There is no ‘one size fits all’ maturity level for data management projects and firms must consider factors including size, focus, core expertise, business requirements and constraints before embarking on such a projects, according to the latest white paper from LakeFrontData. Different capability levels are therefore appropriate for different business requirements and firms must be careful not to overreach themselves in these endeavours.
The white paper, Understanding and Optimising your Firm’s Data Management Capabilities Using Maturity Models, also introduces the vendor’s own data management capability maturity model. The vendor claims this model has been designed to quantify the capabilities and readiness of firms to successfully implement, integrate and operate their data management systems with consuming business applications.
Firms can use these models to benchmark their current capabilities and identify, prioritise and address shortcomings that are evident in their data management practices, says LakeFrontData. Its own model can be used to provide gap analysis in this way and it has seven capability areas and five stages of maturity for each of these capabilities, claims the vendor. The seven capability areas comprise: governance and organisation; policy and stewardship; business engagement process; data content and coverage; data quality management; technology solution and architecture; and operations.
“Our recommended approach would be to initially identify and assess your business priorities and primary pain points when it comes to data. At all stages of maturity, this effort requires and benefits from a collaborative investigation/effort among key stakeholders including business, IT and operations,” says the white paper.
The vendor cautions that enhancements in technology alone will not solve problems: “without the efforts around data stewardship, data workflow capabilities and governance, the longer term goals are unlikely to be met”, it elaborates. The sophistication level of the technology is often over-egged, according to LakeFrontData. It claims that firms can often select a less sophisticated platform than they have chosen to meet their research requirements and thus spend less on this area.
“In most cases, research’s instrument universe and content requirements are large; but the solution typically does not need to handle such things as matching multiple feeds, complex data cleansing rules and strict entitlement controls,” the vendor explains.
LakeFrontData identifies data quality as a much more difficult area to tackle with regards to these projects than technology. There are no vendors out there that offer to tackle every issue with regards to bad data, after all, says the vendor. This is where the tracking of metrics using a data management capability maturity model comes into play, it says: “It allows you to initially gauge your maturity, take corrective action and track your improvements over time.”
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