Automatically Archive Data After A Specific Period Of Time
Having to archive or anonymize data manually can be tedious. Now you can set a defined retention period for personally identifiable data contained in records.
For example, you can establish rules to anonymize or archive specific fields in a claim record after a certain period of time once the claim is closed.
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Data Protection: Governance Risk Management And Compliance
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Routledge & CRC Press eBooks are available through VitalSource. The free VitalSource Bookshelf® application allows you to access to your eBooks whenever and wherever you choose.
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What Is Data Classification
There are two concepts within Data Classification:
Data Category is based on the identity of the owner of the data or type of data subject. Data Categories might include the following:A. Business Information
D. SensitiveE. Restricted
Data classification needs to be defined within your organization and has to be constantly updated. Data classification is a living process. In terms of contracts, this needs to be communicated to all of your third party vendors. How is data treated? What kind of access control is implemented around data? How is the data processed or stored? What are the audit rights for this data? 4th and Nth parties need to be identified upfront and your third party vendor needs to have their own TPRM program and all terms/conditions laid out and issued to their third parties.
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Types Of Data Governance Tools
Implementing your data governance program can be made much easier with the help of various technology and software tools. Here are some of the core types of data governance tools that you should consider using.
- Data cataloging: Software that uses automated data discovery to create catalogs for better organization and standardization.
- Data management: Collects data from multiple sources and provides a master view for data governance purposes.
- Visualization: Consider implementing a tool that helps visualize your entire data ecosystem in a single, easy-to-use interface and report for data owners.
- Data lineage: Trace data lineage by parsing code from data sources, applications, tools, and source code automatically.
- Policy management: Some software automates policy enforcement and assignment of business rules to ensure full compliance with your governance program.
- Threat detection: On the security front, youll want software that alerts you to potential threats, before your data gets compromised or stolen.
These are just a few key tools that can aid your Data Governance efforts. By working with an experienced compliance partner like Varonis, you can gain better clarity on the tools and technology stack that best suits your needs.
Why A Framework For Data Governance Software Is Needed
Its become a cliché, but todays organizations run on data. And we are producing ever-increasing amounts of it every day. In fact, over the last two years alone, 90% of the data in the world was generated. With the rise of our connected society and the advent of the Internet of Things, that volume of data will just keep growing faster.
What a data governance framework does is put a structure in place for an organization to manage it all. Without such a framework, organizations are more likely to treat their data haphazardly, developing policies around such issues as data privacy and data security reactively and randomly, rather than proactively in a systematic fashion. One can even think of data governance as an insurance policy, helping companies mitigate any risk that might arise from their data and reducing their liability.
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Data Governance And Automation
When data governance was just oriented around compliance, the scope of data and the governance requirements were controlled and prescriptive. This narrow focus made it possible to use manual processes for governance and stewardship activities. In the new world of business value-based data governance the sheer scale of data, and the collaboration required across all organizational functions makes automation critical to success.
We now have data lakes with petabytes of data, being updated in real time with streaming sensor data, social data, and mobile location data. There are tens of thousands of users accessing the data across finance, sales, marketing, service, procurement, research and development, manufacturing, logistics, and distribution. Its at least a thousand-fold increase in scale and complexity. At this scale the only way you will keep up is with AI-powered automation.
Gartner predicts, through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated the addition of machine learning and automated service-level management. During Informatica World 2019 we showcased AI/ML innovations for data governance and privacy, including automated:
- Domain discovery
- Mapping of business terms to technical metadata
- Linking of data across structured and unstructured sources
- Data quality rule generation and execution
Data Governance From The Actuary And Risk Management Perspectives
Considering the practices and current and future legislation in Turkey and around the world, the Solvency II framework and new International Financial Reporting Standards regulations are areas where there has been discussion recently from the actuary and risk management perspectives as well as the data dimension. Given that the framework and regulations are data-focused, and the right way to apply them depends on data quality, the importance of data governance can be seen. Figure 1 summarizes the framework and regulations.
Considering the responsibilities of actuary and risk management functions within the Solvency II framework and IFRS regulations, and risk managers general job description, the quality of the data used for all calculations, modeling and reporting is very important and critical to outcomes. Since the data used for calculations, modeling and reporting are kept on information systems in all institutions, ensuring data quality is mainly the data owners job, but the IT department is also responsible because it retains the data.
