Data and Analytics Operating Models
An effective operating model is essential for data leaders to successfully implement their data and analytics (D&A) strategy. Are leaders satisfied with their D&A operating models and are these evolving as needs change?
One minute insights:
- Leaders are mostly satisfied with D&A operating models but face challenges with data standards and documentation
- Most have hybrid data storage and a federated D&A function
- Diagnostic and descriptive analytics are common capabilities, along with self-serve analytics
- Most D&A operating models are undergoing changes driven by the need to improve data access and/or quality
Leaders are mostly satisfied with their D&A operating models, but lack of standards and documentation are pervasive challenges
Most respondents are satisfied with the information architecture (76%), organizational structure (75%), culture (73%) and personnel (71%) of their organization’s D&A operating model.
Lack of data standards (58%) and documentation (56%) are some of the most difficult challenges for more than half of respondents. Nearly half (49%) identified legacy data architecture as one of their most difficult challenges.
Our major challenge regarding analytics is pulling together data from the various systems, including granting access to the databases as well as determining how to relate the various disparate systems.
We find our current model effective for our small and less well resourced business units but less effective where business units have resources and try and bring their own resources in.
Hybrid data storage is the norm and most have a coordinated/federated structure for their D&A function
Over half (52%) of respondents have a coordinated or federated data and analytics function where teams informally work together, while 21% have a hub and spoke model with formally coordinated teams.
Our data and analytics operating model is evolving because of cloud environments.
Most analytics capabilities fall under descriptive or diagnostic, and the majority have self-serve analytics
Data is the key but we have issues in discovery of data. Our analytics team tries hard to generate meaningful insights but fail[s] to do so as our data is not in [the] right format at [the] right place.
Most D&A operating models are undergoing changes, usually driven by the need for better data access and quality
The most common driving factors for changing operating models are the need to improve data access (67%), data quality (63%) and cost efficiencies (51%).
It is very important to have a more flexible operating model capable of adopting new technologies.
We need to have [a] better Data and analytics model within the organization [so] as to have better data visibility, control on legacy data and overall data access.
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