The State of Distribution Intelligence
Olmstead conducted a study of distribution intelligence practices by organizing a dozen conversations with distribution intelligence professionals. Through these discussions, we identified six common themes that best describe why firms have found success in their intelligence practices and developed a model to rate asset managers.
The asset management industry’s data management efforts are generally in a state of transformation. We found staff dedicated to the propositions at all the firms we spoke to, but with a wide range of success as defined by each enabler. Below we have rated the industry and its state for each enabler.
Olmstead’s DI Impact Model assesses the industry on 6 key enablers of a best-in-class DI function
1. Aggregated Data
The industry is making extensive investments in blending data and making it accessible to the firm.
Governing data has become a much more common practice in the industry. While some protocols exist, many do not have defined data rules and definitions or implemented many data validations, leading to a lack of trust in the data.
Self-service has been a long-term goal for many years but has not been fully realized by the industry; user uptake of BI tools is on the rise, but still early days.
4. Executive Champions
As executives realize distribution intelligence’s impact on their bottom line, they are more willing to invest in personnel, technology, and processes.
While there is executive buy-in, in practice, distribution intelligence staff are not involved in many strategic conversations where their insight is critical.
6. Internal Engagement
Many firms only see 20% engagement with their technologies and processes. This prevents the firm from realizing the ROI on their investments. Best practices see a high degree of model collaboration between DI team and sales.
- Distribution Intelligence is Mainstream – the industry recognizes its potential impact and is materially investing in DI people, data, and solutions.
- ROI is Elusive – impact is anecdotal as DI is at the front-end of its Maturity Curve in terms of optimally moving the needle.
- Wrangling Dominates – while it is recognized that there will always be data wrangling, the DI A2W ratio (value-add business analytics to data wrangling) is heavily weighted towards the data chase at 80%.
- Unified Data is Lagging – the complexity and effort of marrying varied and complex internal and external data sets is the challenge.
- Culture Matters – without a culture that integrates distribution intelligence, data, and use of technology, no venture can be modernized.
- Evolution of Lead Gen – a top DI use case is maturing lead generation models as data has enabled more sophisticated models.