By definition, balance means a condition in which different elements are equal or in the correct proportions. Are you finding a balance between business agility and governance, data use and data governance?
Data governance is the overall management of data availability, relevance, usability, integrity and security in an enterprise. It helps organizations manage their information and answer questions about business performance, allowing them to better understand data, and govern it to mitigate compliance risks and empower information stakeholders.
Data Drives Decisions
Data is king, which is why organizations are practicing data-empowered decision making to help them improve processes and drive business value.
There are two types of enterprise data-related decisions:
- Macro decisions: Data stewardship and ownership at the organizational level. One example of this is being able to answer a seemingly simple question: Who owns data in your organization?
- Micro decisions: Decision making at the data element level. For example, who can define and what is the definition of “customer.”
Data governance plays a vital role in successful decision making on both of these levels. How? Data governance ensures that an enterprise’s data assets are understood and formally managed, guaranteeing trust in the quality and consistency of the data. Without a proper governance program, you won’t have a single view of your customer; and without that, your ability to make advancements, stay ahead of the competition and evolve, falters.
Pain Points that Data Governance Can Alleviate
Do any of these scenarios sound familiar to you?
- I don’t trust this data.
- Why do I have two different values for the same metric on these two reports?
- Where did this data come from?
- Who owns data at our organization?
- How many different ways are there to calculate this metric at our organization?
Show Me the Money ROI
We bring good news. The return on investment that data governance can bring to your organization is vast. Some benefits include:
- Accelerating management decisions involving multiple systems with centralized accountability, documented escalation process for issue resolution and improved information for management decisions
- Reducing duplication of data, number of system interfaces and manual data entry processes
- Increasing the accuracy and consistency of reports and dashboards leading to improved decisions, fewer expensive errors based on poor or inconsistent data and reduced rework from these errors
- Reducing compliance issues
But wait, there’s more. A study by NewVantage Venture Partners, states that 84 percent of companies surveyed launched advanced analytics and big data initiatives to bring greater accuracy and accelerate their decision-making. Furthermore, big data delivers the most value to organizations as it decreases expenses by 49 percent and creates new avenues for innovation and disruption by 44 percent.
Balancing Business Agility and Data Governance
In an ever-increasing digital world, organizations have an unquenchable thirst for data and the associated insights. Business leaders demand agility; they want the value and outcomes promised from investments in data and analytics capabilities―and they expect it to be delivered quickly.
However, governance is a necessary counter-balance to the business agility everyone craves. Governance helps to make sure that your data is: accurate, defined, understood, and is used securely.
Striking the right balance between agility and governance can be tricky, but maintaining that balance means stronger returns on your data insights.
Data Value Cycle
Across industries, data follows a cycle: it is created, it is leveraged, it delivers value back to business operations.
This cycle begins with business operations and customer interactions that happen every minute of every day. The data generated is diverse in type and quantity. In order for data from varied sources to be used in a valuable way, it first needs to be integrated and organized. Then, data can be analyzed and business insights can be generated.
Those insights are then presented to decision makers; when action is taken with data and associated insights, value is generated back to the business.
Good data governance and process tools support this data value cycle. For each step in the cycle, your organization should assign accountability and define a clear process for understanding the data and then making decisions for that data. A strong governance strategy will give you the framework to ensure that each step is frictionless and that value is returned to the business seamlessly.
Guiding Principles for a Data Governance Strategy
When you start forming the foundations of a new data governance program, or when you being to enhance an existing one, it’s important to keep these principles in mind.
- Data governance is not a quick project; it is a program that requires people, processes, and technologies to be successful.
- Data governance needs dedicated resources to be successful; it’s not just a part-time job.
- Data governance processes across all data systems, not just EDW/analytics. It has to be able to influence source/transactional systems data decisions and include all data in the organization.
- Start small. Pick a real problem to solve. and tell the success story far and wide. Use one victory to fuel another and another until it’s standard.
- Data governance champions must coordinate closely with other data stakeholders in your organization (i.e., compliance, legal, IS security).
- Invest in tools to document metadata, data lineage, stewardship assignments, and data policies/procedures.
- The business owns the processes that create data, therefore business and technical data stewards must work together for successful data governance.
Implementing a successful data governance program doesn’t happen overnight. Learn how to get started by checking out our Balancing Act of Data Governance Webinar.
