Optimizing VMO – Vendor Analytics
This is the second in a five-part series about optimizing VMO for modern IT organizations.
The VMO’s role is to ensure that vendors are delivering the best business value. Unfortunately, many either don’t collect and analyze vendor data or do so in an unorganized and ineffective way. This ultimately leads to a situation in which organizations are using providers inefficiently without maximizing their real business value. To build a more effective IT organization and get the most value from vendors, it is critical that the VMO develop more effective analytic strategies.
Why better vendor analytics are critical
The foundation of good vendor management is the ability to measure vendor performance and how it relates to business goals. Without this ability, IT leaders will be unable to make the best decisions about IT service sourcing. Analytics allow the organization to find the most cost effective options that support business unit needs. Without effective vendor analytics, companies will be making decisions blindly and vendors will overcharge and under-deliver.
Optimizing Your Vendor Analytics Strategy
Given the importance of effective vendor analytics, it is critical that companies work to optimize their data gathering and analysis process so that it better helps meet business goals. This means building dedicated vendor analytics teams that can look at data across the entire company and advise IT leaders.
Create a vendor analytics initiative – The first step of developing a more effective vendor analytics strategy is to create a well-funded initiative to gather data and analyze it. Many companies simply don’t devote the necessary resources to collect data and use it to extract information to make better decisions. The success of the initiative will depend on buy-in from other business and IT leaders. It is important to help them understand that data based decision making will allow them to reduce costs and improve efficiency within the organization. Those companies that aren’t actively performing analytics are already behind.
Tie metrics to business goals – Many companies that already have a vendor analytics system in place simply measure SLA performance without connecting those metrics to business goals. This creates a common problem where vendors are meeting their predefined targets but business units aren’t getting the performance they need. To prevent this, metrics must be tied to concrete business goals. Vendors shouldn’t just be delivering service at certain technical specifications, they must be delivering service that offers strategic value.
Perform cross-vendor analytics – In today’s highly cloud oriented IT environment, many vendors must work together to deliver the complete set of services that business units require. To account for this, the VMO should both be able to look at vendors individually and across the entire company. Cross-vendor analytics can help form a more complete picture of vendor performance and where problem areas lie. These metrics shouldn’t be tied to any one vendor, but rather to the end service or function. This also helps tie metrics to results rather than unproductive technical specifications. VMO staff should have a consolidated vendor database with access to metrics for every provider across the company. It is important to strive to move analytics away from operational staff and into a centralized system to form a more cohesive image of the company’s IT operations.
Vendor analytics form the backbone of an effective vendor management office. It provides the insight necessary to make better, more informed decisions about sourcing issues. IT leaders must work to build a centralized analytics team, dedicated to ensuring that providers are delivering business value and performing as efficiently as possible. This helps drive strategic goals by reducing costs and delivering better service to end users. Data is a major component of effective modern IT operations and companies can no longer afford to not invest the necessary time and resources into consolidated, comprehensive analytics.
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