The Different Types of Business Intelligence
Business intelligence (BI) has many different facets and can be confusing to the layperson, so it’s important to have an understanding of what each type entails and how each can be helpful to your business. Luckily, there are seven key types of business intelligence that you’re sure to come across in your line of work, and we’ve outlined them below so you know exactly what you’re looking at when it comes time to select a solution for your company. So let’s start with the basics...
Data Warehousing
Data warehouses are repositories for the storage and retrieval of data. Data is often stored in these locations in order to be easily accessed from a central location. The data stored within this type of intelligence can be in any form, such as log files or even images. Data warehouses can also be used to store aggregated data that has been obtained from a variety of sources over time. With this type of intelligence, it is possible to analyze trends and patterns in your data that may not have been discovered without this method.
Data Mining
Businesses should consider investing in data mining if they want better insight into their customer's buying habits, traffic patterns, employee needs, and more. The key to data mining is in understanding what you want to find out and being willing to look for it. It’s not simply running a series of complicated algorithms and using that information for decision-making; rather, it’s about letting your analysis lead you where it needs to go. Data mining is a powerful tool that every business should invest in. As long as your team knows how to use data mining properly, you can reap significant rewards from increased productivity and improved sales.
Web Analytics
Businesses also might use competitive intelligence to measure the performance of their competitors' websites or social media activity in order to better understand their own marketing efforts. There are many other types of BI that every business should know about, and which one is best for you depends on your industry and your goals. For example, a retail company would likely find Retail Point-of-Sale Analytics more helpful than Web Analytics since they would have a physical store location. If you're looking for help deciding which BI tool is right for you, consider using this flowchart from Compuware.
Text Analytics
Reading through this post on text analytics might help you determine which type(s) of BI would be best suited for your own business needs. Using text analytics for your business is a great way to get information about your customers, improve search engine optimization and boost website traffic. Here are some examples: Improving search engine optimization (SEO) -- If you use services such as Google Analytics or Google AdWords to track site traffic, you'll be able to see which keywords people use when they visit your site. You can then take those terms and incorporate them into relevant web pages so that you start ranking for specific terms in organic searches on engines like Google, Yahoo, and Bing. This helps increase organic traffic that converts into more leads or sales.
Knowledge Management
There are many types of business intelligence that a company can choose to implement. There is an expansive list below, but the most common types are:
1. Customer Relationship Management (CRM) - A CRM system is an online database where businesses can store information on their customers and potential customers. One example of this type is Salesforce, which is used by companies such as Nike and Adobe. CRM systems help businesses create new leads, increase customer loyalty, and better understand buying habits to better target marketing campaigns.
2. Data Analytics - This type of BI includes the ability to collect data from various sources for use in internal reporting or external analysis through statistical techniques like predictive analytics, data mining, machine learning, etc.
3. Operational Analytics - This type of BI focuses on using data to optimize key business processes, and improve efficiency and decision-making. This can be done through predictive analytics. Examples of operational analytics are inventory management and order fulfillment.
