For a company’s operations to become informed by data, it will want to employ a business intelligence, or BI, strategy. BI tools allow an organization to make decisions that are guided not just by anecdote or a small collection of data, but with a complete picture of data. In this article, we will cover in detail what business intelligence means, how it can support a business, and what aspects to look for in business intelligence tools for your own organization.
What is business intelligence?
Business intelligence is the process of surfacing and analyzing data in an organization to make informed business decisions. BI covers a broad spectrum of technologies and methods, from the way that data is organized and analyzed, all the way to how findings are reported. BI is used to answer how a business performed in the past and why those outcomes came about.
Execution of a successful business intelligence strategy requires a strong organization of how data is used from start to finish:
- Data collection: A business needs to understand where they can collect data from visitors and customers, and how they can be organized into a form that can be analyzed.
- Data storage: Data relevant to businesses are numerous and often large in scope. In order to be useful, that data must be stored in a place that data stakeholders can reliably access, such as in a SQL database. A storage solution should always be up-to-date so that a company can act on changes in data quickly.
- Data analysis: The core of business intelligence is focused on descriptive and diagnostic analytics, which answers questions of where your company has been, where it is now, and why things are the way they are now. BI tools need to be able to draw from data storage to conduct these different types of analyses.
- Data reporting: All of our data and analyses will do no good if they do not reach decision makers and other stakeholders. BI should convey data and insights in ways that people with less context can still quickly understand and use them to make decisions.
In order to execute these steps, multiple tools and products need to be employed. There are two subsets of tools to consider here.
Data pipeline: data collection and storage
One class of tools are those used to collect and store data. Tools like Salesforce and Hubspot collect data on various aspects of a company’s visitors. Products like Amazon Redshift, Google BigQuery, and Snowflake allow businesses to store their data in scalable data warehouses. And other products like Fivetran and Stitch can make it easy to connect data generators into data storage.
While products that perform data collection and storage are important parts of the BI process, they generally aren’t what people think of when it comes to business intelligence. The efforts put into this part of an organization’s data strategy can serve not just their business intelligence needs, but other parts of their data analytics plans. Instead, the strategies used by a business to collect and store data are often known as the data pipeline. The tools used in the data pipeline will fall under the label of data engineering.
Business intelligence tools: data analysis and reporting
Other tools are used to analyze and report on data; these are the products that are referred to as business intelligence tools. Setting up these BI tools allow you to connect to and query data repositories in order to analyze the data. They let you create visualizations and dashboards that are easy to read and understand. Good BI tools let you generate and send out reports to stakeholders so they can monitor performance indicators at a high level.
Business intelligence vs. business analytics
The term “business analytics” (BA) is a term related to business intelligence, with plenty of confusion over where they overlap. A common distinction between business intelligence and business analytics comes from the type of data analysis being performed.
Business intelligence is often characterized as concerned with the descriptive and diagnostic levels of analysis. That is, BI tries to address questions of what has previously happened, what is the current state of things, and why the observed pattern in the metrics came to be.
Business analytics, on the other hand, is concerned with predictive and prescriptive analytics. This type of analysis is concerned with predicting what will happen next, or what a company should be doing next. Performing BA tends to be a more specialized pursuit, since it requires a good descriptive and diagnostic foundation that comes from BI.
While some definitions of BI and BA make distinctions between them based on analysis methods or strategies, the differences in domain between past and present vs. future are fairly standard. This separation between business intelligence and business analytics can help narrow down what kinds of functionality you want a BI tool to have, and what can be left out.
How business intelligence supports businesses
The overall objective of business intelligence is to allow a business to make informed decisions. A company with a working BI strategy will have data that is accurate, complete, and organized. Business intelligence can be used to show historic patterns to help stakeholders gauge the health of their organization, alerting them to problems as well as potential improvements.
Business intelligence can also help organize teams, keeping them aware of key performance indicators (KPIs). Awareness of KPIs through dashboards and reports keeps teams aligned and focused on their goals. Easy access to metrics and KPIs also frees up time and energy to execute on the tasks that will impact the company’s performance.
