We live in a digitally driven world where organizations need to handle a lot of data for their day-to-day operations. But simply collecting and storing it somewhere is not enough. The data needs to be structured and stored in a way that makes it easily accessible.
In addition to that, we also need to be careful about who can access the data. All the business data cannot be made available to everyone. Furthermore, businesses also need to handle various types of personal data, whether it is of their employees or their customers. All the critical information needs to be kept private and protected from various threats that may cause data leaks or loss. That’s why we need data management.
In this blog, we will discuss what data management is, why it is important, as well as how you can develop strong data management for your organization.
What is Data Management?
Data management is the proactive process where we collect, organize, and store data in a way that it stays secure from various types of threats. Data management also ensures that data stays accurate, updated, and helps organizations stay compliant with various laws and regulations.
Importance of Data Management
In today’s business world, data is an important commodity. We need to manage it strategically to enable optimum utilization. Listed below are a few advantages and benefits of data management that make it important.
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Keeps data updated and accessible so you easily get the correct data, saving time and improving productivity.
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Safeguards your organization from any legal trouble by helping you stay compliant with the policies, such as GDPR and DPDP.
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Keeps information organized so people don’t need to waste time finding the right information and improves operational efficiency.
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A good data management strategy gives customers confidence regarding the privacy and security of their sensitive information. And when people know their data will be used responsibly, they are more likely to trust the organization.
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A scalable data management solution provides you with the necessary agility that helps you grow seamlessly without data management worries.
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Eliminating data silos and reducing storage waste cuts down operational costs.
Let’s explore its core components that ensure data management.
Components of Data Management
Effective data management relies on the five key elements:
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Data Security
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Data Privacy
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Data Recovery
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Data Audit
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Data Authorization Control.
Let’s understand these components in detail.
1. Data Security
Data security ensures that the data remains protected from any type of internal or external threats, such as cyberattacks or unauthorised access. Various tools and technologies are used in data security to ensure data integrity and availability throughout its lifecycle.
The following are some of the most effective data security measures:
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Advanced encryption is used to convert data into an unreadable format unless you have the right key. That keeps data secure in the storage as well as when it is being transferred somewhere.
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Strong passwords and multi-factor authentication are set to ensure that only those who have the right permissions can access particular data.
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Many organizations use a firewall to protect their organization’s network by blocking access attempts by anyone outside the network.
Data security helps protect data against threats and contributes to compliance with various regulations, while also establishing trust that the organization safeguards sensitive information
2. Data Privacy
Data privacy ensures that the personal information of customers or employees is protected and handled responsibly. Companies need to ensure that the way you collect, store, and use the data does not compromise privacy.
It’s not just an ethical obligation but also a legal requirement for organizations to handle personal data responsibly, ensuring it is collected, stored, and used only for authorized purposes.
Let’s look at the key principles that help us ensure data privacy:
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Data privacy uses the concept of data minimization, which says that you should collect information that is necessary to serve a specific purpose. Nothing more than that, and remove it once the motive is attained.
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Limit the utilization of the data to the original purpose of collection. Do not use it for anything else without valid reasoning or permission. This proactive approach is also called purpose limitation.
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Go with the right consent management, where people are well informed about the purpose of data collection and give you voluntary permission to use it. Organizations also need to maintain a record of these consents.
By implementing strong privacy practices, organizations can reduce compliance risks, avoid legal penalties, and ensure personal data is handled responsibly. In addition to that, aligning your data management strategy to data privacy requirements like the GDPR and DPDP Act shows your commitment towards responsible data management practices.
3. Data Recovery
When it comes to data, businesses need to be prepared for unforeseen circumstances. Even with strong data security measures, your data can be damaged by hardware failure, natural disaster, or human error. That’s where data recovery comes into the picture.
The data recovery process involves making a copy of the data and storing it in a safe and secure place, from where it can be easily restored if needed. It gives your organization an additional safety net, so your business processes won’t get interrupted.
A solid data recovery framework includes:
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Daily or real-time backups should be created to ensure all your data, including the latest changes, is secure.
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Define a clear disaster recovery plan with detailed steps to recover systems and data after an incident.
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Off-site server or cloud storage is used to keep the backup separate from the primary system and also easily accessible.
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Regularly test the recovery processes to ensure that they work when needed.
Even a small data loss can cost you a huge financial loss by halting business processes or attracting legal penalties. In the long run, having a good data recovery framework will save you a lot of cost and peace of mind.
4. Data Authorization Control
Threats to data are no longer restricted to simply those who are outside the organization; errors or even intentional misuse can happen even from inside the organization. That means we need stricter control over who can access what, and that’s where data authorization control comes in.
