What Is Business Analytics?
What is business analytics, and what are its benefits and applications?
Business analytics is the practice of combining key data about organizational performance to improve planning and performance. Business analytics can be used to evaluate an organization’s overall performance, or specific KPIs that are critical to a business unit, process, project or product.
According to Gartner, business analytics “includes data mining, predictive analytics, applied analytics and statistics, and is delivered as 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).”
Business analytics makes extensive use of statistical and quantitative analysis (and, to a lesser degree, qualitative analysis). Statistical analysis identifies trends, with the ultimate goal of employing predictive analytics to determine next moves. Quantitative analysis uses statistical and mathematical modeling, measurement and research to analyze data to determine the effects of a past decision on the business. Quantitative analysis provides hard numbers to help determine how things can be done better in the future.
What Is Business Analytics: Contents
What is the purpose of business analytics?
The purpose of business analytics is to gain a complete understanding of the “how” and “why” of past events, by identifying, collecting and analyzing key performance data, which can improve decision making going forward.
Business analytics solutions make it possible to synthesize historical data across an entire business. Applying business analytics can guide — and accelerate — business decisions and improve performance, helping organizations deliver better customer experiences, improve products, optimize marketing and enhance business processes.
What does business analytics do?
Business analytics turns data into actionable insights that can inform strategic and tactical decisions, such as improving business planning, understanding and enhancing customer loyalty, and improving the performance of a contact center or help desk.
For example, Gatwick Airport uses business data to build a stronger customer experience. By monitoring data from its own systems and social media activity, the airport can more accurately predict passenger flow ahead of time.
Business analysis software tools, from data platforms (such as relational databases) to data visualization (like dashboards), collectively help business stakeholders manage their business operations more effectively.
What is the importance of business analytics?
Business analytics is an important tool for businesses seeking a competitive edge in fast-moving industries. Active data analysis for faster, better decision making is seen as crucial to business success. Business analytics software makes it easier for nontechnical users to glean insights from performance data.
Analytics and business expert Tom Davenport has said that business analytics tools “are increasingly making it possible for those without analytical skills to find data and specify the analytics they need. This opens up the possibility of data-driven decision making to many more parts of organizations. This trend started several years ago and will, we believe, continue for many more.”
What are the benefits and applications of business analytics?
The benefits of business analytics include cost savings, resource allocation, process improvement, products and customer experience, and the ability to better meet future needs.
Business analytics solutions make it possible to synthesize key data collected across an entire business regardless of the source. This lets organizations find the answers they need so that they can make smart decisions quickly. Looking at various industries, business analytics provides significant benefits, including:
- Financial services are able to gain a clear understanding of how clients interact with their online offerings, such as loan application processes, to understand how to better improve their customer experience.
- Healthcare companies can discover new ways to reduce costs, such as detecting delayed or misfield insurance claims.
- Retailers can get a better understanding of how customers interact with their websites, leading to a more effective and satisfying customer experience.
- Manufacturers can get a better understanding of their supply chain, in terms of quality, cost, timeliness, etc. They can also more accurately understand demand trends to optimize their warehouse space and be ready for surges in demand.
What is the difference between business analytics and big data analytics?
Big data analytics is often used in business analytics and refers specifically to the process of collecting and examining large amounts of disparate electronic data that’s not perfectly structured in a table to find patterns, correlations, trends and other insights. Big data analytics often requires advanced techniques such as predictive modeling, statistical algorithms and predictive, what-if analysis.
What is the difference between business analytics and business intelligence?
Business analytics is the broad umbrella term for the disciplines that support business decision making. Business intelligence (BI) and big data analytics are subsets of business analytics. BI focuses on pure summary rollups; for example, Finance might want to know the sum of all customer bookings to see how much money was made in a quarter.
Additionally, operational intelligence (OI), is a collection of business analytics systems designed to exploit data that is generated in real-time to aid decision-making in real time.
What are the different types of business analytics?
The four categories of business analytics are descriptive, diagnostic, predictive and prescriptive:
- Descriptive analytics is the process of examining data to find out what happened and what’s going on.
- Diagnostic analytics is characterized by techniques such as data discovery, data mining, data drill-downs and correlations, and invokes advanced methods to determine what has happened, and why.
- Predictive analytics addresses what might happen. The goal is to predict future outcomes. Techniques include forecasting, predictive modeling, regression analysis, pattern matching and multivariate statistics.
- Prescriptive analytics looks at how something can improve. Data is analyzed to find the best course of action moving forward. Methods include recommendation engines, complex event processing, graph analysis and simulation. Tools may incorporate neural networks and machine learning.
How do you get started with business analytics?
Getting started with business analytics requires a strategy. Here are eight key steps, from setting your initial goals to developing a culture that values data-driven decisions.
- Set clear goals: Identify areas and decisions that business analytics will affect the most. What are the biggest challenges ahead, and what is the organization trying to achieve over the next one to three years? What are the key performance indicators (KPIs) and most important metrics to measure? Do you have the needed data management techniques in place? Rank and prioritize your data-centric business goals to identify the low-cost, high-return opportunities.
- Develop a prototype: Apply predictive analytic techniques to ensure it will provide the desired answers or improvements. Prototypes can also be useful for demonstrating the benefits of an analytics program.
- Map the relevant data: What data do you need to collect and analyze to find the answers to specific problems?
- Choose your software tool(s): After thoughtful research and discussion, choose tools based on the data sets you want to analyze, the insights you want to gain and the stakeholders who will use the tools. Remember that you need ways for business users, not just data scientists, to interact with your analytics software.
- Support the initiative: Make sure you have the right talent and infrastructure in place.
- Provide access: Ensure that key decision makers can easily access actionable insights. Choose software tools that are useful and provide value to nontechnical people.
- Name a point person: Designate a leader who will promote the benefits of your analytics program throughout the organization.
- Build a data culture: Create an open culture in which anyone can participate in analytics and get recognition for their contributions.
How do you get the most value out of business analytics?
Getting the most out of business analytics may require a cultural shift. When organizations merely buy a tool to “do analytics,” they can be handicapped by cultural obstacles. Improve your business analytics initiative with the following steps.
- Adapt the decision-making process to include analytics: Insights are no good if you don’t put them to use. Make sure that reports and dashboards are available to the right decision makers at the right time.
- Communicate the benefits of analytics: Explain the advantages to key audiences to increase adoption and support.
- Break down walls: Don’t silo analytics in one area. Foster collaboration and share insights across the organization.
- Create a data-driven culture: Use dashboards and visualizations to promote analytics and transparency across the organization.
- Focus on talent: Hire and train for the skill sets to support and develop the systems and processes you put in place. Include training and change management efforts around all new tools and processes.
Bottom line: Analytics tools are essential to a digital world
An ever-increasing amount of data creates an irresistible arena in which to tackle business problems with analytical tools. Leveraging your organization’s data or customer data for a competitive edge will lead to smarter decisions, helping your business adapt to an evolving landscape. From improving customer experience and service quality, to optimizing marketing and sales operations, business analytics will help organizations survive — and thrive — in an increasingly competitive environment.
Learn more about the value of business analytics from the Splunk blog: