As your business grows, so do your data needs…and your data woes. Without proper governance of data, the same data you need to make decisions can start to have a breakdown in quality, reducing confidence in its accuracy and thus, its value, which always impacts data-dependent processes. And during the digital transformation age, every aspect of your enterprise is a data-dependent process.
Time spent consolidating and validating data wastes money and inhibits the organization’s ability to make time-sensitive, data-driven business decisions. That’s why it’s important to have practices from the start that make it easy to keep data organized and up to date.
Creating a Scalable, Automated Data Solution
A well-known organic pet food company was growing rapidly and realized that a complete Data Governance solution was critical to facilitate that growth and to maintain its brand status. At the time, there were no real processes or rules around data, but the company leadership was open to suggestions.
The solution was a three-phased approach starting with data quality monitoring, and eventually growing to include data lineage and data cleansing and matching. This process leveraged HANA, SLT, SAP Information Steward (data quality component), SAP Data Services and SAP Data Quality as an integrated data governance solution.
Automation was the first key component of the new Data Governance process to proactively detect data quality issues. Next, so decisions could be made rapidly, data insights were critical to communicate timely data quality status to key stakeholders. Finally, to ensure the process would stick and continue to be leveraged as the organization and its needs evolved, useful documentation to guide users had to be created to improve user confidence and provide answers when any numbers were questioned.
Moving to a Proactive Approach to Data Management
From a Data Management Maturity Model perspective, the company moved from Reactive (tactical fixes, no specialist tools, resource-intensive, limited scope) to Proactive (culture change, specialist tools, master data validation, fix root cause).
The Data Governance team, which hadn’t existed before, became the central data maintenance hub for all SAP master data; everything now moved through the Data Governance team.
There was greater confidence among the user community in the quality of the data, which enabled better and faster decision making.
Not only did this Data Governance project organize the data in a valuable way, it also created cost savings by eliminating costs in the hundreds of thousands of annual expenses for manual data quality reporting and deduplication cleansing. In addition, the marketing and sales organization gained transparency into the cost of sales that was not available previously, enabling what had before been impossible optimization of sales channels.
Key Tenets for Any Data Governance Project
This case study illustrates the importance of having proper governance of data. Whatever your data situation, you can apply these nine key data tenets.
1. Perform assessments to understand the key needs of your organization.
Identify gaps from a capability standpoint to fill once the Data Governance strategy is in place. Consider what technology is already in place and what additional tools you will need.
2. Develop a comprehensive data management strategy.
Every organization has different needs. So tailor the Data Governance strategy to your environment: What do you want to achieve? 100% data quality? Robust governance? Compliance with specific regulations? New market entry? Re-organization? Cost savings, and if so, where, when, and how? Ultimately, the more detailed you get with aligning your strategy to the business objectives, the more effective you will be in realizing those goals.
3. Engage with senior management from Day 1 and always have your elevator speech ready.
If the decision-making process is complex and involves many stakeholders, it will take extra work, so start early to get their support. Be able to explain, from a “what’s in it for me” perspective, the aim of the project. Data doesn’t exist in a vacuum; it is the foundation from which business value is realized, so build broad alliances and effectively message the common purpose.
4. Identify and communicate resources and tech requirements upfront.
Identify and invest in the best resources to make the project a success. Communicate why each is a benefit to the team. They’ll want to know how it impacts them or disrupts their workflow, so be ready to show how much smoother things will go as a result of these data initiatives.
5. Engage with business stakeholders, especially Data Owners and Data Stewards.
Communication isn’t a nice-to-have; it’s essential. Data Owners will need to be able to identify education and training opportunities, so keep them in the loop throughout the project. Engage and leverage those relationships. If your organization doesn’t have the role of the Data Steward fully defined, this in itself is an opportunity to use a Data Governance initiative to train and mature the relevant stakeholders to embrace Data Stewardship as a business function.
6. Establish a Data Governance council with each functional area represented to ensure that data-related decisions are made universally.
No one team should feel left out from contributing to the project, so by having a council, you are inclusive of all needs and opinions, you are able to get your findings validated collectively, and you are enabled to get buy-in from all stakeholders for any changes or harmonizations that may be required, the need for which is frequently more common than not.
7. Establish definitions and rules for the Data Lifecycle and how it is accessed and used from creation to retirement.
What is the process to get data created or updated? Who needs to know about which change to any data? Who should have access to what data? How often is the data cleansed? How is the data protected? How is the data reviewed and removed from use? These are just some of the questions that require clarity to ensure that your organization is in control of its data rather than at its mercy.
8. Develop and foster a culture of Data Stewardship to ensure your data governance initiatives sustain and continue to generate value.
Data Stewardship as a culture takes concerted effort and attention to implement and to mature into a potent arm of business intelligence. Data Stewards are champions that lead during and after the project to ensure that data is getting the attention it needs and that the organization doesn’t fall back into bad data habits. The success of any data project should be measured in its lasting effect one or more years out, so do not overlook the opportunity to provide lasting business value by creating a robust Data Stewardship framework.
9. Develop and publish meaningful metrics to show improvement and progress against business objectives.
Leadership will want to see substantive evidence that your Data Governance initiative is positioned to succeed, so make sure to establish a regular cadence to share metrics and reports. Metrics should always refer back to the underlying business objectives to demonstrate attainment against business value. Data initiatives frequently have the ability to start generating value during the initiative well before completion, as any incremental improvement in data quality and availability can generate immediate insights to drive better decision making. Use these opportunities to refine the plan, solicit feedback and clarify evolving business needs to maintain your Data Governance initiative’s focus and value proposition.
Data Governance shouldn’t be an option at the bottom of a long list of priorities. As an organization grows, the need for rules and processes around data only increases. Starting early will ensure that, even at scale, data is manageable and effective in its use. So rather than being an afterthought, transform your enterprise data to be the key driver to unlock additional capabilities across your organization.