The Complete Introduction to Business Intelligence
What is Business Intelligence?
Business intelligence (BI) is all about turning an organization’s data into insights that can be used to inform business decisions. BI analysts will use BI tools, software or services to access and analyze datasets and translate their findings into reports, summaries, dashboards, graphs, charts or maps.
In recent years, the advent of modern data visualization and reporting tools has transformed the discipline, empowering businesses to use big data insights to identify, develop and create new business opportunities.
Business Intelligence Definition
Howard Dresner played a key role in popularizing business intelligence during his time as a VP and Research Fellow at Gartner. In 1989, he defined business intelligence as an umbrella term which describes concepts and methods that can help businesses use “fact-based support systems” to improve their decision-making.
Today, Forrester defines business intelligence as “a set of methodologies, processes, architectures and technologies that transform raw data into meaningful and useful information”. This can then be “used to enable more effective strategic, tactical and operational insights and decision-making”.
This definition acknowledges that data cannot be effectively analyzed or used to generate meaningful insights if it is poor quality.
Some would argue that data management and ingestion should not be the business intelligence team’s responsibility. But it is widely acknowledged that BI professionals currently spend a significant amount of their time on data preparation and cleaning data.
BI should not be confused with ‘business analytics’. Business intelligence is descriptive and uses metrics to generate clear snapshots of business performance. Meanwhile, business analytics is predictive, and describes what organizations should do in future to generate better outcomes.
How Using Big Data Insights Can Boost ROI
Big data can help businesses generate ROI in three main ways:
- It can reveal operational inefficiencies
- It can uncover valuable customer insights
- It can be used to create products or services that can then be sold
The role of BI in this picture is to generate reports into key parts of a business’ operation that can be used to measure and assess their performance.
This might include reports or ‘dashboards’ measuring supply chain efficiency, marketing campaign performance, regional product demand, employee satisfaction, customer retention and much more besides.
A company’s business analysts or management team can then use these insights to understand what’s going on, adjust their strategies and set measurable goals or KPIs for the future.
Business Intelligence Examples
There are two main strands within BI. The first is traditional BI, where IT professionals use in-house transactional data to generate reports. The second is modern BI, where business users interact with agile, intuitive systems to analyze data more quickly.
Organizations generally use classic BI for types of reporting where accuracy is paramount and the questions and datasets used are standard or predicable. This might include regulatory or financial reports.
Meanwhile, modern BI tools are best suited to situations where business users need insight into fast-changing dynamics so that they can respond to events as they unfold. For example, American restaurant Chipotle uses a self-service BI platform to create a centralized view of its operations and track their effectiveness.
Similarly, Coca-Cola Bottling Company uses mobile BI dashboards to put timely, actionable CRM data in the hands of its sales teams.
What’s more, BI dashboard specialist Cumul.io uses its ‘sales assistant dashboard’ to show its reps where each of their prospects are in the sales pipeline and highlight which actions they should take in each case.
Business Intelligence Tools (Free and Paid)
BI tools use data to provide access to quick, easy-to-digest insights about an organization’s current state. A wide range of BI tools are available to help analysts with everything from extracting data from various sources to data storage, cleaning messy data, creating data visualizations or dashboards and beyond.
One common mistake for organizations at the start of their data journeys is start investing in best-in-class BI tools prematurely.
Hartnell Ndungi, CDO at African bank Abisa, explains: “Once you’ve decided to have a data strategy, the first thing most people do is go and buy hardware and software.
“So, you go and start buying best in class BI solutions and all those kind of things before knowing exactly, ‘Is that the tool that’s really going to solve your problems? Do you have a simpler way of doing this? Do you prefer to do this organically so that you can grow over time?’”
Enterprise BI tools are designed to increase efficiency and productivity in complex business environments. A centralized IT team will often need to oversee them – and they are more demanding from a data management perspective, as they are designed to handle vast quantities of data.
As such, small and mid-size businesses may find that less expensive (or even free) self-service BI tools meet all their reporting, dashboard creation and ad hoc querying requirements when they’re just starting out with BI.
Popular enterprise BI tools include Oracle BI, IBM Cognos BI and SAP BusinessObjects. Meanwhile, Tableau, Qlik, Cluvio and more provide a range of free and affordable BI tools for small and mid-size organizations.
Artificial Intelligence vs Business Intelligence
As data leaders seek out ever more valuable insights about their companies’ customers and operations, the use of artificial intelligence (AI) to augment BI capabilities is becoming increasingly common.
AI is a ‘catch all’ term referring to any technology that uses computer systems to mimic various attributes of human intelligence, such as sensory perception, problem solving, learning and decision-making.
In a BI context, the potential applications for AI range from automating reports to visualizing inventory or sales data in real-time, automatically combining datasets, mining data for insights and more besides. Many companies also use BI to better understand their customers.
How Machine Learning Enhances BI
Machine learning (ML) is an application of AI that allows systems to automatically learn and improve from experience, without being explicitly programmed. It involves the development of computer programs that can access data and use it to ‘learn’ for themselves.
Since a key goal of BI is to make better business decisions more quickly, many companies have turned to ML to perform complex tasks accurately and more efficiently than a human ever could.
