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Agile Strategy For Data Collection And Analytics

However, the trend of online businesses in New Jersey is gaining momentum but still people are facing huge digital development challenges due to slow servers, uncooperative CMS’s, or bad mobile experiences impacting their online success extensively. The new digital strategy developed in the software/web community that can make an online business a huge success is known as Agile Development.

The concept of agile development can be directly applied to data collection, analytics, and optimization. Now-a-days companies consider to outsource data collection services for better data optimization. For this reason, data collection service providers practice an agile development approach to meet their client’s requirement. With this, companies can leverage the benefits of rapidly accessing data, and safeguarding it against consistent web variabilities.

Learning from Feature Driven Development

Though, an Agile Development focuses on several development methodologies and practices, but one of the efficient methods I would like to discuss is- Feature Driven Development.

In Feature-Driven Development, we begin with planning and strategizing the overall project requirements as a whole. Once done, then the project is divided into separate pieces, where the designing and development of each piece is done as a component that can be added back to the whole. This way, the project’s components work as a one complete and fully functioning unit.

Phased Implementation (Not Iteration)

Though many people believe Agile Development is known as the iterations made upon products. However, to some extent, it is true but it is completely opposite of what an ideal Agile approach is. The iterations, as u know, are made at the times of planning to implement a new feature. With this, we are not emphasizing on adding layers upon existing features, but are planning to see business objectives as a whole.

Step 1: Develop an Overall Model

Planning: Initiate with planning to get better, accurate and more actionable data.

Understanding the system: It is all about digitalization which involves lot of moving parts. Analyze how your digital presence affects your physical business and your overall business strategy. There could be some components which can be affected by the collected data.

Seek multiple perspective: Evaluate data needs and pain-points of your business. It is critically important to analyze that how implemented processes and decisions you made can affect your business. Consider interacting with the people who make decisions on the data available.

Be strategic: It is always good to define your goal initially and work accordingly. You can’t achieve Agility unless you prepare yourself for the future possibilities. Identify your digital presence and the occurring changes and check that how redesigning and platform changes can affect your overall strategy.

Step 2: Define the Parts of Your Plan

This could be really exciting as, in numerous ways you can divide your analytics strategy. Consider dividing the project into discrete and independent part, which can be further regrouped on the basis of similarity and development process.

By Web Property and Section: Generally a company develops a huge web presence. Chances are, there could be different sections on web with different objectives, out of which few can create a bigger impact on your organization’s goals and thus, you consider to prioritize it differently.

By Data Use: Also, data-collection needs can be based on end use. For example, there is an ecommerce store where different teams are handling different tasks including merchandising, planning, social campaigns, etc. and requires different data for each initiative. Consider such data requirements as different parts of your strategy.

Step 3: Prioritize

This is the one of the critical steps in terms of lowering costs and shortening time to data-driven action. With right planning and right decisions, you can leverage maximum benefits. Keep an eye on your ultimate goals and time. Make your development team understand the time it will take to implement the code and collecting data. Also, don’t forget to estimate overall cost that would be required for data collection.

Step 4: Implementation Cycle (Plan, Implement, QA)

Here the data collection begins. Complete a feature and release it, as done in Agile analytics. A proper planning defines the way of data collection and thus, improves the overall process efficiency. Always be clear about the documentation and stay open to questions.

Quality Assurance: Ensure that the reported data is accurate and clear at the time of implementing code on the site. Remember that, this implementation should work well, in case changes are required, be modest, just as in implementation.

Start Optimizing: Though Agile is not simple, if you want to speed up the time to data-driven action, plan it up front. Stay proactive in your approach. Only a planning can make an agile data collection efficient.

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