Data Remediation Plan – 95% of companies say that managing their data is a constant problem and challenge. And it’s a challenge that will only increase given the huge amount of data generated every day. Adding to this challenge is the fact that nearly 80% of data is unstructured and vulnerable to breaches. All this combined creates many ongoing problems for an organization’s security teams. Generally, the more data an organization has, the greater the risk of error. And data errors cause inefficiencies in the workplace, hinder the decision-making process, incur unnecessary costs and, perhaps most importantly, can put organizations at risk of legal compliance. This is why data curation is essential to help organizations ensure the quality and security of their data.
Simply put, data correction is the correction of errors and inaccuracies in data to eliminate data quality problems. This is done through the process of cleaning, organizing and migrating data to better meet business needs. The ultimate goal of data remediation is to help your organization decide whether to retain, delete, migrate or archive information.
Data Remediation Plan
While it may seem like a daunting task, the long-term benefits of data remediation to the organization outweigh the effort. Because of the many benefits, organizations should consistently incorporate data curation as part of their business activities. The main advantages are:
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Ideally, data remediation should be an ongoing business process to ensure the quality of an organization’s data and protect it from risk. Additionally, an organization should consider data remediation in the following situations:
To help your organization succeed in data recovery, let’s talk about how to prepare your team for this important process.
Your first step is to assess the current state of your data. You want to understand how much data your organization has.
Now that you understand how much data you have, you want to understand what’s inside that data. First, you can identify redundant, obsolete, and trivial (ROT) data that can be deleted, archived, or reclassified. Other non-ROT data can be categorized by sensitivity and value.
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Now that you have a complete data inventory, you can implement your internal data governance policies. These guidelines will tell you what data is outside the policy and needs to be addressed.
Your organization will now determine appropriate data remediation strategies for data that falls outside the policy. Common methods of data remediation include data redaction, masking and de-identification.
The final step is to implement the data recovery strategy that you have developed based on the progress steps.
Data remediation is an involved process that can be costly and time-consuming for your organization. But it doesn’t have to be. Data Sentinel can help make your organization’s data improvement process cost and time efficient.
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With Data Sentinel, you’ll know your data is protected as policy violations are detected across your farms, regardless of scale, location or data type. On-premise, cloud or hybrid cloud environments.
Comply with data protection and privacy regulations such as GDPR, CCPA, CPPA, PCI DSS and HIPAA by using data de-identification methods such as encryption, quarantine and data masking.
Data Sentinel is always on the lookout for exposed data that does not follow data management policies. Once found, the system initiates data correction actions specific to the rules you define within the workflow.
By automating the detection and remediation of exposed data nonconformities, your organization can minimize the risk of financial loss through fines, lawsuits, and reputational damage. This will assure you accurate, cheap and fast data.
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Dynamic data masking – Data Sentinel can trigger dynamic data masking actions on structured and unstructured data as sensitive data anomalies are detected. It is a user-defined rule-based feature that ensures that sensitive data is hidden in real-time. Immediately mitigate risk and ensure compliance with data privacy standards.
Data Sentinel may use a variety of masking techniques, depending on the policies and use cases to be addressed, examples include:
Data Quarantine – In addition to dynamically hiding non-matching data, Data Sentinel can identify and isolate specific records during data quarantine. This ensures that any records that do not comply with your data management policies will remain vulnerable. This allows us to check for policy violations and correct the log if needed. When this happens, a data subject matter expert will be notified to ensure that your organization complies with policies and data privacy regulations.
Data Minimization – ROT (redundant, obsolete, or trivial) data, or simply duplicate data across the organization, creates significant additional risk to the business without significant additional cost. Data Sentinel automates the process of finding and deleting ROT data.
Keys To Mitigating Data Risk
If you’re ready to discuss strategies to ensure the quality and security of your data, let’s talk. Click here to schedule a free research call. On this call, we’ll discuss your data management challenges and your business goals so we can develop a tailored plan to help minimize risk, ensure compliance, and maximize business growth.
95% of companies say that managing their data is a constant problem and challenge. And it’s a challenge that will only increase given the huge amount of data generated every day.
The California Privacy Bill of Rights presents additional compliance challenges for companies that have made significant efforts over the years to ensure compliance with the California Consumer Privacy Act.
You know that strong privacy practices are necessary to comply with Canadian and international privacy laws, but have you considered your approach to privacy as a means of building customer goodwill and attracting investors? A database is like a garden; It requires constant attention, says Robert. Howells. Here, he presents part 2 of his series of articles on data strategy and business goals. It examines the development needs of small and medium-sized enterprises and the data needs of SMEs.
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In the first article in this series—aimed at getting started—I recommended focusing on data quality rather than quantity. By this I did not just mean quality in the sense of precision and standardization. I emphasized the importance of a data strategy that was closely aligned with business goals. When building a business, you should start with a clear emphasis on replicating your primary target market in your data set.
As a company grows, of course, the marketing journey gets mixed up. To paraphrase Mike Tyson, “No plan survives a punch in the face.” No marketing database stays in a stable or tidy state after a few campaigns or over time. You have to work hard to turn a foundation into a scalable process based on real results.
So for startups, if it’s strategic focus, detailed planning, early data acquisition, and building a data platform, there are four key drivers of a strong but growing company:
Testing and analytics, the process of measuring what works and what doesn’t across a wide range of marketing variables, was in danger of becoming a lost art, at least in campaign terms, in the early stages of the digital age. And it is still a risk in poorly managed marketing organizations. With email’s low transaction costs and emphasis on speed to market at the expense of targeted and relevant messages, many companies fall into the trap of skipping testing and jumping straight into campaign rollout.
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Fortunately, this ‘suck and see’ syndrome was overcome by three factors. First, the tools available for real-time analysis are now widespread, so the “testing takes time” argument no longer applies. Second, while the transaction costs of email remain low, the costs of implementing a functional marketing technology stack are relatively high. To leverage the stack—from marketing automation and data integration platforms to business intelligence tools—and return true ROI, you need to market smarter. Finally, the growth of ‘inbound marketing’ makes it an absolute necessity to analyze and identify incoming inquiries.
In other words, while you still see “spray and pray” email, it’s largely in businesses where there’s a disconnect between the capital expenditures spent on martech tools and the transaction costs of the campaign.
Of course, there are many variables that you need to test, measure and understand. But let’s focus on how testing and analytics can positively impact data acquisition and improvement. Here are the points you should evaluate after looking at the results of both campaigns and web queries:
Now that you know, at least partially, what works and what doesn’t, you can prioritize your resources and budget to refine and improve your data. Here’s a simple chart to help you score your wish list. Of course, the scores are hypothetical – the magnitude of the impact will vary from case to case. You may have a very detailed database, but limited coverage, or
Getting Started With Data Governance
This method will allow you to initiate a comprehensive data remediation plan that addresses the most pressing business issues first. And stay focused on measuring improvement. But you can also see how
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