Tips for Building a Data Science Capability
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THE DEPLOYED MODEL<br />
As with the diffused model, data science teams are<br />
embedded in the business units. The difference is<br />
that the embedded teams in the deployed model<br />
report to a single chief data scientist as opposed to<br />
business unit leaders. In this model, also called the<br />
matrixed approach, teams are generally assigned to<br />
individual business units, though they are sometimes<br />
also assigned to broader product lines, or to mission<br />
sets comprised of members from several business<br />
units.<br />
This model often works best in organizations with<br />
medium-sized data science capabilities— ones<br />
that have a sufficient number of teams to handle<br />
multiple projects, but must still carefully target their<br />
resources. This model has many of the advantages<br />
of both the centralized and the deployed models;<br />
the data science capability is more of an organic<br />
whole, yet the embedded teams are close to the<br />
business units.<br />
CHIEF DATA<br />
SCIENTIST<br />
DATA SCIENCE<br />
TEAMS<br />
BUSINESS UNIT<br />
LEADS<br />
<strong>Data</strong> science teams are overseen by a chief data<br />
scientist and <strong>for</strong>ward deploy to business units.<br />
Because the deployed model is often seen as the<br />
best of both worlds, organizations may be quick to<br />
adopt this approach. But it is also the model with the<br />
THE DEPLOYED MODEL<br />
ADVANTAGES CHALLENGES PLACES EXTRA FOCUS ON…<br />
+ Shared benefits of both the<br />
centralized and diffused model<br />
+ <strong>Data</strong> science teams collectively<br />
develop knowledge across<br />
business units, with central<br />
leadership as a bridging<br />
mechanism <strong>for</strong> addressing<br />
organization-wide issues<br />
+ Access to data science is<br />
organization-wide, and close<br />
integration with business units<br />
promotes analytics adoption<br />
+ Project diversity both motivates<br />
data science teams and<br />
improves recruiting and<br />
retention<br />
+ Central leadership streamlines<br />
career management approaches,<br />
tool selection, and business<br />
processes/approaches<br />
+ Deployed teams are responsible to<br />
two bosses—staff may become<br />
uncertain about to whom they are<br />
ultimately accountable<br />
+ <strong>Data</strong> science teams may face<br />
difficulty being accepted into<br />
business units, where long-time<br />
relationships have been established<br />
+ Access to analytics-resources<br />
may still feel competitive between<br />
business units, and data science<br />
units risk alienating business<br />
units whose proposed projects<br />
are not selected<br />
+ Conflict Management. The chief<br />
data scientist should proactively<br />
engage business unit leaders to<br />
prevent competing priorities from<br />
becoming the data science teams’<br />
responsibility to resolve<br />
+ Formal Per<strong>for</strong>mance Feedback.<br />
Agree to per<strong>for</strong>mance goals at the<br />
onset of each project, and collect<br />
feedback during the life of project,<br />
including at its conclusion<br />
+ Rotation. Allow data science teams<br />
to work on projects across different<br />
business units, rather than within a<br />
single business unit—take<br />
advantage of one of the main<br />
benefits this model af<strong>for</strong>ds<br />
+ Pipeline. Regularly communicate the<br />
data science project pipeline,<br />
allowing business units to see how<br />
their priorities are positioned<br />
Aligning <strong>Data</strong> <strong>Science</strong> | 25