Examples of business intelligence usage
Business intelligence tools can be used by all teams at a company, including sales, marketing, and customer support. Team members and executives can both make use of BI tools’ output. Data engineers and data analysts can also make use of the convenience of a BI tool when performing their own investigations.
Examples of how business intelligence is used include:
- Visualize the volume of visitors and users on a website over time
- Track potential customers through a sales pipeline
- Measure performance of business metrics against benchmarks and goals
- Evaluate performance of marketing campaigns and experiments
- Segment users by demographic characteristics
- Generate reports for team and executive decision-making
Modern business intelligence and self-service BI
A major driving force behind modern business intelligence is increasing the accessibility of data analysis to a larger audience. Traditionally, calculation of metrics and compilation of reports required a dedicated data professional or team to create. This was a significant bottleneck between a user noticing an interesting or concerning trend and being able to diagnose their observations.
Now, business intelligence solutions are becoming aligned with self-service BI. With self-service BI, anyone is able to access data directly and perform analyses without needing to go directly through a data team member. Self-service BI tools typically have graphical interfaces so that common data tasks are easy to perform without query language knowledge. While data teams are still important for maintaining data and who gets access to it, self-service BI can free up data specialists to perform more intricate and advanced analyses.
Features of business intelligence software
In order to perform business intelligence tasks, we need data to be collected and stored with data engineering tools, then made available to business intelligence tools for analysis and reporting. When looking for solutions to let your business draw insights from your data, pay attention to the points below to make sure that they satisfy your needs.
Connections to data sources
First and foremost, you need to be able to get access to data in order to perform business intelligence. It’s important to make sure that your analysis-side BI tool is able to connect to your other solutions that take care of data storage. These data sources can include databases like MySQL, data warehouses like Amazon Redshift and Google BigQuery, or even ad hoc data files in CSV format. Make sure that your BI tool is also able to access the most up-to-date data in order to make timely decisions. Try to avoid workflows that require custom data pipelines to be set up, since this can create disruption if there is a change or unexpected event in the raw data.
Data querying
In addition to being able to connect to data sources, it is also worth checking how easily the BI tool is able to connect between data sources. A good BI tool will make it easy to take queries from different data sources and join them into a new one. Connecting and merging data from multiple data sources provides the opportunity for additional insights that are not possible on their own.
Data visualization and dashboards
Data visualization is a core component of most business intelligence applications. A good chart can convey insights faster than a plain table of numbers. When considering BI tools, see what kinds of charts they have available and the amount of customization possible with them. A surprisingly large amount can be done with a fairly small number of chart types, but think about your use case carefully to see if you need software that can support a particularly specialized chart type for your organization.
A BI tool should also be able to arrange groups of charts and tables into dashboards. Dashboards allow for continual tracking of important business metrics in a single location. Make sure that your chosen BI tool is able to automatically update its dashboards so that viewers are always getting the most up-to-date information possible.
Data analysis
It’s important to keep in mind the type of analysis that can be handled by the data that is available. When there is an unexpected change in metrics, a BI tool should allow users to dig deeper into the data. A modern BI tool will allow users to modify and add on to previous queries in order to get deeper insights into the data. Another feature that supports analysis and exploration are dashboard-level filters that can affect multiple charts at the same time.
Recall as well that BI tends to be focused on descriptive and diagnostic analysis. While it might seem attractive for a BI tool to include more advanced capabilities such as machine learning or artificial intelligence, they are far from necessary. Making sense of these advanced techniques still requires specialized knowledge of the business and statistics to properly interpret what the algorithms find. A mature data team may well be better off performing predictive and prescriptive analyses outside of the bounds of BI tools’ functionality.
Data availability
It’s important to consider the freedom that business intelligence tools can provide to an organization. Modern BI tools can make it easier for data stakeholders to perform the investigations they need to themselves, freeing up data teams to perform more in-depth analyses.
Consider how easy it is for new users to be added to a BI tool and how easily they can access the data they need. Take note if there are different account types, if there are separate creator, editor, or viewer user accounts. Check if it is possible for multiple users to work collaboratively on the same dashboard.
