The Five Key Principles of Agile Business Intelligence

Since the introduction of Agile in 2007, agile practices have been widely adopted in software development projects worldwide. A less popularly known fact is that agile methodology and practices can also be adopted in other verticals such as Business Intelligence (BI). This article discusses how to leverage agile methodology in business intelligence (Agile BI).

We will outline 5 key principles for a winning BI approach and outlay actionable ways to put these practices to use on your next BI project.

Whether you are a BI analyst, a CTO or a senior BI lead, we’re confident this would be of great help.

Ready?

Let’s dive in!

Agile + BI = A Winning Combination?

Before diving into the principles of Agile Business Intelligence, it’s important to understand what the AGILE approach entails.

Traditionally, business intelligence has been a linear process, with defined steps that follow a strict sequence.

However, with the rise of Agile methodologies, the business intelligence process has become more iterative, focusing on collaboration, flexibility, and continuous improvement.

An Agile Business Intelligence approach is:

1. Iterative and Incremental: The Agile Business Intelligence approach is iterative and incremental, which involves breaking down a large project into smaller, manageable pieces. This allows for continuous feedback and improvement throughout the project, rather than waiting until the end to make adjustments.
2. Collaborative: Collaboration is a key element of Agile Business Intelligence, with cross-functional teams working together to achieve a common goal. This approach encourages open communication, idea-sharing, and collective problem-solving.
3. Data-Driven: Agile Business Intelligence is data-driven, meaning that decisions are based on facts and data rather than assumptions or intuition. Data is gathered and analyzed throughout the project, and insights inform future decisions.
4. Flexible: The Agile Business Intelligence approach is flexible, meaning that it can adapt to changing business needs and priorities. This approach emphasizes agility, with the ability to pivot and adjust as needed.
5. Continuous Improvement: Continuous improvement is a core tenet of Agile Business Intelligence, focusing on delivering value incrementally and improving over time. This approach encourages experimentation, testing, and learning from failures.

5 Principles For Leveraging Agile Methodologies in BI

Principle 1: Continuous Planning and Delivery

The first principle of agile BI is continuous planning and delivery. This means that instead of spending months or even years developing a solution before releasing it, agile BI focuses on delivering small, incremental improvements on a continuous basis. Let’s explore this principle in more detail:

Why is continuous planning and delivery important in agile BI?

• It allows for faster delivery times: Instead of waiting months or years for a solution to be developed, agile BI allows for delivering smaller, more frequent improvements. This means that businesses can start seeing value from their investments sooner.
• It provides flexibility: Businesses can quickly adapt to changing needs and priorities by delivering smaller improvements.
• It reduces risk: By delivering smaller improvements continuously. Businesses can identify and mitigate risks early on in the development process.

How is continuous planning and delivery achieved in agile BI?

• Agile methodologies such as Scrum and Kanban are often used in agile BI to help teams manage their work and prioritize tasks.
• Continuous planning and delivery involve breaking down larger projects into smaller, more manageable chunks, often called “user stories.” These user stories are prioritized and delivered in short iterations, typically two to four weeks.
• Teams work collaboratively and communicate regularly to ensure everyone is aligned and working towards the same goals.

Principle 2: Collaborative and Cross-Functional Teams

Have you ever been on a team where everyone worked in their own silos, with little communication or collaboration between departments? Maybe you worked on a project where the marketing team created a campaign without consulting the sales team, only to find out later that the campaign didn’t resonate with customers. This lack of cross-functional collaboration can lead to missed opportunities, wasted resources, and, ultimately, failure.

That’s where the second principle of agile BI comes in: collaborative and cross-functional teams. In this approach, teams work collaboratively and iteratively to ensure everyone is aligned and focused on the same goals. Here’s why it matters:

• Improved communication: By breaking down silos and fostering collaboration, teams can communicate more effectively and avoid misunderstandings or conflicting priorities.
• A better understanding of business needs: With cross-functional teams, each member can bring their unique expertise to the table and contribute to a more comprehensive understanding of business needs.
• Ability to quickly address issues: When teams work together, they can identify and address issues more quickly, leading to faster resolution and better outcomes.

Simply put, cross-functional teams help ensure that everyone is rowing in the same direction. In fact, a report by McKinsey found that companies with more diverse teams are more likely to have financial returns above their national industry medians.