Actuaries and risk managers, the parties who use the data produced by the business functions and employ the data to produce new data, are indirectly responsible for assessing and questioning data quality. Their responsibilities continue as data owners when they create, model and report the data.
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Why Data Risk Management Is Important
When business data is exposed or put in jeopardy, there are both direct and indirect costs associated with the malpractice. When data is at risk, a company can be liable for expenses to cover:
- Repairing the damage an attack wreaked on its IT infrastructure
- Costs associated with leaked assets, including regulatory fines and costs for legal consultation
- Increased manual labor for the time taken to contain an incident
- Data center downtime and lost business continuity
- Lost brand value and reputation
Data risk that isn’t known, managed, and mitigated often end up as data breaches, which are particularly costly. According to the 2018 Cost of a Data Breach Study by Ponemon, the global average cost of a data breach is $3.86 million, the average cost for each lost or stolen record containing sensitive and confidential information is $148 per record, and the costs for breaches have risen year-over-year.
As costs associated data risk continue to rise, protecting and maintaining data is essential for organizations.
What Is Data Risk Management
Youd be in the minority if you havent seen a headline or been personally affected by one of the thousands of data breaches that have occurred over the last decade. To name a few:
- In 2013, Yahoo was breached, exposing more than three billion accounts on its servers, which included users names, birth dates, phone numbers and passwords.
- In 2018, 500 million Marriott guests had their names, mailing addresses, phone numbers, email addresses, passport numbers, dates of birth, gender, Starwood Preferred Guest loyalty program account information, arrival and departure times, and reservation dates exposed after a breach.
- Hundreds of millions of Facebook users have had their passwords exposed due to Facebook storing the information in plain text, which was visible to more than 20,000 employees.
While large organizations are the ones likely to catch headlines, research by Beazley Breach Response Services shows that 71% of ransomware attacks in 2018 actually targeted small businesses.
Though data breaches arent the only types of data risks an organization needs to manage, they are often the most visible. To avoid becoming another headline, lawsuit, or unsecured organization, data risk management has become an integral component of IT infrastructure.
What is data risk management, and how can you implement it in your organization?
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How Wyntec Secures Your Data
Wyntec provides Microsoft-driven tools that discover, classify, encrypt, and protect data. This ensures that even if someone does get hold of sensitive information either accidentally or intentionally, it will be impossible for them to share outside authorised. We also enable cyber security protection and data backup to prevent your business from being held to ransom. The tools we use secure your office network, staff working at home, and even mobile devices so if they lose a phone, we can remotely wipe any sensitive documents from the device.
We also understand your internal processes and roles and embed them in a structure that enables easy data governance management. You dont have to become an expert in governance systems to get governance right: you just need a partner that understands both technology and your business at a deep level.
This means achieving data compliance becomes relatively easy, as most data compliance hinges on your data being secure and your processes being documented and followed. The net benefit is reduced risk for your business and greater comfort for your staff, that they can work securely, anywhere.Visit our Cyber Security page for more on how we can protect your data.
Success By Integrating Risk Management Principles Into Your Data Governance Policy
RUNNING AN EFFECTIVE DATA GOVERNANCE PROGRAM
Learn how to plan, design, build, and maintain a successful Data Governance program with our live online training October 24-27, 2022.
Data Governance enables us to harness the right data for purpose of raising an organizations confidence and trust in their data. There is a definite value associated with leveraging the right data for business functions. At the same time, there is also risk related to data and its operations. This risk is a business risk and should be business owned, as is the Value. So, are your thresholds for Data Quality, Metadata Management, security and privacy, Architecture, and content management in line with the risk appetite and tolerance of your enterprise? What does a zero appetite for privacy incidents and loss of price-sensitive data mean to data management and Governance? How often do your organizational risk priorities, appetite, tolerance and limits change annually or bi annually?
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Q: Is Data Governance A Program Or A Project
A: Data governance should be viewed long-term strategic business program, not a single short-term project. Implementing data governance requires structural changes to a companys current data policies and practices, in addition to redefining the roles and responsibilities of data handling personnel.
What Is Data Governance In Risk Management
Data governance refers to the practices and processes associated with data management. This can include a companys data handling practices and policies, data storage, and encryption practices, along with data collection and deletion practices.
