Data Analytics Drives Business Intelligence

Traditional business intelligence wasn’t made for the digital age. It can’t keep pace with the high speeds, wide varieties, and massive volumes of Big Data. Expert-level knowledge is often required to create reports and analyze findings. Organizations must upgrade existing business intelligence capabilities to become truly data-driven.

Otherwise, organizations will struggle to keep up with savvier competitors and fend off new, digitally-native entrants. Big Data, along with advances in AI, automation, and machine learning, is changing the game for BI—making it accessible to a broader audience, and providing a faster path to actionable insights.

In this article, we examine how business intelligence and data analytics come together to create value for businesses across all sectors.

Traditional Business Intelligence No Longer Makes the Cut

Traditional BI operates on a publisher-consumer model. Business users essentially put in an order, and a trained data scientist or analyst creates reports and/or dashboards for the “consumer.” This arrangement is responsible for the massive lag time between submitting a request for a report and actually receiving those insights.

In many cases, businesses wait on critical insights for weeks, or even months. In today’s world, where real-time insights unlock true competitive advantage, a delay in receiving insights means businesses that still rely on legacy BI are making decisions based on outdated information.

Modern business intelligence and data analytics tools bring more control to end users, allowing self-serve report generation that enables them to dig into datasets and come up with their own queries. They can also scan massive datasets and serve up insights in real-time, allowing business users to make decisions based on what’s happening right now.

Data Mining for Business Analytics

Business intelligence is a term that refers to a collection of processes and applications used to turn data into actionable insights. BI aims to help organizations understand the competitive landscape, better serve their customers, and act on opportunities to drive operational efficiencies.

According to Deloitte, modern business intelligence tools should include the following capabilities:

• Data ingestion
• Data preparation
• Data discovery
• Advanced analytics

Turning Big Data into business intelligence starts with data mining. Analysts rely on data mining to collect information in a specific format. Businesses need solutions that can instantly capture the right data, clean and prepare it, then serve it up in a way that users can understand.

The term data mining refers to the process of identifying patterns or correlations across multiple fields and data sets in large, relational databases. The process involves searching through massive datasets for relevant data sources while business analytics uncovers patterns and trends that provide context and help inform decision-making.

Data mining typically follows a process that looks something like this:

• Extract, transform, and load (ETL) data onto the data warehouse system.
• Store and manage data in a multidimensional database system.
• Analyze data using a BI application.
• Present insights as a report that users can easily understand.

Increasingly, insights are often represented as visualizations or graphs. They may even include AI-generated explanations and recommendations, which help users act on insights faster.

Data mining paves the path for business intelligence. When data is collected, it’s typically raw and unstructured, making it near impossible for human users to analyze those insights and arrive at any real conclusions. Data mining is a critical step in decoding complex datasets, providing a clean, accurate version for users to analyze.

There are two types of data mining for business intelligence: descriptive, which provides information about existing data; and predictive, which analyzes data and provides forecasts and recommendations based on factors like historical patterns, market trends, and external conditions. Predictive data mining is achieved using AI, neural networks, and statistics to serve up the advanced analytics today’s businesses need to make informed decisions.

Long story short: data mining is all about collecting the whereas advanced analytics and reporting tools—with some help from the machines—is about uncovering the , the , and the .

How BI Is Evolving: Emerging Trends

According to a report from Gartner, augmented analytics is one the top trends in data analytics. Because business intelligence and data analytics are closely connected, we can expect to see more growth in the BI arena in the near-term.

Here’s a look at some of the emerging trends:

NLP is humanizing data. BI tools like Tableau and Microsoft’s Power BI are already using natural language processing (NLP), which makes it easier for users to ask questions and analyze insights hiding in massive datasets. Conversational analytics takes things even further, allowing business users to enter queries using natural language, much like a Google search. In the report mentioned above, Gartner predicts that NLP and conversational analytics will lead to an increase in BI adoption, from 35% of employees to more than 50%.

Mobile BI. Mobile BI is emerging in response to increasing pressure for users to make smarter decisions from any location. With remote work becoming the norm, mobile BI enables decision-making on the go, which speeds up the decision-making process by making data more accessible. It’s also worth noting that mobile BI goes beyond bringing business insights to mobile devices. Organizations looking to adopt mobile solutions will need to develop a strategy that addresses security issues that emerge when workers conduct business on private devices, as well as come up with a plan for standardizing reporting tools across different suppliers and systems.

Data visualization. Data discovery systems are becoming more accessible. Users can quickly access data and extract useful insights while visualization capabilities make it easier to interpret findings and collaborate with other stakeholders. The main goal of data visualization is to make it easier for users to quickly pick out patterns, correlations, and anomalies in large datasets by translating complex information into a visual context.

Wrapping Up

Between data mining for business intelligence and the whole range of new tools that bring actionable insights to the masses, it’s clear the BI landscape is changing dramatically. Traditional BI is too slow and hard to manage for the workers who rely on intelligence to do their jobs—whether they’re in sales, marketing, HR, or part of the C-suite.

[adinserter name=”Data Analytics CTA”]