Data Classification Guide: Challenges, Use Cases, Best Practices

Dealing with data is often a challenge, even for mature organizations. Whether it is about improving overall efficiency, driving informed decision-making, or moving toward achieving regulatory compliance—the first step is understanding how to tame dynamic flows of information. 

Data classification is a dedicated machine learning practice that not only helps companies accurately handle increasing volumes of information but lays the foundation for their large-scale success with advanced analytics, automation, and AI. 

Working with enterprises and challenger businesses undergoing transformations, we regularly receive questions like “Do we have enough data collected?”, “How do we organize our data?”, “What should we do next to start seeing results?”. 
While these questions apply to a variety of generic scenarios, below we provide a comprehensive overview of real-life business use cases, challenges, and best practices that help organizations get started with data classification and make the very most of it in today’s hyper-dynamic realities.

What is data classification?

Data classification is the process of categorizing and organizing data based on its attributes, properties, or characteristics to facilitate effective management, analysis, and utilization across various domains and applications. 

Traditionally, this term is mostly used in four different areas:

• Data management
• Business analytics
• Machine learning
• Cybersecurity 

Hence, it’s the context that defines the understanding of data classification. In this article, our primary focus will be on understanding its significance, objectives, and best practices within the realms of data management and machine learning—areas where Trinetix excels in expertise.

Classification in data management

From the data management perspective, data classification is the process of tagging data to make it more searchable and trackable for entities in charge. This involves categorizing data into relevant classes based on characteristics like format, source, content, and intended audience. To achieve that, data is accurately tagged and, as a next step, organized into distinct categories or classes. 

This allows organizations to effectively manage and govern information throughout the data lifecycle, embracing collection and pre-processing, storage, processing, and sharing stages and shaping the way organizations analyze, manage, and derive value from their data assets.

Classification in machine learning

Classification in data mining and ML is a supervised learning technique that involves categorizing data into predefined classes or labels based on the characteristics and patterns identified in the dataset, enabling the prediction of class labels for new, unseen data.

For example, the data classification practice is essential to develop a spam filter for an online inbox. In this case, we refer to building a data classification model, training it, and evaluating its efficiency on test data. When deployed in production, this model can perform predictions on new unseen data.

The goal of data classification

In the context of data management and machine learning, data classification serves several crucial purposes:

• Enhances understanding 

By categorizing data, organizations gain insight into the value of the information they possess, enabling them to prioritize resources and efforts effectively.

• Facilitates informed decision-making

By properly classifying data, organizations can unlock their full potential for analysis and decision-making, enabling them to derive valuable insights and drive business growth.

• Ensures regulatory compliance
  

In data management, data classification enables organizations to assess whether their information meets regulatory requirements, helping them identify areas where adjustments may be needed.

How data classification works in practice

While categorization had been a common practice for mankind far before Charles Babbage and the ENIAC, till now the understanding of data classification and the variety of its options, processes, and types have experienced several changes, becoming more diverse and flexible.

Types of data classification systems

Depending on the ways to manage and organize information and the level of technology enablement achieved, data categorization systems are divided into manual, automated, and hybrid.

When is the right time to approach data classification?

Over the past years, controlling and managing online data has become the number one objective for global authorities, businesses, individuals, and any other party involved in digital. Along with that, mastering data analytics has emerged as the first step companies need to take to build smarter operations, drive substantial investments, undergo digital transformation, and facilitate innovation enablement.   

So, when do organizations usually recognize the need to get started with data classification?

• Data volumes grow

New customers and partnerships result in businesses accumulating vast amounts of diversified information including personal records, corporate information, and transactional history.  At some point, these growing volumes of data require businesses to implement data classification to efficiently handle large datasets, enabling streamlined analysis, actionable insights, and informed decision-making.

• The nature of data changes

With the variety and difficulty of business tasks growing, companies are more likely to adopt AI and ML. The need to solve these tasks and the subsequent technology adoption naturally make data more complex and dynamic. Additionally, the integration of diverse sources of information, including sensor data from IoT devices, social media streams, and real-time customer interactions, contributes to the shifting nature of data and requires organizations to adapt their strategies to get more value and remain operational. 

