Data Science Strategy: Memulai Rencana Data Driven Transformation

Getting Started As organizations collect more data and build more accurate models, change managers will be able to confidently use them to prescribe strategies to enable organizations to meet their goals. They’ll be able to answer important questions, such as these: 62 PART 1 Optimizing Your Data Science Investment» » W Which stakeholders are involved? What type of change approach works with groups that share these characteristics? » » What risks are associated with programs that share these features? » » What are the techniques that accelerate the delivery of business benefit, and what are their relative costs? » » What is the cause-and-effect of specific types of investment? All these questions can be answered with data and will underpin data-driven transformation plans. Developing these sorts of metrics isn’t quick or easy. They aren’t one-off instal- lations, but rather multiyear commitments to capture data, build models, and refine dashboards. Establishing stable and reliable data sets takes time. Data quality is an issue everywhere, and so is the need for a common data language that allows organizations to know that they’re measuring what they intend to measure. This has been a problem for data analytics in other fields; there’s no reason to think that change management will be any different. Although it will take time, you’ll eventually be able to close the causal loop and make reliable predictions for how an action or initiative in a change program will impact a given metric. This will move investment in change from being an act of faith to being a data-driven decision. Change management will move from a project-based discipline that’s struggling to justify adequate investment to one that is advising on business outcomes and how to deliver them. This will lead to a decline in the one metric that is well known across change programs — the failure rate. And, as part of introducing data-driven change management, it should finally be possible to solve the great puzzle of why so many transformation efforts fail.