What is business analytics?

Business analytics involves companies that use data created by their operations or publicly available data to solve business problems, monitor their business fundamentals, identify new growth opportunities, and better serve their customers.

Business analytics uses data exploration, data visualization, integrated dashboards, and more to provide users with access to actionable data and business insights.

Business intelligence (BI) enables better business decisions that are based on a foundation of business data. Business analytics (BA) is a subset of business intelligence, with business analytics providing the analysis, while the umbrella business intelligence infrastructure includes the tools for the identification and storage of the data that will be used for decision-making.

Business intelligence collects, manages and uses both the raw input data and also the resulting knowledge and actionable insights generated by business analytics. The ongoing purpose of business analytics is to develop new knowledge and insights to increase a company’s total business intelligence.

Business analytics can be used to answer questions about what happened in the past, make predictions and forecast business results.¹ An organization can gain a more complete picture of its business, enabling it to understand user behavior more effectively.

Data scientists and advanced data analysts use business analytics to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis which uses previous sales data to estimate customer lifetime value, and cluster analysis for 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, plus all industries, including healthcare, financial services and consumer goods.

Business analytics uses analytics—the action of deriving insights from data—to drive increases in business performance. 4 types of valuable analytics are often used:

As the name implies, this type of analytics describes the data it contains. An example would be a pie chart that breaks down the demographics of a company’s customers.

Diagnostic analytics helps pinpoint the root cause of an event. It can help answer questions such as: What are the series of events that influenced the business outcomes?Where do the true correlation and causality lie within a given historical time frame? What are the drivers behind the findings? For example, manufacturers can analyze a failed component on an assembly line and determine the reason behind its failure.

Predictive analytics mines existing data, identifies patterns and helps companies predict what might happen in the future based on that data. It uses predictive models that 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 is projected to have warmer temperatures.

Predictive modeling² also helps organizations avoid issues before they occur, such as knowing when a vehicle or tool will break and intervening before it occurs, or knowing when changing demographics or psychographics will positively or negatively impact their product lines.

These analytics help organizations make decisions about the future based on existing information and resources. Every business can use prescriptive analytics by reviewing their existing data to make a guess 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.

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use it to turn that data into insights. Here are some of the most common tools, disciplines and approaches:

• Data management: Data management is the practice of ingesting, processing, securing and storing an organization’s data. It is then used for strategic decision-making to improve business outcomes. The data management discipline has become an increasing priority as expanding data stores has created significant challenges, such as data silos, security risks and general bottlenecks to decision-making.
  
  
• Data mining or KDD: Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets and is a significant component of big data analytics. The growing importance of big data makes data mining a critical component of any modern business by assisting companies in transforming their raw data into useful knowledge.
  
  
• Data warehousing: A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources, including apps, Internet of Things (IoT) devices, social media and spreadsheets into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI) and machine learning (ML). A data warehouse system enables an organization to run powerful analytics on large amounts of data (petabytes and petabytes) in ways that a standard database cannot.

• Data visualization: The representation of data by using graphics such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easier to understand, being especially helpful for nontechnical staff to understand analytics concepts, and helping show patterns in multiple data points. Data visualization can also help with idea generation, idea illustration or visual discovery.
  
  
• Forecasting: This tool takes historical data and current market conditions and then makes predictions as to how much revenue an organization can expect to bring in over the next few months or years. Forecasts are adjusted as new information becomes available. When companies embrace data and analytics with well-established planning and forecasting best practices, they enhance strategic decision-making and can be rewarded with more accurate plans and more timely forecasts.
  
  
• Machine learning algorithms: A machine learning algorithm is a set of rules or processes used by an AI system to conduct tasks, most often to discover new data insights and patterns, or to predict output values from a given set of input variables. Machine learning algorithms enable machine learning to learn, delivering the power to analyze data, identify trends and predict issues before they occur.
  
  
• Reporting: Business analytics runs on the fuel of data to help organizations make informed decisions. Enterprise-grade reporting software can extract information from various applications used by an enterprise, analyze the data and generate reports.
  
  
• Statistical analysis: Statistical analysis enables an organization to extract actionable insights from its data. Advanced statistical analysis procedures help ensure high accuracy and quality decision-making. The analytics lifecycle includes data preparation and management to analysis and reporting.
  
  
• Text analysis: Identifies textual patterns and trends within unstructured data by using machine learning, statistics and linguistics. By transforming the data into a more structured format through text mining and text analysis, more quantitative insights can be found.

