Eashani Krishna’s Post

Are you struggling to implement data governance in your organization? You're not alone. Many businesses face the challenge of balancing continuity with change when it comes to data management. While businesses prioritize operational stability, the Data Management Office (DMO) aims to improve data quality, security, and compliance through structured data governance. To overcome this tension, a collaborative approach is key. The DMO should work closely with business units to understand their needs and constraints. Clear communication about the benefits of data governance, such as enhanced decision-making, risk mitigation, and regulatory compliance, can help in gaining buy-in. Additionally, phased implementation and pilot programs can demonstrate value without overwhelming the organization. By aligning data governance initiatives with business goals and involving stakeholders throughout the process, organizations can achieve a harmonious balance between maintaining operational stability and enhancing data management practices. This approach ensures that the transition is smooth and that the benefits of data governance are fully realized. #data #data_governance #data_management #governance

I saw many topic regarding to classification for data and roles for departments in the project but not considering the owner for the data how responsible and accountable to his data. In implementing a successful data classification project, it is crucial not only to assign roles and responsibilities across departments but also to empower data owners with the knowledge to classify their data appropriately. The classification of data should be based on a CIA assessment (Confidentiality, Integrity, and Availability), helping data owners determine the sensitivity and importance of their data. considerations include: 1. Educating Data Owners: Data owners must understand the importance of data classification and be able to assess whether their data is highly sensitive, moderately sensitive, or non-sensitive. This understanding ensures data is appropriately classified and handled. 2. CIA Framework Application: Data classification should rely on the CIA assessment with data owner. 3. Departmental Accountability: While it is important to define the responsibilities of various departments in protecting classified data, the focus should be on guiding data owners to actively manage and classify their data in accordance with company policies. 4. Continuous Monitoring and Assessment: Data classification should not be static. Regular assessments, on a quarterly basis or as dictated by company policy, are essential to ensure data remains accurately classified and security measures are adjusted as needed. #DataProtection #DataClassification #DataOwner

Change Control Part 6: Impact Analysis Conducting an impact analysis for data is crucial for understanding the potential consequences of changes to data structures, systems, or processes within an organisation as well as the effects on data quality, integrity, security, and regulatory compliance. Here is some guidance for how you might structure your approach to conducting such an analysis as part of your Change Control process: 1. Identify Key Stakeholders: Begin by assembling a multidisciplinary team of stakeholders from various departments, including IT, data management, operations, and compliance. 2. Define Objectives: Clearly outline the objectives of the impact analysis, such as assessing the effects of data changes on business operations, compliance with regulations, and customer experience. 3. Inventory Data Assets: Create an inventory of all data assets, including databases, applications, files, and data flows, to understand the scope of the analysis. 4. Assess Dependencies: Identify dependencies between data assets, systems, and processes to determine how changes in one area may affect others. 5. Analyse Risks: Evaluate potential risks and challenges associated with the proposed changes, considering factors such as data integrity, availability, security, and regulatory compliance. 6. Quantify Impacts: Assess the magnitude and extent of impacts on various aspects, such as business processes, workflows, reporting, analytics, and decision-making. 7. Mitigation Strategies: Develop mitigation strategies to address identified risks and minimise negative impacts, including contingency plans, data backup procedures, and training programs. 8. Communication and Documentation: Communicate findings to stakeholders and decision-makers, providing clear and concise documentation of the analysis results, recommendations, and action plans. By following these steps, you can confidently cover all bases and maximise the usefulness of your analysis. In turn this can lead to better informed decision-making, risk mitigation, and a successful implementation of data-related changes. Do you routinely conduct Impact Analyses? You could save a lot of unnecessary disruption by doing so. Add it to your Change Control schedule! Next week in Change Control Part 7: Documentation and Audit Trail #data #dataanalytics #datagovernance #datasystems #damauk

Data Governance: What is #Data_Governance? Data governance refers to the overall management framework that ensures high data quality, availability, usability and security within an organization. So unlock the potential of data governance for effective management and strategic utilization of organizational data assets. Why #Data_Governance? So data governance is required for data quality assurance, risk management and security regulatory compliance, support for decision making and strategic planning, and operational efficiency and consistency.

