Managed Metadata in SharePoint - Part Two

In part one of this post, I described using metadata in SharePoint. In this part two, I will describe metadata management.

Managed metadata makes it easier for Term Store Administrators to maintain and adapt your metadata as business needs evolve. You can update a term set easily. And, new or updated terms automatically become available when you associate a Managed Metadata column with that term set. For example, if you merge multiple terms into one term, content that is tagged with these terms is automatically updated to reflect this change. You can specify multiple synonyms (or labels) for individual terms. If your site is multilingual, you can also specify multilingual labels for individual terms.

Managing metadata

Managing metadata effectively requires careful thought and planning. Think about the kind of information that you want to manage the content of lists and libraries, and think about the way that the information is used in the organization. You can create term sets of metadata terms for lots of different information.

For example, you might have a single content type for a document. Each document can have metadata that identifies many of the relevant facts about it, such as these examples:

• Document purpose - is it a sales proposal? An engineering specification? A Human Resources procedure?
• Document author, and names of people who changed it
• Date of creation, date of approval, date of most recent modification
• Department responsible for any budgetary implications of the document
• Audience

Activities that are involved with managing metadata:

• Planning and configuring
• Managing terms, term sets, and groups
• Specifying properties for metadata

Planning and configuring managed metadata

Your organization may want to do careful planning before you start to use managed metadata. The amount of planning that you must do depends on how formal your taxonomy is. It also depends on how much control that you want to impose on metadata.

If you want to let users help develop your taxonomy, then you can just have users add keywords to items, and then organize these into term sets as necessary.

If your organization wants to use managed term sets to implement formal taxonomies, then it is important to involve key stakeholders in planning and development. After the key stakeholders in the organization agree upon the required term sets, you can use the Term Store Management Tool to import or create your term sets. You can also use the tool to manage the term sets as users start to work with the metadata. If your web application is configured correctly, and you have the appropriate permissions, you can go to the Term Store Management Tool by following these steps:

1. Select Settings and then choose Site Settings.

2. Select Term store management under Site Administration.

Managing terms, term sets, and groups

The Term Store Management Tool provides a tree control that you can use to perform most tasks. Your user role for this tool determines the tasks that you can perform. To work in the Term Store Management Tool, you must be a Farm Administrator or a Term Store Administrator. Or, you can be a designated Group Manager or Contributor for term sets.

To take actions on an item in the hierarchy, follow these steps:

1. Point to the name of the Managed Metadata Service application, group, term set, or term that you want to change, and then click the arrow that appears.

2. Select the actions that you want from the menu.

For example, if you are a Term Store Administrator or a Group Manager you can create, import, or delete term sets in a group. Term set contributors can create new term sets.

Properties for terms and term sets

At each level of the hierarchy, you can configure specific properties for a group, term set, or term by using the properties pane in the Term Store Management Tool. For example, if you are configuring a term set, you can specify information such as Name, Description, Owner, Contact, and Stakeholders in pane available on the General tab. You can also specify whether you want a term set to be open or closed to new submissions from users. Or, you can choose the Intended Use tab, and specify whether the term set should be available for tagging or site navigation.

Managed Metadata in SharePoint - Part One

Using metadata in SharePoint makes it easier to find content items. Metadata can be managed centrally in SharePoint and can be organized in a way that makes sense in your business. When the content across sites in an organization has consistent metadata, it is easier to find business information and data by using search. Search features such as the refinement panel, which displays on the left-hand side of the search results page, enable users to filter search results based on metadata.

SharePoint metadata management supports a range of approaches to metadata, from formal taxonomies to user-driven folksonomies. You can implement formal taxonomies through managed terms and term sets. You can also use enterprise keywords and social tagging, which enable site users to tag content with keywords that they choose. SharePoint enable organizations to combine the advantages of formal, managed taxonomies with the dynamic benefits of social tagging in customized ways.

Metadata navigation enables users to create views of information dynamically, based on specific metadata fields. Then, users can locate libraries by using folders or by using metadata pivots, and refine the results by using additional key filters.

You can choose how much structure and control to use with metadata, and the scope of control and structure. For example:

• You can apply control globally across sites, or make local to specific sites.
• You can configure term sets to be closed or open to user contributions.
• You can choose to use enterprise keywords and social tagging with managed terms, or not.

