Tag: data mapping

  • How To Build a Business Data Map

    How To Build a Business Data Map

    Recently on devopsy.kinsta.cloud, Anais Baig provided a brief overview of the privacy process and capabilities that developers need to think about to comply with new global privacy laws such as Europe’s GDPR and California’s CCPA. Top of the list for complying with these laws? The need to build a business data map.

    It’s no exaggeration to say that without a comprehensive, accurate data map—or, in other words, a complete inventory of the personal data the business possesses at any given time—it’s impossible to comply with these modern privacy regulations. Developers will understand better than most the difficulty in creating and then maintaining this record.

    But it can be done, and today we’re going to dive into the requirements and some of the technical processes that go into crafting a data map.

    What’s in a Data Map?

    In simple terms, a data map shows the personally identifiable information (PII) your business possesses, why you possess it, where it lives and how it’s being used. To answer these four questions, you’ll need to audit the databases in your business. Be sure to include those that are part of third-party SaaS applications as well as your own proprietary data systems; figure out the fields of information contained in each. Then you’ll need to coordinate with your team to understand the business purpose for which each of those data points is being used.

    The bulk of the work that developers typically do is that first step: auditing the business data system and documenting the database structures. At the very least, these are the things that you’ll need to be able to say about every field of personal information you possess:

    Source: How was it collected?

    Use Case: What is the business purpose for which it is being used?

    Storage: Where is it being stored for additional processing, transformation or record-keeping?

    Access: What business units have access to the data?

    Retention: What is the schedule for deleting this data or renewing the user’s consent to possess it?

    Consent: Was this data obtained through explicit customer consent?

    If you’re a small business, sometimes a data map can be “visualized” with a simple spreadsheet, but it very quickly uses its utility as data infrastructure scales. Larger businesses often find it very useful to construct a data map like an actual map, with a visual representation of the data relationships that exist in the business. In this form, a data map most often contains nodes and links to show how different systems that contain any personally identifiable information link together in your organization.

    Here’s one built by Ethyca:

     

    As you can imagine, without automated tools it takes a good bit of grunt work to get to this point of elegant visualization. For those just getting started mapping data in their business, we recommend a hybrid mapping approach.

    The Hybrid Approach to Mapping

    The hybrid approach means using an automated baseline process to identify and record the individual personally identifiable information that your organization is processing into one central repository, and then using manual label analysis data analysis to annotate and verify entity relationships across applications in your stack.

    Label analysis involves using a data attribute’s name to decide which data dictionary element the attribute identifies. For example, an attribute named ‘email_id’ can be identified as ‘person.contact.email-address’.

    When the attribute’s name alone is not enough, the name of the entity to which the attribute belongs is included in the analysis. For example, the attribute named ‘name’ in an entity named ‘customer’ can be identified as a name of a person, whereas a similarly named attribute in an entity named ‘city_master’ can be identified as a name of a city.

    Sometimes a group of attributes occurring together can be identified easily. For example, attributes with names ‘lat’ and ‘lng’ can be identified as latitude and longitude of a location. Similarly, attributes with names ‘f_name’ and ‘l_name’ can be identified as parts of a person’s name.

    Data analysis lets you manually confirm the relationships posited in your label analysis by using actual data from your databases to test.

    The presence of certain values or patterns in the actual data can confirm certain mappings. For example, if the data for an attribute follows ‘foo@bar.com’ pattern, it can be confirmed that this attribute captures an email address.

    Range values captured in attributes of different tables can be checked to see if those attributes are referring to the same piece of information, such as a user_id, that can be used for linking the tables with specific relationships. Similarly, the absence of values in an attribute can confirm the opposite.

    If you’re not using an off-the-shelf data mapping solution, the label and data analysis processes can be arduous. But if done correctly, by the time you’ve completed the exercise, you’ll have a comprehensive understanding of how the data in your business links together, and you’ll be ready to start answering the compliance-related questions about each PII field you hold. You can also begin thinking about processes for maintaining the map’s accuracy as your data inventory flexes and evolves.

  • Data Mapping and GDPR: How Are They Related?

    Data Mapping and GDPR: How Are They Related?