Data authorization control refers to the set of rules, practices, and technology that decides what device or user can access what part of the data, as well as what they can do with it. These fine-grained access limits are necessary to ensure that only trusted entities have access to sensitive data and prevent data leaks or unauthorized use.
The primary Data Authorization Control strategies include:
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User Permissions : Define who can access the data as well as what actions they can take on this data, such as view, edit, download, and more. This can be defined based on their data needs to efficiently complete their day-to-day tasks.
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Strong Authentication : Use strong authentication methods such as two-factor authentication or biometrics to verify identities. So, only authorised users can get access even if the password is compromised.
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Access Limits : Users should get access to only the necessary data, which is also called the principle of ‘least privilege’. It minimizes the exposure and avoids data leaks.
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Regular Reviews : Periodically review all the permissions and adjust them to changes in the roles and responsibilities. This helps to immediately identify and remove access from employees who no longer need the data.
Data Authorization Control ensures that people only access the data they are permitted to view or use, and keep the data protected. It is essential to balance access with protection to ensure data security and organizational productivity. It ensures both security and privacy, by restricting access to sensitive personal or organizational data.
5. Data Audit
Threats do not always come from a distant outside source. It often comes from unsuspected directions. It can be someone with the right access but wrong intentions, an employee who is not careful enough while editing or moving data, multiple failed login attempts that go unnoticed, and much more.
Organizations always need to keep a watch on data to ensure nothing is missed, and data monitoring helps you achieve exactly that. Data auditing is a process of monitoring every user, activity, and device, so you get a complete picture.
So, let’s take a look at how data monitoring works:
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All the activities are tracked in real-time to spot any unusual patterns like heavy downloads or huge external data transfers.
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The system keeps an audit log for every access, which makes it easier to trace the issue if something goes wrong.
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Various tools are used to detect any corrupted data or malware.
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In case of any suspicious activities system sends instant notifications to concerned teams or people. So, they can quickly take necessary action to prevent any damage.
Effective data auditing ensures that business operations run smoothly, and minor issues never get transformed into major calamities.
Having covered the essential components, let’s dive into the data management process to see how data is handled from creation to disposal.
The Data Management Process
The data management process is a structured approach to efficiently handle data throughout its existence in the organization. It ensures data is secure, accessible, helps achieve organizational goals, and is eventually deleted when it’s no longer needed.
Below are the key stages in the data management lifecycle.
1. Data Collection
Data collection involves gathering data from multiple sources, such as various devices, customers, or third-party service providers. This has to be done in a way that keeps data secure in transit and ensures personal data is collected and used responsibly.
2. Data Storage
The collected data needs to be securely stored with an appropriate option, such as cloud-based platforms, on-premises servers, or hybrid systems. And the access to personal or sensitive data is restricted according to privacy policies.
You need to consider scalability, compliance, and accessibility to select the right data storage option. Storage strategies should support scalability, accessibility, compliance, and the company’s data retention policies.
3. Data Organization
Data is utilized well only when it is properly structured. So, you need to categorise and label the data according to various functions and departments. Also, remove any duplications to optimize the storage. Data organization makes data easier to find and utilize.
4. Data Access and Usage
Ensure that data is accessible only to authorized people for data security and that personal data is used strictly for its intended purposes for data privacy. Apply encryption and limit external access to reduce risks.
5. Data Exchange and Collaboration
Use a secure data exchange and collaboration platform so that data can move efficiently internally. The right data collaboration tools ensure that all departments can seamlessly get together to work towards the common goal.
6. Data Maintenance
Data, as well as data requirements, change over time. That means you need to regularly check the data duplication, irrelevant data, any errors, or data corruption to ensure higher data quality.
7. Data Archiving
Not all data needs to be used frequently, but at the same time, a lot of data needs to be stored for compliance or company records. That’s where data archiving comes into play. This inactive but important data can be moved to the archive to store it cost-effectively for a longer period of time, yet keep it accessible whenever needed.
8. Data Disposal or Deletion
Every piece of data has an end where it’s no longer needed. This data needs to be deleted, not only to save storage costs but also to stay compliant with laws like GDPR to ensure privacy.
Managing data lifecycle well helps you keep data secure, ensure data privacy, stay compliant, and optimize work processes at your organization.
Let’s look at some of the challenges that organizations commonly face in data management.
Challenges in Data Management
Data management is not always that simple. You need to carefully lay out the strategies and be consistent with implementation to get the desired results. Even organizations with the best intentions sometimes struggle when it comes to data management.
Let’s focus on the common challenges that turn data into a mess rather than a valuable asset it should be.
Data Silos
Data silos happen when various teams and departments in the organization use different tools and platforms to store their data. These separate systems are like speaking a separate language.
This fragmentation makes it difficult to get a clear picture for everyone. That slows down knowledge transfer, complicates communication, and delays the decision-making process. Companies should use a single data storage and collaboration platform to avoid data silos.