The managed metadata features in SharePoint enable you to control how users add metadata to content. For example, by using term sets and managed terms, you can control which terms users can add to content, and who can add new terms. You can also limit enterprise keywords to a specific list by configuring the keywords term set as closed.

When the same terms are used consistently across sites, it is easier to build robust processes or solutions that rely on metadata. Additionally, it is easier for site users to apply metadata consistently to their content.

Metadata Terms

A term is a specific word or phrase that you associated with an item on a SharePoint site. A term has a unique ID and it can have many text labels (synonyms). If you work on a multilingual site, the term can have labels in different languages.

There are two types of terms:

Managed terms are terms that are pre-defined. Term Store administrators organize managed terms into a hierarchical term set.

Enterprise keywords are words or phrases that users add to items on a SharePoint site. The collection of enterprise keywords is known as a keywords set. Typically, users can add any word or phrase to an item as a keyword. This means that you can use enterprise keywords for folksonomy-style tagging. Sometimes, Term Store administrators move enterprise keywords into a specific managed term set. When they are part of a managed term set, keywords become available in the context of that term set.

Term Set

A Term Set is a group of related terms. Terms sets can have different scope, depending on where you create the term set.

Local term sets are created within the context of a site collection, and are available for use (and visible) only to users of that site collection. For example, when you create a term set for a metadata column in a list or library, then the term set is local. It is available only in the site collection that contains this list or library. For example, a media library might have a metadata column that shows the kind of media (diagram, photograph, screen shot, video, etc.). The list of permitted terms is relevant only to this library, and available for use in the library.

Global term sets are available for use across all sites that subscribe to a specific Managed Metadata Service application. For example, an organization might create a term set that lists names of business units in the organization, such as Human Resources, Marketing, Information Technology, and so on.

In addition, you can configure a term set as closed or open. In a closed term set, users can't add new terms unless they have appropriate permissions. In an open term set, users can add new terms in a column that is mapped to the term set.

Group

Group is a security term. With respect to managed metadata, a group is a set of term sets that share common security requirements. Only users who have contributor permissions for a specific group can manage term sets that belong to the group or create new term sets within it. Organizations should create groups for term sets that will have unique access or security needs.

Term Store Management Tool

The Term Store Management Tool is the tool that people who manage taxonomies use to create or manage term sets and the terms within them. The Term Store Management tool displays all the global term sets and any local term sets available for the site collection from which you access the Term Store Management Tool.

Managed Metadata column

A Managed Metadata column is a special kind of column that you can add to lists or libraries. It enables site users to select terms from a specific term set. A Managed Metadata column can be mapped to an existing term set, or you can create a local term set specifically for the column.

Enterprise Keywords column

The enterprise Keywords column is a column that you can add to content types, lists, or libraries to enable users to tag items with words or phrases that they choose. By default, it is a multi-value column. When users type a word or phrase into the column, SharePoint presents type-ahead suggestions. Type-ahead suggestions might include items from managed term sets and the Keywords term set. Users can select an existing value, or enter a new term.

Social Tags

Social tags are words or phrases that site users can apply to content to help them categorize information in ways that are meaningful to them. Social tagging is useful because it helps site users to improve the discoverability of information on a site. Users can add social tags to information on a SharePoint site and to URLs outside a SharePoint site.

A social tag contains pointers to three types of information:

• A user identity
• An item URL
• A term

When you add a social tag to an item, you can specify whether you want to make your identity and the item URL private. However, the term part of the social tag is always public, because it is stored in the Term Store.

When you create a social tag, you can choose from a set of existing terms or enter something new. If you select an existing term, your social tag contains a pointer to that term.

If, instead, you enter a new term, SharePoint creates a new keyword for it in the keywords term set. The new social tag points to this term. In in this manner, social tags support folksonomy-based tagging. Additionally, when users update an enterprise Keywords or Managed Metadata column, SharePoint can create social tags automatically. These terms then become visible as tags in newsfeeds, tag clouds, or My Site profiles.

List or library owners can enable or disable metadata publishing by updating the Enterprise Metadata and Keywords Settings for a list or library.

In the second part of this post, I will describe managing SharePoint metadata.

Role of Automatic Classification in Information Governance

Defensible disposal of unstructured content is a key outcome of sound information governance programs. A sound approach to records management as part of the organization’s information governance strategy is rife with challenges.