    Every business enterprise receives data from an array of diverse data points. The volume of data is growing, and businesses understand the importance of leveraging data and converting them into actionable insights. Owing to data diversity, it becomes important to integrate them in a standard format. This is where data mapping takes a precedence. It helps establish relationships between different data models. Although data mapping offers several business benefits, it is also an integral aspect of the General Data Protection Regulation (GDPR) compliance. This post discusses the concept of data mapping and how it helps you adhere to GDPR.

    What is Data Mapping?

    As the name suggests, data mapping is a term for mapping the journey of data in an organization. A data map answers questions ranging from the origin of data to its journey through the organization. Today, most business enterprises are using various types of data mapping software, including ETL data mapping tools, to achieve their data mapping targets. Depending on the capabilities of these software applications, data mapping is used to perform a wide range of data management tasks such as data integration, data migration, data warehousing and data transformation.

    How Does a Typical Data Map Look Like?

    Typically, a data map includes the following information:

    • Type of data collected
    • Basis of data processing (personal or sensitive)
    • Purpose of data collection
    • Storage period of data
    • Place of storage
    • Conditions of data storage
    • Various data transfer destinations
    • Location of third party vendors (especially, international third party vendors)
    • Protocols followed during the data transfer (to international third party vendors)
    • Legal basis for data processing

    GDPR Compliance and Data Mapping

    The GDPR mandates business enterprises to ensure fair handling and security of the information they own. To assure the security, it is important to track the data journey from its point of origin to a conclusion. Without a detailed insight into data lifecycle, it is difficult to implement any security. Thus, data mapping becomes an essential step to achieve GDPR compliance. The following GDPR articles and their requirements will help you understand the importance of data mapping.

    • Article 30: Maintaining a written record of data processing activities in an organization is mandatory. A data map serves as a best written record, which organizations can easily retrieve and present at the time of GDPR audit.
    • Article 25: Data mapping helps organizations showcase their commitment towards data privacy.
    • Article 6: This article governs personal data processing under a lawful basis. Data mapping helps organizations to show the basis of processing–personal or sensitive.
    • Article 28: Data maps enable GDPR auditors to understand the extent of third party access to organizational data. All organizations are required to maintain a list of third-parties with whom they might be sharing their business data for various reasons.
    • Article 35: This article discusses Data Protection Impact Assessment (DPIA), and data maps enable business enterprises to meet this requirement effectively. To analyze the risk of a particular process, an organization needs to understand the data flow across an organization–how the data is being collected, where it is stored, who uses the information, how long it will be retained and so on.

    How an Efficient Data Mapping Software Helps in Achieving GDPR Compliance

    Now, what is an efficient data mapping software? It can be any application that aids a business organization to exploit the benefits of data mapping while adhering to the GDPR regulations. The following are a few important features of such GDPR compliant data mapping tools, which helps distinguish them from others.

    • Ability to Identify and Map Personal Data: Advanced data mapping software applications, which may or may not involve ETL data mapping software, allows users to identify and map personal data flow in an organization. They are designed to handle a large influx of data. These solutions utilize metadata to identify various data elements. The metadata helps in creating data maps that answer questions ranging from the origin of data, type of data collected, data subjects involved in process, service level agreements, among others. Thus, advanced data mapping software applications enable users to maintain, manage and monitor data maps for business efficiency and GDPR compliance.
    • Helps Ensure Data Security: Professional-grade data mapping software solutions are equipped with various security features that enable users to secure their database while providing them access to data, whenever required. They also allow organizations to conduct risk analysis of their data and ensure adherence to DPIA.
    • Assures a Better Control: Earlier reference to GDPR articles suggest how GDPR mandates enterprises to ensure data protection while handling personal data. An efficient data mapping software will help them address this need. They enable users to modify their applications to establish control, as well as safeguard personal data effectively.

    Achieving GDPR compliance is not an easy task. However, you can easily address it at various stages and it will help your business adhere to privacy standards while effectively meeting customer demands. You can take advantage of various data mapping software applications including advanced ETL data mapping tools to create effective data maps, stay compliant and, above all, drive your business forward.

    — Chandra Shekhar