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up frequently and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors who do not.

Faster and better-informed decisions: Having a flexible and expansive view of all the data an organization possesses can eliminate uncertainty, prompt an organization to take action faster, and improve business processes. If an organization’s data suggests that sales of a particular product line are declining precipitously, it might decide to discontinue that line. If climate risk impacts the harvesting of a raw material another organization depends on, it might need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies.

How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics enables them to use thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.

Single-window view of information: Increased collaboration between departments and line-of-business users means that everyone has the same data and is talking from the same playbook. Having that single pane of glass shows more unseen patterns, enabling different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.

Enhanced customer service: By knowing what customers want, when and how they want it, organizations encourage happier customers and build greater loyalty. In addition to an improved customer experience, by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.

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 hands-on analytical and communication skills.

Here are some of the employees that they need to take advantage of the full potential of robust business analytics strategies:

Data scientists: These people are responsible for managing the algorithms and models that power the business analytics programs. Organizational data scientists either use open source libraries, such as the natural language toolkit (NTLK) for algorithms or build their own to analyze data. They excel at problem-solving and usually need to know several programming languages, such as Python, which helps access out-of-the-box machine learning algorithms and structured query language (SQL), which helps extract data from databases to feed into a model.

In recent years, an increasing number of schools offer Master of Science or Bachelor’s degrees in data science where students engage in degree program coursework that teaches them computer science, statistical modeling and other mathematical applications.

Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted, and placed into a master database. They are often responsible for helping to ensure that data can be easily collected and accessed by stakeholders to provide organizations with a unified view of their data operations.

Data analysts: They play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they might collect and analyze the data sets and build the data visualizations, or they might take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

To maximize the benefits of an organization’s business analytics, it needs to clean and connect its data, create data visualizations and provide insights on where the business is today while helping predict what will happen tomorrow. This usually involves these steps:

First, organizations must identify all 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 a company's data remains uncleaned, rendering it useless for accurate analysis until addressed.

Here are some reasons why an organization’s data might need cleaning:

• Incorrect data fields: Due to manual entry or incorrect data transfers, an organization might have bad data mixed in with accurate data. If it has any bad data in the system, this has the potential to render the entire set meaningless.
  
  
• Outdated data values: Certain data sets, including customer information, might need editing due to customers leaving, product lines being discontinued or other historical data that is no longer relevant.
  
  
• Missing data: Companies might have changed how they collect data or the data they collect, which means historic entries might be missing data that is crucial to future business analysis. Companies in this situation might 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 might need to merge the data so it’s all in one place. While the foundation of any business analytics approach is first-party data (data the company has collected from stakeholders and owns), they might want to append third-party data (data they’ve purchased or gleaned from other organizations) to match their data with external insights.

Companies can now query and quickly parse gigabytes or terabytes of data rapidly with more cloud computing. Data scientists can analyze data more effectively by using machine learning, algorithms, artificial intelligence (AI) and other technologies. Doing so can produce actionable insights based on an organization’s key performance indicators (KPIs).

Business analytics programs can now quickly take huge 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 best practices include understanding which 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 help ensure that the audience understands what they’re viewing.

Ongoing data management is conducted in tandem with what was mentioned earlier. 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.

• Financial and operational planning: Business analytics provides valuable insights to help organizations align their financial planning and operations more seamlessly. It does this by setting rules for supply chain management, integrating data across functions, and improving supply chain analytics and demand forecasting.
  
  
• Planning analytics: An integrated business planning approach that combines spreadsheets and database technologies to make effective business decisions about topics such as demand and lead generation, optimization of operating costs, and technology requirements based on solid metrics. Many organizations have historically used tools including Microsoft Excel for business planning, but some are transitioning to tools such as IBM Planning Analytics.
  
  
• Integrated sales and marketing planning: Most organizations have historical data about their lead generation, sales conversions and customer retention success rates. Organizations looking to create more accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data are using business analytics to allocate resources based on performance or changing demand to meet business objectives.
  
  
• Integrated workforce performance planning: As organizations undergo digital transformation and otherwise react to changing landscapes, they might need to ensure they have the right workforce with the right analytical skills. This is especially true in a world where employees are more likely to leave a company for a new job. Workforce performance planning helps organizations understand their workforce requirements, identify and address skill gaps, and better recruit and retain talent to meet the organization's needs today and in the future.