Developing And Evolving A Data Organizational Structure To Meet Business Needs

I have built data organizations for the last 8 years. Before that, I built and led teams in traditional software engineering organizations. Organizational design and development require repetition to master. Most companies struggle because they don’t have the experience.

In February, I will be building my 21st organization (18 data and 3 traditional technology). Blackjack! You probably expect me to rattle off a rigid design and call it good, but that’s an amateur approach.

The biggest lie in data science is that there is 1 data science, 1 data scientist, and 1 data science lifecycle. The field doesn’t work that way, and I won’t insult your intelligence by dropping a single organizational chart down and calling it good.

I must provide a framework for data organizational development that will generalize across company sizes, maturities, and industries. Workforce engineering creates a specification for talent needs that aligns with business needs. Organizational design principles are implemented to build the structure. Hiring and internal training programs represent the execution framework.

The process begins by categorizing the business. The points are:

1. Business vs. Competitor’s Data Maturity

2. Opportunity Discovery Output: Use Cases and Needs

3. Continuous Transformation Strategy

4. Company Size and Budget

5. Percentage of Revenue Growth Dependent on Data Initiatives

6. Data Product and Infrastructure Roadmaps

Nothing works until the foundation is in place. Companies struggle when they hire data scientists and decide what to do with them. At the same time, most companies are midway into their organizational journey. Clients bring me in with some pieces in place, but they have realized they need structure to generate value and keep costs under control.

Categorizing The Business And Defining The Organizational Development Roadmap