Understanding what’s happening across all areas of the business is critically important if you expect to remain competitive. However, when data is stored in silos, decision-makers struggle to even track key performance indicators (KPIs). Forget advanced predictive analysis.

Getting that valuable information into the hands of key decisions-makers is the promise of today’s business intelligence (BI) systems. These projects aren’t necessarily simple, but the payoff is more effective, real-time decision-making and performance optimisation across the enterprise.

What Is Business Intelligence (BI)?

Business intelligence (BI) refers to the systems an organisation uses to drive strategy, analyse data and extract insights to inform decision-makers. An effective BI practise enables all members of the organisation — from leaders and managers to front-line support and operations personnel — to act based on shared intelligence derived from a single, reliable source of data.

BI overlaps a number of other data-driven disciplines. It’s important to understand the differences among them as well as how they work together to deliver greater value.

Business intelligence vs. data science

BI and data science are closely linked but distinct disciplines.

  • Data science is an interdisciplinary field that extracts meaning and insight from increasingly large, varied and complex data sets. It is both predictive, forecasting future outcomes, and prescriptive, determining the best actions to prepare for those outcomes.
  • BI refers to the analysis of business data in order to understand company performance and provide actionable insight. It analyses what has already happened.

Business intelligence vs. data analytics vs. business analytics

  • Data analytics refers to the examination of datasets or creation of analytical models to uncover patterns and draw conclusions about information. Business analytics is a more specific application of data analytics, referring to the analysis of business data.
  • Business analytics includes data mining, machine learning and statistical analysis to make predictions about the future and guide decision-making.
  • BI paints a picture of what has and hasn’t worked to inform what a business might want to do next; business analytics offers visibility into what is likely to happen.

Increasingly, companies are combining business analytics and BI to drive both day-to-day and forward-looking planning and decisions.

Traditional BI vs. contemporary BI

Business intelligence as we know it today has been around since the late 1990s. It is in wide use across industries, geographies and in companies of all sizes. However, the strategic value of BI has grown exponentially in recent years, driven by advancements in database technology. As data science and data analytics capabilities have matured and expanded, modern BI software has gained the ability to ingest and analyse more of the big data sets companies now have access to — in all their increasing volume, variety and velocity.

That has enabled BI systems and professionals to provide greater value to the business now and contribute more to long-term success. At the same time, the development of more user-friendly self-service BI tools and the addition of machine-learning capabilities and intelligent automation within BI systems is democratising access to data-driven insights.

Key Takeaways

  • Business intelligence comprises the data analysis strategies and technologies used to deliver insights that power better decision-making.
  • While business intelligence has been around for decades, it is now an indispensable, strategic, technology-enabled practise.
  • Digitisation of information and advances in technology have democratised and amplified the power of business intelligence.
  • Use cases for business intelligence span most corporate functions and, increasingly, most business roles.
  • Effective business intelligence can deliver a number of business benefits, from increased revenue and agility to improved efficiency and productivity.

Business Intelligence (BI) Defined

Business intelligence refers to the technologies and strategies involved in the comprehensive collection, integration, analysis and presentation of business information that guides all levels of decision-making. The best BI systems collect data from a variety of internal systems and external sources so that they can provide historical, current and predictive views of business operations using key performance indicators (KPIs). The data is accessible to authorised members of the organisation’s workforce in the form of reports, data visualisations and dashboards.

How Does Business Intelligence Work?

Business intelligence systems provide detailed analyses of business operations and performance. The process begins with collecting data that exists in multiple internal enterprise software applications and from external sources. The data may be structured or unstructured, historical or real-time. Often, this data is gathered into a central data warehouse or smaller data marts. It may also exist in data lakes, where raw data, like log files, typically reside. Data integration and management tools can be used to extract, transform and load the raw data into a warehouse.

Where Data Resides

Data lakes. Data lakes are large — sometimes huge — storage repositories containing a wide variety of raw, structured, semi-structured and unstructured data. Each data type stays in its native format while in the data lake. Data may be dumped into the lake from many internal and external sources. The storage medium used tends to be inexpensive, and extracting, transforming and loading the data so it can be used for analysis often requires specialised expertise.

Data warehouses. These contain data that may have been extracted from a data lake or deposited directly. Data in a data warehouse is in an assigned format and uses a defined schema. It may be structured or semi-structured, such as video files that contain metadata describing the contents. Data warehouses are usually large, containing data from all corners of a company, though not the size of a typical data lake. The data is readily accessible to authorised business users and applications, but companies often prefer to slice sections off into data marts for security and speed of access.

