Agile Analytics Framework Overview and its Best Practices

What is Agile Analytics?

Like Agile Methodology, Agile Analytics also consist of some set of guiding principles and core values. It is a style for building Data Marts, Business Intelligence application as well as Analytics application. Its core values include best practices of project planning, management, and monitoring.

It is not a framework, not even a methodology; it's just a development style that focuses on the client’s end goals to make better decisions using data-driven prediction. Client satisfaction is the topmost priority for project delivery achieved through Agile Analytics rapid delivery of usable predictions. These values aim to create high-quality, high-value, working DW/BI systems.

Agile Helps organizations to focus on meeting user needs, prioritizing delivery, helping people to collaborate. Click to explore about our, Agile Thinking Benefits and Best Practices

How does it work?

Consider all the data insights along with the analytics requirements, and then based on that, we recommend Agile Development methodology. It enforces practices and techniques tailored according to customer demands and adopted in any organizational culture to continuously deliver business value.

Successful, timely completion of end-to-end work involves setting up all the data sources, their ETL scripting, Wrangling, Storing, Visualizing and insights, and till deployment, adopting Agile methodology. It provides an opportunity to assess the project delivery path throughout development. The primary objective is a high-quality, high-value, working Business Intelligence/Data Warehouse. It focuses on some goals,i.e.

• Incremental, Iterative, and Evolutionary—Agile is an incremental, iterative, and evolutionary style of development. Work in small chunks or iterations, no longer than 3 weeks. Develop a system in small increments of user-valued functionalities and evolve the working software by adapting frequent user feedback.

• Value-Driven Development - The goal of each iteration is the production values feature. Every iteration must end with the delivery of one or more features. The main agenda is to get value from the iteration.

• Production Quality—Each developed feature must be tested and debugged during its development stage. As Agile says, it’s not like building hollow prototypes; it's all about incrementally evolving to the right solution. Rigorous testing must be planned in the development process. A user feature is done when it is of production quality.

• Automation—Automate as many processes as possible. Test Automation is a challenge but most beneficial when added to the process. If the processor routines are developed more than twice, automate them and focus more on developing user features.

• Collaboration - Establishing a collaborative team workspace is essential for successful Agile projects. Daily collaboration with the technical team is critical.

• Self-organization and Self-managing team - Hire the best people, give them the tools they need as well as support, then stand aside and allow them to be successful, the role of Agile Project Manager to enable the team members to work and facilitate a high degree of collaboration with other team members.

The User story is the fundamental that help us to understand the purpose behind any work. Click to explore about our, Agile User Story Principles

What are the benefits of Agile Analytics?

Developing and delivering Successful Business Intelligence and Analytics systems are very complex. As per the stats record, more than 50% of projects fail in on-time delivery and meeting and satisfying the requirement end goal. Such kind of issues are resolved by defining the scope in the earlier stage. So, in a nutshell -

• It promotes the responsibility of success quality on all the users.

• Early iteration result allows users to share feedback and make changes quickly to avoid late surprises and poor user satisfaction.

• It reduces project cost by implementing the scale-out version of the system across multiple functions.

• ItAllow Self Service.

• It provides diversity.

• It allows Data Visualization.

• Gets connected with the Data-Driven revolution.

• Better management and Governance.

Why Agile Analytics Matters?

Traditional project managers focus on planning by WBS, which involves predefined scheduled tasks. Project managers develop project plans according to the inputs get from the developers, here the primary measure of success is how the team completed the scheduled task as per planned. Traditional methods focus more on the task manager and ensure project executions conform to the plan.

Agile Project Management focus more on team management rather than task management. They ensure the development team has what they need to succeed. They help the team from stress and disruption. They enable the team to self-organize and self-manage task completion.

Agile analytics practice daily coordination for 15 minutes with the team members. So, in brief, Business requirements for analytics change rapidly, and clients demand Predictive Analytics that can support decisions today. Clients require more timely and actionable analytics. Data warehouses reduce latency in the data used by predictive models. Innovation directly impacts the analytic workflow itself.