The Harvard Business Review reports that business applications for ML usually fall into one of three categories – process automation, insight generation or employee/customer engagement.
These technologies can be used to augment a company’s BI capabilities in a variety of ways.
For example, First National Bank South Africa is developing ML algorithms to combat cybercrime by creating a unified view of its customers. The bank’s ML algorithms already monitor customer transactions and handle ID verification tasks, and will soon be able to automatically combine all this information into a single report for its analysts.
“Whenever a customer is triggered for a specific reason, you need the analyst to easily look at them and assess them accurately and make the right decision,” explains Mark Nasila, CAO, Consumer Banking and Chief Risk Office at FNB South Africa.
“The bank uses AI algorithms to handle ID verification tasks, spot fraudulent documents and transactions and proactively flag suspicious accounts,” he adds. “So, instead of the analyst doing all this work, we’re having the analyst just doing quality assurance and making the decision.”
This is just one example of the many ways BI tools can gather data from disparate sources and present it in a cohesive, unified format. In this way, businesses can gain a deeper understanding of who their customers are and how to serve them best.
So, BI and AI are distinct but complementary. BI can help companies understand the huge volumes of data they collect. But simple visualizations and dashboards may not always be sufficient.
ML-powered BI tools can clarify the importance of datapoints on a granular level, and help human operators understand how that information can translate into real business decisions.
ML can also lead to the development of smarter, more adaptive BI tools. As these tools gather more data through performing the functions they are designed for, they can ‘learn’ what kinds of recommendations and analyses are most useful and self-adjust accordingly.
AI rather than human software engineers, may ultimately provide the incremental improvements that take BI tools to the next level.
What A Business Intelligence Analyst Does
Business Intelligence Analysts are responsible for assessing the data an organization generates or acquires to meet its BI, reporting and data analysis needs. They work closely with clients and IT teams to turn data into insights that can be used to make sound business decisions.
Since BI Analysts must use data to identify and address operational challenges and opportunities, the role requires a deep understanding of the business in question.
BI Analysts will typically be involved in any data literacy programs an organization runs to help colleagues or clients use data as an analytical tool. They also work with organizations to determine business requirements, define KPIs and develop BI and data warehouse strategies.
In practice, this generally means working with business and development teams to design dashboards, alerts and reports.
The findings documented in these dashboards, alerts and reports will then be distributed to all levels of management and may form part of the business cases for specific process changes or IT projects.
Executive management, operations and sales are the three primary roles driving BI adoption, according to Dresner Advisory Services’ Wisdom of Crowds Business Intelligence Market Study. The study also shows that small businesses with up to 100 employees had the highest rate of BI penetration and adoption in 2018.
While BI adoption is increasingly common in a wide range of industries, this suggests that ‘digital native’ companies are building data-driven capabilities into their business processes as a matter of course.
On the other hand, large enterprises often find that legacy systems make it hard to use their vast data stores efficiently. Replacing these outdated technologies can be a daunting challenge for businesses looking to embrace data and analytics.
The use of BI techniques is especially prevalent in traditionally data-driven industries, such as banking and insurance. What’s more, our research shows that 83% of government organizations are using data to justify making changes to the way they operate.
However, the pace of BI adoption is now accelerating in a wide range of sectors. For example, data and analytics in healthcare is forecast to grow at a compound average growth rate of 20% right through to 2027.
Aspiring BI Analysts will need to secure a bachelor’s degree in computer science or business management with a focus in technology. They should also have good communication skills and be able to grasp the needs of the institutions and industries they work for.
Some employers also require BI Analysts to have additional certifications in computer languages or programs. These certifications are industry-created and generally involve passing an exam.
In some cases, multiple technology certifications and experience as an intelligence analyst can make up for the absence of a degree.
Junior BI Analysts will work on low-to-medium complexity tasks within one or more functional areas of an organization. They will act as a member of a larger team and provide BI support for the wider user community.
Meanwhile, Junior BI Developers will be responsible for designing, developing and maintaining BI solutions. They will craft and execute data queries upon request, presenting information using reports and data visualization tools.
BI Engineers help to fine-tune BI-based platforms, processes and tools, working closely with BI Analysts and BI Developers as well as clients, customers and internal departments. They are also responsible for the ongoing strategic implementation of these tools.
Business Intelligence Analyst Career Path
As BI professionals climb the ranks within their organizations, they will gain new responsibilities. Ultimately, they will start to direct, organize and lead BI workstream projects, as well as the implementation and use of new BI software tools and systems.
Over time, many will move into consultancy roles. This sector usually offers advancement, lucrative pay, the ability to play many roles, a wide variety of activities and great learning potential.
Training and Conferences
There is a wide range of BI certifications and training programs available to professionals looking to advance their careers. BI solutions providers offer many of these certifications, such as the IBM Certified Designer: IBM Cognos Analytics Author V11 or Microsoft’s MCSA: BI Reporting and MCSA: SQL 2016 BI Development qualifications.
At the same time, BI conferences and events are often the best way to stay abreast of the latest industry innovations and trends. That’s why our vast portfolio of global BI, data and analytics events are attended by the world’s most senior BI professionals.