Business analytics is a type of analytics that helps organizations mine, process, and visualize important business data and take advantage of patterns in their businesses that they would not see otherwise.
Business analytics is the process by which companies use data either created by their operations or publicly available data, to solve business problems, monitor their business fundamentals, identify new opportunities for growth and better serve their customers. As the saying goes, you can’t measure what you can’t see.
Business analytics involves either individual pieces of data or data sets which are either stored on-premise or in the cloud. Data sets that increase beyond a certain threshold are commonly referred to as big data, which requires significant computational power to access and analyze. Business analytics uses data exploration, data visualization, integrated dashboards and more, to allow users access to usable data and insights.
As companies increasingly digitize their businesses, business analytics is more important than ever before. Delivering advanced data analytics and AI with an integrated workflow drives organizations to implement smarter, faster and more accurate data-driven decisions.
Business analytics also delivers business optimization strategies that help organizations visualize and take advantage of patterns in their businesses that they would not see otherwise.
The world changes so fast, and organizations need to adapt quickly based on the information. Success today depends on many elements, but, primarily, organizations need access to the right data and insights fast so executives can act decisively.
Those who can make quick strategic decisions with the right information at hand often have a huge competitive advantage. With business analytics, organizations can make confident business decisions informed by real metrics and insights and take the guesswork out of decision making.
Therefore, many companies have business analysts, whose jobs depend on identifying business intelligence that can help the company make smarter and quicker decisions that produce an advantage over competitors.
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Business intelligence, which has been around for many years, involves using data on hand to make important business decisions that impact the entire organization. Business intelligence is often thought of as the act of identifying and storing data so as to be used for decision making.
Business analytics1(link resides outside of ibm.com) takes business intelligence a step further by using that data to ask and answer specific questions about what happened in the past that either a) may happen in the future exactly the same or b) will happen differently because of new or different contexts.
It provides a complete picture of a business, allowing organizations to explain user behavior more effectively. Not only that, but business analytics can also forecast what’s coming in the future, making predictions about changes to business results.
Business analytics benefits data scientists and advanced data analysts to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis, such as using previous sales data to estimate customer lifetime value, and cluster analysis, such as analyzing and segmenting high-usage and low-usage users in a particular area.
Business analytics solutions provide benefits for all departments, including finance, human resources, supply chain, marketing, sales, or information technology; and all industries, including healthcare, financial services and consumer goods.
Business analytics practices involve several tools that help companies make sense of the data they are collecting and use to turn that data into insights. Here are some of the most common tools, disciplines and approaches.
Business analytics leverage analytics, the action of deriving insights from data, to drive increases in business performance. There are three types of valuable analytics that are often employed in business analytics situations.
As the phrase implies, this type of analytics describes the data contained within. An example would be a pie chart that breaks down the demographics of a company’s customers.
This form of analytics mines existing data, identifies patterns and helps companies predict what may happen in the future based on that data. It uses predictive models that data can be fed into to make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season was projecting warm temperatures.
These analytics3(link resides outside of ibm.com) help organizations make future decisions based on existing information and resources. Every business can use prescriptive analytics by using the existing data to make guesses about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.
To maximize an organization’s business analytics, it needs to clean and connect its data, create stunning data visualizations and provide insights on where a particular business is today while helping predict what will happen tomorrow. It usually involves the following components:
First, organizations must identify all of the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.
Unfortunately, much of the data a company may be sitting on today is not “cleaned,” rendering it useless for real analysis unless it is addressed.
Here are some reasons why an organization’s data may need cleaning:
Incorrect data fields: Due to manual entry or incorrect data transfers, an organization may have bad data mixed with good data. If it has any bad data in the system, it has the potential to render the entire set meaningless.
Outdated data values: Certain data sets, like customer information, may need editing due to customers leaving, product lines being discontinued, or other historical data that no longer is relevant.
Missing data: Companies may have changed how it collects data or the data they collect, which means historic entries may be missing data that is crucial to future analysis. Companies in this situation may need to invest in either manual data entry or identify ways to use algorithms or machine learning to predict what the correct data should be.
Data silos: If an organization’s existing data is in multiple spreadsheets or other types of databases, it may need to merge so that it has all the data in one place. While the foundation of any business analytics approach is first-party data (e.g., data the company has collected from stakeholders and owns), they may want to append third-party data (e.g., data they’ve purchased or gleaned from other organizations) to match their data with external insights.
Companies now can query and quickly parse gigabytes and terabytes of data instantaneously with additional cloud computing. Data scientists can analyze data more effectively using machine learning (ML), algorithms, artificial intelligence (AI) and other technologies. Doing so can produce actionable insights based on an organization’s KPIs.
A company’s data is only as good as it can be understood by humans. Programs can now quickly take voluminous amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders. Data visualization serves several purposes for organizations, helping non-technical people understand analytics concepts, helping others see patterns in multiple data points, or demonstrating a business’s growth or decline. They can help with idea generation, idea illustration, or visual discovery. Data visualization best practices include understanding what visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to ensure the audience it is shared with understands what they’re viewing.
Data management is conducted in tandem with the above, an organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.
Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision making.
Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent analytical and communication skills.
Here’s the type of employees they will need to have to take advantage of the full potential of robust business analytics strategies.
Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up every year and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors that do not.
The flexibility of spreadsheets. Control of a database. The power of integrated business planning. Now available as a Service on AWS
AI-powered automation and insights in Cognos Analytics enable everyone in your organization to unlock the full potential of your data.
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Learn more about business analytics by reading these blogs and articles.
1 Business Intelligence vs. Business Analytics (link resides outside of ibm.com), Harvard Business School
2 How predictive analytics can boost product development (link resides outside of ibm.com), McKinsey, August 16, 2018
3 What is prescriptive analytics? (link resides outside of ibm.com), Harvard Business School Blog, November 2, 2021