Glossary Index

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Glossary Index

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Glossary Index

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Glossary term

Business Analytics 

Quick definition

Business analytics means using data science to build models that help inform decision-making to improve organizational processes through a variety of methods.

Key takeaways


●     There are several different types of business analytics, including descriptive analytics, predictive analytics, and more. Each of these types of analytics has its own purpose and method for improving business performance.

●     Business analytics is the best and most effective way to gain insights about what’s working — and what’s not working — in your business processes, in order to make your organization more successful.

●     In the future, artificial intelligence and machine learning will have a big impact on business analytics. AI in business analytics will make the process much more democratized.

Nate Smith is a group manager of product marketing for Adobe Analytics Cloud. He oversees strategy development, product launches, positioning and messaging, pricing and packaging, sales enablement, product requirements, and competitive analysis for several Adobe products. Nate has been with Adobe for almost 11 years, and before his time at Adobe, he had six years of experience as a marketing strategist.

Q: What is business analytics?

A: Business analytics refers to the use of data science to build models that help people make decisions to improve organizational processes. Every day, a company receives huge amounts of data sets about customer behaviors, revenue, conversions, and more. This data comes from a variety of places but is most likely stored in one large data repository. Business analysts turn this data into insights to help improve their organization using a variety of methods and models.

Q: What are the different types of business analytics?

A: There are a few different types of business analytics.

Descriptive analytics involves using historical data to identify trends within a company’s business processes. Some real-world examples of descriptive analytics would include a content marketing agency reporting on sales data, marketing campaigns, social media usage, and engagement data.

Diagnostic analytics, a deeper version of descriptive analytics, looks into the reasons behind certain outcomes. It means taking data and determining correlations. A real-world example of diagnostic analysis would be a cybersecurity team drawing correlations between employee email password strength and the number of security breaches in an organization.

Predictive analytics means using historical data to determine likely outcomes or events. With predictive analytics, you can employ machine learning and artificial intelligence for more accurate predictions. A real-world example of predictive analytics would be an e-commerce company using data to identify the customers who are most likely to abandon their carts.

Prescriptive analytics is a more advanced form of predictive analytics and is used to recommend actions that businesses can take to reach their goals. A real-world example of prescriptive analytics would be a jewelry company finding out that most of their customers who buy a certain bracelet also buy a particular necklace, and advertising that necklace to customers who only bought the bracelet.

Most business analysts primarily use descriptive and predictive analytics, but the other two types of analytics can also prove valuable for businesses.

Q: What’s the difference between business analytics and data analytics?

A: With business analytics, to a large extent, you’re using the same technologies as data analytics. But though both types of analytics employ the same types of tools, business analytics is more focused on existing workflows and processes and is performed by business analysts. When reviewing data, a business analyst looks at things like purchase processes, revenue optimization, and other ways to improve business processes. It’s a much more focused area of data analysis.

On the other hand, data analysis is performed by data scientists and is the broader, more technical part of the process. Business analysts aren’t as technical as data scientists. Data scientists transform data, then business analysts take the transformed data sets and communicate information to other parts of the organization about how to optimize existing processes and metrics. Data scientists take deep dives into data, determining trends and connections. Business analysts translate that work into useful insights about organizational processes.

Q: What’s the difference between business analytics and business intelligence?

A: Most of the time, business intelligence (BI) is just data visualization. You start with a large amount of big data in a data warehouse, and then you use a data visualization tool to report on it. But this data is historical data — it doesn’t determine future outcomes.

This is where business analytics is different from business intelligence: because BI is only for descriptive reporting, not making predictions. Business analytics can be used for diagnostic and predictive insights, while business intelligence cannot. It could be said that business intelligence is actually just an aspect of business analytics.

Q: What are the requirements to perform business analytics?

A: First and foremost, your organization needs a fair amount of data to draw insights from. You can gather this data from a variety of sources and keep it all in a data warehouse. You should also have proper data management tactics to keep things organized.

After you have your data sets, a team of data scientists explore the data and prepare it for visualization with a data visualization tool. Then, once the data has been visualized, a team of business analysts can use methods like descriptive and predictive analytics with analytics tools to gain business insights. Then they can present their findings to the appropriate stakeholders, who can make business decisions based on those data insights.

Q: Is it always necessary for a business to use business analytics?

A: Business analytics is a necessary capability to be competitive in your industry. It’s extremely important for optimizing your business — how it runs, how customers interact with it, and how it earns revenue. Business analytics is the most effective way to gain insights about what’s working — and what’s not working — in your business operations.

The only time that business analysis could be considered optional is if your business is just starting out. It’s all right to focus first on building your products and making profits, but once your business has grown, you should start to use business analytics to make better decisions about how you run your company.

Q: What are the challenges that come with business analytics?

A: One large challenge with business analytics is how siloed these analytics groups can be, especially because not everyone in the organization needs — or knows how — to perform these tasks. There are many people involved with business processes who could improve them if they had access to the right data. But in many cases, only the analytics groups have the data and can make sense of it. It’s critical to democratize business analytics as broadly as possible so that people throughout the organization have data to help them make better decisions.

Q: How will business analytics look in the future?

A: In the future, artificial intelligence and machine learning will have a big impact on business analytics. As we’ve discussed, more people than just data analysts and business analysts need to understand the insights that surface from business analytics. Not all of them will be trained analysts, but they need to know how to gain data insights in order to do their jobs correctly. In the future, there will be purpose-built AI technology that can do the more advanced tasks of business analytics. This shift in technology will optimize the process of business analytics and make it more accessible to people who aren’t data scientists.

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