No matter the size of your business, or what industry you operate in, data is critical to its growth. Understanding how the numbers stack up is key to making well-informed, profitable decisions – whether those decisions relate to your marketing strategy, product-development plans or employee-engagement efforts.
Also critical to growth is adaptability. Business teams must be able to react quickly to market changes, and managers leading those teams must have relevant information at their fingertips from which to take direction.
This is where agile business intelligence and data analysis play their role.
What Is Agile Business Intelligence?
To define agile business intelligence, we need to look at its component parts.
A modern definition of business intelligence (BI) is the gathering and analysis of key business data to inform strategic direction. This is done through BI tools and technologies that assist with data mining, data visualization and data analytics.
Essentially, through the application of BI, organizations form a detailed picture of business health, track performance against operational goals and identify opportunities for improved processes.
Rather than a tactic reserved for multinational conglomerates and major corporations, BI applies to every business operating in today’s data-driven world.
If you have a website, run social media accounts or use SaaS tools like CRMs and customer-review platforms, you have access to data that can help your brand grow. Business intelligence systems consolidate and transform that data into actionable insights.
When we refer to agile business intelligence, we simply refer to the methodology used in BI projects.
Unlike traditional waterfall methodology – a linear, rigid process that focuses on the delivery of an end product – agile is a flexible approach. Parts of a solution are delivered incrementally and are designed in collaboration with the business client. It allows for much quicker turnaround and partial changes where necessary without disrupting an entire BI system.
In short, agile business intelligence is a process whereby BI systems continually evolve to meet the ever-changing needs of a business user.
The Benefits of Agile Business Intelligence
Now we’ve established a definition of business intelligence, we know it has multiple inherent benefits.
Adopting BI systems allows for consolidated data and intelligent analytics. This leads to smarter decision-making, which in itself can lead to:
- More effective marketing with greater ROI
- Improved customer-satisfaction rates
- Increased employee engagement, productivity and retention
- Streamlined and cost-efficient operational practices
- Better supply-chain partnerships
- More customer-centric product development
Almost any aspect of business can be improved through data and earn you a competitive advantage. But what are the specific benefits of applying agile methodology to BI projects?
Low Initial Outlay
Agile BI systems are designed to integrate seamlessly with existing tools and software. This makes them relatively inexpensive compared to traditional systems which require significant investment in both software and hardware.
Low Cost of Ownership
Agile BI systems are cheaper to run in the long term, requiring less maintenance and less involvement from IT teams.
Easy to Use
Agile solutions are designed to be user-friendly and intuitive. They conform to the ‘self-service’ model, meaning anyone can pull intelligent reports without the need for coding knowledge or technical expertise.
Because agile solutions are web-based, they allow for unrestricted access to data (geographically speaking). This, combined with their ease of use, enables more effective information-sharing and collaboration – vital if you’re working with remote decision-makers.
Agile takes an iterative approach, meaning any given part can be updated to a smarter version of itself and rolled out quickly, without the need to start an entire BI project from scratch.
An agile solution is adaptable to your needs. That means that whatever direction your business takes, or whatever market shifts occur, your business intelligence can be tailored to suit.
You’ll always have at your disposal the tools you need for current circumstances, and with decisions based on up-to-date information, you’ll see a greater return on your BI investment.
Six Phases of Agile Business Intelligence and Data Analytics
The whole concept behind agile business intelligence is that an effective, usable solution is delivered quickly, then subject to continual evolution and improvement.
To achieve this, teams must complete the six phases of the agile project life cycle.
This is the initial planning phase of your agile BI project – the point at which you’ll ask yourself what questions you’re looking to answer as a business, and what data sources will help you find those answers.
It’s important to involve all stakeholders in this phase for a holistic view of BI goals. The result should be an overview of business objectives in priority order, a loose vision of BI system architecture, and a list of practical considerations for its design and implementation.
‘Loose’ is the operative word here. Remember, the whole point of an agile approach is adaptability so avoid any rigid plan and don’t focus too much on the finer details, as these will be explored later.
In the design phase, you’ll dig down into the granular detail, outlining the specifics of each feature. Think of it as designing a roadmap for the final BI system, and discuss with the wider team the expected capabilities of each feature.
With agile methodology, these features will be built and released in short development cycles known as sprints. It’s best practice in the design phase to construct a top-level plan of these sprints, feature releases and associated deliverables, and seek sign-off from all stakeholders. This will prevent any need to repeat phases one and two, and allow the development team to quickly roll out features in their first iterations.
This is where the first version of each system feature will come into being, and the phase to which you’ll repeatedly return after each sprint.
A sprint is typically one to three weeks long, and to ensure they run smoothly it’s advisable to hold short, daily stand-up meetings. This helps to confirm that everyone has the resources they need to complete their tasks, as well as aiding with accountability.
If you’re outsourcing the development of your BI system, it’s vital to hold regular catch-up sessions with your development team to collaborate on system functionality.
At the end of each sprint, a fully functioning feature should be ready for beta testing.
This is a crucial phase in which the feature released will be road-tested by a small group of users working with sample data. This test group will refer to the original plan for the feature release to ensure the required capabilities are met.
It’s at this stage bugs are identified and fixed to create a BI system feature ready for deployment.
The deployment phase is exactly what you’d expect. It is the point at which a feature is made live and put into real-world use with real-world users.
Anyone involved in your business intelligence strategy should be fully trained in the relevant software at the deployment stage. It’s also imperative to continually engage stakeholders as the feature rollout goes company-wide. After all, it’s their user experience and feedback that will help inform future iterations.
This isn’t so much of a phase as an ongoing process. Every part of your BI system should come under regular review – not just the system features and their functionality, but also your business objectives and methods of data-gathering and interpretation.
This is the essence of agile business intelligence. It’s about flexibility and adapting to changing business needs.
How to Succeed with Agile Business Intelligence
Until this point, we’ve focused on the benefits of agile business intelligence and the phases that an agile BI project goes through. However, if you want to develop a strategy that works, you need the right building blocks in place, and the right mindset.
Focus on the Right Data
Knowing that data analysis is integral to growth, many brands start collecting data without much focus on the what or why. Be sure to clarify what problems you’re facing, and what data sources can provide you with solutions.
Find First-Party Data Opportunities
This is particularly relevant for direct-to-consumer businesses, like eCommerce, where the age of third-party data is coming to an end. These businesses must seek out first-party data (i.e. voluntarily given) to understand and improve the customer experience. Review-collection and loyalty programs are good examples of first-party data collection.
Train Your Team
To garner strategic insight from data gathered, those involved must be data-literate and confident in the evaluation and interpretation of the information available. Not every business will have professional data analysts at their disposal, and for those that don’t, upskilling their existing workforce should be a priority.
Whilst data can provide highly profitable business insights, it can also incur significant costs if not stored, handled and used correctly. Put compliance front and center of all business-intelligence activity.
Constantly Question Your Approach
There’s no point in investing in agile business intelligence if you’re going to stick with the status quo. To be successful, you need to continuously review and refine your systems and processes. Hold regular sessions to address how your market has shifted, what new trends are on the horizon, and how you can answer future business concerns with intelligent data sourcing.
If you’re a small to medium-sized company, you may view business intelligence and data analytics as irrelevant, but in a highly competitive business world, those that succeed are the ones that make intelligent, data-driven decisions.
What agile BI offers is an accessible, adaptable approach for brands of all sizes to gain actionable insight and grow at scale.