What is the first thing data leaders need to get right to avoid problems with self-serve analytics?
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Director of Data Architecture in Mediaa year ago
It depends on what kinds of problem. Generally, the operating model and creating services persona's of what capabilities can be self service based on the use cases, along with measurement plan are foundational items.Data & AI Practice Leada year ago
The first key step for data leaders to ensure successful self-serve analytics is establishing robust data governance. This involves setting up policies, procedures, and standards for data quality, security, compliance, documentation, ownership, usage, training, and monitoring. By doing so, organizations can provide a solid foundation for users to conduct self-service analytics while maintaining data integrity and security.Chief Data and Analytics Officer in Banking5 months ago
self service capability requires a certain level of discipline and comfort from the end users. Data leaders need to ensure that there is sufficient level of support, data literacy and solution that's fit for purpose for the organization. Self service capability is not a one size fit all. Nevertheless, to ensure consistency in usage and accessibility a self service standard or framework may be necessary.
Chief Data Officer in Software5 months ago
At the risk of sounding glib, the best way to avoid problems with self serve analytics is to avoid self service analytics. Success with self service depends on levels of data, governance, and process maturity that most D&A functions simply dont have.
Where Bronze means the report or analysis was generated by a self-serve community member with few demands for enterprise standard alignment. While it could be directional, insightful and interesting, recognize that the asset is not Gold certified.
Where Gold means that (1) all data elements and transformations have been reviewed and signed-off by the business data steward, (2) all data is sourced from an authoritative data source, (3) production of the report is automated and monitored for quality, (4) an owner is identified and ready to provide support through the lifecycle of the report.
Differentiating assets in this way will allow business users the speed and flexibility they need to create rapid insights, while differentiating these assets from Gold assets which require a high level of quality and accountability.
Of course, this type of transparency might be the 'first' or early thing to get right. However, in an ideal world we'd have complete and high-quality data assets for all to use, with guardrails and standards that prevent erroneous reports/analyses to be produced, and all on a platform that is monitored for quality and timeliness such that there is no longer a need to distinguish between 'Bronze' and 'Gold'. It will all be Gold. But we don't live in that world.