Every organization has content/information that needs to be treated as confidential. In some cases, it’s easy to know where this content is stored and to make sure that it is secure. In many other cases, this sensitive or confidential content is created and stored on shared drives or in insecure locations that employees could stumble upon or hackers could take advantage of. Especially in larger organizations that have been in operation for decades, sensitive content and data that has been left and forgotten in unsecured locations is a common, high-risk problem. We call hidden and risky content ‘Phantom Data’ to express that it is often unknown or unseen and also has the strong potential to hurt your organization’s operations. Most organizations have a Phantom Data problem and very few know how to solve it. We have helped a number of organizations address this problem and I am going to share our approach so that others can be protected from the exposure of confidential information that could lead to fines, a loss of reputation, and/or potential lawsuits.

We’ve consolidated our recommended approach to this problem into four steps. This approach offers better ways to defend against hackers, unwanted information loss, and unintended information disclosures.

1. Identify a way to manage the unmanaged content.
2. Implement software to identify Personally Identifiable Information (PII) and Personal Health Information (PHI).
3. Implement an automated tagging solution to further identify secure information.
4. Design ongoing content governance to ensure continued compliance.

Manage Unmanaged Content

Shared drives and other unmanaged data sources are the most common cause of the Phantom Data problem. If possible, organizations should have well-defined content management systems (document management, digital asset management, and web content management solutions) to store their information. These systems should be configured with a security model that is auditable and aligns with the company’s security policies.

Typically we work with our clients to define a security model and an information architecture for their CMS tools, and then migrate content to the properly secured infrastructure. The security model needs to align with the identity and access management tools already in place. The information architecture should be defined in a way that makes information findable for staff across business departments/units, but also makes it very clear as to where secure content should be stored. Done properly, the CMS will be easy to use and your knowledge workers will find it easier to place secure content in the right place.

In some cases, our clients need to store content in multiple locations and are unable to consolidate it onto a single platform. In these cases, we recommend a federated content management approach using a metadata store or content hub. This is a solution we have built for many of our clients. The hub stores the metadata and security information about each piece of content and points to the content in its central location. The image below shows how this works.

Once the hub is in place, the business can now see which content needs security and ensure that the security of the source systems matches the required security identified in the hub.

Implement PII and PHI Software

There are a number of security software solutions that are designed to scan content to identify PII and PHI information. These tools look at content to identify the following information:

• Credit card and bank account information
• Passport or driver’s license information
• Names, DOBs, phone numbers
• Email addresses
• Medical conditions
• Disabilities
• Relative information

These are powerful tools that are worth implementing as part of this solution set. They are focused on one important part of the Phantom Data issue, and can deliver a solution with out-of-the-box software. In addition, many of these tools already have pre-established connectors to common CMS tools.

Once integrated, these tools provide a powerful alert function to the existence of PII and PHI information that should be stored in more secure locations.

Implement an Automated Tagging Solution

Many organizations assume that a PII and PHI scanning tool will completely resolve the problem of finding and managing Phantom Data. Unfortunately, PII and PHI are only part of the problem. There is a lot of content that needs to be secured or controlled that does not have personal or health information in it. As an example, at EK we have content from clients that describes internal processes, which should not be shared. There is no personal information in it, but it still needs to be stored in a secure environment to protect our clients’ confidentiality. Our clients may also have customer or product information that needs to be secured. Taxonomies and auto-tagging solutions can help identify these files. 

We work with our clients to develop taxonomies (controlled vocabularies) that can be used to identify content that needs to be secured. For example, we can create a taxonomy of client names to spot content about a specific client. We can also create a topical taxonomy that identifies the type of information in the document. Together, these two fields can help an administrator see content whose topic and text suggest that it should be secured.

The steps to implement this tagging are as follows:

1. Identify and procure a taxonomy management tool that supports auto-tagging.
2. Develop one or more taxonomies that can be used to identify content that should be secured.
3. Implement and tune auto-tagging (through the taxonomy management tool) to tag content.
4. Review the tagging combinations that most likely suggest a need for security, and develop rules to notify administrators when these situations arise.
5. Implement notifications to content/security administrators based on the content tags.