• Data privacy becomes an emerging concern

With a heightened focus on data privacy, businesses acknowledge the imperative to protect sensitive information. Data classification serves as a strategic response to this concern, empowering organizations to identify, label, and safeguard private data, fostering trust among customers, and complying with evolving privacy regulations.

• Cybersecurity concerns start hindering business success

Data makes businesses vulnerable to diverse cybersecurity threats—phishing, ransomware, DDoS, and insider risks. Insecure IoT devices and supply chain vulnerabilities also pose significant risks, addressing which requires proactive measures. By systematically categorizing and prioritizing data based on sensitivity, businesses can implement targeted security measures that safeguard the integrity, confidentiality, and availability of digital assets.

• Regulatory landscape evolves

As the importance of safeguarding online information grows, a surge in regulations emerges to protect individuals and organizations. GDPR (General Data Protection Regulation), PCI DSS (Payment Card Industry Data Security Standard), and other industry-specific mandates directly relate to data, setting stringent standards for its handling, storage, and protection. Data classification serves as a proactive strategy, enabling organizations to categorize information according to regulatory specifications, thereby ensuring adherence to standards and minimizing legal risks.

• The next strategic move is required

Throughout their evolution, organizations naturally engage in transformative endeavors such as mergers and acquisitions (M&A), robust digital transformation, innovation adoption, and global expansion. In these strategic moves, data classification empowers businesses to effectively categorize and protect critical information, ensuring seamless integration of diverse datasets, compliance with regulations, and informed decision-making. 

Common data classification challenges

While data classification is a commonly used practice that has shaped our digital experiences and made online shopping, correspondence, and other day-to-day operations possible, it is still characterized by a variety of process-specific imperfections and challenges organizations face in practice.

Manual practices hindering accuracy and efficiency

Although adopting technology to automate data processing is an ongoing promise for 90% of global companies, in reality, 48% of organizations are still taking early steps toward intelligent automation. This means that so far every second company in the world continues to go for manual processes.  

Apart from hindering the overall data accuracy and lowering operational efficiency, manual practices became a barrier to compliance success for at least 16% of organizations. This has a very distinct impact on business outcomes.

• Manual handling increases the risk of sensitive data getting lost in data silos, rendering it undiscoverable and unprotected.
• Mishandling sensitive information not only leads to potential embarrassment for clients but also results in the loss of future revenue opportunities.
• Organizations face fines and penalties for the mishandling of regulated data, impacting their financial health.
• Breaches in client information can give rise to lawsuits, ruining the organization's good reputation.

Siloed organizational structures and lack of data culture

For the past decade, the level of data-related responsibility among executives has encountered a myriad of changes triggered by emerging security risks, technology's rapid evolution, and global political changes that have a dramatic impact on business.

At the same time, only a couple of years ago, the share of organizations investing in building in-house data strategies was 13%. For the situation to change for the better, the main shift should be the one in leaders’ minds, as so far the global data culture adoption landscape still looks as follows.

• Leadership often adopts a "it won't happen to us" mindset, potentially underestimating the importance of proactive data management.
• Data and privacy concerns take a back seat to other pressing priorities like sales, marketing, expansion, and product expenses.
• Companies struggle to effectively locate or identify their data.
• Organizations find themselves out of sync with existing compliance regulations.
• Companies are putting too much effort into overcoming data classification process complexities, which makes them disconnected from getting practical results.

Underestimation of data privacy concerns

As we mentioned before, in some sense, data equals privacy. And while consistently leveraging data classification practices allows companies to respect users’ privacy, on the other hand, it also becomes a cornerstone for successful data strategy adoption. 

The truth is that for many organizations data classification policies are often theoretical rather than operational, meaning while they exist on paper and in reality bring no measurable impact. This is proved by the fact that according to the Data and Analytics Leadership Executive Survey, only 24% of companies can state they are doing enough to ensure responsible and ethical use of data in 2023.

So, what are the privacy concerns organizations still overlook?

• Data privacy remains topical for specific organizational levels and never becomes a company-wide concern.
• Understanding who is responsible for data ethics and making sure they have the necessary assets to implement and maintain the required level of privacy. 
• Controlling how confidential information is shared with other entities.
• Establishing an actionable plan B to apply in case privacy gets compromised.

