You’ve probably heard the term “business intelligence (BI)”—but what does it actually mean? Gartner defines BI as “the applications, infrastructure and tools and best practices that enable access to, and analysis of, information to improve and optimize decisions and performance.”
Given this, data analysis software is a central requirement for any business that wants to improve BI. In this guide, we’ll outline the main role of data analysis tools and describe their functionality. This can help buyers select the best solution for their needs. We’ll cover:
While the term "data analysis" is self-explanatory, it’s usually encountered in the context of BI, which is much more complicated. Thus, it’s helpful to begin by explaining how software for data analysis fits into the larger BI context.
The diagram below shows the major components of a BI system. All the data originates from the various data sources on the left, is colocated in the data warehouse (in the center) and then is analyzed by end users—using data analysis tools—on the right.
Data analysis tools are often the final, or second-to-last, link in the long chain of BI: This chain begins with loosely related and unstructured data, and ends with actionable intelligence.
Data analysis tools are also known as “data analytics” tools. No matter what you call them, these should be able to handle the five key capabilities identified in our report, “5 Core Data Analytics Software Capabilities For SMBs.” Here they are:
Dashboards. These provide a real-time overview of key performance indicators (KPIs) in a visual format that is easily sharable. Some data analysis tools allow users to create their own dashboards so they can get a clearer picture of specific business operations.
Data set creation. Your business forecasts are only as reliable as the quality and quantity of data behind them. Software for data analysis should allow users to scrub, aggregate and split data as needed.
Interactive exploration. Static pie charts and line graphs are practically passé by today’s standards. Tools that enable interactive exploration provide eye-catching ways to visualize trends, such as heat maps and time motion views. Heat maps are 2D representations of data where values are represented by colors and time motion views are moving images depicting how data changes over time.
Sharing. Collaboration and social sharing functionalities are important because they allow business leaders to work together more efficiently. When everyone is seeing the same data sets, it’s easier to interpret the information in the same way.
Ease of use. A good data analytics tool should not require an advanced computer science degree to operate. While usability will vary depending on how robust and technical your platform is, the interface should be intuitive enough for trained staff to use with minimal support.
As you research the various options available in the data analysis software market, you’ll notice that vendors describe and name their solutions differently. This can make product selection, and apples-to-apples comparisons, difficult. So, here’s an overview of the general categories of software for data analysis:
|Querying||Querying is the process of “asking” the database a question. The answer is returned via the analysis tool in the form of data or data patterns, and is often presented as a “report.”|
|Reporting||Reports are the results returned from a database query. They can take many different shapes and sizes depending on the software used, the available data and the specific nature of the query.|
|Managed Query Environment (MQE)||MQE functionality lets administrators set role-based restrictions on the types of queries employees can submit, which databases those queries will access and the types of reports they’ll produce.|
|Online analytical processing (OLAP)||Analyzes multidimensional data from multiple perspectives. It’s usually comprised of three analytical operations: data consolidation, data sorting and classification ("drill-down") and analysis of data from a particular perspective ("slice-and-dice").|
|Predictive analytics||Makes predictions about future risks and opportunities. For example, predictive analytics could be used to test the effectiveness of marketing campaigns based on the performance of past campaigns.|
|Semantic and text analytics||Analyzes large volumes of text to identify patterns, relationships and sentiment. These tools are often applied to comments on social media, live chat conversations and emailed communications.|
The benefits of data analysis tools are, essentially, the benefits of business intelligence. They vary tremendously depending on the individual case. Generally, however, they help identify, interpret and predict trends and patterns that affect the business.
Data analysis software can help, for example:
By extracting these kinds of meaningful insights from your data, you’re in a better position to understand what it will take for your business to increase profitability. When you analyze a data set, you’re reflecting on what is (or isn’t) running smoothly at your business. The great advantage of using software for this purpose is it’s often more reliable—and less time-consuming—than manual data coding methods.
Businesses hoping to purchase data analysis tools in the near future should consider the following when evaluating solutions:
Customer support. Even in the most user-friendly of data analysis programs, it’s common to encounter some technical difficulties. Be sure to evaluate all the training and support services a vendor offers before committing to their system. This may include 24/7 live chat, online case submissions, webinars and more.
Mobile access. Business leaders who travel frequently will be especially interested in ensuring their data analysis tools are compatible with various mobile devices, such as tablets and smartphones. Click here for a more comprehensive guide on mobile BI software.
Integrations. Make a list of all the different software your business currently uses to check what the integration process is like with BI platforms on your shortlist. For example, a vendor’s BI platform may not integrate with your existing CRM system.
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