Data marts. These are collections of data relating to one subject or department, like finance, sales or marketing, and may be standalone or partitioned off from a data warehouse. Data marts structured and accessible to authorised business users and applications.

Databases. These are the organising elements of data warehouses and data marts.

erp business intelligence
Business intelligence systems analyse business operations and performance by collecting structured and unstructured data from various sources into central warehouses or data lakes for detailed processing.

Next, BI software provides a variety of data management, reporting, analytics and communication capabilities, including data preparation, querying capabilities and advanced analytics including data mining, predictive analytics, text mining and statistical analysis. It also distributes the resulting KPIs and other intelligence to business users, conveying insights to help guide tactical and strategic action.

erp business intelligence workflow
The business intelligence workflow involves gathering, organising, cleaning, analysing, and presenting data to inform strategic decision-making and enhance business performance.

BI Use Cases

Because business intelligence delivers value across business functions and units, BI use cases continue to grow along with available data and technology capabilities. Most can be categorised into the following types of business value:

Performance management: Most organisations track multiple KPIs, both metrics that inform the overall business and within key functions, like accounts payable or inventory. One of BI’s most important benefits is its ability to easily monitor and report on a wide range of KPIs, not only to confirm advances toward business goals, but to uncover shortcomings and issues early on. Leaders and managers can also use BI tools to help surface root causes and solutions.

Better, faster decision-making: Advanced BI tools give business users a more comprehensive and easy-to-digest view of the data that helps them do their jobs. Informed, data-driven decision-making is more important than ever in a hypercompetitive and dynamic marketplace.

Business process optimisation: Leaders and managers can use BI capabilities to identify inefficiencies and productivity bottlenecks hidden within mass volumes of operational or transactional data. By collecting and analysing this data, BI enables leaders and managers to rebalance processes — on the shop floor, along the supply chain, in sales and marketing workflows, on IT networks or within customer or employee experiences.

Nuanced understanding of markets and customers: Sales and marketing teams can adopt BI to mine large data stores for in-depth knowledge about the needs and buying behaviours of current and prospective customers. Modern BI tools blend big data from inside and outside the company to optimise customer-facing approaches and ultimately increase sales, market share, customer lifetime value and loyalty.

Increased insight for strategic planning: BI provides insights to drive organisational strategies and direction. Executive teams, board members, strategists and research and development professionals can deploy BI tools, along with business analytics, to better understand not only the current business environment, but possible future scenarios.

Benefits of Business Intelligence

It’s clear that BI gives business users across functions and levels key information and realistic grounding to help them work smarter. That leads to a number of business benefits and positive outcomes, including:

  • More accurate and detailed understanding of business performance;
  • Better communications amongst decision makers;
  • Early warning of business, financial and operational challenges;
  • Ongoing, accurate benchmarking and competitive analysis;
  • Better access to more complete, higher-quality business data;
  • Enhanced ability to predict cash flow, market and demand trends;
  • Increased operational efficiency or productivity; and
  • More informed, and hopefully more accurate, faster decision-making.

These should lead to increased revenue, better profitability, more accurate financial planning and analysis (FP&A) and improved risk management.

How to Build a Business Intelligence Strategy

While business intelligence software is capable of visualising data to help with decision making, success requires a well-thought-out BI strategy that ties together the people, processes and technologies necessary to achieve desired outcomes.

Creating a BI strategy is a multistep process that begins with an assessment of the current data landscape and the selection of an executive sponsor who is committed to the project. Key stakeholders (internal and external) and a BI governance team that comes from different parts of the business should be identified. A chief data officer, if the business is large enough to employ one, will also be instrumental for strategic insight.

With the right people in place, the focus shifts to what the business wants to accomplish with its BI strategy. That includes determining achievable, specific objectives, key performance indicators to measure progress and a plan for how data will be shared and documented. Other keys to success: a clear BI implementation road map to be shared with stakeholders; selection of a new BI platform and tools; agreement on architectural and data sourcing decisions; data mapping and infrastructure preparation; and the establishment of a BI governance process.

Companywide BI projects are a major commitment, with returns on investment that can be seen in faster data collection, analysis and decision-making — all of which can help reduce costs and improve revenue. Tracking is essential, as is reviewing the BI strategy at least annually to assess its success and where improvements can be made.

