Business intelligence (BI): Empowering data-driven decision making
Introduction to business intelligence (BI)
Today’s company leadership is faced with the necessity of making big business decisions faster and more accurately than ever. Fortunately, the right strategies and tools can help you make data-driven decisions quickly and accurately.
Defining business intelligence: Purpose and core concepts
Business intelligence (BI) is the collection of processes, technologies, skills, and software applications used to make informed, data-driven business decisions. BI includes data collection, data aggregation, analysis, and meaningful presentation that facilitates decision making.
Data-driven organizations use a variety of BI tools to access historical and real-time data in a data repository to perform queries, generate customized reports, and predict future trends. These tools include advanced analytics performed by trained data scientists as well as insights generated autonomously by machine learning (ML) algorithms.
Business intelligence encompasses a broad range of methods for collecting, storing, and analyzing sources of business operations data. BI has become an umbrella term for a number of processes and activities that help leaders gather data to then optimize the business. These include:
- Querying: using specific questions to obtain answers from datasets
- Data mining of databases and other data sources that uses machine learning to discover trends in big data
- Reporting and sharing data analysis with decision-makers
- Descriptive analytics that help ascertain what occurred
- Performance metrics and benchmarking to compare current data to historical data and track performance
- Data visualization that turns gathered data into visual charts, dashboards, and graphs to help make data easier to digest and interpret
- Visual analysis that explores data through visual means and stories to readily communicate analysis results
- Statistical analysis to help determine how and why trends happen
- Data preparation that compiles multiple data sources and prepares it for data analysis
Business intelligence vs. business analytics: Understanding the differences
BI is often confused with business analytics. Business analytics (BA) refers to statistical methods used to measure performance and optimize business processes.
Data analytics is the process of analyzing sets of data to gain insights. Two types of data analytics are:
- Predictive analytics — Analyzing historical data to determine the most likely outcome.
- Prescriptive analytics — Running hypothetical scenarios to determine most likely outcome of a certain action.
Data analytics is a primary component of BI and BA, but only one part of the overall system.
BA is a similar yet separate process with a different function. BA mines historical data for trends and insights to drive business change. BI uses historical and real-time data to enable making decisions in the present: i.e., evaluate what works, what doesn’t, then decide how to best move forward. BI primarily helps run a business today, BA is primarily used to predict what will happen in the future.
The importance and benefits of business intelligence in modern business
The growing accessibility of big data is making modern business decisions more crucial but also more difficult to obtain. An enterprise data warehouse often contains a terabyte or more of raw data that needs to processed and made ready for analysis. BI systems allow for comprehensive analysis of data — often in minutes — to respond to specific business requests.
For example, SKF is a global manufacturing enterprise, so they need to be able to accurately forecast the size of the market for its products and the demand for specific product types.
"What products the company should produce and in what volumes? Where to invest or divest and how to react to emerging industry trends? Talend is helping us do that." — Fritz Ulrich Dettmer, Manager of Business Intelligence, SKF
F+W is a content and ecommerce company dedicated to innovation and creativity. That means their entire team needs to be able to access the data necessary to evaluate success and drive progress.
"With Talend, our cloud and on-premises systems are now speaking together. This has empowered the organization as a whole and is moving us from making decisions based on gut feel to making them based on consistent data." — Greg Sitzman, VP of Business Intelligence, F+W
Other key benefits of BI include:
- Accelerated time to answer — In-memory analytics with cloud-based data warehouse solutions can analyze data in real time, providing fact-based information in minutes.
- Better business decisions — BI extracts facts and transforms data into actionable information that can be trusted.
- Improved operational efficiency — BI makes the interconnections between different components of the business more visible, so problems and inefficiencies can be identified and dealt with more quickly.
- Increased ROI — BI helps identify resources needed to reach goals, increases productivity by making data analysis quicker, and aids in the discovery of new revenue streams.
- Faster reporting — BI provides real-time reporting of up-to-the-minute, accurate datasets, giving organizations a competitive edge in solving complex business problems.
- Accurate strategies — BI helps identify important trends and patterns in data that can be utilized to set priorities and allocate resources to meet desired goals.
- Satisfied customers — BI provides data on KPIs that improve core business functions (improved product or service, decrease in time to market) resulting in higher customer satisfaction scores (CSATs).
