What is Business Intelligence?

Definition

Business Intelligence (BI) is the practice of extracting insights from data to measure and improve business performance. Business intelligence uses data supported with technology to deliver insights at scale to multiple constituents: analysts, decision-makers and action-takers to improve business performance. 

Purpose

The purpose of business intelligence is to provide a consistent, accurate and actionable set of insights about the business to a broad array of users from data. These insights are based on a common set of data, sourced, transformed, structured and queried to address common questions delivered via structured output of many forms, including data extracts, reports, spreadsheets, visualizations, dashboards and presentations. 

Primary Uses of Business Intelligence Software

Business Intelligence is used to improve business performance through better business awareness, decisions and actions based on insights created from data. Business Intelligence supports a broad set of users and use cases, potentially addressing the needs of most every user in a business enterprise to address business questions better based on insights created from data. Business Intelligence addresses two major uses: reporting and analysis as follows:

Reporting – Providing structured output of a consistent set of data in common format for a repeated, scheduled presentation to a standard set of users. Reporting typically focuses on the “what” is happening.

Analysis – Providing insights from data to address specific business questions, often focusing on the “why” more than the “what”. Analysis activity is focused on creating queries of the data that are more tailored and often ad-hoc to specific situations, users and uses, whereas reporting queries are more standardized and repeated and delivered to a wider audience.

Benefits of Well-Executed BI

The benefits of business intelligence are to deliver improved business performance through improved awareness, insights, plans, decisions and actions using data. Key impact drivers are being able to make decisions and take actions with greater relevance, speed, clarity, alignment and confidence as follows:

Relevance – Insights delivered from data are relevant to the business subject and user needs. Data are available, accurate, timely, understandable and comprehensive to address the business users’ needs.

Speed – Insights created from data enable actions to be taken faster, because the insights are structured to address business questions more timely and effectively.

Clarity – The insights are seen as being clear, compelling and accurate in such a way that conclusions reached from the data are consistent across the audience of users, and data are sufficient to address questions posed from multiple interpretations of the data and insights.

Alignment – Because the data is relevant, timely, accurate and comprehensive and the insights are presented in a clear, consistent way that is easily understood, interpretation of the data is consistent, supporting improved alignment regarding decisions and actions to be taken.

Confidence – The insights created from data are trusted, and performance of decisions and actions are measured using data such that the relationship between insights and effective, impactful decisions and actions is direct, positive and improving.

Common Roles and Responsibilities of a Business Intelligence Program

Business Intelligence and the resulting creation of actionable insights from data delivered to business users involves the following key roles:

• Insights Creators – Insights creators (e.g. data analysts) are responsible for creating insights from data and delivering them to insights consumers. Insights creators typically design the reports and analyses, and often develop them, including reviewing and validating the data. Insights creators are supported by insights enablers. 
• Insights Enablers – Insights enablers (e.g. data engineers, data architects, BI engineers) are responsible for making data available to insights creators, including helping to develop the reports and dashboards used by insights consumers.
• Insights Consumers – Insights consumers (e.g. business leaders and business analysts) are responsible for using insights and analyses created by insights creators to improve business performance, including through awareness, plans, decisions and actions.
• Insights Consumers – Insights consumers (e.g. business leaders and analysts) are responsible for using insights and analyses created by insights creators to improve business performance, including through improved awareness, plans, decisions and actions.

Key Business Processes Associated with Business Intelligence

The process for delivering business intelligence involves a nine (9) step process, as follows:

1. Access – Data, often in structured ready-to-analyze form and is made available securely and available to approved users, including insights creators and enablers.
2. Profiling – Data are reviewed for relevance, completeness and accuracy by data creators and enablers. Profiling can and should occur for individual datasets and integrated data sets, both in raw form as was a ready-to-analyze structured form. 
3. Preparation – Data are extracted, transformed, modeled, structured and made available in a ready-to-analyze form, often with standardized configurations and coded automation to enable faster data refresh and delivery. Data is typically made available in an easy to query form such as database, spreadsheet or Business Intelligence application.
4. Integration – When multiple data sources are involved, integration involves combining multiple data sources into a single, structured, ready-to-analyze dataset. Integration involves creating a single data model and then extracting, transforming and loading the individual data sources to conform to the data model, making the data available for querying by data insights creators and consumers.
5. Extraction / Aggregation – The integrated dataset is made available  for querying, including, including aggregated to optimize query performance.
6. Analyze – Process of querying data to create insights that address specific business questions. Often analysis is based on queries made using business intelligence tools using a structured database that automate the queries and present the data for faster, repeated use by data analysts, business analysts and decision-makers. 
7. Synthesize – Determine the key insights that the data are indicating, and determine the best way to convey those insights to the intended audience. 
8. Storytelling / Visualize – Design of data storyline / dashboards and visuals should be prepared and then developed based on the business questions to be addressed and the queries implemented. It is important to think about how the data will be presented so that the insights are understood and addressed.
9. Publish – Results of queries are made available for consumption via multiple forms, including as datasets, spreadsheets, reports, visualizations, dashboards and presentations. 

