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.

• Predictive modeling: Companies will often design or develop new products, enter into new markets or otherwise explore new opportunities for which they have little prior experience or historical data to mine. This is where predictive modeling and predictive analytics shine. Predictive modeling²(link resides outside of ibm.com) helps organizations avoid issues before they occur, like knowing when a vehicle or tool will break down and intervening before it occurs or knowing when changing demographics or psychographics will positively or negatively impact their product lines. 
• Data mining: This is an extremely important component of business analytics, where mostly automated tools unearth and make sense of raw data to identify patterns, producing key insights. The growing importance of big data makes data mining, also known as knowledge discovery in data (KDD), a critical component of any modern business. Although companies often struggle with scaling their data mining activities as they seek to uncover more insights.
  
• Data science: The study of how data creates business insights, incorporating elements from mathematics, statistics and computer science. With the increase in data sources and the importance of analyzing that data, data science is become one of the most important jobs in Corporate America and organizations are increasingly reliant on it to certain create actionable insights that impact business outcomes.

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 helps organizations to align financial planning and operations more seamlessly. It does this by setting rules for supply chain management, integrating data across functions, and improving demand forecasting.
• Planning analytics: Planning analytics is an integrated business planning approach that combines uses spreadsheets and database technology to make effective business decisions about topics such as demand and lead generation, operations costs, and technology requirements. Many organizations have historically used tools like Excel for business planning, but some are transitioning to tools like IBM Planning Analytics.
• Integrated sales and marketing planning: Every organization is sitting on historical data about their lead generation, sales conversions, and customer retention success rates. Organizations looking to create accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data using business analytics to easily 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 need to ensure they have the right workforce with the right skills. It 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.

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.

• Data scientists: These employees are usually responsible for managing the algorithms and models that power the company’s business analytics programs. Organizational data scientists either leverage open source libraries, like NTLK, for algorithms to use or build their own to conduct analysis on data. They excel at problem-solving and usually need to know several programming languages, like Python, which helps tap into access out-of-the-box machine learning algorithms, and SQL, which helps extract data from databases to feed into a model. In recent years, an increasing number of schools now offer master of science or bachelor’s degrees in data science where students engage in a 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 one master database. They are often responsible for ensuring that data can be easily collected and accessed by stakeholders to provide organizations with a single-pane-of-glass view of their data operations.
• Data analysts: Data analysts play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they may be involved in collecting and analyzing the data sets and building the data visualizations or they may just take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

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.

• More informed decisions: Having a flexible and expansive view of all the data an organization sits on can eliminate uncertainty and prompt an organization to take action quicker. If an organization’s data suggests that sales of a particular product line are precipitously declining, it may decide to discontinue it. If climate risk will affect the harvesting of a raw material another organization depends on, it may 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 allows them to utilize thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.
• Single-pane view of information: Increase collaboration between departments and line-of-business users because everyone has the same data and is talking from the same playbook. That can expose more unseen patterns, allow 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 and when and how they want it, organizations will drive happier customers and, therefore, engender greater loyalty. Additionally, 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.