Organizations across the globe are collecting more data than ever before, and many of them are struggling to keep track of what data they store, who collected it, why it was collected, and how it is being accessed and used. It’s important to remember that data is very valuable.
Companies can use the data they collect to derive meaningful insights into consumer and industry trends. Cyber-criminals want to get their hands on this data in order to sell it on the dark web or use it for other nefarious purposes.
Without a well-thought-out data governance strategy, companies will put their customer’s privacy at risk, fall out of alignment with the relevant data privacy laws, and fail to use the data to maximize their productivity.
Common Data Governance Challenges
Below are some of the main challenges that organizations will encounter when developing and maintaining a data governance program.
- Limited Resources
- Data Silos
- A Lack of Leadership
- Managing ROT Data
- A Lack of Control
- Balancing Security, Privacy, Accessibility and Usability
- Training and Education
- Data Quality and Consistency
- Cross-functional Collaboration
- Understanding the Business Value of Data
1. Limited Resources
In general, organizations are not overly keen on the idea of allocating large amounts of their budget to data security, as it doesn’t appear to yield any direct rewards. As a result, many security teams are required to operate on a tight budget and are usually too busy with other tasks to focus on improving/updating their data governance program.
The problem is that a failure to do so could end up costing them more resources in the long run. Not only is it important to ensure that you have a sufficient level of funding and staff to maintain your data governance program, but it’s also a good idea to focus on automating as many tasks as possible, which includes carefully selecting the right technologies and streamlining business processes.
2. Data Silos
Data security techniques, such as micro-segmentation, can help to make networks less vulnerable to attack. However, such techniques require planning and consideration. When data becomes unintentionally siloed, it will become even harder to protect. For example, when companies collect too much data too quickly (which is often the case), it tends to get scattered around in unknown, unmonitored, or unsuitable locations.
Data can become siloed when companies adopt new technologies, data sources, processes, or infrastructure. In some cases, all it takes is a bit of friction between teams/departments for data silos to form. It is important that we pay close attention to the processes that determine how and where our data is stored, otherwise, we will struggle to keep it secure, or use it in a meaningful way.
3. A Lack of Leadership
Many companies lack leadership when it comes to data governance – a problem that is exasperated by the serious lack of cyber-security professionals. In the absence of a dedicated data governance officer, it is necessary to appoint a member of staff who will give directions to the security team, develop policies, discuss ideas, address concerns, deliver presentations, and so on.
4. Managing ROT Data
Organizations collect and store large amounts of data that isn’t really useful to them or their customers. This type of data is typically referred to as ROT (Redundant, Obsolete or Trivial). Hoarding large amounts of ROT data is generally a bad idea as it creates clutter and hinders visibility.
To ensure that organizations are only storing the data they need, they will need a solution that will automatically discover and classify their data, based on whether it is public, private, or restricted. Some data classification solutions will also come with pre-sets that allow you to classify data in accordance with the relevant data privacy laws. You may also want to consider using a data deduplication solution that will scan your repositories for duplicate files, and replace them with a reference to the original file.
5. A Lack of Control
Naturally, if you don’t have control over your assets, you will likely fall out of compliance with the relevant data privacy laws. In cases where organizations are overwhelmed by the amount of data they collect, they will often just store the data anywhere they can, without concerning themselves about who is responsible for it. If nobody is responsible for it, then you won’t have control over it. Again, you will need a data classification software that can automatically classify data at the point of creation/modification, which the relevant personnel can review for inconsistencies.
6. Balancing Security, Privacy, Accessibility and Usability
Ensuring that your data is secure, compliant, and confidential, while also making it accessible, usable, and valuable for your business goals and processes, can be a monumental task.
To effectively balance data security and privacy with data accessibility and usability, the first step is to understand your data landscape by identifying what data you have, where it comes from, and how it is processed. Assessing the quality, relevancy, and risks associated with the data is crucial for prioritizing data governance efforts. The next step involves defining a data strategy that aligns data initiatives with business goals and addresses security and privacy challenges. Implementing security measures according to data sensitivity and regulatory requirements is essential to reduce the risk of breaches and protect sensitive information. Monitoring and optimizing data performance ensures that data delivers expected benefits, while adapting to changing data needs and expectations allows businesses to stay competitive and relevant.