6 Ways BI Helps Businesses Make Data-Driven Decisions

As mentioned earlier, there are many valuable business use cases for BI across functions. But what does that look like on the ground? Here are ways various business functions use BI capabilities to make data-driven decisions.

  1. Finance: BI tools and processes provide real-time strategic visibility into finance and operations. Finance team members from controllers to CFOs turn to BI to improve financial and management reporting, drive down operational and capital costs, manage financial risk and maintain compliance using trusted data.
  2. Information technology: The CIO and IT leadership team must deal with changing business models, a dynamic technology and market environment and the challenges of globalisation and remote operations. BI empowers IT leaders with insights so they can better manage their own operations and raise their profile in the business by partnering with key stakeholders to bring together data and systems for better decision-making in an ever-changing market.
  3. Sales: BI can turn data from disparate systems (inventory, shipments, billing, customer financials, payments) into insight that helps sales leaders and teams forecast, plan, budget and set realistic sales targets. Real-time visibility into customer and sales data can enable users to optimise the sales pipeline, from leads to order processing.
  4. Operations: BI helps COOs achieve operational excellence. Manufacturers in particular are under pressure to manage distributed facilities with the utmost efficiency and begin to wrangle big data with industrial IoT projects. BI arms operations managers with global manufacturing insights and real-time visibility to identify and head off bottlenecks and supply chain disruptions so they can keep quality up and costs down.
  5. Customer support: An integrated BI solution that draws customer and transactional data from siloed software gives customer support managers and agents a comprehensive, up-to-date view of customers. This speeds up case resolution, lowers service costs and improves satisfaction and loyalty.
  6. Marketing and customer experience management: Today’s customers expect to get what they want, how, where and when they want it. BI provides customer experience and marketing leaders with insights to take a customer-centric approach, producing a more personalised, relevant and consistent experience across touchpoints and products.

What Are Business Intelligence Techniques?

Modern business intelligence involves a variety of processes to help business users monitor and improve performance. So how does business intelligence work? Some of the most important functions to understand include:

Querying. Querying is the act of asking datasets to answer specific questions using database programming language. It occurs at the beginning of the BI process.

Data preparation. This is the process of getting raw data ready so it can be analysed. It involves collecting data from multiple sources, assessing what’s there, cleansing and validating it and transforming or enriching it if necessary.

Data mining. Mining business data is the process of identifying patterns, trends or anomalies in large data sets using statistics, artificial intelligence or machine-learning algorithms, for example.

Reporting. This refers to sharing data analysis with others in some form, such as tables, spreadsheets or PDFs. Users can see data trends and work with the information themselves by, say, slicing and dicing tables to uncover new relationships between variables.

Data visualisation. A step beyond reporting, data visualisations offer data analysis in easier-to-digest forms, such as charts, graphs or histograms.

Benchmarking. BI tools help users benchmark or compare their performance data to other companies of the same size or industry.

Descriptive analytics. In the context of BI, data analysis that is descriptive explains what has or is happening in the business.

Statistical analysis. Statistical analysis is the collection and interpretation of data to uncover patterns and trends. In the BI context, statistical analysis further scrutinises the results of descriptive analytics to explain why something happened or when it might happen again.

Types of Business Intelligence (BI) Tools

Business intelligence has many different technology capabilities and approaches. Some of the most common include:

Online analytical processing (OLAP): Working behind the scenes in many BI applications, OLAP performs multidimensional analysis of business data. It enables complex calculations, trend analysis, predictive scenario-planning and sophisticated data modelling.

Ad hoc analysis: Ad hoc analysis or reporting refers to business users creating real-time data reports as needed, often to answer a specific or timely question without outside assistance. OLAP may be used to enable this capability.

Real-time BI: This is the ability to analyse data as it happens to help make better, faster decisions, versus the traditional BI approach of analysing historical data to determine what has happened.

Operational intelligence (OI): A subset of real-time BI, OI runs queries against operational data, such as streaming data feeds or event data. Analysis is delivered as operational instructions or intelligence for short-term planning and decision-making.

Software-as-a-service BI: Unlike on-premises BI solutions that are supported within an organisation’s own data centre, software-as-a-service (SaaS) BI applications are hosted by a vendor and accessed online.

Open-source BI (OSBI): OSBI software is created by a community of developers who continue to improve it. It doesn’t require a software licence, but there may be charges for support, documentation and code that’s been fine-tuned for specific implementations. OSBI requires technical knowledge to run.