Learn how document management systems integrate into your data strategy for enhanced security and regulatory compliance: #DocumentManagement #DataStrategy

🚀 New Blog Post Alert: Ensuring Data Integrity and Accuracy in Record Keeping! 📊 In the age of data-driven decision-making, maintaining data integrity and accuracy is crucial for any organization. My latest blog post dives into the essential practices for ensuring reliable and precise record-keeping. I cover everything from establishing robust data governance frameworks to leveraging automated data management systems! 🔍 Key Takeaways: -The importance of a comprehensive data governance framework -Regular data audits and reviews -Implementing data validation and verification processes -Utilizing automated data management systems -Employee training and awareness programs -Strong access controls and security measures -Reliable data backup and recovery plans 🛠️ Build a solid foundation for effective data management and confidently make informed decisions. Check out my blog post to learn more about prioritizing data integrity and accuracy in your organization. 👉 Read the full blog post here: https://lnkd.in/gnwGAAaM #DataIntegrity #DataAccuracy #DataManagement #InformationGovernance #BusinessIntelligence #DataSecurity #Compliance #RecordKeeping #AutomatedSystems #EmployeeTraining #DataGovernance Feel free to share your thoughts and experiences in the comments! 💬

What are the Data Governance Controls? Your data governance controls are the procedures and technical measures you put in place to ensure that your data governance program is effective. These controls should be designed to meet the specific needs of your organization. Your data governance controls should include: • Access Control — Establish procedures for granting and revoking access to data. • Change Control — Manage changes to data, including who can make changes and how those changes are tracked. • Version Control — Track different versions of data to ensure that everyone is working with the most up-to-date information. • Auditing — Monitor compliance with data governance policies and procedures. • Data Retention — Establish procedures for archiving and deleting data that is no longer needed. • Data Backup and Recovery — Put procedures in place to protect data from loss and ensure that it can be recovered if it is lost. Feel free to share your insights! #datagovernance #controls #data #datamanagement

📊 Data Governance: Ensuring Data Quality and Compliance 🗂️ Data governance involves managing the availability, usability, integrity, and security of data in enterprise systems. Effective data governance ensures that data is accurate, consistent, and compliant with regulations, enabling better decision-making and risk management. #DataGovernance #DataQuality #Compliance #BusinessIntelligence #KairosCoders

Entity management, the process of organizing and maintaining information about entities (e.g., customers, products), is important for data accuracy and efficiency. However, unwanted situations like duplicate entries or outdated information can still arise. These issues often stem from: 1. Inconsistent data entry: Manual data entry can lead to typos, inconsistencies, and missed fields. 2. Lack of standardization: Without clear data format guidelines, variations can creep in over time. 3. Inadequate data cleansing: Unreviewed data accumulates errors and inaccuracies. Here's how to minimize these risks: • Standardize data entry: Implement data validation rules and controlled vocabularies to ensure consistency. •Schedule regular data cleansing: Proactively identify and rectify duplicate or outdated entities. By prioritizing data quality through proactive entity management, organizations can avoid the complications associated with inaccurate information. #regulation #compliance #data #management

Changing a law firm’s practice management system typically occurs every ten to fifteen years. The drivers for change varies however, progressive firms invest in this change more often to capitalise on the benefits associated with modernisation, intuitiveness, security and integration capabilities.   Often the greatest risk when transitioning to a new system is the data conversion process. Given your data is an invaluable and unique asset it is important to ensure that it is managed carefully and mapped correctly so that it behaves as expected in the new system. If not, major operational problems can arise including being unable to bill your clients or relying on inaccurate information regarding the firm’s performance that can lead to poor decision-making with dire outcomes.      Some of the data conversion risks we are engaged to mitigate include: Poor system performance when the data is not de-duplicated and cleansed to be fit for purpose   Processing errors at the time of live operation leading to loss of confidence in the system at the outset   System imbalances when financial data has not been validated for quality assurance   Excessive effort spent on multiple test conversions leading to project delays and higher costs   On-going data legacies that require manual workarounds for the life-cycle of the new system. To avoid data conversion risks and costly complications, the engagement of trusted specialists is recommended. Specialists come equipped with proven methodologies and tools, and have developed the skills and experience necessary to overcome the complexities, which will surface regardless of the system(s) involved.   If you would like to learn more about our data conversion services, visit us at https://lnkd.in/gawx6SPq or give us a call.   #dataconversion #riskmanagement #trustedspecialists #modernisation