Data Classification Outsourcing: Benefits & Challenges
By Label Forge | 10 May, 2024 in Data Classification | 3 mins read
Data classification is critical for companies dealing with sensitive, confidential and regulated data. It involves separating and organizing data into groups as per their characteristics. These include degree of sensitivity, risks, and compliance regulations protecting them. It also helps in ensuring:
- 1. Data is handled, stored and shared in an appropriate manner.
- 2. Businesses can optimize data management, security and compliance strategies.
- 3. Businesses can unravel the value of data for business intelligence and decision-making.
Companies must be cautious in handling every group of data in the best possible manner so that it is accessed only by authorized people (internally and externally). They must ensure that the data complies with all the regulations. Accurate data classification allows for efficient, proper use and protection of data. However, it is frequently overlooked as organizations are unable to comprehend its purpose, scope and capability.
Significance of Data Classification
1. Data security: Data classification assists businesses in protecting their data from unauthorized access, use and modification. It applies appropriate security measures and controls to each category of data (public, private, internal and confidential) as per the sensitivity of data.
2. Supports regulatory compliance: Data classification assists in determining the location of regulated data within the enterprise by making it traceable and searchable through proper security controls. It helps companies in complying with legal and regulatory requirements.
3. Data governance: Data classification assists in monitoring and measuring the quality, accuracy and completeness of data. It helps in identifying and resolving issues, enhance trustworthiness and usage of data for supporting business goals and decisions.
4. Data ethics: Data classification assists in respecting the rights and interests of data subjects and stakeholders. It also assists in balancing the risks and benefits of your data activities along with addressing ethical dilemmas. It aids in fostering a culture of data responsibility and accountability within your organization.
Benefits and Challenges of Outsourcing Data Classification Services
Finding companies to outsource your data classification process can be a blessing as well as a daunting task. A structured and transparent selection of service providers will assist in selecting the best option for your business. This might involve sending out request for proposal, studying the proposal, negotiating contract and implementation and monitoring of the project. Let’s now take a broader look at the benefits and challenges of outsourcing your data classification services.
| S. No. | Benefits | Challenges |
|---|---|---|
| 1. | Saves time and resources: This is a key benefit of outsourcing data classification. It saves companies time and resources in manually sorting, cleaning and standardizing data. By delegating this task to a provider who has the right tools and skills, the work can be completed quickly and accurately. |
Finding the right provider: A primary challenge faced during outsourcing is in selecting a provider who’s capable of comprehending your business context, goals and expectations. Also, one that is capable of delivering a customized and flexible solution which complies with your standards and specifications. |
| 2. | Divert focus to strategic activities: Outsourcing assists companies in diverting their internal staff’s focus and energy on strategic and value-added activities like data analysis, insight development and implementation of action plans. |
Data security and confidentiality: This must be ensured by verifying your service provider has the policies, procedures and systems to protect your data from unauthorized access, misuse or breach. Ownership, access and control rights must also be defined along with the way it needs to be stored, transferred or disposed off. |
| 3. | Limits errors and data inconsistencies: Outsourcing aids in limiting errors and data inconsistencies which can impact the quality and reliability of your analysis. |
Finding a qualitative, cost-effective and customized service provider: To successfully outsource data classification, one must evaluate and opt for a service provider offering qualitative, cost-effective and tailored service. Some of the important criterias for selecting a service provider are their experience, market reputation, credentials and certifications, success stories, core competencies, skills, and knowledge in data classification. |
| 4. | Application of industry best practices: Your external service provider can ensure that industry best practices, methodologies and advanced technologies like artificial intelligence and machine learning are applied to cater to your company’s specific requirements. |
Scope and scalability: These should be evaluated for handling varying volumes, sources, formats and kinds of data. One must also carry out a comparison of their pricing models and structures within budget and return on investment. |
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