Once the tagging solution is in place, your organization will have two complementary methods to automatically identify content and information that should be secured according to your data security policy.

Design and Implement Content Governance

The steps described above provide a great way to get started solving your Phantom Data problem. Each of these tools is designed to provide automated methods to alert users about this problem going forward. The solution will stagnate if a governance plan is not put in place to ensure that content is properly managed and the solution adapts over time.

We typically help our clients develop a governance plan and framework that:

• Identifies the roles and responsibilities of people managing content;
• Provides auditable reports and metrics for monitoring compliance with security requirements; and
• Provides processes for regularly testing, reviewing, and enhancing the tagging and alerting logic so that security is maintained even as content adapts.

The governance plan gives our clients step-by-step instructions, showing how to ensure ongoing compliance with data protection policies to continually enhance the process over time.

Beyond simply creating a governance plan, the key to success is to implement it in a way that is easy to follow and difficult to ignore. For instance, content governance roles and processes should be implemented as security privileges and workflows directly within your systems.

In Summary

If you work in a large organization with any sort of decentralized management of confidential information, you likely have a Phantom Data problem. Exposure of Phantom Data can cost organizations millions of dollars, not to mention the loss of reputation that organizations can suffer if the information security failure becomes public.

If you are worried about your Phantom Data risks and are looking for an answer, please do not hesitate to reach out to us.

The post The Phantom Data Problem: Finding and Managing Secure Content appeared first on Enterprise Knowledge.

One of, if not the most prominent, causes of this inability to quickly find information that EK has seen more recently is that an organization possesses multiple content repositories that lack a clear intention or purpose. As a result, users are forced to visit each repository within their organization’s technology landscape one at a time in order to search for the information that they need. Further, this problem is often exacerbated by other KM issues, such as a lack of proper search techniques, organization mismanagement of content, and content sprawl and duplication. In addition to a loss in productivity, these issues lead to rework, individuals making decisions on outdated information, employees losing precious working time trying to validate information, and users relying on experts for information they cannot find on their own. 

Along with a solid content management and KM related strategy, EK recommends that clients experiencing these types of findability related issues also seek solutions at the technical level. It is critical that organizations take advantage of the opportunity to streamline the way their users access the information they need to do their jobs; this will allow for the reduction of time and effort of users spent searching for information, as well as the assuage of the aforementioned challenges. This blog will explain how organizations can proactively mitigate the challenges of siloed information in different applications by instituting a unique set of technical solutions, including taxonomy management systems, metadata hubs, and enterprise search, to alleviate these problems.

With the abundance and variety of content that organizations typically possess, it is often unrealistic to have one repository that houses all types of content. There are very few, if any, content management systems on the market that can optimally support the storage of every type of content an organization may have, let alone possess the search and metadata capabilities required for proper content management. Organizations can address this dilemma by having a unified, centralized search experience that is able to search all content repositories in a secure and safe manner. This is achieved through the design and implementation of a semantic layer – a combination of unique solutions that work together to provide users one place to go to for searching for content, but behind the scenes allow for the return of results from multiple locations.

In the following sections, I will illustrate the value of Taxonomy Management Systems, Enterprise Search, and Metadata Hubs that make up the semantic layer, which collectively enable a unique and highly beneficial set of solutions.

Taxonomy Management Systems

In order to pull consistent data values back from different sources and filter, sort, and facet that data, there must be a taxonomy in place that applies to all content, in all locations. This is achieved by the implementation of an Enterprise TMS, which can be used to create, manage, and apply an enterprise-wide taxonomy to content in every system. This is important because it’s likely there are already multiple, separate taxonomies built into various content repositories that are different from one another and therefore cannot be leveraged in one system. An enterprise wide taxonomy allows for the design of a taxonomy that applies to all content, regardless of its type or location. An additional benefit of having an enterprise TMS is that organizations can utilize the system’s auto-tagging capabilities to assist in the tagging of content in various repositories. Most, if not all major contenders in the TMS industry provide auto-tagging capabilities, and organizations can use these capabilities to significantly reduce the burden on content authors and curators to manually apply metadata to content. Once integrated with content repositories, the TMS can automatically parse content, assign metadata based on a controlled vocabulary (stored in the enterprise taxonomy), and return those tags to a central location.

