Enabling Consistent Data Management Through Data Assets Governance
Effective data governance is essential for businesses to gain valuable insights from their data and mitigate risks related to data quality, privacy, and security.
- Data quality: Data quality is a key element of data governance that ensures that data is accurate, consistent, and of high quality. This involves establishing clear standards for data quality and ensuring that data is regularly monitored and maintained.
- Data ownership: Data ownership is another important element of data governance that defines who is responsible for managing and maintaining data. This involves establishing clear roles and responsibilities for data ownership, and ensuring that data is properly secured and protected.
- Master Data Management: Master data management is the process of managing the core data elements that are essential to an organization’s operations.
- Data discoverability: Data discoverability is the process of making data easily accessible and discoverable by stakeholders within an organization.
By effectively managing these core elements of data governance, businesses can ensure that their data is accurate, secure, and well-managed. This enables them to make informed decisions, mitigate risks, and drive growth and innovation.
What is Data Governance?
The Data Governance Institute defines data governance as a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.
Data governance is the set of policies, procedures, and standards that govern how data is collected, stored, and managed.
Data Quality Dimensions vs. Metrics
We leverage an Enterprise Data Management Framework to gain understanding around your current people, process, technology and data.
- Business can set benchmarks to simply measure the quality of their data.
- These dimensions and metrics allow for business leaders to determine justifiable levels of data quality based on the most important business factors (data timeliness, missing data, incorrect data types).
Dimensions are categories of data quality issues with a shared reason why they are important and often have similar underlying causes.
- Why is the data in my warehouse not up-to-date?
- Why is the data in my operational tool six hours behind?
- Why does my dashboard take so long to refresh?
Metrics describe how specifically a dimension is measured, both quantitatively or qualitatively, and can be tracked over time.
- Difference between dashboard access time and latest refresh time of data
- Number of hours in which service level agreement was not met
- Average latency between ELT load and reverse ETL operationalization
What is Master Data Management (MDM)?
Enabling Consistent Data Management Through Master Data Management
Master data management (MDM) is the process of creating, maintaining, and utilizing master data across an organization. This process includes identifying and defining master data, creating and maintaining a central repository of master data, and ensuring data quality and consistency across all systems that use the master data.
- Data Consistency and Data Quality: MDM helps ensure that data is consistent and accurate across different systems and applications. It provides a single, authoritative source of key business data, which reduces the risk of data inconsistencies and errors.
- Cost Savings: MDM can help reduce costs by eliminating data redundancies and improving data quality. This results in more efficient processes, fewer errors, and lower operational costs.
- Regulatory Compliance: MDM can help organizations comply with industry-specific regulations by ensuring that data is accurate and complete.
- Unified View: MDM helps organizations create a unified view of their customers, patients, facilities…etc. allowing the organization to understand their from a comprehensive perspective and ultimately, better business outcomes.
Overall, MDM is critical for organizations looking to establish a solid data foundation, reduce costs, comply with regulations, improve decision-making, and drive business success.
Why is Managing Control Over Data Availability Crucial to Data Governance?
Managed control over your data availability leads to consistency in its governance, which is crucial to its usability, integrity, and security, supporting the growth and flexibility of your enterprise.
- Establishing a governing body or council with a defined set of mandates, goals, and processes will help to ensure that consistency is maintained.
- Automating your processes and enforcing the procedures that have been established will support consistency.
- Consistency in the data shared across departments, such as customer or part numbers, or vendor and employee IDs, will remove duplicates and standardize the data, reducing the amount of incorrect data entering your core enterprise systems. This will also greatly lessen the amount of chaos and confusion that can come from inconsistent data and information shared within a company.
Consistent data governance will allow your departmental management to make informed operational decisions, while strategic decisions that are made by the corporate management will be based on information that can be trusted as being a clear and correct snapshot of the operation. One version of the truth will benefit your entire enterprise.
What are the Benefits of Consistent Data Governance?
Every group or department in your private equity organization is going to need their own view of your data. For example, if they are looking at year-end sales, the focus will be on all the business your sales team closed in the previous year. The group controller is going to have different line-of-business needs, and will be looking at all closed business that has been booked to the ledger. A portfolio company CEO will get a different answer to a query involving those year-end sales figures depending upon who he or she is asking. The private equity managing partner might like to look at each individual portfolio company or their entire portfolio rolled up to benchmark metrics. Establishing a common set of data parameters that your whole organization follows will not be simple, but consistent data governance will have a huge beneficial aspect to your organization.
A few of the benefits of consistent data governance are:
- Less time spent on report reconciliation and inconsistent performance results.
- Less data entry errors due to data being manually entered into multiple systems
- A consistent set of definitions for key performance indicators across your enterprise
- Data being distributed across your organization consistently
- Better management of key corporate master data, such as adds/deletes/changes
- Eradication of bottlenecks in your change management process that can delay the approval of adds/deletes/changes
- It will ensure accurate procedures with relation to regulation and compliance activities and increase the transparency required in these areas.
- The value of your data will increase, and resolution of past and current data issues will be supported.
- The costs that can be associated with other areas of data management will decrease and the overall revenue of your enterprise will increase.
Why Do You Need a Good Policy for Data Governance?
Your business will need a strong policy for your data governance so that as training and education practices around the management of your data assets is implemented, direction and compliance within the organization is clear and consistent. A good data governance policy will help maintain effective processes when it comes to managing all the information of your business.
As you analyze all the internal and external factors that can affect your business objectives, a data strong and effective governance policy can be formulated which will determine the implementation of your strategy when it comes to handling your data and will help your enterprise gain better control over all your data assets, including methods, behaviors and technologies that surround the proper management of data.
A strong data governance policy considers your goals. Establish goals and performance metrics for each area listed here:
- Define key roles throughout your business that are related to data. For every system, choose stewards to manage that data and focus on quality; the owners who will make decisions and define quality standards, and the IT team who will provide technical support and monitor all compliances.
- Document and inventory all your sources for data and review the inventory regularly to ensure it is up to date.
- The purposes for which you collect data should be identified and communicated to your team and clients and reviewed regularly.
- Policies should be in place that define how your records will be created, maintained, and disposed of.
- Quality and access. Appropriate staff members should be assigned the responsibility for data quality and to conduct regular audits while all permissions and access should be clearly defined.
- Data security policies need to be defined with respect to access and sharing of data. Include a risk management section that outlines risk and probabilities.
You want to know that the formal management of all the data assets within your organization is efficient and cost-effective and is bringing the most return on your investment possible. A well-thought out policy will do just that by ensuring that all your team members know who can take which actions, what methods are to be used, and under what circumstances, reducing wasted time due to unclear direction or incomplete sets of information available.
How your policies define your business will influence your ability to manage it, keeping everyone focused and in sync, striving towards the same goals.
If you are interested in more information, complete the form below to have an LBMC sales team member contact you.