柯y Challenges For Data GovernanceDespite benefits of high-quality data available, most companies are still in the process of developing their data governance systems

The power of data in driving business growth is well-documented and effective data governance allows organizations to get the most benefits from their most valuable asset. With high-quality data, businesses are able to gain insights for better business decisions, and increase efficiency and productivity. Moreover, data governance also protects the business from compliance and regulatory issues which may arise from poor and inconsistent data. With a set of processes that provides the framework to effectively manage data assets throughout the enterprise, data governance ensures the quality, integrity and security of data as it stands against established internal data standards and policies.

然而,尽管有这些好处,大多数公司基于“增大化现实”技术e still in the process of developing their data governance systems.

Gartnerpredicts that through 2022, only 20 per cent of organizations investing in information governance will succeed in scaling governance for digital business.

Due to roadblocks when implementing data governance programs, many companies lag behind in implementing data governance policies that ensure company data can be used for decision making and supports critical business processes.

Here are five common obstacles organizations face when establishing data governance frameworks:

Data silos

Inflexible legacy data systems often hinder the free flow of data and information across the digital ecosystem. This makes it difficult to share, organize, and update information within the organization. With siloed, stale and disorganized data, establishing data governance, whether it involves tracing data history, cataloguing data or applying a granular security model can be challenging.

Poor data quality

According toNASSCOM, India's analytics market is expected to grow at a CAGR of 26 per cent reaching approximately $16 billion by 2025. However, despite the investments directed towards big data and analytics, many organizations are not seeing sufficient results. Data governance involves oversight of the quality of the data coming into a company as well as its use throughout the organization. Data stewards need to be able to identify when data is corrupt, inaccurate, old, or when it is being analyzed out of context. They should be able to set rules and processes easily to ensure that company data can be trusted. The ability to trust data is a cornerstone for data-driven organizations that make decisions based on information from many different sources.

Data opacity

Data governance requires companies to achieve data transparency. Information such as what kind of data does the organization have, where does this data reside, who has access and how this data is used, should be accounted for. However, legacy systems obscure the answers to these questions. A data management process should be implemented to establish strategies and methods for accessing, integrating, storing, transferring and preparing data for analytics.

Unsecure data

With the proliferation of data sources both inside and outside enterprises, data breaches are also on the rise. Like successful data management, data security hinges on traceability. IT teams should be able to track where the data originated, where it is located, who has access to it, how this data is being used, and how to delete it. Data governance sets rules and procedures, preventing potential leaks of sensitive business information or customer data so data does not get into the wrong hands. However, legacy platforms create siloed information that is difficult to access and trace. Without a consolidated data repository, siloed and untraceable data increases security risks.

Lack of control over data

Businesses often begin thinking about data governance when they need to comply with regulatory policies such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI-DSS) and the US Sarbanes-Oxley (SOX) law. In India, companies need to comply with the provisions of ICLG. All these regulations require organizations to have data governance structures that show traceability of data from source to retirement, data access logs, and how and where data is used. With set regulatory standards, companies are able to protect sensitive information from getting into the wrong hands and establish control over their data.

Most digitalization and modernization issues stem from poor data management. Organizations must take a closer look at their data governance policies and identify what needs to be prioritized. Breaking down data silos, ensuring data quality and clarity, securing data and achieving regulatory compliance are vital steps toward data governance. By addressing these challenges, organizations are laying the groundwork for the success of future digital transformation plans.