5 Pitfalls to Avoid When Designing an Effective Data and Analytics Organization
To deliver real business outcomes from data-driven programs, an effective data and analytics organization is required. But what are the main design challenges of the data and analytics organizational model and what do data and analytics leaders need to do to overcome them?
- Low level of data and analytics maturity: An inability to understand the levers of change will result in complications when trying to leverage data and analytics across the enterprise.
- Difficulties influencing cultural change: Cultural issues are at the root of many failed data and analytics initiatives, yet most enterprises do not assign explicit responsibility for culture.
- Shortage of skills: The desire to deliver data and analytics solutions is often slowed down by a shortage of the right skills and people.
- Unclear business value: A disconnect between the data and analytics organizational model and the business outcome fuels uncertainty about the potential value of data and analytics.
- Lack of data literacy: An overreliance on a formal framework makes it unclear what data is needed or even available, while its quality frequently remains unknown.