GRC
HR
SCM
CRM
BI
Expand +


Article

 

4 Steps to Build a Business Case for Data Governance

by Jim Barker | SAPinsider, Volume 17, Issue 4

October 10, 2016

Data touches every part of an organization, and drives all business operations and analytics. This means that properly managing, governing, and securing that data is mission-critical to the business. Even so, stakeholders need to hear a clear and persuasive case for investing in the comprehensive level of data governance required to ensure data quality while complying with regulations. Learn how to craft a convincing business case for an effective data governance program. 

Data is mission-critical to the business. It touches every department of an organization — from finance to the supply chain to master data — with all operations and analytics relying on it. However, it can be difficult to know where to start to improve data governance. And without a clear idea of where to start, making the case for investing in improved data governance capabilities can be challenging.

To gain the attention and support of your leadership team, you need to be able to clearly communicate the value of your data, which needs to be maintained through a data governance program that can ensure data quality and proper management, while complying with regulations to keep the data safe and effective (see Figure 1).

Figure 1 These are the six areas of activity you need to establish in your data governance initiative 


A 4-Step Process for Better Data Governance

To create a dynamic business case for your data governance program, follow this four-step process.

  1. Identify. Talk to business leaders who frequently leverage data across lines of business to pinpoint the organizational and operational issues in your current data governance practices.
  2. Document. Work with business leaders to document process-heavy data tasks, such as creating new materials, setting up new vendors, or creating complex sales orders in SAP systems. Document the creation and change of master data by each functional area and the related impacts across the business. Important impacts to make note of include the time spent by these functional areas in modifying data as well as any recurrent data quality issues, such as additional man hours, time delays, or additional costs.
  3. Research. Work with data experts within your organization to research potential solutions and create a strategic plan for implementing a data governance program. The information they provide can help you build a quantitative, meaningful case for data governance.
  4. Communicate. Present a clear and persuasive business case that outlines program vision and risk, business alignment, and the cost of missed opportunities caused by bad data. To make your business case effective, present a solution that includes a set of tools that address data quality, data visualization, data correction, and data integration capabilities.
Complete Visibility into Master Data

Building an effective business case is the first step to finding and implementing a solution that provides complete transparency for monitoring all master data elements, giving you the visibility to identify process inefficiencies before they cause business disruptions. These solutions, such as Winshuttle Studio, allow you to identify erroneous data at the point of entry and correct it in Microsoft Excel before posting to an SAP system. There are also process automation solutions such as Winshuttle Foundation that streamline data collection and provide powerful workflow capabilities.

To learn more about how to build a strong use case for enhanced data governance solutions, download the ebook “How to Build a Dynamic Data Governance Framework” at winshuttle.com/data-gov-ebook. For more information about Winshuttle Studio and Winshuttle Foundation, visit winshuttle.com/studio and winshuttle.com/foundation.

An email has been sent to:





 

Jim Barker
Jim Barker

Director of Product Management
Winshuttle



More from SAPinsider



COMMENTS

Please log in to post a comment.

SAPinsider
FAQ