Metadata Hub

The next piece of this semantic layer puzzle is a metadata hub. We often find that one or more content repositories in an organization’s KM ecosystem lack the necessary metadata capabilities to describe and categorize content. This is extremely important because it facilitates the efficient indexing and retrieval of content. A ‘metadata hub’ can help to alleviate this dilemma by effectively giving those systems their needed metadata capabilities as well as creating a single place to store and manage that metadata. The metadata hub, when integrated with the TMS can apply the taxonomy and tag content from each repository, and store those tags in a single place for a search tool to index. 

This metadata hub acts as a ‘manage in place’ solution. The metadata hub points to content in its source location. Tags and metadata that are being generated are only stored in the metadata hub and are not ‘pushed’ down to the source repositories. This “pushing down” of tags can be achieved with additional development, but is generally avoided as not to disrupt the integrity of content within its respective repository. The main goal here is to have one place that contains metadata about all content in all repositories, and that this metadata is based on a shared, enterprise-wide taxonomy.

Enterprise Search

The final component of the semantic layer is Enterprise Search (ES). This is the piece that allows for individuals to perform a single search as opposed to visiting multiple systems and performing multiple searches, which is far from the optimal search experience. The ES solution acts as the enabling tool that makes the singular search experience possible. This search tool is the one that individuals will use to execute queries for content across multiple systems and includes the ability to filter, facet, and sort content to narrow down search results. In order for the search tool to function properly, there must be integrations set up between the source repositories, the metadata hub, and the TMS solution. Once these connectors are established, the search tool will be able to query each source repository with the search criteria provided by the user, and then return metadata and additional information made available by the TMS and metadata hub solutions. The result is a faceted search solution similar to what we are all familiar with at Amazon and other leading e-commerce websites. These three systems work together to not only alleviate the issues created by a lack of metadata functionalities in source repositories, but also to give users a single place to find anything and everything that relates to their search criteria.

Bringing It All Together

The value of a semantic layer can be exemplified through a common use case:

Let’s say you are trying to find out more information about a certain topic within your organization. In order to do this, you would love to perform a search for everything related to this certain topic, but realize that you have to visit multiple systems to do so. One of your content repositories stores digital media, i.e. videos and pictures, another of your content repositories stores scholarly articles, and another one stores information on individuals who are experts on the topic. There could be many more repositories, and you must visit each one separately and search within each system to gather the information you need. This takes considerable time and effort and in a best case scenario makes for a painstakingly long search process. In a worst case scenario, content is missed and the research is incomplete.

With the introduction of the semantic layer, the searchers would only have to visit one location and perform a single search. When doing so, searchers would see the results from each individual repository all in one location. Additionally, searchers would have extensive amounts of metadata on each piece of content to filter to ensure that they find the information they are looking for. Normally when we build these semantic layers the search allows users the option to narrow results by source system, content type (article, person, digital media), date created or modified, and many more. Once the searcher has found their desired content, a convenient link is provided which will take them directly to the content in its respective repository. 

Closing

The increasingly common issue of having multiple, disparate content repositories in a KM technology stack is one that causes organizations to lose valuable time and effort, while hindering employees’ ability to efficiently find information through mature, proven metadata and search capabilities. Enterprise Knowledge (EK) specializes in the design and implementation of the exact systems mentioned above and has proven experience building out these types of technologies for clients. If your company is facing issues with the findability of your content, struggling with having to search for content in multiple places, or even finding that searching for information is a cumbersome task, we can help. Contact us with any questions you have about how we can improve the way your organization searches for and finds information within your KM environment.

The post The Importance of a Semantic Layer in a Knowledge Management Technology Suite appeared first on Enterprise Knowledge.