Implementing a self-service BI tool can be a great way to drive an organization towards being data-driven. This is especially true for smaller businesses, which may not have the level of personnel to handle a more traditional BI strategy centered around a dedicated data team. When it is easier for users to get up to speed with a BI tool, the faster an organization can make use of and act on their data.
Deployment method
Another big consideration in choosing a business intelligence tool is how it will be deployed. Traditional BI software required an on-premises deployment, including hardware setup to software installation.
Modern business intelligence follows a cloud-based deployment model. Cloud-based BI tools require no specific hardware setup, sometimes just requiring an online connection. Since resources accessed remotely, a cloud-based BI strategy is quicker to get running and easier to scale with a company’s data needs. It is now much easier to perform complex analyses due to this scalability. While on-premises deployment can have some small advantages with customizability, it will be in your best interests to stick with a cloud-based BI solution.
Application support
Each BI application has its own learning curve that can take some time to overcome. This can be an important consideration especially if you want many people actively using the software – including those who may not have much technical or analytical experience. Check to see what resources each BI tool has for using their product, like documentation, tutorials, and FAQs. Certain providers may also offer active support lines to provide direct help on specific customer questions.
Make an honest attempt at using a BI tool in a product trial in order to see if it suits your needs. Before and during a trial, plan out and try implementing some of your use cases in the product. Pay attention to not only whether the product’s features actually solve your problem, but also where you get stuck and how the BI tool’s support resources help you out. Other users will encounter those problems after purchasing the product, so knowing the kind of support you need should be a factor in choosing a BI tool.
Best practices for implementing business intelligence
Implementing business intelligence doesn’t stop with just choosing the right tools: it also requires the proper support from the organization and its people. Keep in mind the following tips to ensure that when you invest in a BI strategy, the information it returns will be valuable to your company.
Ensure that the tool meets the company’s needs
Before you purchase a BI tool, make sure that its capabilities actually act as solutions for the organization’s questions before finalizing a commitment. Plan out how you expect the BI tool to be used by the company. Use the list of business intelligence features above to understand the priorities that you need in your BI strategy. Each BI tool provides tradeoffs between its features, so determine which features are most important for your business and select the tool that aligns best with your needs.
Establish a working data pipeline
Getting meaningful outputs from a BI tool requires meaningful inputs. Having a functional data pipeline for data collection and storage is a prerequisite to performing data analyses. When data is not clean, then it may be difficult to work with the data. If data is not thorough, then gaps in the data may result in bias in the results.
Make sure that your data is structured and organized before you start to analyze it. The people who manage the data will need to be in sync with what users need in order for analysis to be useful and actionable. Collecting data from many different sources and storing them in data lakes, warehouses, and marts can be a considerable effort in both time and money. Exercising good data management is a necessary step towards becoming data-informed, and work that you put in at the start can save you more pain later on.
Encourage active usage of business intelligence tools
It can take some effort to educate employees to use the BI tools you purchase. Education on proper tool use is necessary to ensure that users are drawing accurate insights from the data. However, while BI application interfaces have become more intuitive, learning how to work with a tool still requires patience.
It can help to have someone in charge of spearheading BI adoption efforts. Let them be an evangelist and leader to get more people engaged in using BI tools. Team leaders and executives can also provide valuable buy-in energy to support the building effort. When more people are using analysis tools, the more effective its implementation will be. Not only will more insights be generated, users can rely on each other to obtain the insights they need to perform informed decision making.
Conclusion
Business intelligence greatly enhances how a company approaches its decision-making by using data to answer questions of the company’s past and present. It can be used by teams across an organization to track key metrics and organize on goals. Modern business intelligence tools use self-service solutions to make it easier for stakeholders to access their data and explore it for themselves.
When evaluating a BI tool, make sure it satisfies your company’s needs in terms of:
- Connections to data sources
- How data can be queried and connected between data sources
- Data visualizations and dashboards
- Report generation
- Depth of data analysis and drilldown
- Accessibility of data
- Help, documentation, and active application support
Business intelligence is a key investment to making a business more informed by data. When a BI tool is used in alignment with a business’s use cases, it can free up time for employees to take meaningful actions to keep the business moving forward. For more perspectives, check out our whitepaper on how BI can benefit an organization and how to choose the right BI tool.