Some of the challenges are explosive content volumes, difficulty with accurately determining what content is a business record comparing to transient or non-business related content, eroding IT budgets due to mounting storage costs, and the need to incorporate content from legacy systems or merger and acquisition activity.

Managing the retention and disposition of information reduces litigation risk, it reduces discovery and storage costs, and it ensures organizations maintain regulatory compliance. In order for content to be understood and determined why it must be retained, for how long it must be retained, and when it can be dispositioned, it needs to be classified.

However, users see the process of sorting records from transient content as intrusive, complex, and counterproductive. On top of this, the popularity of mobile devices and social media applications has effectively fragmented the content authoring and has eliminated any chance of building consistent classification tools into end-user applications.

If classification is not being carried out, there are serious implications when asked by regulators or auditors to provide reports to defend the organization’s records and retention management program.

Records managers also struggle with enforcing policies that rely on manual, human-based classification. Accuracy and consistency in applying classification is often inadequate when left up to users, the costs in terms of productivity loss are high, and these issues, in turn, result in increased business and legal risk as well as the potential for the entire records management program to quickly become unsustainable in terms of its ability to scale.

A solution to overcome this challenge is automatic classification. It eliminates the need for users to manually identify records and apply necessary classifications. By taking the burden of classification off the end-user, records managers can improve consistency of classification and better enforce rules and policies.

Auto-Classification makes it possible for records managers to easily demonstrate a defensible approach to classification based on statistically relevant sampling and quality control. Consequently, this minimizes the risk of regulatory fines and eDiscovery sanctions.

In short, it provides a non-intrusive solution that eliminates the need for business users to sort and classify a growing volume of low-touch content, such as email and social media, while offering records managers and the organization as a whole the ability to establish a highly defensible, completely transparent records management program as part of their broader information governance strategy.

Benefits of Automatic Classification for Information Governance

Apply records management classifications as part of a consistent, programmatic component of a sound information governance program to:

Reduce

• Litigation risk
• Storage costs
• eDiscovery costs

Improve

• Compliance
• Security
• Responsiveness
• User productivity and satisfaction

Address

• The fundamental difficulties in applying classifications to high volume, low touch content such as legacy content, email and social media content.
• Records manager and compliance officer concerns about defensibility and transparency.

Features

• Automated Classification: automate the classification of content in line with existing records management classifications.
• Advanced Techniques: classification process based on a hybrid approach that combines machine learning, rules, and content analytics.
• Flexible Classification: ability to define classification rules using keywords or metadata.
• Policy-Driven Configuration: ability to configure and optimize the classification process with an easy "step-by-step" tuning guide.
• Advanced Optimization Tools: reports make it easy to examine classification results, identify potential accuracy issues, and then fix those issues by leveraging the provided "optimization" hints.
• Sophisticated Relevancy and Accuracy Assurance: automatic sampling and bench marking with a complete set of metrics to assess the quality of the classification process.
• Quality Assurance : advanced reports on a statistically relevant sample to review and code documents that have been automatically classified to manually assess the quality of the classification results when desired.

Dublin Core Metadata

The word "metadata" means "data about data". Metadata describes a context for objects of interest such as document files, images, audio and video files. It can also be called resource description. As a tradition, resource description dates back to the earliest archives and library catalogs. The modern "metadata" field that gave rise to Dublin Core and other recent standards emerged with the Web revolution of the mid-1990s.

The Dublin Core Schema is a small set of vocabulary terms that can be used to describe different resources.

Dublin Core Metadata may be used for multiple purposes, from simple resource description, to combining metadata vocabularies of different metadata standards, to providing inter-operability for metadata vocabularies in the Linked data cloud and Semantic web implementations.

"Dublin" refers to Dublin, Ohio, USA where the schema originated during the 1995 invitational OCLC/NCSA Metadata Workshop hosted by the Online Computer Library Center (OCLC), a library consortium based in Dublin, and the National Center for Supercomputing Applications (NCSA). "Core" refers to the metadata terms as broad and generic being usable for describing a wide range of resources. The semantics of Dublin Core were established and are maintained by an international, cross-disciplinary group of professionals from librarianship, computer science, text encoding, museums, and other related fields of scholarship and practice.

