What is Business Intelligence (BI)?
Business intelligence (BI) incorporates a variety of concepts to improve business decision making by using fact-based decision making. Business intelligence is the ability to collect information about the business, analyze it and react according to it.
Why is it important now?
BI is a comprehensive term that refers to a combination of analytics and reporting tools that identifies trends and patterns in historical data. Organizations are facing increased competition and lower survival rates in today’s digital environment where knowledge is the power that helps businesses gain a competitive edge.
There is an increasing need for democratization of analytics. Though more organizations want to increase their data-driven decision making, data scientists are a scarce resource. McKinsey Global Institute estimates that there will be a shortage of approximately 250,000 data scientists by 2024 in the United States. Companies can make up for this scarcity by enabling their employees who are not data literate to act as citizen data scientists. Therefore implementing BI software help organizations turn data into actionable insights about without hiring multiple data scientists.
BI software can also improve efficiency in organizations’ processes. It makes data more accessible within the organization and, as a result, the productivity of departments increases.
What is BI Software?
Business intelligence software allows non-technical users access and analyze data with ease to make data-driven business decisions. Wikipedia provides a more formal definition: "Business intelligence comprise the strategies and technologies used by enterprises for the data analysis of business information."
BI software solutions are critical as companies try to expand data-driven decision making across the organization. BI tools are targeted toward non-technical users and usually don’t require coding experience. Analytical training and BI knowledge and some experience with the tool is sufficient for a non-technical users to effectively use a BI tool.
Business intelligence processes have two components: Data management and BI tools. Some BI vendors focus on data management. They aggregate data from different resources (e.g. internal and external) and focus on data storage, organization and cleaning. Other vendors focus on the analytical aspects of BI such as ad hoc queries, scorecards, dashboards and analysis.
What are its benefits?
- Single source of truth: Use of the same BI tool allows the whole company to be aligned on the key metrics with limited effort. In companies without a central BI tool, significant management and personnel time is spent aligning different departments on how to measure and track KPIs.
- Constant and effortless business monitoring: Dashboards allow all levels in the business to monitor key metrics with ease. This allows management to choose metrics to focus on and helps them align business efforts to improve these metrics.
- Data driven decision making: BI tools allow companies to make data-driven operational decisions efficiently with the help of templates and historical data. Highest level strategic decisions in most Fortune 1000 companies are driven by strategy and business teams, sometimes supported by consultants, that rely on data and insights. However, as decision importance decreases, cost of analysis to make data driven decisions increases relative to the business value generated by improved decisions. For example, a multi billion acqusition may generate multi billion benefits to the company and therefore spending $10-20m on due diligence may be worthwhile. With the same logic, selecting the exact bid price for a <$20k bid to a new customer, should only warrant an analysis cost of $100-200 at most. However, a comprehensive analysis taking into account the competition, the potential lifetime value of time customer, cost to serve the customer etc. would cost a lot more than $100-200 if completed ad-hoc with no specialized tools. BI tools dramatically reduce the cost of such analysis by providing easy access to historical data, analytical functions and enabling collaboration via templates. Therefore, BI tools ensure that even operational decisions are data driven.
- Improved operational efficiency:Creating dashboards and deriving insights from data helps companies to optimize their business processes. By using BI software, companies can generate reports on which processes to be improved and which processes integrate with each other better without wasting too much time. Therefore, BI software stands alone when operational efficiency is necessary to be improved and gives companies the opportunity to focus more on improving their products instead of spending time on data analysis and compiling reports.
- Faster and more accurate analysis or planning:By using the right BI software, a company can produce intuitive dashboards much faster and more accurately, which helps companies to save lots of time that will be wasted otherwise.
- Improved data quality:BI software is advantageous when the quality of data in hand is low. Preprocessing of data is often time-consuming and challenging without the proper know-how. Therefore, a company can gain an advantage by using BI software to improve the structure of the collected data and categorize it efficiently.
- Improved customer and employee satisfaction: Optimizing some business processes can significantly increase customer satisfaction levels. Verizon, a multinational telecommunication company, was able to reduce the number of support calls by 43 percent by performing analysis on customer support chat data. Additionally, often IT departments are given access to data manipulation and analysis tools as they have the technical knowledge. However, a BI software with a practical user interface can increase employee satisfaction a lot as non-technical users can also generate powerful insights and visualizations from data.
