Business intelligence (BI) mainly refers to computer-based techniques used in identifying, extracting,[clarification needed] and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.
BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and predictive analytics.
Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS). Though the term business intelligence is sometimes used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. Business intelligence understood broadly can include the subset of competitive intelligence.
What is business intelligence?
Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting.
Companies use BI to improve decision making, cut costs and identify new business opportunities. BI is more than just corporate reporting and more than a set of tools to coax data out of enterprise systems. CIOs use BI to identify inefficient business processes that are ripe for re-engineering.
With today’s BI tools, business folks can jump in and start analyzing data themselves, rather than wait for IT to run complex reports. Although BI holds great promise, implementations can be dogged by technical and cultural challenges. Executives have to ensure that the data feeding BI applications is clean and consistent so that users trust it.
What are some benefits of business intelligence efforts?
A broad range of applications for BI has helped companies rack up impressive ROI figures. Business intelligence has been used to identify cost-cutting ideas, uncover business opportunities, roll ERP data into accessible reports, react quickly to retail demand and optimize prices.
Besides making data accessible, BI software can give companies more leverage during negotiations by making it easier to quantify the value of relationships with suppliers and customers.
Within the walls of the enterprise, there are plenty of opportunities to save money by optimizing business processes and focusing decisions. BI yields significant ROI when it sheds light on business bloopers. For example, employees of the city of Albuquerque used BI software to identify opportunities to cut cell phone usage, overtime and other operating expenses, saving the city $2 million during three years. Likewise, with the help of BI tools, Toyota realized it had been double-paying its shippers to the tune of $812,000 in 2000. Companies that use BI to uncover flawed business processes are in a much better position to successfully compete than companies that use BI merely to monitor what’s happening.
What are some potential problems?
User resistance is one big barrier to BI success; others include having to winnow through voluminous amounts of irrelevant data and poor data quality.
The key to getting accurate insights from BI systems is standard data. Data is the most fundamental component of any BI endeavor. It’s the building blocks for insight. Companies have to get their data stores and data warehouses in good working order before they can begin extracting and acting on insights. If not, they’ll be operating based on flawed information.
Another potential pitfall is BI tools themselves. Though the tools are more scalable and user friendly than they used to be, the core of BI is still reporting rather than process management, although that’s slowly beginning to change. Be careful not to confuse business intelligence with business analytics.
A third impediment to using BI to transform business processes is that most companies don’t understand their business processes well enough to determine how to improve them. And companies need to be careful about the processes they choose. If the process does not have a direct impact on revenue or the business isn’t behind standardizing the process across the company, the entire BI effort could disintegrate. Companies need to understand all the activities that make up a particular business process, how information and data flow across various processes, how data is passed between business users, and how people use it to execute their particular part of the process. And they need to understand all this before they start a BI project, if they hope to improve how people do their jobs.
How should you implement a BI system with help of ATN & RK Software Ltd?
When charting a course for BI, companies should first analyze the way they make decisions and consider the information that executives need to facilitate more confident and more rapid decision-making, as well as how they’d like that information presented to them (for example, as a report, a chart, online, hard copy). Discussions of decision making will drive what information companies need to collect, analyze and publish in their BI systems.
Good BI systems need to give context. It’s not enough that they report sales were X yesterday and Y a year ago that same day. They need to explain what factors influencing the business caused sales to be X one day and Y on the same date the previous year.
Like so many technology projects, BI won’t yield returns if users feel threatened by, or are skeptical of, the technology and refuse to use it as a result. And when it comes to something like BI, which, when implemented strategically, ought to fundamentally change how companies operate and how people make decisions, CIOs need to be extra attentive to users’ feelings.
Seven steps to rolling out BI systems:
- Make sure your data is clean.
- Train users effectively.
- Deploy quickly, then adjust as you go. Don’t spend a huge amount of time up front developing the “perfect” reports because needs will evolve as the business evolves. Deliver reports that provide the most value quickly, and then tweak them.
- Take an integrated approach to building your data warehouse from the beginning. Make sure you’re not locking yourself into an unworkable data strategy further down the road.
- Define ROI clearly before you start. Outline the specific benefits you expect to achieve, then do a reality check every quarter or six months.
- Focus on business objectives.
- Don’t buy business intelligence software because you think you need it. Deploy BI with the idea that there are numbers out there that you need to find, and know roughly where they might be.