Data-driven decision making and performance metrics
Gone are the days when businesses could afford to make decisions based on human judgment alone. Modern businesses run on data and need business intelligence to enable data-driven decision making. Business intelligence also delivers performance metrics, which help leaders understand, in real time and historically, what is working and what needs to be changed. This makes businesses more agile and responsive to market changes. It also helps them capitalize on trends and sudden changes, such as spikes in demand for products based on unexpected events.
Leveraging BI for competitive advantage and business optimization
Businesses need BI to help optimize the business and maintain a competitive advantage. Data enables the insights into the company’s performance, but also on a competitor’s performance and trends. This helps businesses stay abreast of strategy and make decisions that will guide them to the best way to maintain and increase revenue and to meet customer expectations.
The role of BI in supply chain, customer behavior, and forecasting
Business intelligence offers rich data on customer behavior across the buying process, as well as real-time visibility into the supply chain, which helps analysts with forecasting inventory, sales, and trends that impact growth and revenue. It can also help with product development, as BI delivers data on most-used features and customer feedback to iterate development more accurately.
Key features and functions of business intelligence tools
Self-service BI and data management for business users
One of the benefits of modern BI are the many BI tools designed to provide self-service BI and data management for business users. This frees business users to focus on the data and reporting they need in the moment to make decisions, rather than having to wait for data scientists to gather data and perform data analysis for them. Many BI tools make it easy for users to query and visualize results so that data is easy to work with and share on a daily basis. A key advantage to self-service BI is that it helps businesses make decisions faster and in a more democratized way, enabling the business to maintain the speed it needs to meet customer expectations and take advantage of market opportunities.
Dashboards, interactive graphs, and data visualization
BI tools should provide ways to make data easy to understand. These include easily configurable dashboards, interactive graphs with predictive modeling capabilities, and data visualization tools to make data easy to see, interpret, compare, and comprehend.
Data sources, data warehouse, and data integration (ETL and OLAP)
Business intelligence tools draw on multiple data sources to provide a complete picture of data for informed decision making. Much of this data is stored in data warehouses and many companies use various processes of data integration, such as extract, transform, and load (ETL), to integrate data sources into their BI tools. Whereas ETL loads data into a warehouse, online analytical processing (OLAP) retrieves it for multi-dimensional analysis at high speeds on large datasets from a data warehouse or unified, centralized data repository.
Talend Data Fabric and Stitch help companies integrate their data sources and data stores with easy-to-use processes, connectors, and pipelines to popular BI tools such as Qlik, Tableau, Microsoft Power BI, and IBM.
Real-time analytics and predictive analytics
Many BI tools provide real-time analytics, predictive analytics, and use online analytical processing (OLAP) to enable powerful insights from multiple data sources. Real-time analytics helps companies make decisions in present time based on current insights. Predictive analytics provides insights into what is likely to happen given different scenarios, based on current and historical data. This type of analytics is used in generating product recommendations based on a customer’s purchase history, for example.
Selecting the right business intelligence tools and platforms
Use criteria for choosing the best BI software for your organization
The best BI software should:
- Connect to a broad array of disparate data sources, datasets, and data systems; wherever your data lives, a BI tool should be able to connect to it
- Generate deep analysis that helps users easily see patterns, trends, and connections
- Display results in easy-to-digest data visualizations, reports, maps, charts, and graphs
- Facilitate comparisons and multiple scenarios
- Provide drill-down, drill-up, and drill-through features so users can get as granular with the data as they need
Advanced BI and analytics systems use artificial intelligence (AI) and machine learning to automate and streamline their processing and reporting.
Read Gartner's BI Magic Quadrant
Many companies turn to analyst group Gartner’s Business Intelligence Magic Quadrant report to evaluate BI tool solutions and vendors that will meet their needs. This report compares vendors based on Gartner’s evaluation of their products, company, history, and innovation path.
Real-world business intelligence success
Lenovo: Harnessing real-time BI for improved performance
Hardware and PC giant Lenovo was experiencing explosive business growth and exponential increases in data volume. The company knew that supporting analytics solely on premises would not be sustainable, so the company decided to move to the cloud. With Amazon Web Services (AWS), Talend, and other partners, Lenovo built a hybrid-cloud platform for analytics.