- Improved competitive advantage: Another practical feature of BI software is benchmarking. Companies need to analyze the market that they are competing in-depth to understand the structure of the market and their performance within the market. BI software enables businesses to keep up with changes such as customer behaviors in the market and help them overcome their competitors with actionable information.
What are the challenges of implementing BI software?
Due to poor collection and categorization of data, BI tools cannot operate as efficiently as they would with clean data. When analyzing data from multiple sources or reviewing customer insights, the structure of data is an important factor for BI software to work properly. Therefore, BI software, with an integrated data pre-processing tool where restructuring is necessary, is one of the solutions to overcome the costly impacts of poor data quality.
Absence of execution and training
Even when a company has the right BI software to solve complex tasks and derive insights from data, business users’ lack of technical knowledge is a challenge over the effective adoption of software in the company. Therefore, the selected tools should be east-to-use and employees should be aware of what they can produce with the implemented tool.
Measuring correct metrics and KPIsOften businesses are not sure about which metrics or KPIs should be measured to derive actionable insights from data. Instead of applying ad-hoc analysis, BI software with an ability to perform tasks such as filtering data, adding new data, slicing data fields and comparing any combination of data can be useful.
Cost of BI implementation
Implementing BI software may bring some additional costs along. Not only it is expensive to implement BI software that satisfies the needs of the company, some software may also require additional hardware resources which might bring high infrastructure investments along.
Lack of coding knowledge and BI strategy
A business should analyze their business processes in-depth to select the right BI software that will suit their needs. A BI strategy is crucial to know the pros and cons of the implemented software. Also, coding skills may be necessary for some BI software to deal with advanced calculations and filtering of data. Their businesses should also consider adding the coding skills to their teams while setting up their BI strategy. Implementing software that does not require additional coding can be very handy in order to overcome this challenge.
How does machine learning impact BI software?
Combining your BI solution with AI and machine learning can fasten data collection and processing processes. We’ve written an article about AI’s role in analytics tools, feel free to check it out.
Where does SaaS come into play?
As with other software, there is a significant amount of interest in the SaaS/cloud deployment model for BI and Analytics software. However, only a few companies have emerged that are pure-play SAAS. This is because of the complexity involved in delivering a pure SaaS BI product due to data privacy challenges, slower online response times due to data upload bottlenecks etc. Therefore, in a significant share of BI installations, data remains on-premise, even for those software vendors who focus on SaaS and cloud products.
What are BI Use Cases?
BI tools are general purpose analytical tools enabling a wide variety of functions such as reporting, data analytics, visualization, discovery, to business performance management. We can put them under 3 specific categories;
Reporting and Performance Management: Vendors in reporting space help to query data sources to produce a human-readable output, such as financial and operational data. It’s used cases include benchmarking of business processes or performance metrics to those of other companies or industries.
Data Analytics and Visualization: Discovering and communicating meaningful patterns in data and Communicating clearly and efficiently through visual means, e.g., graphics and charts with simple user experience with drag and drop features.
Complex Event Processing and Data Discovery: User driven approach to finding patterns and data points through the use of visual tools (mouse clicks: behavior of the users or maps.) and breaking down the complex processes to draw conclusions.
What are example case studies?
Air Canada improves safety by implementing AI and analytics
Air Canada uses Sissense BI software to collect and analyze data generated from their planes. BI software enables front-end employees to access data and make immediate decisions in real-time regarding safety issues. For example, the organization uses an AI-powered BI software for predictive maintenance operations. Software constantly delivers reports on safety-related KPIs so that users can predict any potential problems about planes’ parts before the problem arises.
Telecommunication equipment company democratizes data
Telecommunication equipment company designs and markets wireless telecommunications products and services. They updated their BI tool to an augmented analytics tool, IBM Cognos Analytics, that empowers business users with self-service analytics and data visualizations so that they can identify risks, make smart decisions and seize new opportunities. With the new update of BI software, the organization
- avoids the risk of running unsupported software
- gains faster insight into data
- give business users more ownership and accountability for the reports they generate and use.