By 2022, the total volume of enterprise data is estimated to be over 2.02 petabytes. Therefore, companies working in data-intensive environments need strong data management capabilities to monitor, manage, store, access, secure, and share information in a streamlined and standardized manner.
You will want your business to have best practices in data governance
By consolidating and scaling more data sources and assets, the right data governance architecture can help organizations maximize the value of data, minimize risk, and eliminate unnecessary operating costs.
An investigation report from Gartner reveals that 55% of organizations lack a standardized approach to data governance and identify it as the most significant barrier to achieving data goals. However, a carefully designed data governance strategy can solve many problems in terms of consistency, data standardization, and better business results.
Here are five key best practices for successful data governance:
Define the right process in alignment with the right people
Bridging the gap between strategic teams and data processes is key to building an inclusive, data-driven organization. To master the data and control it from a centralized location, it is essential to create a unified database, as well as a master data set.
This structure can standardize how data is used in various areas of the organization. Defined roles are essential to any data governance program, and assigning levels of ownership is essential. So the right people can access the most relevant data when they need it to deliver the best insights and get the best results.
However, the data governance team should be cross-functional, from data managers to senior executives. The team should include a group of subject matter experts, data security experts, project managers, and data governance visionaries who can provide a front-line and cross-functional experience across the entire organization. ‘organization.
Build the roadmap that guarantees reliable data at all times
Organizations looking to improve decision-making and business outcomes must align their business goals with creating and implementing high-quality data. According to a harvard business review report, 47% of data records are created with critical errors that affect work.
Companies often lack processes to validate data characteristics, including data accuracy, uniqueness, completeness, relevance, and timeliness. Additionally, organizations need to put in place data quality controls to develop better insights and meet required data standards – tackling and identifying erroneous or inadequate data.
Organizations performing analyzes without quality data can lead to inaccurate interpretations and decisions. In addition to data goals, any additional goals that are specific to the business or necessary to achieve specific organizational goals should also be considered.
However, ensuring better and cleaner data should be paramount for companies aiming for digital transformations and business analytics.
Consistently become compliant with regulatory requirements
Meeting essential compliance and regulatory requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is crucial to every data governance assessment. Ensuring that organizations consistently meet all levels of regulatory requirements is critical to minimizing risk and reducing operational costs.
Compliance ensures that data processing meets applicable regulatory requirements, whether from government, accreditation bodies or the business itself. These regulations are designed to protect data against misuse, loss and theft.
According to a survey conducted by erwin and UBM (erwin dotcom), 60% of organizations believe that regulatory compliance is the most critical factor in strengthening data governance. Regulations vary widely by geography and industry, making it difficult to manage them.
However, organizations must follow best practices to ensure compliance and protect against compliance failures that could lead to tarnished brand image and subsequent downfall of the business.
Assess risks at all levels
The need to protect data and reduce risk is a critical factor in driving data governance in many organizations – with data security and data privacy being the most visible. According to a survey by Gartner42% of data and analytics leaders do not assess, measure or monitor their data and analytics governance.
Data security starts with understanding the risks of data spread across sources such as data lakes, data warehouses, and individual silos. Additionally, it is important to protect data across the organization to control data leaks, which mostly come from inappropriate data access permissions.
The organizational structure should ensure appropriate access to data while maintaining adequate confidentiality. Ever-evolving security threats are unpredictable.
The best way to protect against data loss or theft is to be aware of security risks, detect them and respond to them in time.
Bring the Right Data Management Platform to the Table
Regardless of the industry, unlocking the potential of data is only possible through good data management. A data management platform is the backbone of any enterprise data strategy. choose the right data management platform means selecting the long-term success of the business.
A forward-thinking organization must look to the future, aligning its data governance expectations with its technology stack to implement robust quality controls, risk assessments, and continuous monitoring and testing mechanisms . This can be done by opting for an artificial intelligence (AI)-based cloud platform that can deliver value, adapt to data needs, and scale with organizational changes.
A cloud-based data management platform will allow organizations to quickly connect to robust, cost-effective functionality and avoid the overhead required for on-premises servers. It can also radically simplify complex legacy operations, reducing operational costs, improving agility, and achieving breakthrough performance that delivers real business value.
Protect the future with data governance
Implementing advanced procedures and appropriate policies is key to achieving reliable data governance outcomes. Every business needs to unlock the value of data and drive trusted business decisions, regardless of size and industry.
A recent study by McKinsey & Company reveals that companies invest on average between 2.5% and 7.5% of their IT expenditure in data governance. This will improve the strategic, operational and tactical levels of an organization and bring value, scale and speed to the governance process.
Additionally, to achieve futuristic analytics, visualization, and automation goals, enterprises must strive to improve data quality (pimcore dotcom) and data access.
Organizations must also realize that an integrated data governance architecture is critical to improving decision-making and ensuring successful outcomes. Data should be well documented and easily accessible; moreover, it must be secure, compliant, and confidential to manage risk and improve business decision-making.
Data governance is not a one-time strategy; it is an ongoing process that includes organizational duties and responsibilities, regulatory requirements and industry protocols. Data, one of any organization’s most important assets, impacts decision-making and risk mitigation, and it must be governed accordingly.
Additionally, senior executives and management should ensure organization-wide data awareness and quality improvement initiatives. Ultimately, they need to understand that data governance is unique to every organization and data needs to be designed accordingly to meet business requirements.
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