Seven Steps to Implement Analytics Programs
Ray Kurzweil, renowned technology forecaster and director of engineering at Google predicts that at the comparative rate for technological change in 2001, the twenty-first century will experience twenty thousand times more than did the twentieth century, accelerating techno-economic evolution beyond the ability to reliably forecast.
According to Thornton A. May, renowned futurist, “Organizations must look for new methods to harness this opportunity. They must truly transform their thinking to embrace more responsive strategies, align teams more dynamically and creatively solve resource shortages. Big data paired with analytics is one solution to harness these opportunities.”
How are you leveraging analytics to differentiate your organization? Are you creating strategic advantage? Are you creating strong financial returns?
“We use the same seven step process to implement analytics programs as other large scale implementations yet within each step, analytics projects have significant nuances not seen in other programs. These programs are implemented using Agile development methodology rather than waterfall. The different methodology changes the cadence of the implementation efforts as well as the pace of usable deliverables. It is also critical to address culture changes necessary to ensure changes ‘stick’.”
Seven steps using Agile development methodology
1. Create a vision
The success of analytic initiatives hinges on starting with a strong vision and realistic assessment of the organization’s ability to structure the effort. It also hinges on the leader’s ability to prepare the stakeholders for a journey, rather than just a traditionally structured implementation project. The transformation vision needs to focus on leveraging data and analytics to provide a business outcome that is not currently available. That outcome is best described in financial terms or customer centric metrics.
2. Build team
Start building your team after you’ve defined the vision and scope, and gained sponsor and stakeholder support. It’s actually more appropriate to say you can start building your teams (plural). Unlike more definitive initiatives, it’s critical to build teams that include the sponsors, steering committee, project team, extended team members, and subject matter experts. To help identify the necessary teams, you can review the data sources previously identified, the type of analytics to produce, the outcomes to be produced, and the measurements identified.
3. Analyze situation and strengths
This is a perfect opportunity to refresh the previously completed stakeholder analysis. By expanding it to include the broader organization responsible for the transformation, the team can create a plan that addresses barriers “top down” as well as “bottom up.” The leader can help ensure long-term success by driving messages in both directions. For an analytics initiative, data owners must maintain their confidence in the use of the data as well as modeling team to ensure the models can be operationally aligned.
4. Plan journey
As the team plans the actual analytic work, they should avoid the traditional “waterfall” implementation methodology. Data and analytic initiatives are extensively based on incremental discovery. The traditional approach of in-depth requirements, design, development, testing, and acceptance (which works very well for the implementation of a prescribed solution), isn’t appropriate for a process that must adapt to ongoing discovery. The team will make substantially faster progress by working in short “sprints” typically associated with Agile development methodologies.
While assessing the organization earlier in the process, the team should have a good understanding of the organization’s support for the change. If this is their first analytics initiative, additional communications will be necessary to build awareness of the approach and tools. For organizations that have leveraged data and analytics previously, the team still needs to provide ongoing communications to build understanding of the current effort and maintain awareness of the progress. As the team prepares for implementation, the need for adoption and operationalization will require significant communications. The key point to recognize is how the purpose of the communications will change throughout the initiative.
6. Implement and measure
The execution phase of an analytics initiative has some similarities to other efforts, but is also very unique. The similarity is the extensive use of systems and processes; the use of databases and servers is quite common. However, some of the types of databases or servers will be unique to analytic efforts. Due to the volume of data or use of statistical analysis, new infrastructure may be required. It is critical that the infrastructure is available and validated prior to attempting any model development. Establishing core infrastructure design and the data for initial analysis contains the risk as small models are built incrementally. Additionally, the data discovery methods may be unique to many team members.
7. Embed transformation
The measure of the outcome and process control cannot be taken lightly. It’s critical that the new methods are delivering the intended results. While analytic models are robust, it’s important to recognize they should be monitored and refined. The leader should guarantee that an ongoing process to monitor the results has been institutionalized. That process should include a feedback loop for ongoing model refinement and initiation of future initiatives.
Many leaders will use traditional approaches to organizational transformation when implementing analytics programs. Even the most effective leadership will fall short if the approach is not tailored specifically toward analytics leveraging Agile developmental methodology and building an Agile culture.
Building yourself as a leader who is effective in this environment will be a critical differentiator in your success and the success of your organization.
image credits: bigstockphoto.com
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Metcalf & Associates provides training, coaching, team building, and organizational transformation to help leadership teams become more innovative, and thrive in a rapidly changing environment. She is author of Innovative Leadership Fieldbook, a free on-line Innovative Leadership assessment and co-author of Leaders Workbook to Implementing Analytics Programs.
James Brenza, is co-author of the Innovative Leaders Workbook to Implementing Analytics Programs. He is Advisory Board Member, Chief Data Officer at InXite Health Systems, and Vice President at Pillar Technology, a data and analytics practice.
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