Agile Analytics Framework Overview and Best Practices - XenonStack

What is Agile Analytics?

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

Agile Analytics is not a framework, not even a methodology, it's just a development style that focuses on 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 hold the primary objective of creating high-quality, high-value, working DW/BI systems.


How Agile Analytics Works?

Consider all the data insights along with the analytics requirements and then on the basis of that we recommend Agile Development methodology. It enforces practices and techniques tailored according to customer demands and adopted in any of organization culture for the Continuous Delivery of business value.

Successful timely completion of an end to end work involves setting up of all the data sources their ETL scripting, Wrangling, Storing, Visualizing and insights and till deployment, adopt Agile methodology. It provides an opportunity to assess the project delivery path throughout the development process.

The primary objective is 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 iteration 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 production values feature. Every iteration must end with delivery of one or more feature. The main agenda 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. User feature is done when it is of production quality.

Automation – Automate as many processes. Test Automation is a challenge but most beneficial to add in the process, if the processor routines developed more than twice, automate them and focus more on developing user feature.

Collaboration – Establishing a collaborative team workspace is an essential ingredient 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.


Benefits of Agile Analytics

The development and delivery of Successful Business Intelligence and Analytics system are very complex. As per the stats record, more than 50% of projects fail in on-time delivery as well as meeting and satisfaction of the requirement end goal. Such kind of issues resolved by defining 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.
  • Agile Analytics Allow Self Service.
  • Agile Analytics provides diversity.
  • Agile Analytics allows Data Visualization.
  • Gets connected with the Data-Driven revolution.
  • Better management and Governance.

Why Agile Analytics Matters?

Traditional project managers focus on the 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 conforms 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 to 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 reduced latency in the data used by predictive models.

Innovation directly impacts the analytic workflow itself.


How to Adopt Agile Analytics?

There is three consistent truth related to it i.e. development project fails very easily or often, and it's better to fail fast and adapt them than to fail late after the budget spent. Simply serve the goal for Agile analytics i.e. –

  • Big data
  • Agile Delivery
  • Lean learning
  • Advanced Analytics
  • Impact
  • Solution thinking
  • Ethics

Big Data

Big Data defined as everything that can be quantified and tracked. Data is messy now than ever before.

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 up into small incremental goals to show to stakeholders after every few days of iteration, for review and further Decision- Making towards 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 how to explore and solve specific problems and 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 at fast as possible

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

Advanced Analytics

Advanced analytics involves prediction, transformation, and anticipation of business into new markets and expand new growth opportunities.

Analytics services include –

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

Data is very critical and getting true value from data required a lot of techniques and analytics power beyond Conventional BI reporting.

It Includes –

  • Machine Learning
  • Statistics

Solution thinking

The word solution thinking widely used but it’s rare to understand what it actually means. It involves the following steps –

  • 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 and workable plan and make it happen.

Ethics

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

It involves –

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

Best Practises of Agile Analytics

Agile Analytics targets toward achieving few goals i.e. team collaborations, customer satisfaction, instead of last minutes surprise there should be iterative development, daily basis team engagement. To build Business Intelligence or Analytics application Agile Analytics comes into the picture to focus 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.

  • Agile Analytics follows incremental delivery after every few time period.
  • Change in requirements welcomed even late in development.
  • Software delivered frequently after few weeks iterations.
  • Give Business Intelligence experts an environment and support 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.
  • The track at regular intervals how to become more productive and adopt a result oriented approach.
  • Teams collaboration and self-management, work together to make plans and figure out 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 the highest priority of Agile Analytics 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, just follow these three questions while adopting Agile Analytics –

  • Will the goals of delivering customers value early and respond 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?