy access to data, decision-makers accelerated reporting on key projects from quarterly to monthly, gaining a more up-to-date business case for Chipotle leaders to make strategic business decisions.

The AI Revolution in Business Intelligence

While the business intelligence systems described above are useful in tracking a company’s success over time and creating a data-driven business, their value is inherently limited by their descriptive nature and reliance on an employee’s ability to ask the right questions. Sales managers and other business users can, for example, easily analyze whether their customer acquisition strategy is working but lack insight into “why?”, “how?”, and “what’s next?”. AI helps answer these higher-level questions. And by integrating BI, business analytics, and artificial intelligence into one seamless, self-service BI platform, a true business intelligence revolution occurs.

In fact, just as BI software democratized access to business data, AI-driven business intelligence democratizes access to machine learning-powered predictive and prescriptive analytics. These algorithms drive enormous value; however, with data scientists in short supply, it’s costly and difficult to build models from scratch. That’s where modern BI systems come in to help guide users on their data queries.

Predictive analytics accomplishes this by crunching enormous amounts of data to identify trends, relationships, and other insights a human never could due to the sheer volume of required steps. Predictive analytics models learn from historical data sets to then predict future outcomes. These models are based on past data as well as the relationship between data points, which machine learning helps identify. In manufacturing, for example, algorithms analyze machine usage, factory temperature, past failure rates, and a wealth of other information to identify components at risk of imminent failure. One study found that such an approach could theoretically save between one and two percent of the value of an organization’s total sales. This type of predictive analytics applies to a wide range of use cases, from inventory and sales predictions to customer churn and even Netflix recommendations.

While related to predictive analytics, prescriptive analytics specifically focuses on actionable recommendations. This type of analytics engine utilizes machine learning algorithms to examine vast amounts of structured and unstructured data and then run “if, then” scenarios to prescribe the optimal course of action.

Prescriptive analytics has a variety of use cases in the real world, from product pricing to business processes improvement. Sutherland, for example, worked with one insurance company to transform its claims process through a holistic analysis of current operations. The Sutherland analytics engine learned from unstructured data—in the form of customer and agent feedback as well as demographic data—to find process inefficiencies and quickly deliver prescriptive solutions to various pain points.

Put together, prescriptive and predictive analytics take business intelligence a step further, moving beyond the purely descriptive usage of traditional BI platforms. That’s why the application of AI to BI is considered a wholesale change, not just a minor improvement.

AI + BI = A True Competitive Advantage

Ultimately, the BI revolution puts machine learning models in the hands of everyday employees, arming them with data-driven insights that improve productivity and profitability. From descriptive analytics and reporting capabilities to actionable recommendations and data-driven forecasting, AI-driven business intelligence is one way organizations overcome a shortage in data scientists, make the most of the data they collect, and seize a competitive advantage.

To learn more about Sutherland business intelligence solutions and the application of AI-driven analytics, get in touch today.

How AI Revolutionizes Business Intelligence

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