Big data analytics is an emerging technology process that more and more organizations are investing in. Before joining this trend, it is important to understand what your company will accomplish through big data analytics. You need a plan to guide you through your implementation. Choosing whether or not to implement a big data analytics process is an important decision, as it could require a large up-front cost, and could result in even bigger benefits if done right. So, it is critical to include a goal that you wish to accomplish through your big data analytics, and the means to develop the ability to accomplish the goal.

Maybe you have tried to implement big data in the past and failed, or maybe your want to do it the most efficient way possible. Whatever the case may be, agile will provide a set of core values and principles to guide your big data processes. Agile provides an adaptive approach, allows for fast feedback, and the ability to change direction in your delivery. This can all be accomplished, and the first step is implementation with these top 5 ways to introduce agile to your big data analytics.

  1. Build acceptance with a pilot project.

    Often times, IT can be opposed to a change in data-management policy. Agile will make data clearer for the business-units, and benefit IT as well. First, choose a project that is involved with both IT and a business unit, and of high value to the business. Getting both parties involved on a project that carries weight will gain their attention and show what agile can accomplish. Successes, failures, milestones, and impact should be recorded and communicated throughout the duration of the project. Due to this attention to detail, best practices can be documented, and the process can be improved upon during the next project.

  2. Implement agile data teams.

    Agile teams should be cross functional, including members from IT and business-units. Teams should be involved in important decision making, especially regarding data migration and architecture. Teams need to be fully immersed in the lab, testing and learning, without waiting on approvals or other outside distractions. Planned, direct communication between teams and leadership needs to be developed, and help needs to be given to teams in a timely manner.

  3. Keep data and technology infrastructure up to date.

    Big data might require improvements to current systems and technologies, including platforms, tools, and skillsets. These new requirements must also fit in with the rest of the existing IT structure. Existing strategies, integrations, and support may need rethought, as new infrastructure or strategies could be used in more than one place.

  4. Improve and emphasize communication.

    Communication within agile data labs differs from communication elsewhere in an organization. Monitor outcomes, and record a comparison of completed work against expected results. Share relevant information with other business units, as well as supervisors. Implement a platform where IT and business-unit leaders can discuss trends and topics in the industry. These communication channels exist to ensure all parties have necessary information, problems are solved in a timely manner, and the importance of agile can be made clear.

  5. Understand and utilize key performance indicators.

    Metrics that examine agile methods should be tracked and examined during performance evaluation. This is necessary to ensure the long-term success of agile methods. Weekly or biweekly work plans called sprints should be monitored by a project manager. Team members understand clear roles, and know what results they are responsible for. Key statistics relating to the team’s accomplishments and efficiency should be available.This allows for feedback to be given to the team, and for the team to make improvements.

With the right team structure, data and technology infrastructure, communication, and feedback, IT and business units will welcome the benefits of agile. It is important to have everybody on board, so that communication and processes are supporting the organization’s goals. Agile will improve your big data analytics, but first make sure you are ready.