Lenovo Unified Customer Intelligence (LUCI) Sky is an AWS-based platform that runs the analytics workload for the entire Global Analytics and Operations (GAO) organization. For data integration and data quality, Lenovo chose Talend Data Fabric.
At Lenovo, waiting around for answers is not an option. Since reducing the amount of data used by 40 percent, LUCI Sky’s performance has met all SLAs. It ingests over 40 GB from more than 60 data sources each day. It runs 4,000+ processes, turns tickets around in less than three hours, and its compute is 800 cores. And with all that, it delivers analytics every four hours or so, feeding dashboards and other intelligence tools. LUCI Sky has also delivered on its annual goal of 10X return on investment.
The future of cloud-based business intelligence: AI and machine learning (ML)
AI and ML enable modern cloud BI tools to perform faster, more intelligently, and with greater accuracy than traditional models. By using AI and ML to engage with data and train analytic models, BI tools can handle greater volumes and a wider scope of business intelligence data gathering and analysis. As AI and ML advances, it will help businesses ascertain broader impacts and uncover more hidden connections among data so people can make more confident, data-driven decisions.
Currently, leading BI tools like Qlik use AI to deliver a smoother, enhanced interaction between user, tool, and data — and to make data analysis easier for business users. By taking much of the data science complexity off of the front-end, AI-enabled BI makes rich data insights accessible to anyone in the organization. It also speeds up time to value for BI projects.
Getting started with business intelligence: A guide for beginners
The role of business intelligence analysts and data scientists
Given the role of AI and ML, do organizations still need BI analysts and data scientists? The answer is yes. BI analysts and data scientists provide expertise and professional knowledge to help guide the business in asking the right questions, connecting the right data sources, and designing or configuring BI tools according to the business problems that company needs to solve. Human insight, experience, and the ability to understand context —as well as to be mindful of issues such as compliance, culture, and privacy concerns — is crucial to a successful business intelligence strategy.
To get started in BI, companies will need to work with their data engineers to ensure BI tools are set up properly and to guide the overall strategy.
Developing key performance indicators (KPIs) and benchmarking
A key next step is to understand that to measure anything in the future, you must first start where you are. It’s crucial to develop key performance indicators (KPIs) that outline what success looks like, and benchmarks to know where you’re starting and what results will indicate that you’ve achieved the next level of success. Setting baselines, KPIs, and key benchmarks gives you a map to navigate from. Again, these will be tied to the problems the business is trying to solve. You’ll want to work with business leaders and your senior teams to understand where to prioritize BI efforts and what problems you’re trying to solve.
Implementing business intelligence solutions: best practices and tips
A successful BI initiative requires several key best practices:
- Gain buy-in from the business. Without organizational-wide buy-in to the value of BI, it will be very difficult to make any headway toward success. The business needs to understand the value BI will bring and how it will help shape a different path forward.
- Work with key stakeholders in IT, Engineering, Security, Platforms, and Data Management. All of these stakeholders hold part of the key to success — with necessary contributions toward designing a BI initiative that is integrated, has data governance, is secure, is aligned with platform team guidelines, and supports data management practices.
- Collaborate with key business stakeholders. The end users in Corporate, Sales, Marketing, Finance, Procurement, HR, Supply Chain — all have much to benefit from BI. Start with the teams that will be impacted first, but include everyone in the bigger story of how BI can make their lives easier, more certain, and more data-driven.
- Identify a priority use case to pilot. Choose a business problem that is identifiable, measurable, and small enough to have displayable impact when BI analytics is applied to it.
- Choose a BI tool provider that will serve as a partner, not just a vendor. The provider should have demonstrable success in the BI industry, experience, a commitment to innovation, and expertise in AI adoption. They should work with your teams to help you achieve success. Pay attention to their professional services reputation and how available they are in customer support.
- Report your success. Evangelize the value of BI across the business by having the pilot team share their story with others. Consider holding a townhall meeting where others can ask questions, learn from the experience, and brainstorm on what is possible to achieve or change with support from BI.
- Expand your BI scope. Once you’ve proven the value to the business through a pilot project, mindfully and strategically expand the scope of your BI efforts. Eventually, BI should incorporate all major departments of the business and drive overall data-driven decision making for the enterprise.
Business intelligence is a key component of an organization’s strategy to drive growth, remain relevant, resolve problems, re-imagine customer and employee experience, and move with speed and agility in today’s market.