What Changes Must Happen On The Business Side To Support Data Science? Part 1
Part 1 covers the initial assessment, creating a technology model, business model transformation, and operating model transformation. In part 2 I will cover the transformation roadmap and timeline.
This two-part post series is a partial summary of one section from my Business Strategy For Data Scientists course. It is tough to personalize recommendations to your specific business needs, so I created a framework you can follow to build out a Transformation Roadmap and Timeline.
What I'm describing is what I do for clients. I take this framework and customize each piece based on:
Opportunities identified by the current core business strategy and objectives
Threats from existing and potential competitors
Growth plans and investors’/board of directors' expectations
The ability of the business to transform and current maturity level
Realism is required, and that starts with an honest assessment of the business and current Machine Learning capabilities. I look for disconnects between the technical organization, senior leadership, front-line business units, and customers. They all have different opinions on how well the business is leveraging Machine Learning and how much value the technology generates.
I select the AI Strategy KPIs that best fit the business's strategic goals and opportunities. Those bring the company's actual state into focus and clarify the scope of change necessary to achieve the business's goals.
Significant needs and obvious value must justify the change. Transformation happens over years (it's really never done), is complex to manage, and impacts every part of the business.