The Dublin Core Metadata Initiative (DCMI) provides an open forum for the development of inter-operable online metadata standards for a broad range of purposes and of business models. DCMI's activities include consensus-driven working groups, global conferences and workshops, standards liaison, and educational efforts to promote widespread acceptance of metadata standards and practices.

In 2008, DCMI separated from OCLC and incorporated as an independent entity. Any and all changes that are made to the Dublin Core standard are reviewed by a DCMI Usage Board within the context of a DCMI Namespace Policy. This policy describes how terms are assigned and also sets limits on the amount of editorial changes allowed to the labels, definitions, and usage comments.

Levels of the Standard

The Dublin Core standard originally includes two levels: Simple and Qualified. Simple Dublin Core comprised 15 elements; Qualified Dublin Core included three additional elements (Audience, Provenance and RightsHolder), as well as a group of element refinements (also called qualifiers) that could refine the semantics of the elements in ways that may be useful in resource discovery. Since 2012 the two have been incorporated into the DCMI Metadata Terms as a single set of terms using the Resource Description Framework (RDF).

The original Dublin Core Metadata Element Set which is the Simple level consists of 15 metadata elements:

Title

Creator

Subject

Description

Publisher

Contributor

Date

Type

Format

Identifier

Source

Language

Relation

Coverage

Rights

Each Dublin Core element is optional and may be repeated. The DCMI has established standard ways to refine elements and encourage the use of encoding and vocabulary schemes. There is no prescribed order in Dublin Core for presenting or using the elements. The Dublin Core became ISO 15836 standard in 2006 and is used as a base-level data element set for the description of learning resources in the ISO/IEC 19788-2.

Qualified Dublin Core

Subsequent to the specification of the original 15 elements, an ongoing process to develop terms extending or refining the Dublin Core Metadata Element Set (DCMES) began. The additional terms were identified. Elements refinements make the meaning of an element narrower or more specific. A refined element shares the meaning of the unqualified element, but with a more restricted scope.

In addition to element refinements, Qualified Dublin Core includes a set of recommended encoding schemes, designed to aid in the interpretation of an element value. These schemes include controlled vocabularies and formal notations or parsing rules.

Syntax

Syntax choices for Dublin Core metadata depends on a number of variables, and "one size fits all" forms rarely apply. When considering an appropriate syntax, it is important to note that Dublin Core concepts and semantics are designed to be syntax independent and are equally applicable in a variety of contexts, as long as the metadata is in a form suitable for interpretation both by machines and by human beings.

The Dublin Core Abstract Model provides a reference model against which particular Dublin Core encoding guidelines can be compared, independent of any particular encoding syntax. Such a reference model allows users to gain a better understanding of descriptions they are trying to encode and facilitates the development of better mappings and translations between different syntax.

I will describe some applications of Dublin Core in my future posts.

Hadoop and Big Data

During last ten years the volume and diversity of digital information grew at unprecedented rates. Amount of information is doubling every 18 months, and unstructured information volumes grow six times faster than structured.

Big data is the nowadays trend. Big data has been defined as data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.

Hadoop was created in 2005 by Doug Cutting and Mike Cafarella to address the big data issue. Doug Cutting named it after his son's toy elephant It was originally developed for the Nutch search engine project. Nutch is an effort to build an open source web search engine based on Lucene and Java for the search and index component.

As of 2013, Hadoop adoption is widespread. A number of companies offer commercial implementations or support for Hadoop. For example, more than half of the Fortune 50 use Hadoop. Hadoop is designed to process terabytes and even petabytes of unstructured and structured data. It breaks large workloads into smaller data blocks that are distributed across a cluster of commodity hardware for faster processing.

Ventana Research, a benchmark research and advisory services firm published the results of its groundbreaking survey on enterprise adoption of Hadoop to manage big data. According to this survey:

• More than one-half (54%) of organizations surveyed are using or considering Hadoop for large-scale data processing needs.
• More than twice as many Hadoop users report being able to create new products and services and enjoy costs savings beyond those using other platforms; over 82% benefit from faster analysis and better utilization of computing resources.
• 87% of Hadoop users are performing or planning new types of analysis with large scale data.
• 94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data.
• 63% of organizations use Hadoop in particular to work with unstructured data such as logs and event data.
• More than two-thirds of Hadoop users perform advanced analysis such as data mining or algorithm development and testing.

