Data has become the lifeline that is necessary for most businesses to function with increased efficiency and operational performance. Organizational leaders and staff are racing towards developing effective data governance models that focus on high quality data in order to drive the business forward. Systems such as ERP, CRM and supply chain management are dependent on the high quality and complete data across the entire organization.
These data challenges have led to many organizations overemphasizing a data governance model at the expense of refocusing the business to implement a data driven approach.
Organizations should, therefore, strive towards avoiding data governance hype and achieving a healthy balance between high quality data and the proper implementation of data driven processes for the good of the business.
Defining data governance
Everywhere you turn, you will hear organizations channeling resources towards data governance. Data governance spans across many different functional areas, and each business can have its own precise definition for the term.
In a general sense, data governance can be described as an organizing framework for managing organizational data in order to align strategy, define objectives, and establish relevant policies for handling enterprise information.
Approaching data governance in terms of data quality
Data governance involves data quality, ownership and security, metadata, and analytics processes. In most organizations, the word “governance” tends to throw off staff, who can become confused with what data governance entails in the organization and what their specific role is.
In order to clear up the role of data governance in the business, it should be defined more in terms of data quality and how higher quality data can advance the efficiency of the business. High data quality should be the fundamental aim for any data governance campaign, and it should be the key area of focus. In fact, research by Gartner showed that poor data quality cost organizations an average of $8 million a year.
Challenges to the proper implementation of data governance
Many businesses fall prey to spending too much time on defining a data governance model, such that they end up hindering their organization from becoming data driven.
There are several common mistakes that businesses make when putting together a data governance model. These include:
Designing data governance
Many organizations that are getting hung up on data governance often fail to properly design their data governance model. Designing such a model means recognizing your company culture, decision-making processes, operational setup, and ownership environment. Each organization needs to understand how it processes and shares information across different functional areas in order to implement a process where data can be used to drive the organization forward.
When designing a data governance model, specific challenges and benchmarks that span across functional areas should be clearly defined. For example, if data governance can lead to more secure data for the business, or better communication processes with customers, then the model should be defined in terms of specific goals and objectives that can be targeted by the entire organization.
Approaching data governance as a finite project
The hype surrounding data governance makes most organizations implement the model as a separate and discrete initiative. Often, this approach does not intertwine with the rest of the organization’s processes.
In order for the implementation to be successful, it should be systematic, clearly defined, and continuous. Changes in information type and data quantity should be accommodated into the framework, and decisions on accessing and processing new incoming data should be incorporated into a formal structure.
Failure to build up on existing processes
Most data governance models tend to ignore current systems that have been put in place to handle and process data. Current decision-making staff who handle data processes should have their input considered when designing a data governance model.
The input of these professionals can help define the scope of the model and the complexities that will be involved in data governance within the organization. Ignoring the unique perspectives of internal data professionals can lead to an unbalanced data governance model with no internal checks and balances.
Achieving data governance success
Organizations need to go beyond the stage of designing a data governance model. They need to begin implementing data driven strategies that will enable them to tackle their analytical problems. Companies that are successful at data governance normally have the following processes within their strategy:
People, policies and procedures
An effective data governance process involves the combined efforts of people, policies, and procedures working in collaboration. People are important in working together to provide input into defining the data that needs to be governed, as well as designing the relevant policies and procedures for the implementation of the model.
The policies are the rules and regulations that will surround access and usage of data, and procedures are the governing structure that will guide the organization in accessing, processing and improving data usage.
Treating data governance as a product
Organizations should approach data governance in the same way as they approach designing a consumer product. The product development process is continuous, futuristic, and targeted towards specific needs.
Data governance should be implemented in much the same way, with a focus on efficiency, business goals and objectives, and a long-term approach that is aimed at continuous growth and improvement.
Data governance as part of the governing structure
Data governance involves the process of how data is collected, processed, and accessed for use in business initiatives. Due to the scope of this framework, data governance should not be implemented in isolation. Rather, it should be a core component of other governance procedures within the organization.
IT, corporate and data governance should all work together towards a complete data governance process.