This is part 3 of a multi-part series on the Analytics Operating Model.
In part 2 of our blog series, Equipped for Success: Analytics Governance and Organization, we explored the paramount importance of governance and organization structure to a new analytics capability. As this series continues, we explore strategies for developing analytics talent and capability in an era where data is abundant, but the ability to quickly derive business value has yet to be fully unleashed.
Universities and data science boot camps are fervently churning out data science graduates in response to what seems to be an insatiable business demand. Study after study predicts the long-term demand for data science expertise will continue to rise. While many institutions and companies focus primarily on developing the technical side of their analytics horsepower, those that take a more cross-functional approach will deliver business value on a faster, more sustainable basis.
What exactly does a cross-functional approach to analytics talent and capability look like? Consider a capability framework and sourcing approach that stretches beyond traditional technical expertise, as illustrated in Figure 1.
Figure 1. The cross-functional approach requires a combination of business, change management, and technical talent and capability to deliver value.
This framework recognizes technical expertise as a necessity, while also emphasizing the importance of business knowledge and change management competence as key enablers of value generation:
- Business Domain includes understanding business drivers, deep knowledge of critical business processes, data and systems, and identification of use cases and business impact
- Change Management includes stakeholder management, increasing organizational awareness of analytics capabilities, storytelling with data, and driving adoption of integrated business processes that leverage analytics
- Technical Expertise includes data architecture and data modeling skills, data science and expertise (algorithms and machine learning), analytics toolset implementation, and deployment experience
Conventional roles in business and technology are rarely sufficient for delivering valuable and insightful analytics solutions. These roles must be transformed into the pieces an analytics team with horsepower – one that can deliver meaningful products.
Begin by conducting a clear-eyed assessment of existing talent: understand when talent can be developed internally and which gaps must be supplemented from outside. The assessment should consider demand as well: Do the demands call for a more simplistic descriptive approach or something more advanced, requiring prescriptive models? The intersection of both technical and business skill sets must also be considered. In this cross-section, you will find employees with mathematical or statistical knowledge, technical experience in structuring and using data, business domain understanding, and change management skills. Arguably, change management skills can make the difference between a team that delivers from day one versus a team that takes months, or even years, to produce something useful to the business.
Developing the capability of any analytics organization is driven by two, often opposing, considerations:
- How to align team capability with future analytics demand
- How quickly current demand needs to be met
Often the answer lies in some combination of internal training, selective hiring, and even more selective sourcing from analytics vendors. Identify the essential roles within both business and technology to the capability. If analytics is new for your organization or enterprise, create a plan to progressively fill and develop the following roles:
|· Data Engineer||· Data Architect||· Data Scientist|
|· Business Analyst – Data||· Business Analyst – Process||· Visualization Specialist or Story Teller|
|· Facilitator and Manager||· Business SME– Power User||· Business SME – Citizen Data Scientist|
When analytics demand exceeds your team’s capability, try bridging the gap by either sourcing analytics vendors or cultivating power business users with self-service tools to catalyze their work. If you pursue the third-party route, be an informed consumer. There is an overwhelming chorus from service providers, operations contractors, and many others who have pivoted from their core offerings to include something with the term “analytics” in it. Consider evaluating analytics providers based on criteria that augment your internal capability. Below is an example of guiding questions for evaluating providers.
Figure 2. Evaluate analytics providers against standard criteria, like the ones above, to help navigate the seemingly saturated market.
Lastly, beware of the temptation to hire or contract data scientists, without first building the necessary supporting cast. When building an analytics team, think beyond technical expertise to include business domain knowledge and change management expertise so your product is meaningful, relevant and, most importantly, usable by the business.
Click here to learn more about our Analytics & Information Management practice. Want to continue the conversation? Contact us at firstname.lastname@example.org, and follow the rest of this series as we dig deeper into each element of the Analytics Operating Model.