Data Governance: The Secret to Great Resource Management

Managing the many moving parts of a modern professional services company can be almost impossible without a strong grasp on data. This is where data governance plays an important role, as it is a collection of the processes, policies, roles, standards, and metrics that help businesses use information to achieve their goals.

Strong Resource Management Needs Consistent and Accurate Data

In the world of resource management, executing strategic resource planning for professional services firms is significantly enhanced through proper data governance. And to ensure that successful data governance is fully leveraged, more and more businesses are creating resource management offices. The competitive advantages of a strategic resource management office include more predictable operations and labor force costs, as well as overall brand quality recognition.

At the core of these advantages is the use of resource-centric data management, which applies the focus of data governance directly to resource management needs. The resource data that business must capture and maintain in their data management includes:

• Resource Roles
• Resource Availability
• Resource Skills
• Cost Rates
• Resource Locations
• Language Proficiency
• Other Fields Personalized to Your Organization

Capturing and maintaining this information allows resource managers to better align resource supply with current and forecasted demand. Additionally, if individual contributors have data reflective of their abilities and preferences, resource managers can position them on projects that align with their skills and desires. Beyond improving project quality and hitting deadlines, this can also lead to improved employee satisfaction and engagement. 

High quality data governance can also contribute to project deployment success. If resource managers can match resource supply and demand accurately and quickly, the process to collaborate and approve staffing plans is streamlined. In addition, projects are more likely to start on time and the amount of change requests throughout the duration of a project is likely to be reduced.

Today, many organizations have bottlenecks around data governance. Whether it’s because there is only one individual administrator responsible for governance or crucial software has been improperly deployed, these bottlenecks can cause data inconsistencies and timing issues. Democratizing data governance with appropriate approvals is one approach to avoiding administrative bottlenecks. This means allowing your employees to upload and update their skills, career ambitions, and project preferences. Administrators can then approve these updates rather than update them for every employee, saving time and making data validation easy. Having a single source of truth and leveraging a technology provider to contain and manage this data is essential. This prevents duplicating efforts, stops confusion around data entry, and promotes stronger data integrations into other platforms that can leverage this data book of record.

Is Your Data Governance Model Right for You?

Take a look at your data governance model being used today. Are you experiencing the aforementioned bottlenecks or other challenges? What processes or systems would you change to improve your governance? If you had strong data governance, what competitive advantages would that unlock for your business? Could you execute more strategic resource planning and achieve organizational objectives with confidence?

The four most common data governance models are:

• De-Centralized Data Governance Model with Single Business Units – Typically consists of individual business users who create, manage, and use their own data.
• De-Centralized Data Governance Model with Multiple Business Units – Various business units may be sharing customers, vendors, and other interests, and as such share a set of master data.
• Centralized Data Governance Model – May be single or multiple business units with centralized maintenance of the master data. Business units, or consumers of the data, make requests and a central organization manages the master data.
• Centralized Data Governance Model with Decentralized Execution – Centralized data governance entity responsible for defining the data governance framework and policies, and individual business units are responsible for creating and maintaining their portion of the master data.

All of these have 3 elements of proper data governance in common. These are a policy framework designed by stakeholders (those in your organization who are custodians of your data) to outline how data will be treated, an actionable implementation plan defining tools and technology and assigning responsibility to data stakeholder, and commitment to ongoing assessment of policies and plans against business objectives.

Whichever model you choose, it’s important that you work to unite the whole enterprise around a common data governance strategy by involving different departments to better understand the current data situation and requirements, provide practical workshops on governance, and analyze the data problems being encountered.

Understanding what your data governance model looks like today and carefully mapping out its effects on your business is the first step that every professional services organization should take in improving governance. This knowledge will guide you in making informed decisions and calculated improvements to how your data is handled and applied. Whether this is in small changes, database expansions, or transferring to a brand new software solution altogether, knowing where your shortcomings are and where you need to be is crucial for the continued success of a business today.

Learn How Kantata Can Support You

Kantata is dedicated to providing data governance-backed resource management that provides a level of insight and control that every business needs today. Want to learn more about the Kantata Professional Services Cloud? Contact us today.