The term Business Analytics refers to the practice or discipline, often with the aid of specialized software tools, of extracting information and insights from collected data that can be used to inform effective planning and decision-making in a business context. Business analytics, as with other types of analytics, makes use of mathematics, statistics, predictive modeling, and other investigative tools to discover and interpret patterns in data. As organizations amass increasingly large data set, the use of real time data and analytics software is increasingly important for implementing effective business analytics.
Business analytics can be considered a catch-all term that encompasses many types of more specific analyses that can inform business decisions. Some online retail businesses conduct customer journey analysis to discover ways to optimize the customer experience during purchase and checkout. Behavioural analytics is used to investigate data about how users interact with a given application, while supply chain analytics could be used to find efficiencies in a logistics company.
Whatever the case, the practice of business analytics is continuing to grow and develop, especially with innovative analytics technologies like machine learning and pattern recognition that are being deployed to help organizations extract value from larger and more complex data sets.
Business organizations across industry verticals generate massive amounts of data through the software applications that support their daily functions. Each application deployed in an organization's hybrid cloud environment generates event logs that provide information about network traffic, application usage and other factors. These logs can be analyzed in real time using a four-step process designed to deliver insights and information that drive business decisions.
Step One: Descriptive Analytics
Descriptive analytics is the first and simplest form of business analytics. The purpose of descriptive analytics is simply to use the data to describe the state of the system and to give security analysts an organized, accurate and detailed perspective on system performance. Descriptive analytics can be used to answer questions such as:
- Which advertisement banner generated the most positive responses from users?
- What was the average speed of database queries on a given application?
- How long did users spend on a particular step of the checkout process?
Descriptive analytical tools today can aggregate data from across applications in real-time and analyze it to answer basic questions about application performance and user interactions.
Step Two: Diagnostic Analytics
While descriptive analytics helps organizations gain visibility into what is going on with their applications and other IT infrastructure, diagnostic analytics helps organizations investigate the causes, sequences of events or specific factors that led to a specified event or outcome. Diagnostic analytics can be used to answer questions such as:
- Why was there a sudden increase in website or application traffic?
- Why did the application server restart unexpectedly?
- Why did sales decrease by 80% over the past week?
Diagnostic analytics incorporates techniques such as data mining, correlation, drilling down and data discovery to discover the "Why" - the chain of causation behind an observed event.
Step Three: Predictive Analytics
By now, you should be starting to see how each stage of business analytics feeds into the next one. In stage one, we used descriptive analytics to get the facts and increase our understanding of what is going on in the business. In stage two, diagnostic analytics was used to investigate the cause of a known event. As businesses (and software) begin to understand the underlying factors that drive key performance indicators, predictive analysis can be used to develop models that can estimate the likelihood of a future event happening.
Predictive analysis can help businesses address questions like:
- What will be the demand level for a given service on Christmas day this year?
- How many cases of wine should we ship to restaurant #141 for Valentines day?
- How will our customers react to a new product or advertisement with specified characteristics?
Today's business analytics tools use machine learning and pattern recognition to dig deep into the strongest causal and correlating factors in the data and use that to drive predictions of future performance.
Step Four: Prescriptive Analytics
Prescriptive analytics is the final stage of business analytics. In this stage, the business leverages big data analysis of its past events and performance to generate insights into how it should handle a given situation in the future. Prescriptive analytics may use software-based simulation or optimization engines to determine how a business might best react to a specified event.
Prescriptive analytics can help answer questions like:
- What route should our truck driver take to minimize fuel consumption?
- How should we protect our data in case of an application layer security breach?
- Which aspects of our application should be refined to improve the customer experience?
The ultimate goal of business analytics is to provide actionable information and insights that can be used to assist the business in making the best decisions that further its strategic goals and objectives. The exact pathway to realizing that value depends on the type of business analytics that is implemented.
Descriptive analytics gives organizations unprecedented visibility into the behavior and activities of their services, applications and users. Diagnostic analysis enables forensic analysis of events and a more thorough understanding of how chain-of-causation can affect the performance of applications and services that comprise the IT infrastructure. With predictive analysis, organizations benefit from foresight and may be able to anticipate and prepare for events before they happen. Finally, with prescriptive analytics, software goes beyond providing data and actually recommends a decision path for the organization.
Managing the vast quantities of log data needed to generate useful business analytics and insights is a major challenge for businesses and the IT organizations that serve them. Log data is generated everywhere on the network, including by servers, application, virtual machines and databases, and while organizations can benefit hugely from its implementation, there are significant technical challenges associated with managing such a high volume and velocity of newly created data.
Sumo Logic's cloud analytics platform makes it easy for businesses to collect valuable data from existing systems, increase visibility into application and user activity and take the right steps to reach their most important business goals and objectives.
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