Data: It’s what we all rely on to give us the right answer to any number of questions. If you’re an attorney, you may need to respond to a harassment claim and have several questions regarding what the parties said and did. If you’re a customer service representative, you may need to respond to a customer who wants to know what personal data your organization has on them and how that data is being used.
These Situations Have 3 Common Objectives:
- Find accurate data that’s relevant to the situation as quickly and easily as possible.
- Access the data in a way that facilitates its analysis.
- Enable the data’s reviewers to quickly identify and deliver the appropriate information.
Your organization’s ability to meet these objectives and ensure its response provides value, while minimizing the organization’s risks, depends on its data governance policies and procedures and how they are implemented.
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Traditional Approach to Information/Data Governance
For years, information/data governance programs have been implemented by organizations using an approach like this:
- The legal and compliance teams create the policies.
- The records management team trains and guides content creators and system administrators how to manage and protect that content.
- The content owners / system administrators assume responsibility for following policies and procedures.
The problem with this approach is most employees don’t consider managing data to be a priority. It’s not their primary job responsibility and, done manually, can take a lot of time. In the heat of the moment, we can easily forget, cut corners, and make mistakes. We’re human. And as a result, data being managed using this approach is being managed inconsistently and ineffectively.
With the ever-growing volumes of data and increasing number of data management regulations, particularly around data privacy and security, a new approach is desperately needed. According to Okta’s 2023 Business at Work report, the average number of applications implemented by organizations with more than 2000 employees is 212. So, a customer service representative at this size organization who needs to respond to a customer’s request for their personal information will likely need to collect data from marketing, sales, financial, support, fulfillment, and many other systems. The significant rate of data growth and the fact that data associated with a single person is distributed across multiple systems makes data governance and eDiscovery particularly challenging.
Data Governance for the Future
With advances in cloud computing, artificial intelligence and machine learning, a new approach to data governance is possible, where data management success no longer depends on content owners. The data governance process can now be automated using a set of governance rules. And instead of automating these governance rules one application at a time, it can be administered from a single platform. Given the ever-increasing volume of data, however, this platform must be able to scale to manage all types of data, not just communications, and do so securely and cost-effectively.
Unlike many of our competitors, Archive360’s cloud-native platform was designed from the beginning to be the most scalable, secure, and extensible data governance platform on the market, serving the legal, compliance, and analysis needs of organizations worldwide. Recently, we updated our website to better communicate our platform’s capabilities. Check out our updated site and learn more about our Unified Data Governance Platform.
Your legacy application is built on outdated technology, such as unsupported hardware or software, which makes it difficult to maintain and poses significant data security risks.
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WEBINAR
Steering Clear of the Pitfalls: Essential Data Governance Strategies for Effective AI Compliance
This webinar delves into the complexities and challenges of integrating data governance within AI compliance frameworks and vice versa.
Watch the replay to learn how Data Governance and AI Should deliver:
- Foundation of Trust
- Regulatory Navigation
- Avoiding Shadow AI
- Risk Management
- Practical Frameworks
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PaaS vs SaaS Archives: What You Need to Know
All cloud archives are not created equal. There are major differences between archives deployed in a SaaS model versus a PaaS model that affect the security, accessibility and functionality of your archived data. This Technical Guide explores what you will need to consider in order to make an informed decision about PaaS versus SaaS.
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Jim is the Director of Product Marketing at Archive360 and has over 24 years of experience with multinational corporations and technology start-ups in the financial services, pharmaceutical, and business services industries. His expertise includes over 10 years in archiving, data governance, risk, and compliance, making him a seasoned professional in these critical areas.
