7 Business Intelligence Best Practices
At Ibotta, we process terabytes of data each day, powering business insights for hundreds of internal customers and clients. Ibotta is not just an app that saves consumers millions of dollars in cash back per year, but an example of how modern business intelligence can make a profound impact! Our world and the fabric of contemporary business decisions rely heavily upon the acuity of data science now more than ever before. With the ascent of big data capabilities, businesses are able to produce actionable insights from analytics that inform key enterprise decisions.
The scope of business intelligence (BI) encompasses a wide range of software and services used for the assemblage, integration, and presentation of business information. Examples that comprise the scope of BI include enterprise analytics, relational databases, online analytical processing (OLAP), data visualization, data and process mining, benchmarking, and data architecture with its corresponding implements. The best practices outlined below for business intelligence are industry standards that, if followed, will enable your company to achieve greater efficiency, awareness, and success!
- Secure organization-wide buy-in.
The key element in business intelligence is the business. It is ill-advised to move forward with a BI enterprise without the organization adopting the initiative universally. From the very beginning, it is imperative to acquire sponsorship from company administrators, and to involve key stakeholders in strategic planning and collaborative efforts to ensure that all parties concerned are in agreement on project priorities and key enterprise objectives.
For example, getting the right departments involved such as IT and Data Engineering can ensure that appropriate data governance and security measures are adhered to. Attaining investment from the right roles and departments in the early stages of a business intelligence process helps to provide clarity, supplies an additional knowledge base, and garners a company-wide focus toward a successful BI implementation.
2. Prepare a BI blueprint.
Prior to a quality BI implementation, it is important to outline the project scope and assess requirements to formulate a cohesive strategy. Establish a baseline for technological and reporting needs while planning for scalability. At this stage, identify data sources and integration methods, collaborate with all stakeholders to reach agreement on expectations, achieve uniformity in desired metrics, and pin-point the KPIs that matter most for performance tracking.
Design a strategy with concise, measurable, and distinct objectives. Define ownership of each task so it is clear what each individual/department’s role is in the initiative. This collaborative stage creates a strategy outlining main objectives and methods for implementation. Laying this groundwork in the beginning sets a solid foundation to build upon in later stages of development. When a strategic blueprint is formed for a BI initiative, all stakeholders can reach alignment in priorities and have a healthy conversation on how to approach key business objectives.
3. Implement a quality data governance framework.
Data governance is a broad and complex subject that encompasses the policies/standards, decision rights, accountabilities, and enforcement methods for individuals and information-systems as they execute information-related operations. There are many benefits to data governance which include: greater security, decreased operational expense, streamlined data management, improved decision-making, functional efficiency, ameliorated data accountability and understanding, regulatory compliance, and higher data quality. A well-organized and maintained data governance framework is a powerful enterprise asset and an essential element in any business intelligence fulfillment.
4. Execute your BI strategy in stages.
As mentioned earlier, at the genesis of a business intelligence endeavor it is important to have a strategy mapped out for how to achieve business objectives. With this blueprint, start small and build out as you go. Execute tasks that are influential and less time-consuming to implement first, then build out complexity in iterations. Frequently review tangible deliverables. In this stage of development, the keys to success are project management, communication, and organization. As the analysis evolves, communicate with stakeholders on any changes in implementation or impactful findings to keep them well informed. Manage deadlines and expectations accordingly for better time management and transparency.
5. Design dashboards with impact.
One of the most valuable tools in business intelligence reporting is the dashboard. When designing dashboard visualizations, there are four main considerations: simplicity, clarity, conveyance of meaning, and consistency. In-depth analysis can quickly build in complexity. It is vital to produce analysis that is as concise and simplified as possible so your audience can extrapolate trends and significant data points effortlessly.
Place elements with the most important benchmarks at the top of the dashboard layout, including more detailed analysis as needed progressing downward. Dashboard elements must succinctly convey answers to questions and accurately represent data in an appropriate format to display underlying trends. Be sure colors, labels, and formatting are consistent for easy readability. The main objective in business intelligence reporting is to enable the audience to quickly derive underlying KPIs and patterns from the data that inform actionable, data-driven decision making. Remove elements that do not add value and remember that the details matter!
6. Stay nimble.
Data is frequently growing and changing. To be effective in business intelligence, you must remain agile and adapt quickly. Previously mentioned was the importance of taking an iterative approach to development, evolving analysis as we progress. Developing in iterations allows us to continually make improvements and changes as needed to keep analysis up-to-date. As data and business needs mature, keep open communications with stakeholders to leverage user feedback for continuous optimization and to gain a better understanding of how the data is utilized. Remaining nimble permits business intelligence needs to be satisfied expeditiously, analysis to stay relevant, and customer-orientation to be greatly improved.
7. Educate and empower the broader business community.
Empowering the broader business community with knowledge is an important objective of business intelligence. By training individuals and departments on established company terminology and metrics, you can extensively increase data literacy. Additionally, building capabilities for self-service business intelligence democratizes data for ease of access and faster insights. Effective training and education can elevate comprehension, performance, and investment in business objectives.
Business intelligence is in a constant state of evolution, advancing according to enterprise needs and the expanding landscape of technology. As artificial intelligence and machine learning capabilities grow more powerful, companies who wish to keep pace with advancements will need to remain agile and adapt innovations into their business intelligence strategies. These business intelligence best practices are essential implementations to keep a competitive edge and acquire deeper insights into the nature of your enterprise.