How to get started with data classification and achieve long-term success

When it comes to initiating data classification, the process generally involves establishing the following steps or milestones.

• Assessing the data landscape to gain insights into current data and regulatory requirements.

• Establishing strong data governance policies to ensure compliance and maintain data integrity.

• Systematically classifying data based on sensitivity, enhancing efficient data management.

However, in today’s competitive and dynamic realities, data classification requires a more impact-oriented approach. This means despite the data classification flow still depending on a specific business case, building a data classification model, companies should focus on guaranteeing its long-term value and bringing in a culture of continuous improvement vs following a generally accepted straightforward procedure.

1. Scope requirements 

Clearly outline the project's scope to ensure precise identification and classification of relevant data.

2. Define classification criteria 

Establish specific criteria, such as sensitivity and regulatory implications, to guide accurate data classification.

3. Identify data sources 

Pinpoint all relevant data sources for comprehensive classification, ensuring a holistic understanding of the organization's data landscape.

4. Perform data profiling and discovery 

Conduct in-depth data profiling to uncover hidden patterns, enhancing the model's accuracy and effectiveness.

5. Assign classification labels 

Clearly label data based on established criteria, enabling efficient categorization and subsequent handling.

6. Automate classification 

Implement automated tools to streamline classification, boosting efficiency and ensuring consistency across large datasets.

7. Establish access controls

Define access controls aligned with data classifications to safeguard sensitive information and maintain data integrity.

8. Monitor and review 

Regularly review and update classifications, ensuring ongoing relevance and adaptability to evolving data landscapes

9. Educate and train users 

Provide comprehensive training on classification labels and data handling, fostering a culture of responsible data management

10. Build a culture of continuous improvement 

Develop a company-wide that ensures the data classification model evolves alongside organizational needs and industry changes.

Data classification for strategic planning: a Fortune 500 use case

Now, when we are done with understanding the impact-driven approach to data classification, let’s explore how it works in practice.

The context. Keeping aligned with the global corporate dynamics was one of the strategic objectives for our Fortune 500 client. To provide employees with the consistent ability to maintain awareness about their clients’ business movements, the company invested in the development of a hybrid data intelligence engine that would automatically categorize the information coming from online communiques and disclosures based on objective criteria. This way, employees would timely receive and process relevant information without spending time on manual data classification and filtering.

The results. The solution developed by our team not only allowed the company to reinforce strategic planning but also contributed to improved time efficiency, reduced the human factor, and enabled informed decision-making thus giving our client a strong competitive advantage in the market. Apart from that, leveraging consistent data classification practices gradually built a reliable foundation for generative AI adoption and allowed the client to successfully preserve industry dominance.

How we approach data classification at Trinetix

Here at Trinetix, we contribute to building a responsible data society, where ethical principles and sustainability practices help world-renowned organizations move toward strategic success and measurable growth. 

Our value-centric approach to technology enablement is characterized by consistency and strategic thinking. Focused on delivering long-term value, we provide businesses with a few game-changing advantages:

• Establishing a company-wide data culture vs solving specific problems

Getting acquainted with a company and their business needs, we analyze the existing data culture and provide tailored advisory that eliminates potential doubts about having insufficient data or data stored inappropriately and focuses on squeezing out the maximum of the current situation, clearly defining the next steps and expected outcomes.

• Supporting the full cycle of data enablement: from requirements to deployment

While a common industry practice is bringing in a set of recommendations on data enablement and ML adoption, our team consists of industry practitioners who not only meticulously outline a hands-on implementation strategy but use a set of best practices to put it into action and scale as requirements evolve and data grows.

• Competitive agility to navigate the changing tomorrow 

Working with global leaders, we know the inside of enterprise operations and clearly understand the evolving market dynamics. That’s why our approach combines prioritizing flexibility and ensuring process consistency to allow big players to focus on their business goals while their data keeps working to secure their competitive edge.
If our vision resonates with your ongoing business objectives, let’s chatabout bringing this advanced approach to data classification to the core of your processes and operations, enabling long-term efficiency and facilitating an undeniable strategic advantage.