Data Quality Issues
Using the wrong data often leads to more losses rather than the profits it’s supposed to bring in. When data is outdated, incomplete, or just plain wrong, it can lead to poor decisions and wasted efforts.
Imagine using the wrong email list for a marketing campaign, and what if you totally mess up the target audience? You lose time, money, effort, and maybe even a time-sensitive opportunity. To avoid this, data has to be updated and stored appropriately so everyone can easily find and use the latest version.
Scalability Concerns
Business grows, so does the data. The tools that may work fine today may not be capable of handling terabytes of data tomorrow. If you do not switch to the right scalable data management solution at the right time, it might slow you down when you need to sprint forward.
You can consider secure cloud solutions that seamlessly accommodate your data requirements as you grow. So that you can continue with the same data management solutions and do not need to switch to different ones frequently due to storage constraints.
Regulatory Compliance
Keeping up with various compliance can be challenging too. Especially those organizations that work in multiple sectors and countries. Any instance of non-compliance can mean hefty penalties, lawsuits, and the resultant reputation damage.
To stay compliant, you need to be aware of all the relevant Acts and guidelines, and in addition to that, stay updated with any changes. Select the data management software that aligns with your compliance requirements.
Security Threats
Cyberattacks and phishing incidents are now becoming regular instances, and these
threats are evolving day by day. Even with a decent defence system, one wrong click can make you lose all your data. 2024 Verizon Data Breach Investigations Report
found ‘a 34% increase in attackers exploiting vulnerabilities to gain initial access and cause security breaches compared to the previous year’. These breaches lead to a loss of money as well as a loss of trust from
your customers. Companies need to use the right tools and technology to be shielded from such threats.
Legacy Systems
Managing your data today with old systems can lead to another set of nightmares. It often fails to adapt modern data management essentials such as encryption or cloud storage. Leading to a risky and messy bunch of tools and processes.
While shifting to modern data management software may cost you more initially. But in the long run, it will give the organization better security and greater peace of mind.
You need to tackle these challenges by choosing the right data management software that supports your data management strategy. Let’s take a look at how to define a data management strategy.
Data Management Strategy
Every organization needs an effective data management strategy to ensure that data is easily accessible and always stored and exchanged securely. The data management strategy also helps to keep data well-structured, accurate, and aligned with compliance.
Described below are the essential steps in designing an impactful data management framework.
1. Define Data Governance Policies
The data governance policies are a set of various rules, standards, and procedures. These policies define how data should be handled in the organization throughout the lifecycle.
Here are the essential steps in defining the data governance policies.
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Assign clear roles and define what data they can access to fulfil their job responsibilities.
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Define who owns the data and who will be responsible for maintaining data monitoring and security.
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Establish standards for various aspects, such as naming practices, metadata standards, and the formats to store the data. It helps to establish uniformity and a clear data structure.
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Add guidelines to maintain compliance with industry and location-specific regulations.
2. Implement Data Quality Management
We need data that is accurate and updated for the latest decisions. Inaccurate data can lead to errors, wasted time, and wasted efforts.
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Set a process to frequently check the quality of the data. So, you can detect and correct the errors quickly.
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Use tools to monitor data corruption and duplication. It will save you a lot of time and improve efficiency.
3. Prioritize Data Security and Privacy
As discussed, the privacy and security of sensitive data can’t be avoided. So, take necessary measures to ensure the same.
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Encrypt data at the source as well as in transit to secure it all the time.
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Set strict data access control to ensure data privacy.
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Conduct regular security audits and vulnerability assessments to identify and address risks.
4. Foster a Data-Driven Culture
A successful data management strategy requires organizations to establish a culture that values data.
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Educate employees on data management best practices and the importance of following the data policy.
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Use an easy-to-use and secure data exchange platform to encourage secure collaborations among various departments.
5. Leverage Advanced Technologies
Technology is evolving rapidly, and those who are able to adapt quickly and use it to their benefit often get a competitive advantage. On the other hand, failing to do that quickly enough can also mean losing your market share. So, what can we do to stay in the game?
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Automate repetitive tasks and improve processes like detecting errors, generating reports, and classifying data with the help of AI. This improves accuracy and increases the speed.
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Get a data management software solution that easily accommodates evolving technologies, as well as the growing data management needs of your business.
A good data management strategy should include all the essential aspects, like compliance, security, accessibility, monitoring, flexibility, and much more. So that your organization can rely on the data at hand and stay worry-free when it comes to security.
Summary
Data management is the need of the time. It helps organizations keep the data safe and accessible only to the right people. It improves operational efficiency as the accurate data stays easily accessible, saving time and effort. Having the right data management strategy not only keeps businesses away from legal trouble but also helps to build a strong, trustworthy brand for the company in the long run.