Business intelligence (BI) is not just about building BI tools; it’s about making those tools accessible to users to empower them with the data they need to make decisions. As a BI professional, you need to master the art of communication to ensure that your stakeholders and project partners understand and use the BI systems you create. In this post, you will learn some key communication strategies and best practices that will help you in your BI career. You will also discover the importance of fairness and avoiding bias in BI, and how to promote ethical and inclusive data analysis.
Make BI Accessible to Stakeholders
Communication is a vital skill for any BI professional. You need to be able to simplify technical processes and present data insights in a clear and concise way to a variety of users who might not have the same level of knowledge or expertise as you. You also need to be able to ask the right questions, define project deliverables, and share business intelligence effectively.
To communicate with stakeholders and project partners, you need to consider four main aspects:
• Who is your audience? Different stakeholders have different goals, needs, and expectations. You need to tailor your communication style and content to suit your audience. For example, the sales or marketing team might be more interested in the business impact and the user experience of the BI tools, while the data science team might be more interested in the technical details and the data quality.
• What do they already know? Before communicating with your audience, you need to assess their level of knowledge and expertise on the topic. This will help you avoid over-explaining or skipping over important information. You can use surveys, interviews, or feedback forms to gather this information.
• What do they need to know? Depending on your audience and your purpose, you need to decide what information is relevant and useful for them. You don’t want to overwhelm them with too much data or too many details, but you also don’t want to leave out anything essential. You can use SMART (Specific, Measurable, Achievable, Relevant, Time-bound) criteria to define your communication objectives and scope.
• How can you best communicate what they need to know? After you have identified your audience, their knowledge, and their needs, you need to choose the best way to communicate with them. This might be an email report, a small meeting, or a cross-team presentation with a Q&A section. You also need to choose the best format and medium for your communication, such as text, charts, graphs, dashboards, or videos.
In addition to these aspects, there are some other best practices for communicating with stakeholders:
• Create realistic deadlines. Before you start a project, make a list of dependencies and potential roadblocks so you can estimate how much time you need for each task. Then communicate your timelines and expectations clearly with your stakeholders and update them regularly on your progress.
• Know your project. When you have a good understanding of why you are building a new BI tool, it can help you connect your work with larger initiatives and add meaning to the project. Keep track of your discussions about the project over email or meeting notes, and be ready to answer questions about how certain aspects are important for your organization.
• Communicate often. Your stakeholders will appreciate regular updates on your project status, achievements, challenges, and changes. Use a document or a tool that allows you to share your reports easily and transparently with your stakeholders. You can also use a changelog to provide a chronological list of modifications.
Prioritize Fairness and Avoid Biased Insights
Providing stakeholders with the data and tools they need to make informed, intelligent business decisions is what BI is all about. Part of that is making sure you are helping them make fair and inclusive decisions. Fairness in data analytics means that the analysis doesn’t create or reinforce bias (a conscious or subconscious preference in favor of or against a person, group of people, or thing). In other words, you want to help create systems that are fair and inclusive for everyone.
As a BI professional, it’s your responsibility to remain as objective as possible and try to recognize the many sides of an argument before drawing conclusions. The best thing you can do for the fairness and accuracy of your data is to make sure you start with data that has been collected in the most appropriate and objective way. Then you’ll have facts that you can pass on to your team.
A big part of your job will be putting data into context. Context is the condition in which something exists or happens; basically, this is who, what, where, when, how, and why of the data. When presenting data, you’ll want to make sure that you’re providing information that answers these questions:
• Who collected the data? Knowing the source and the motivation of the data collection can help you assess the reliability and the validity of the data. For example, if the data was collected by a third-party vendor, you might want to check their credentials and reputation.
• What is it about? Knowing what the data represents and how it relates to other data can help you understand the meaning and the value of the data. For example, if the data is about customer satisfaction, you might want to know what factors influence customer satisfaction and how they are measured.
• When was the data collected? Knowing when the data was collected can help you determine the relevance and the timeliness of the data. For example, if the data is about market trends, you might want to know how recent and how frequent the data is.
• Where did the data come from? Knowing where the data came from can help you identify the scope and the limitations of the data. For example, if the data is about user behavior, you might want to know what platforms, devices, or locations the data covers.
• How was it collected? Knowing how the data was collected can help you evaluate the quality and the accuracy of the data. For example, if the data was collected by a survey, you might want to know how the survey was designed, administered, and analyzed.
• Why was this data collected? Knowing why the data was collected can help you align your analysis with the business goals and expectations. For example, if the data was collected to improve customer retention, you might want to focus on finding patterns and insights that can help achieve that objective.
One way to provide context for your data is by clarifying that any findings you share pertain to a specific dataset. This can help prevent unfair or inaccurate generalizations stakeholders might want to make based on your insights. For example, imagine you are analyzing a dataset of people’s favorite activities from a particular city in Canada. The dataset was collected via phone surveys made to house phone numbers during daytime business hours. Immediately there is a bias here. Not everyone has a home phone, and not everyone is home during the day. Therefore, insights from this dataset cannot be generalized to represent the opinion of the entire population of that city.More research should be done to determine the demographic make-up of these individuals.
You also have to ensure that the way you present your data—whether in the form of visualizations, dashboards, or reports—promotes fair interpretations by stakeholders. For instance, you’ve learned about using color schemes that are accessible to individuals who are colorblind. Otherwise, your insights may be difficult to understand for these stakeholders.
Key Takeaways
Being able to provide stakeholders with tools that will empower them to access data whenever they need it and the knowledge they need to use those tools is important for a BI professional. Your primary goal should always be to give stakeholders fair, contextualized insights about business processes and trends. Communicating effectively is how you can make sure that happens.
