Modern Data Governance Problems within Big Enterprises
Technological innovation in the business space has increased pressure on big enterprises to provide stakeholders with secure access to critical applications on demand. It becomes imperative for organizations to have robust modern data governance systems in place in such a situation.
Data Governance requires having “an existing practical and actionable framework to assist data stakeholders across any organization to identify and meet their information needs.” We’ve seen what poor data governance can do to big enterprises from regulatory fines, security breaches, to lawsuits. Classic examples of big enterprises facing the consequence of poor data governance include CitiGroup and Bank of America.
There are significant reasons why big enterprises struggle with modern data governance, and they are explained below:
4 Modern Data Governance Problems Facing Big Enterprises
For an organization to attain the name “big enterprise,” it means it has the needed resources in place. However, knowing how to use these resources and adapt them to its data governance framework is often a problem. Here’s why:
Lack of Data Leadership
Data Leadership is a challenge facing most organizations as it is a governance gap that is easily overlooked. Organizations need to prioritize people responsible for their data the same way technology acquisition to leverage and protect data is prioritized.
Having a Chief Data Officer (CDO) is now equally as important as having both a Chief Information Officer (CIO) and Data Protection Officer (DPO) in modern data governance. The Chief Data Officer fills the void of orchestrating the many data activities of an organization to maximize the impact on the business. This role is particularly important in organizations where ‘the business’ and “IT” are frequently at odds.
Using The Wrong Tools with Business Processes
For data governance to be effective, an organization needs to own the right tools. However, these tools alone do not guarantee adequate data governance to align with business processes. Operational alignment reduces the gap between business requirements, business processes, and software systems.
Some companies place the acquisition of data tools first without considering if it fits into the existing business process. When the tools fail to perform effectively, reactive steps are taken to adapt to the current business process to provide the acquired technology. Not only is this step often ineffective, but it can also prove costly.
There’s no denying the fact that data is king. In the business world, all decision making and operational activities rely on available data. But, at present, the problem for big enterprises’ problem has shifted from data availability to data storage and retrieval.
With survey results showing a potential data increase by 6.6 times the distance between the earth and moon, it has become increasingly important to have the right database. Such databases need to have:
– Optimal Performance
– Planned Downtime
– Compliance with Regulatory Data Protection Requirements
However, not all databases can guarantee the above, and it has become a standard practice for organizations to get performance tuning on SQL servers. This ensures that the organization is not at risk of losing its data or sub-optimal performance.
Redundant Policies and Procedures
Data governance, just like data, needs to be dynamic. Most organizations rely on policies and procedures to guide their actions on data. If the company fails to update its policies and procedures to align with data flows’ dynamic nature, it becomes difficult to utilize the data.
For every new type of data an organization comes across, it is essential for management to consider whether existing practices or policies would help analyze or store it. Reviewing policies and procedures can be quite cumbersome; hence, most organizations fail to update them. However, best practice suggests that organizational policies, especially those on data, be reviewed annually. The periodic review makes it easier to identify data risks and place mitigants as early as possible.
An organization’s top management is responsible for its data governance practices. Once top management does not have the right attitude or implement the right plans, it becomes a matter of time for the organization to have data governance problems. A defined data governance plan or strategy complemented with the right persons, processes, and tools help reduce the risks of data governance problems.