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How to adopt Agile Analytics?

There are three consistent truths related to it, i.e., development projects fail very easily or often, and it's better to fail fast and adapt them than to fail late after the budget is spent. Serve the goal, i.e. -

• Big data

• Agile Delivery

• Lean learning

• Advanced Analytics

• Impact

• Solution thinking

• Ethics

Big Data

Agile Delivery

Agile teams seek to end every iteration ready for deployment; it's better to develop an automated and disciplined process for deployment that adds more value to the business insights as well. Start with a high-value business goal and then chunk it up into small incremental goals to show to stakeholders after every few days of iteration for review and further Decision-making towards the original goal. It Includes -

• Continuous Integration
• Collaboration
• Evolve
• Continuous Delivery

Lean learning

Lean Management tactics enable process improvement through some techniques i.e. Agile Development, Just in Time scheduling and Value Stream Mapping. It correlates with Design Thinking and Agile. Design Thinking revolves around how to explore and solve specific problems, Lean is a framework for testing beliefs and learning ways to get the right outcomes, and Agile is how to adopt iterative conditions with software. Lean learning involves -

• Eliminate Waste

• Amplify learning

• Decide as late as possible

• Deliver as fast as possible

Empower the team to provide motivation from time to time, with a purpose within reachable reality, with the assurance that the team might choose its own commitments. All components work together as a whole, with a balance between flexibility, maintainability, efficiency, and responsiveness.

Advanced Analytics

It involves predicting, transforming, and anticipating business into new markets and expanding new growth opportunities. Its services include -

• Insights, forecasting, and visualization.
• Asset-based Analytics-as-a-Service.
• Advanced Analytics and Data Strategy.

Data is very critical, and getting actual value from data requires a lot of techniques and analytics power beyond Conventional BI reporting. It Includes -

• Machine Learning
• Statistics

Solution thinking

• Evaluating a current problem or situation

• Determining a reasonable Practical plan to attack that problem or situation.

• Possess skills, talents, and resources to discover the solution to the problem by devising a workable plan and making it happen.

Ethics

Ethics means ‘moral principles that govern a person’s behaviour or the conducting of an activity’.It includes radical transparency by the organization conducting analysis and ensuring personal privacy for individuals to control their data. It involves -

• Privacy Controls
• Radical transparency
• Data Democracy
• Open Data

Big data analytics has always been a fundamental approach for companies to become a competing edge and accomplish their aims.Click to explore about our, Trends in Big Data

Best Practises of Agile Analytics

It targets achieving a few goals, i.e. team collaboration and customer satisfaction; instead of last-minute surprises, there should be iterative development and daily basis team engagement. To build a Business Intelligence or Analytics application comes the picture of focusing on the early and continuous or iterative delivery of business value throughout the development lifecycle.

Priority is customer satisfaction and self-organized teams. Agile Alliance has established a set of principles for software development, i.e.

• It follows incremental delivery after every few periods.

• Changes in requirements are welcomed even late in development.

• Software is delivered frequently after a few weeks of iterations.

• Give business intelligence experts an environment to support them and trust them to get the job done.

• Face-to-face conversation is the best way of conveying information to and within a development team.

• Maintain the balance among project scope, schedule, and cost.

• They regularly track how to become more productive and adopt a result-oriented approach.

• Teams, collaboration, and self-management collaborate to make plans and determine the best way to tackle work.

• Rather than measuring progress on a Gantt chart, Agile organizations have three simple measures of progress - better business outcomes, more productive and engaged teams, and happy customers.

Concluding Agile Analytics

It pays to keep in mind that the highest priority of Agile Analytics is to satisfy the customer through the early and continuous delivery of BI features. It is not mandatory to adopt all the recommended practices at once or even to adopt all of them; follow the below three questions while adopting -

• Will the goals of delivering customers value early and responding to change be better served?

• Will team and project be better in the long run?

• Will the cost of adopting these practices justified by its benefits?

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