Embedded BI: Embedded BI refers to the integration of BI reports, dashboards and visualisations from a BI platform into other business applications to improve and speed access to intelligence and decision-making.

Location intelligence (LI): In the BI sphere, location intelligence — also called spatial intelligence — is the process of analysing and visualising geospatial data sets to deliver business insight. For example, a company may layer data such as demographics, traffic and weather on a smart map to better visualise why something specific is occurring in a certain location.

Self-Service Business Intelligence (SSBI)

Self-service business intelligence (SSBI) lets users perform BI functions on their own, without, for example, waiting for a BI expert to deliver a report or dashboard. Business users can employ these more flexible and easier-to-use SSBI systems to analyse data, create reports and visualisations and make data-driven decisions on their own — without data mining, coding, BI or statistical analysis know-how. This enables business users to access BI more quickly, frees up IT and BI teams to focus on higher-value tasks and extracts a higher ROI from the project.

Worth noting: Without clear principles and governance, by democratising BI you can run the risk of creating new silos of data and information. Strong oversight also requires ensuring SSBI tools don’t exacerbate issues related to poor data quality or consistency, bad analyses or lack of compliance.

Business Intelligence Platforms

Business intelligence involves a number of moving parts, including the various technologies that enable analysis and data import. BI platforms bring those together into a single offering.

Business intelligence platforms help companies to build complete BI systems. All BI platforms provide analysis, data delivery and integration. More advanced platforms enable tasks such as importing and cleansing data, performing more complex and varied data analyses and building and distributing real-time reports and dashboards.

How to Choose a BI Platform

Like other technology purchases, choosing a BI platform requires input from key stakeholders about the features and functionalities that will help them better perform their jobs across the business. At a broader level, some important questions to ask of any BI platform vendor during the evaluation process include:

  • Does the platform scale as our data needs increase?
  • Does the platform integrate with our existing systems and third-party systems?
  • What tasks can be automated?
  • What features are included that allow us to analyse our data?
  • Does the platform include prebuilt reporting?
  • Can we customise dashboards, reporting and key performance indicators?
  • How much does the solution cost and where does it run — on-premises or in the cloud?
  • How is our data protected?

Examples of Business Intelligence

Business intelligence offers insights to inform all levels of business decision-making. With its data analysis, reporting and visualisation capabilities, BI helps users more easily understand the environments in which they are operating.

Consider a business that wants to attract a new round of funding. It has volumes of data on hand to manage its business, but now it needs a more fine-tuned way of analysing that data and presenting it to potential investors in a digestible format, such as charts and graphs. These are among BI’s strengths. BI can also help the business better understand whether a certain market is right for targeting a new product, or help to predict financial performance.

The Future of Business Intelligence

Business intelligence has come a long way. Once the province of data scientists and the companies that can afford them. BI is now an essential ingredient in business performance management and strategic planning.

Looking ahead, a number of trends are emerging in the BI space that point to even greater uptake.

The appeal of an enterprise-wide approach: organisations are recognising that BI is not a standalone capability. Not only does it provide greater value when combined with business analytics or operational intelligence, but it may be best conceived and managed as an enterprise-wide capability serving all functions and roles.

BI as a service: As data stores grow and diversity, it seems clear that cloud services will be essential for nearly all data and analytics innovation. Thus, more organisations are moving to the cloud and adopting SaaS BI.

Embedded AI: Giving workers on the ground access to critical, real-time information via their core enterprise applications, mobile devices and intranets will be key to better, faster decision-making in a dynamic business environment.

A push for better SSBI: Facing a dearth of data science and analyst talent, enterprises are looking for SSBI tools to turn all employees into data workers. SSBI allows users to analyse data, create reports and visualisations and make data-driven decisions on their own — without data mining, coding, BI or statistical analysis know-how.

Incorporating more intelligence and predictive analytics: By tying predictive analytics into BI platforms, businesses can automate more aspects of decision-making and use the intelligence for better scenario-planning. BI vendors are also incorporating AI and advanced analytics into their tools.

An eye on visualisation: An appreciation for the art of data presentation and storytelling will increase demand for better visualisation capabilities that are more relevant, user-friendly and real-time.

Greater customer focus: Marketing and customer experience leaders are exploring how BI platforms — particularly those with real-time capabilities — can provide a more nuanced understanding of customer demands and behaviours and enable marketers to create more effective and measurable customer experience programmes.

