Master Data Management
Four common master data governance challenges
Competition among businesses today is colossal, and to avoid going obsolete in this fast-paced scenario, organisations must ensure they utilise their biggest resource - data - to the highest possible extent. Thanks to steady data governance processes and strategies, businesses can efficiently minimise their risks, limit errors, and boost revenues to stay ahead of the curve.
What is data governance?
Data governance is a framework of strategies, principles, standards, and metrics that ensure the effective and efficient use of data throughout the lifecycle of an organisation. It establishes a set of processes and responsibilities that ensure the quality and security of the data utilised by an enterprise. Data governance helps businesses maintain the roles and responsibilities related to the data and ensures accountability across the hierarchy. It establishes codes of conduct and best practices in data management, ensuring that the concerns and needs beyond traditional data and technology areas - including legal, security, and compliance - are addressed consistently.
The importance of data governance
A coherent data governance process is paramount for any enterprise that works with big data. It aids in defining the business processes, strategies and tactics, and organisational responsibilities. A solid and reliable data governance framework can effectively help minimise data errors and misuse. Data governance is important for businesses and organisations because it offers:
- Improved data management: With data governance, organisations can establish a systematic and comprehensive understanding of data. Better data management also means lower costs, higher flexibility, and increased business revenue.
- Enhanced data quality: Data governance helps businesses to organise data assets, leading to higher accuracy, quality, and credibility. Businesses can use this high-quality data to improve their various internal and external channels and analytics.
- Stronger regulatory compliance: Data governance brings about an all-inclusive, detailed data structure, ensuring that the organisation effectively meets all the industry standards and regulations.
Common data governance challenges and their solutions
The volume of data collected is growing exponentially. Therefore, developing an efficient data strategy is crucial. To do so, good data governance helps create a vital framework to balance data collection and management. However, embarking on a data governance journey brings about certain challenges for enterprises. Here are a few of them.
- Unclear goals: If your company’s problem statement and goals are unclear, their data governance solutions would be too. Before defining a data governance strategy, an organisation must define ‘why’ they need it in the first place. Are improving customer experience, managing enormous chunks of data, or preventing frauds and scams the primary goals of data governance? While any data governance process could help meet all these goals, the most efficient strategy is one that has well-defined orders of priorities and requirements.
- Data silos and sequestered information: When an organisation grows, so does the data. Sometimes, this large influx of information from new, unfamiliar sources can result in highly siloed or isolated data. Data silos can also arise from internal friction between various departments and due to the lack of a proper channel to exchange information. Because of such data silos, organisations are unable to realise a successful data governance model since analysing data accurately becomes a cumbersome process. Data governance works better with a single, detailed compendium of business information instead of multiple disconnected datasets. Your enterprise must ensure that a proper pipeline is set in place for data sharing and collaboration across all internal and external platforms.
- Limited resources: Every data governance process requires budget, resources, and workforce. Your business may prioritise other sectors over data governance, which directly affects the quality of data governance output. Investing in ample resources is always profitable in the long run, and you must find ways to properly allocate necessary budgets and manpower for your data governance processes.
- Poor leadership: A data governance program would be ineffective without strong leadership that can direct the governance team. Your enterprise must have a data literate head to oversee the governance program and help develop policies and regulations that must be followed across departments for seamless execution of all the processes.
How can Infosys BPM help?
Our Master Data Management (MDM) solutions offer end-to-end solutions for your business organisations with quick diagnostics and comprehensive insights into the data quality levels. Infosys BPM has noteworthy experience in MDM and data governance across several key domains.
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