Business Analytics

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What is Business Analytics

Business Analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company. Business analytics are performed in order to identify weaknesses in existing processes and highlight meaningful data that will help an organization prepare for future growth and challenges.[1]

Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes Data Mining, Predictive Analytics, Applied Analytics, and statistics, and is delivered asst an application suitable for a business user. These analytics solutions often come with prebuilt industry content that is targeted at an industry business process (for example, claims, underwriting, or a specific regulatory requirement).[2]

Business Analytics in the Business World[3]

Business analytics plays a major role in transforming data into action no matter the type and size of the organization. The figure below provides a visual representation of the role of business analytics in the business world.

Business Analytics
source: Benedictine University

Benefits of Data-Driven Decision Making with Business Analytics[4]

Companies use Business Analytics (BA) to make data-driven decisions. The insight gained by BA enables these companies to automate and optimize their business processes. In fact, data-driven companies that utilize Business Analytics achieve a competitive advantage because they are able to use the insights to:

  • Conduct data mining (explore data to find new patterns and relationships)
  • Complete statistical analysis and quantitative analysis to explain why certain results occur
  • Test previous decisions using A/B testing and multivariate testing
  • Make use of predictive modeling and predictive analytics to forecast future results
  • Business Analytics also provides support for companies in the process of making proactive tactical decisions, and BA makes it possible for those companies to automate decision-making in order to support real-time responses.

Challenges for Business Analytics[5]

  • Strategic Alignment: Most organizations today already have some element of business analytics in place, often in the BI/data warehousing area. Unfortunately, analytics are often viewed by top executives as esoteric research at best, and irrelevant fringe experiments at worst. The issue surrounds not a lack of appreciation for the usefulness of information but a lack of alignment, availability, and trust.

Recommendation: review the Business Goals that support the main strategies for the company, and for each major Business Process that underpins the goals, ask the following questions:

    • “Would we be able to govern and optimize this process more effectively if we could predict how modifications to it would affect the result?”
    • “Would we be able to adapt the process more readily to changes in the external environment if we could more accurately assess the nature and causes of those changes?”

If the answer to either of those questions is “yes”, then the process (and therefore the goal it underpins, and the strategy the goal supports) would benefit from the application of Analytics.

  • Agility: Typically in organizations, the analysts are organized by business domains. Findings working with top-tier, information-driven companies demonstrate that domain-based organizations are not the most effective approach for analytics. Analysts often work independently and create models in ad-hoc environments based on a patchwork of extracts and sources. The results, while advanced and valid, are not easily communicated to the business users for whom they would provide the greatest value.

Recommendation: liberate the organization’s analytical capabilities by pooling analysts into a Center of Excellence highly focused on “analytics-as-a-skill”. Combine members of the CoE with business domain experts into teams who employ agile methods for the development of analytical models, enabling your business users to gain real- or right-time insight into complex, data-demanding questions.

  • Commitment: Analytics software packages often come as a prefabricated solution and are not particularly difficult to implement; however they can be costly, and the ROI is not immediate. By their nature, analytical models will improve in accuracy over time as the predicted results are compared with actual events hitting the warehouse. But this is a complex endeavor that requires dedication to the solution during an extended tuning period. Here is where many deployments fail. Business users do not immediately see the promised results and lose interest, and executives lose trust in the solution and refuse to rely on what the models tell them.

Recommendation: by addressing key challenge 1, stakeholders in analytics will naturally be identified (the process and goal owners). These business owners must take responsibility for establishing the productive analytics environment described in key challenge 2. Realistic timelines that allow the models to take form should be set based on industry standards and best practices.

  • Information Maturity: The world’s best hammer is useless without nails, and so it goes for analytical tools. For an analytics solution to succeed, the “nails” need to be plentiful and not consistently bent or misshapen. Implementations often fail because of the lack or low quality of underlying transactional data. Either data are not available, data sources are too complex, or data are poorly mastered. Even bleeding-edge, sentiment- and context-analysis tools require some level of trust in the data, and for any analytical model the rule is consistent: the more trustworthy the data the more trustworthy the result.

Recommendation: perform a maturity assessment of the company’s information architecture. Identify data sources based on a mapping to analytical requirements; measure the quality of both operational information (transactional data) and aggregated information in the warehouse, and review the existing integration infrastructure’s ability to support new sources and data conduits.

Business Intelligence (BI) vs. Business Analytics[6]

Broadly speaking, Business Intelligence tools include reporting, visualization, and analytics functions used to interpret large volumes of data. These tools and applications are used to analyze data from business operations and transform raw data into meaningful, useful, and actionable information. BI Tools are used both inside the enterprise and within software applications to provide insight from operational data, financial data, and more. Though there can be many variations in the feature sets of BI platforms, the main deliverables of any BI stack are Dashboards, Reports, and more recently Self-Service capabilities. Of the different types of BI, Embedded BI aims to provide seamless integration of analytical capabilities directly into your applications. Business analytics is heavily statistically focused and uses analysis techniques such as descriptive, predictive, and prescriptive analytics. It also consists of a set of solutions, methods, skills, and best practices used to gain insights for understanding current business realities and business planning. The breadth and depth of statistical analysis capabilities found in business analytics products can vary greatly. However, the primary use of business analytics remains the same: to drive decision-making. So what’s the difference? Where does the term “business analytics” fit within business intelligence? The statistical techniques categorized by business analytics are often found within the category of business intelligence. The main focus of a “business analytics” product is to create actionable business intelligence. When in doubt, the term business analytics is used to focus more specifically on the statistical capabilities of a BI.

See Also


Further Reading