Common Technology Categories Associated with Business Intelligence

Technologies involved with business intelligence are as follows:

• Data Engineering – Data engineering is the process and technology required to move data securely from source to target in a way that it is easily available and accessible.
• Data Transformation – Data transformation involves altering the data from its raw form to a structured form that is easy to analyze via queries. Transformation also involves enhancing the data to provide attributes and references that increase standardization and ease of integration with other data sources.
• Data Preparation – Data preparation involves enhancing it and aggregating it to make it ready for analysis, including to address a specific set of business questions. 
• Data Modeling – Data modeling involves creating structure and consistency as well as standardization of the data via adding dimensionality, attributes, metrics and aggregation. Data models are both logical (reference) and physical. Data models ensure that data is structured in such a way that it can be stored and queried with transparency and effectiveness.
• Database – Databases store data for easy access, profiling, structuring and querying. Databases come in many forms to store many types of data.
• Data Querying – Technologies called Online Analytical Processing (OLAP) are used to automate data querying, which involves making requests for slices of data from a database. Queries can also be made using standardized languages or protocols such as SQL. Queries take data as an input and deliver a smaller subset of the data in a summarized form for reporting and analysis, including interpretation and presentation by analysts for decision-makers and action-takers.
• Data Warehouse – Data warehouses store data that are used frequently and extensively by the business for reporting and analysis.  Data warehouses are constructed to store the data in a way that is integrated, secure and easily accessible for standard and ad-hoc queries for many users.
• Data Lake – Data lakes are centralized data storage facilities that automate and standardize the process for acquiring data, storing it and making it available for profiling, preparation, data modeling, analysis and reporting / publishing. Data lakes are often created using cloud technology, which makes data storage very inexpensive, flexible and elastic.
• Data Visualization – Data visualization is a method for visually depicting slices of data formed through queries. Data visualizations are typically available in Query tools (OLAP / BI Applications), as standalone-applications and as libraries.

Caveats / Considerations

Business Intelligence vs Predictive Analytics  – Business Intelligence is typically thought of as being descriptive analytics – the ability to understand data from a “looking back” as history perspective. Predictive analytics is focusing on using mathematical models to predict the future. 

Business Intelligence and Data Science – Data Scientists focus on using mathematical techniques, and often more advanced statistical techniques  applied to data to improve data profiling, understanding, learning and predictions.  Business Intelligence relies more on basic data queries to understand data. Data Science can be applied to improve the value of business intelligence, including creating predictions, prescriptions and optimizations.

Trends / Outlook for BI

Key trends to watch in the Business Intelligence arena are as follows:

Semantic Layer – The semantic layer is a common, consistent representation of the data used for business intelligence used for reporting and analysis, as well as for analytics. The semantic layer is important, because it creates a common consistent way to define data in multidimensional form to ensure that queries made from and across multiple applications, including multiple business intelligence tools, can be done through one common definition, rather than having to create the data models and definitions within each tool, thus ensuring consistency and efficiency, including cost savings as well as the opportunity to improve query speed / performance.

Data Visualization – Given the increased amount of data, most users prefer to visualize insights from the data vs typically using columnar reports. Visualizations have increased in their sophistication as well as availability, including through more applications and libraries.

Automation – Increase emphasis is being placed by vendors on ease of use and automation to increase speed-to-insights. This includes offering “drag and drop” interfaces to execute data-related preparation activities and insights creation / queries without having to write code, including reusing activities and processes, both for repeating use as well as sharing.

Self-service – As data grows, availability of qualified data technologists and analytics are very limited. To address this gap and increase productivity without having to lean 100% on IT resources to make data and analysis available, Self-service is increasingly available for data profiling, mining, preparation, reporting and analysis. In addition tools like the Semantic Layer offered by AtScale, Inc are also focused on enabling business users / data analysts to model data for business intelligence and analytics uses.

Cognitive – Data means nothing if it is not actionable. Actionable means understandable. Increasing effort is being made to make data easier to understand, and this includes applying cognitive capabilities to improve analysis, synthesis, presentation as well as query and question / answer interaction.

Transferable – Increased effort is also underway to make insights easier to consume, and this includes making data available for publishing easier, including using api’s and via objects that store elements of the insights.

Observable – Recently, a host of new vendors are offering services referred to as “data observability”. Data observability is the practice of monitoring the data to understand how it is changing and being consumed. This trend, often called “dataops” closely mirrors the trend in software development called “devops” to track how applications are performing and being used to understand, anticipate and address performance gaps and improve areas proactively vs reactively.

AtScale and Business Intelligence

AtScale is the leading provider of the Semantic Layer – to enable actionable insights and analytics to be delivered with increased speed, scale and cost effectiveness. Research confirms that companies that use a semantic layer improve their speed to insights by 4x – meaning that a typical project to launch a new data source with analysis and reporting capabilities taking 4 months can now be done in just one month using a semantic layer. 

AtScale’s semantic layer is uniquely positioned to support business intelligence automation – the ability to visually view data, including attributes, metrics and features in dimensional form, clean it, edit it, refine it by adding additional features, and have it automatically extracted and made available to any BI tool, whether it’s Tableau, Power BI or Excel. Moreover, this only requires one resource who understands the data and how it is to be analyzed, eliminating the need for complexity and resource intensity. This approach to business intelligence automation also eliminates multiple data hand-offs, manual coding, the risk of duplicate extracts and suboptimal query performance.