Questions to Ask when Choosing a Business Intelligence Solution

The right BI suite or platform gives business users real-time visibility relevant to their roles; enables them to identify trends, opportunities and challenges; and lets them instantly drill down to the underlying issue to take action. Advanced business intelligence software and platforms do this with the power of data analysis, reporting, dashboards and other integrated processes built into the systems.

There are many BI solutions to choose from — from special-purpose one-off tools to suites of software and integrated platforms built by large software vendors, niche suppliers and a growing number of start-ups. Here are some initial questions to consider when selecting a BI solution:

What are your organisation’s data insight and usage needs? Do you have a team of data scientists, or will your users build their own reports, KPIs, and dashboards? Your options will vary based on who needs the data and what they want to do with it.

What kind of business problems are you looking to solve? What questions do you want to answer with BI? Rather than beginning with a BI system and adjusting the business’s needs to that system’s capabilities, start with the business requirements and find the best fit.

Where is your data and what does it look like? It’s important to consider a BI tool in the context of your data environment. An understanding of the existing data infrastructure will guide you toward the solutions that can best help you. If your data is mostly unstructured and siloed, for example, you’ll either need a solution that can clean and prepare it or access to data specialists who can address the issues. The alternative is to limit report creation to experts who can work around the flaws.

What’s your business case and budget? Before looking at any solutions, it’s critical to understand what your organisation can afford. A realistic business case that considers the likely returns the BI solution can deliver will help drive that.

Do you have peers with positive business intelligence experiences? Talk to other companies that have successfully implemented BI solutions, and determine how you can apply their learned best practises to your scenario. Ask also about whether they used a systems integrator or in-house IT.

What business intelligence skills do users need? Today’s BI systems are designed for all levels of business users, from C-suite leaders to operations and sales managers to front-line customer service representatives and factory workers.

The business intelligence market is seeing considerable growth — and for good reason. The importance of shared, reliable, accurate and current insight with which to guide the business forward has never been more important.

With the right approach, BI solutions and services can deliver a wide range of benefits, including optimising internal business processes, accelerating and enhancing decision-making, driving revenue growth, obtaining advantage over competitors and increasing operational efficiency. BI systems also help companies identify market trends and spot business problems that need to be addressed.

NetSuite Improves and Increases the Value of BI for Your organisation

Big data, small data and the volumes of data in between contain a treasure trove of business information just waiting to be found and analysed. NetSuite SuiteAnalytics is the perfect solution for such rich discovery, with embedded analytics and built-in reporting and dashboard functionality that present actionable insights about company performance. SuiteAnalytics centralised real-time business data from across the organisation, with easily configurable dashboards and automated reporting of key performance indicators that are crucial for decision-making. Additional features include savable, sharable and reusable Workbooks for filtering and analysing data with pivoting and charting capabilities, and Saved Searches that, as its name implies, offer fast access to data filtered and matched to a specific set of criteria. SuiteAnalytics also safeguards business intelligence with role-based security that restricts data access to staff based on defined organisational roles.

#1 Cloud ERP

Free Product Tour

Business Intelligence FAQs

Who uses business intelligence (BI)?

Modern business intelligence has use cases across business units and functions. Traditional BI systems required the skills of IT or BI professionals to query data, run reports and deliver dashboards. However, today’s BI systems are designed for users throughout the business from C-suite leaders, to operations and sales managers, to front-line customer service representatives and factory workers. Increasingly, these BI capabilities may be embedded into other applications that business users already use, making the delivery of insight faster and more seamless.

How does business intelligence support decision-making?

Understanding exactly what is happening in all aspects of the business is important to manage business performance and drive data-based decision-making. When that data is stored in disparate systems, however, the ability to perform analytics, calculate and monitor key performance indicators (KPIs), and get that valuable information into the hands of decision makers is difficult. Today’s modern BI systems collect and integrate data from enterprise software applications and from external sources, creating a single, unified data repository for more effective and increasingly real-time decision-making and performance optimisation across the organisation. Capabilities including data preparation, querying, advanced analytics and distribution of the resulting intelligence to business users also guide both tactical and strategic decision-making.

Is business intelligence part of data science?

Business intelligence and data science are closely linked, but they are distinct disciplines. Data science is an interdisciplinary field that covers the methods of understanding, storing, processing and analysing data in order to extract value and communicate it to the organisation. BI refers to the analysis of business data in order to understand company performance and provide actionable insight.