If your company relies on tracking the costs and revenues tied to each asset in order to keep its competitive edge, then you are already familiar with the importance of asset management. Asset management policies ensure accurate cost tracking and valuations, cost-efficient assett maintenance, and early warning of an asset’s failure to perform. If your company treats its data as an asset, then asset management practices can be just as important.
Smart data asset management practices, like smart physical asset management practices, seek to minimize the cost of an asset over the course of its life cycle. Just as a manufacturing machine has a life cycle that includes its design, its commissioning, its maintenance, and its later modifications and decommissioning, data assets have a life cycle that needs to be planned for at the outset. Collecting data, analyzing data, storing data, and deleting data all incur costs; planning for these costs ahead of time will help you design a good data management strategy.
Another feature that sound data asset management has in common with sound physical asset management is its aim to keep the asset as profitable as possible. Companies use data analysis to adapt to a changing workplace. A data asset management plan should therefore be built around the assumption that as the demands of the market will change, the kinds of data that will be useful to your company will also change. Planning on “maintaining” your data assets by pruning them or adding to them as the market shifts will ensure that market changes never catch your company by surprise.
Organizations have significant operational inefficiency that has accumulated as redundant data has increased over time. Each new technology project, whether it be business or technology driven, is creating redundant data if the business is not actively involved in applying business acumen to the management of data assets. Much like debt to asset ratio provides insight on the health and vitality of an organization’s cash-flow pressure created by excess debt and associated service fees, a certified to un-certified data ratio tells us the balance between data that has been “signed off” as fit for use by the business, the quality is known and it is generally being managed well versus data that is in the dreaded “unknown” state. When the balanced of your organization’s data shifts too far towards un-certified the organization has a hidden tax or burden that is placed on it by way of inefficiency, duplicate efforts, rework and even risk in bad business decisions being made due to inaccurate data. This is a very simple view on one way that we can begin to view data as an asset, as it has very similar tendancies to how we analyze and account for other assets within an organization.
As more companies begin to embrace data as a unique asset for which they should invest to achieve future success, good data asset management practices will become increasingly important. Data Clairvoyance is leading innovation in the creation of valuation methods, ways to quantify and methodologies for managing data in more efficient and business driven ways. Click the button below to learn more.