7. Training and Education
Security awareness programs can be burdensome for administrators, who are responsible for selecting and assigning courses, creating content, and dealing with related tasks. Low employee participation is also a challenge, as programs that are difficult to access or require frequent logins can deter users. Additionally, employees often forget what they’ve learned from security awareness training, leading to vulnerabilities in the organization.
One solution is to have ongoing, dynamic courses that are continually updated based on evolving threats. Using a fully managed program can alleviate this burden and ensure high-quality, up-to-date content. It is important to remove resistance to participation by making content convenient and integrating it into employees’ daily routines. Additionally, keeping content engaging and relevant is crucial to maintaining employee interest in security awareness training.
8. Data Quality and Consistency
Data plays a crucial role in the operations of businesses. Whether it is customer data or financial data, businesses heavily rely on data to drive growth and make informed decisions. Nevertheless, the accuracy and reliability of this data can be jeopardized by data quality issues. These issues can result in incorrect decisions and costly mistakes which can cause significant setbacks for businesses.
Below are the most common data quality issues;
- Incomplete or missing data: occurs when necessary data fields are left empty, leading to unreliable analysis and decision-making.
- Inconsistent data formats: creates challenges in integrating data from various sources, resulting in errors in reporting and analysis.
- Inaccurate data: whether from entry errors or outdated information, inaccurate data can lead to increased costs and ineffective decision-making.
- Duplicate data and outdated data: causes data inconsistencies and incorrect reporting.
To mitigate these issues, businesses must prioritize data profiling, validation, and regular maintenance to ensure the accuracy, completeness, and timeliness of their data.
9. Cross-functional Collaboration
Cross-functional collaboration in data governance breaks down silos and creates a shared understanding of data assets, policies, and processes. It aligns everyone involved, clarifies roles and responsibilities, and ensures access to correct data for informed decisions. However, many organizations struggle with efficient cross-functional collaboration due to the following challenges:
Poor communication and documentation: Data teams manage and safeguard data while business stakeholders rely on it for decisions. Misunderstandings often arise due to complex data policies and changing KPIs. Regular meetings, shared documentation, and training can bridge the gap between the two groups.
Poor data management tools and processes: Proper tools and processes are essential for effective data governance. Inadequate tools can lead to low quality data, slow processing, high maintenance costs, and manual processes. Choosing tools that centralize data access, promote collaboration and monitor access to sensitive data can optimize operations and data quality.
Resistance to change and lack of data literacy: Business stakeholders may resist new data technologies and processes due to a lack of data literacy. This can lead to slow adoption, data trust issues, security risks, and friction with data teams. To address this, provide workshops and training to empower non-technical employees with data skills and promote a data-driven culture. Consider using data visualizations to make information more accessible.
10. Understanding the Business Value of Data
Recent reports show that American Airlines has obtained massive loans by using their customer loyalty programs as collateral. The value of these programs, assessed by third parties, exceeds the market value of the airlines by two to three times.
In most companies, there is a persistent cycle where the value of data is not properly quantified, leading to a lack of effective governance, integration, transformation, and accessibility of this crucial asset. As the saying goes, “You can’t manage what you don’t measure” and accordingly, you can’t monetize what you don’t manage. This can result in a diminished ability to generate economic benefits from the data, creating a missed opportunity for the company to fully use and capitalize on their unique data resources.
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How Lepide Helps with Data Governance
The modern attack surface is continually expanding as employees come and go and change roles, leading to access rights becoming unmanageable. To address this issue, data access governance software is employed to identify sensitive data, detect excessive permissions, and monitor user access and behavior.
With the Lepide Data Security Platform, organizations can better regulate access to sensitive unstructured data, enforce zero-trust policies, and ensure compliance. The platform can automatically discover and classify sensitive data, and assign a score to it based on its level of sensitivity. It also prevents unauthorized access by identifying who has access to data and reversing excessive permissions. When anomalous activity is detected, a real-time notification is sent via email or mobile device to the relevant personnel for further investigation. Finally, detailed pre-set reports can be generated at the click of a button, providing visibility into suspicious events and helping companies demonstrate their compliance efforts.
If you’d like to see how the Lepide Data Security Platform can help with data access governance, schedule a demo with one of our engineers.