When examined relative to risk management, data governance brings to mind the General Data Protection Regulation . This is an EU-based regulation that applies to any organization that does business with a citizen of a European Union nation. GDPR has stringent regulations related to data governance, including the right to be forgotten a regulation whereby an organization is required to delete or forget an individuals data upon request.
GDPR also addresses data collection and data storage practices. The risk lies in the potential for tremendous GDPR fines, which can amount to $20 million or 4% of total worldwide turnover for the prior fiscal year This is just one example of a data governance risk that impacts countless companies worldwide.
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Key Questions To Ask Before Developing A Model
Before even starting to develop a data governance framework, ask yourself some fundamental questions:
Why do we need a data governance framework?
Before even starting out, you need to ask: Why does my organization need a data governance framework? Whats motivating you and your organization to have one? Is it because its the shiny new tool someone heard about and now wants? Or are you responding to an adverse event that happened with your data and you want to ensure it doesnt happen again? Regardless, first articulate precisely why you need a framework around data governance.
What does my current data governance look like?
You need to understand what your organizations current data governance framework looks like. Do you even have one? And if you do, what sort of controls and policies are in place? Whats more, you need to understand if your current data governance is up-to-date and reflects current best-practicesor if it was written several years ago and hasnt been updated.
What are we trying to achieve with a data governance framework?
This goes beyond the general question of whether or not you need a data governance framework what do you specifically hope to realize by having one? In short, what is the end goal of having a data governance framework? What KPIs can I attach to its implementation? Its a fundamental question that will help determine what sort, if any, of data governance framework will best help you reach sound business objectives.
Data Governance Use Cases
Effective data governance is at the heart of managing the data used in operational systems, as well as the BI and analytics applications fed by data warehouses, data marts and data lakes. It’s also a particularly important component of digital transformation initiatives, and it can aid in other corporate processes, such as risk management, business process management, and mergers and acquisitions.
As data uses continue to expand and new technologies emerge, data governance is likely to see even wider application. For example, efforts are underway to apply data governance processes to machine learning algorithms and other AI tools. Also, high-profile data breaches and laws like GDPR and CCPA have made building privacy protections into data governance policies a central part of governance efforts.
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Balancing Data Protection With Productivity
When implementing a data protection solution, it is important that you enforce security without sacrificing your teams productivity. There are different types of security policies, each tailored to an organisations specific needs, which determine how to balance these with productivity to keep operations running effectively.
Some businesses are reluctant to implement any data protection measures because they would cause an unnecessary reduction in productivity. However, by implementing mandatory access controls on all users and containers, you can increase the security of the data without affecting the productivity of your company.
Simply allowing open access within your network means exposing your files from all angles and potentially leading to cyber attacks from external threats that could leverage login credentials or other information on company systems.
Some of the most critical applications are those with which employees are involved. These are your day-to-day workplace applications, where you are accessing files, sending an email, or checking customer information. And for some businesses, this is where they need to be agile and responsive for time-sensitive operations or customer engagements.
Data Governance Best Practices
1. Data governance is not data management
Data management is the actions taken to facilitate the data governance software framework. Data governance is the decision making function over data management decisions.
2. Collaboratively made frameworks are the most effective
Employees within the organization who know how to best manage the data should play a critical role in the framework design as this will ensure optimal optimization of the process.
3. Data governance needs to be integrated organization wide
Once the framework is functional ensuring it is implemented across the organization will ensure consistency in data collection to help every team achieve their goals.
4. Risk milestones
Data is valuable and when it is shared within the organization risk increases. Establishing risk milestones will put the spotlight on potential risks to help avoid costly breaches.
5. Continuously refine
As your organization grows, keep revisiting the data governance strategy to ensure it is still meeting the needs of your customers and your organization.
More Data Governance Resources
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Bring It All Together With A Data Governance Playbook For Your Organization
Now that youve got your data structure, processes, and policies to enforce your standards in place, its time to bring it all together for your data governance program with a clearly defined data governance playbook.
Make sure that this isnt set in stone, giving your program room to grow, as data governance is an ever-evolving practice throughout your organization.
Setting up a data governance program is a rewarding journey!
The maturity journey starts with simplifying regulatory compliance, slowly evolves to enabling data intelligence and governance, and ends up enabling responsible AI, ensuring data ethics.