This blog series is written by guest blogger and co-author James Brenza. James is the Chief Data Officer for Labor Genome. He is also Vice President, Data and Analytics Practice at Pillar Technology. He provides over 20 years of technology leadership to drive the use of data and analytics for sustainable competitive advantage.
In this series James has been talking about implementing big data and analytics programs using a composite case study to illustrate the process. Each week he will focus on one of the seven steps giving specific examples to help illustrate how the tools can be used in a very practical manner. This is the second of the series that corresponds with the seven stage implementation model (shown below). More information on that robust model is available in the Innovative Leaders Workbook for Implementing Analytics Programs by Maureen Metcalf and James Brenza (scheduled for release in August 2014).
Define the teams: When leading an analytic initiative, you can start to build 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.
The different teams identified will serve very distinct purposes. The sponsors will be required to meet monthly to help ensure you remain aligned with the organization mission and the initiative vision. They will also be very effective at breaking down high-level barriers. The steering committee should be prepared to meet on, at least, a biweekly basis. Steering committee members that meet frequently will be uniquely positioned to more deeply understand your progress as well as help remove barriers. Another key role for the steering committee members is to provide ongoing communications and updates to sponsors and stakeholders.
The core project team must absolutely embrace all of the core functions necessary to implement the initiative. This will include ingesting large volumes of data, integrating data, establishing data quality, formalizing data definitions, building analytic models, assessing the strength of the models, tuning the models, training the models, and creating the new business processes so the business value can be realized. To be successful, the core team will also need to have extended team members. The extended team members will need to include subject matter experts for the data, the IT infrastructure, the statistical models, and the business processes.
Select team members: When selecting your team members, it will help significantly to create a selection matrix. The rows of the matrix should list specifically identified team member candidates, and the columns represent key selection criteria that the members must exhibit. The selection criteria can include areas of expertise, communication, teamwork, credibility, trust, culture, commitment and developmental perspective. As you assess each team member across the selection criteria, it’s important to make sure you have adequate coverage over all columns. If a candidate has many gaps across the columns, it’s appropriate to select a different team member, or find a second representative to help augment that team member. For any gaps in the coverage for either team members or columns, the leader should consider adding or substituting team members to ensure complete coverage.
For analytic initiatives, the selection of the data scientist is critical. You need to make sure you’re embracing strategic focus, data management, quantitative analysis, business acumen, communication, and problem-solving skills. Attempting to find all of these skills in one individual can be nearly impossible. So rather than hunt for unicorns, the leader can be more successful by focusing on building a small, highly cohesive team—of at least three individuals—to cover all of these areas.
Define the sponsor management plan: After the core project team has been assembled, they can create the sponsor management plan. The sponsor management plan will augment the detailed implementation plan with a list of activities for every sponsor and stakeholder, when an activity should occur, the outcome expected from that activity, and the specific messages that need to be delivered. This will be a precursor to the communication plan that will be developed in subsequent steps. After the plan has been drafted it can be compared to the original list of data sources, analytics, desired outcomes and measures to ensure all aspects of the initiative have been addressed.
How is leading a big data/analytics initiative different than other projects? So let’s take a moment to focus on what’s unique about data and analytic initiatives.
- Due to the analytic nature of the initiative, the team requires extensive balance far beyond traditional teams. These dynamic elements can include:
- Broad diversity of talents that must incorporate technology, analytics and business acumen
- Flexibility to collaborate and respond rapidly to opportunities and challenges
- Ability to simultaneously manage and be managed by multiple organizations.
- That balance needs to include the vision, technical and business acumen, communication, and extensive subject matter expert involvement. Many other initiatives do not need to encompass this many dimensions.
- This will create a unique challenge for the leader to make sure they’re keeping this in mind at all times and ensuring all team members stay fully engaged throughout the initiative.
Defining the team is one of the first challenges. In our next section, we’ll discuss how to assess the situation and strengths to help the team succeed throughout implementation.
Click to purchase the Innovative Leaders Workbook to Implementing Analytics Programs.
If you are interested in reading more by James, you may want to read: Evaluating Big Data Projects – Success and Failure Using an Integral Lens, Integral Leadership Review August – November 2013. You can also listen to the NPR interview that accompanies this paper including a dialogue between James Brenza, Maureen Metcalf, and the host Doug Dangler.
We also invite you to join the LinkedIn group Innovative Leadership for Analytics Programs on LinkedIn curated by James.
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Photo credit: www.flickr.com by Mike Pluta