Organizations collect, process and store enormous amount of data, A growing number of applications/systems, which support various lines of businesses, have been collecting more and more data through various channels. Mergers and acquisitions have made the situation even more complex and confusing when it comes to the management of data and business processes.
Figure 1 shows an example of the way data assets grow over time.
A lack of data management practices (or data standardization processes) results in challenges that are faced by both IT and business. Often, individual applications or systems maintain and manage their own data. This results in data silos or data hubs (logical or physical). The solutions around data redundancies, metadata management and related data issues are always tactical in nature, whereas data anomalies are fixed by temporary patches or left unprocessed. Thus, issues around data integrity, consistency and accuracy make the data unreliable; typical factors impeding clean and conformed data include lack of standards, typos and duplicates, applications being ported from different platforms/languages, lack of standardized quality processes, historical/outdated data, unknown data and many others.
Businesses need clean and conformed data to make better decisions. That’s why data and information quality has become an important factor in making better business decisions.
Such decisions are critical for the following areas:
- Customer services.
- Product optimization.
- Regulatory compliance.
- Managing operation costs and risks.
Without reliable data, business intelligence generated over a period of time is questionable. Inaccurate financial reports and audit reports will not only receive penalties but will have financial implications as well.
Thus, processes/policies and rules that ensure enterprise-wide data asset management are needed. It becomes difficult to maintain and manage data without policies, strategies and dedicated efforts by the team across business functions. Data governance seeks to solve these problems, through information policies, data rules, guidelines for managing key data elements and assigning roles for accountabilities and responsibilities.
What is Data Governance?
Data governance is the processes or policies which guarantee that important data elements that can be trusted. A framework or set of processes is implemented throughout the enterprise, empowering the right people to take control of data and processes. A data governance program also includes technology, which helps identify and fix data issues, resulting in fewer negative events due to poor data. It’s also about the communication, identifying common language that will bridge the gap between IT and business managers.
In short, data governance is about management of the availability, usability, integrity and security of the data. Some of the key focus areas are data quality, data integration, policies around privacy, compliance and security, the data warehouse and BI, architecture integration and analysis and data access, in terms of archival, retrieval and storage.
A data governance program will have certain drivers, such as:
- Identifying data anomalies and fixing them, particularly with regards to key data assets around important business processes.
- Optimizing business processes and defining data rules.
- Designating the right people responsible for information quality and security.
- Creating policies for handling data, in case of initiative changes.
- Coordinating with key business stakeholders to ensure that information policies support business objectives.
The data governance program and initiatives around information quality management need to involve stakeholders representing a cross-functional team to fulfill the objectives.
Stakeholder Involvement and How to Start Data Governance Initiatives
Issues around data management and information quality can be addressed using data governance initiatives. These initiatives need business and IT support, which means stakeholder involvement across the teams. These initiatives start with bringing people together for mutual understanding and educating them about doing the right things in the context of information quality.
Figure 2 shows how different stakeholders within business and IT see data governance differently
Starting a data governance initiative requires answers to three questions around benefits of the program:
- How will the program increase company revenue?
- How can the program lower costs?
- How can the program reduce the risks and address compliance issues?
It would be wise to start with initiatives where data needs to be fixed to minimize the risks or where business users have voiced complaints. Additional initiatives that can be considered may include a CRM implementation, a new data warehouse, BI initiatives and analysis of complaints management.
Information quality surveys can be launched across the line of businesses, with questions about data related to important business processes. For example, the billing and dispatch departments can be asked about the validity of the addresses of the customers, in terms of format. Does the customer get the communication or shipment in time or it is lost or returned? The inventory department can be asked whether the data always reflects the correct inventory amounts and types. The marketing department can be asked about their confidence in contacting potential customers using the email or phone numbers listed in the system.
Prepare a case study for analysis of each problem statement, followed by a detailed business impact assessment. With this, information quality ROI can be calculated, which will significantly help make the case for whether data governance initiatives will be beneficial.
Generating awareness among the business stake holders about latest trends, risks and competitors’ initiatives regarding information quality may also help in selling the importance of data governance program.