So how do you implement this principle in your organization? Here are some tools and techniques used in cross-functional and collaborative teams in agile BI:

• User stories are simple, narrative descriptions of a feature or function that captures the end user’s perspective. User stories can help teams understand user needs and prioritize work accordingly.
• Personas: Personas are fictional characters that represent different user types. They can help teams understand the needs, goals, and behaviors of different user segments.
• Retrospectives: These are meetings where teams reflect on their work and identify areas for improvement. Retrospectives can help teams identify communication breakdowns, process inefficiencies, and other issues that need to be addressed.

Principle 3: Iterative Development and Testing

One of the key principles of Agile BI is iterative development and testing. This means that instead of trying to create a perfect solution from the outset, you create something that works, test it, and then improve it based on feedback. This allows you to adapt to changing business needs and technology trends and ultimately create better solutions.

Maybe I can share a story to illustrate the importance of iterative development and testing in Agile BI:

In 2018, the US Army was developing a new battlefield intelligence system called DCGS-A (Distributed Common Ground System — Army). The project was plagued with problems, and despite spending over $6 billion, the system was still not meeting the needs of soldiers in the field.

One of the main issues was that the system was being developed using a traditional waterfall approach, which meant that each phase of development had to be completed before moving on to the next one. This led to long development cycles, and by the time the system was tested, it was often too late to make major changes.

In 2019, the Army decided to switch to an Agile approach, which involved breaking down the development into smaller, iterative sprints. This allowed the developers to get feedback from soldiers and adjust the system accordingly, resulting in a much more effective solution.

Here are some key benefits of iterative development and testing in Agile BI:

• Speed: By breaking development into smaller sprints, you can deliver working solutions faster and respond quickly to changing business needs.
• Flexibility: Iterative development allows you to adapt to changing requirements and incorporate stakeholder feedback.
• Improved quality: By testing the product at the end of each sprint, issues and bugs can be identified and resolved more quickly.

Principle 4: Data-Driven Decision Making

Here are some concrete and fun facts to illustrate this principle.

• In 2023, data will be even more important in decision-making. According to a survey by Gartner, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
• The traditional approach of decision-making based on gut instincts and intuition is no longer sustainable in today’s fast-paced business environment. Data-driven decision-making ensures that decisions are based on factual information, not just assumptions.
• Data-driven decision-making enables businesses to make faster and more accurate decisions. Businesses can quickly respond to changing market conditions, and customer needs by analyzing data in real-time.
• Data-driven decision-making also enables businesses to identify new revenue streams, improve customer experiences, and optimize business operations.
• Companies like Amazon, Netflix, and Uber are excellent examples of data-driven decision-making transforming their respective industries. By analyzing user data and behavior, they have created personalized experiences, developed new services, and optimized their operations to stay ahead of the competition.

To further illustrate the importance of data-driven decision-making, let me share a story about how Walmart used data to optimize its supply chain.

Walmart analyzed its sales data and discovered that strawberry Pop-Tarts were in high demand before a hurricane hit. Before a storm hit, they used this information to stock their stores with extra Pop-Tarts and other hurricane-related items. As a result, Walmart was able to meet customer demand and increase sales during the hurricane season.

Principle 5: Continuous Improvement and Adaptation

Continuous Improvement and Adaptation is the fifth and final principle of Agile Business Intelligence, and it’s all about learning from experience and making changes based on that learning. In a rapidly changing business environment, it’s important to stay nimble and adaptable; this principle helps businesses achieve that.

How Can You Put these Principles into Practice?

Here are a few strategies to consider:

• Embrace a growth mindset: A growth mindset is an idea that you can always learn and improve, regardless of your current level of knowledge or expertise. By fostering a growth mindset within your organization, you can create a continuous improvement and learning culture.
• Encourage experimentation: Experimentation is a critical part of the continuous improvement process. Encourage your team to try out new strategies and processes, and be open to taking risks and learning from failures.
• Monitor and measure progress: In order to know whether your continuous improvement efforts are paying off, it’s important to monitor and measure your progress over time. Use data analytics tools to track key metrics and identify areas for improvement.
• Embrace new technologies: The world of business intelligence is constantly evolving, with new tools and technologies emerging all the time. By embracing new technologies like Datameer, you can stay ahead of the curve and take advantage of the latest innovations in the field.

Speaking of new technologies, let’s take a closer look at how Datameer can help you put these principles into practice. With its powerful data analytics tools and intuitive interface, Datameer can help you with the following:

• Collect and analyze data from a variety of sources
• Identify key insights and trends
• Collaborate with team members in real-time
• Continuously refine and optimize your data analysis processes

By leveraging the power of Datameer, you can stay agile and adaptable in the face of changing circumstances and maintain a competitive edge in your industry.