Today, Hadoop is being used as a:

• Staging layer: the most common use of Hadoop in enterprise environments is as “Hadoop ETL” — pre-processing, filtering, and transforming vast quantities of semi-structured and unstructured data for loading into a data warehouse.
• Event analytics layer: large-scale log processing of event data: call records, behavioral analysis, social network analysis, clickstream data, etc.
• Content analytics layer: next-best action, customer experience optimization, social media analytics. MapReduce provides the abstraction layer for integrating content analytics with more traditional forms of advanced analysis.
• Most existing vendors in the data warehousing space have announced integrations between their products and Hadoop/MapReduce.

Hadoop is particularly useful when:

• Complex information processing is needed.
• Unstructured data needs to be turned into structured data.
• Queries can’t be reasonably expressed using SQL.
• Heavily recursive algorithms.
• Complex but parallelizable algorithms needed, such as geo-spatial analysis or genome sequencing.
• Machine learning.
• Data sets are too large to fit into database RAM, discs, or require too many cores (10’s of TB up to PB).
• Data value does not justify expense of constant real-time availability, such as archives or special interest information, which can be moved to Hadoop and remain available at lower cost.
• Results are not needed in real time.
• Fault tolerance is critical.
• Significant custom coding would be required to handle job scheduling.

Does Hadoop and Big Data Solve All Our Data Problems?

Hadoop provides a new, complementary approach to traditional data warehousing that helps deliver on some of the most difficult challenges of enterprise data warehouses. Of course, it’s not a panacea, but by making it easier to gather and analyze data, it may help move the spotlight away from the technology towards the more important limitations on today’s business intelligence efforts: information culture and the limited ability of many people to actually use information to make the right decisions.

Benefits of Automatic Content Classification

I had few questions about my posts about automatic content classification. I would like to thank my blog readers for these questions. This post is to follow up on those questions.

Organizations receive countless amounts of paper documents every day. These documents can be mail, invoices, faxes, or email. Even after organizations scan these paper documents, it is still difficult to manage and organize them.

To overcome the inefficiencies associated with paper and captured documents, companies should implement an intelligent classification system to organize captured documents.

With today’s document processing technology, organizations do not need to rely on manual classification or processing of documents. Organizations that overcome manual sorting and classification in favor of an automated document classification & processing system can realize a significant reduction in manual entry costs, and improve the speed and turnaround time for document processing.

Recent research has shown that two-thirds of organizations cannot access their information assets or find vital enterprise documents because of poor information classification or tagging. The survey suggests that much of the problem may be due to manual tagging of documents with metadata, which can be inconsistent and riddled with errors, if it has been done at all.

There are few solutions for automated document classification and recognition. Some of them are: SmartLogic's Semaphore, OpenText, Interwoven Metatagger, Documentum, CVISION Trapeze, and others. These solutions enable organizations to organize, access, and control their enterprise information.

They are cost effective and eliminate inconstancy, mistakes, and the huge manpower costs associated with manual classification. Putting an effective and consistent automatic content classification system in place that ensures quick and easy retrieval of the right documents means better access to corporate knowledge, improved risk management and compliance, superior customer relationship management, enhanced findability for key audiences and an improved ability to monetize information.

Specific benefits of automatic content classification are:

More consistency. It produces the same unbiased results over and over. Might not always be 100% accurate or relevant, but if something goes wrong, it is at least is easy to understand why.

Larger context. Enforces classification from the whole organizations perspective, not the individuals. For example, a person interested in sports might tag an article which mentions a specific player, but forget/not consider a team and a country topic.

Persistent. A person can only handle a certain number of incoming documents per day, whilst an automatic classification works round the clock.

Cost effective. Possible to handle thousands of documents much faster than a person.

Automatic document classification can be divided into three types: supervised document classification where some external mechanism (such as human feedback) provides information on the correct classification for documents; unsupervised document classification (also known as document clustering) where the classification must be done entirely without reference to external information, and semi-supervised document classification where parts of the documents are labeled by the external mechanism.

Automate your content classification and consider using manual labor mainly for quality checking and approval of content.

In my next post on this topic, I will describe the role of automatic classification in records management and information governance.

Unified Data Management

In most organizations today, information is managed in isolated silos by independent teams using various tools for data quality, data integration, data governance, metadata and master data management, B2B data exchange, content management, database administration, information life-cycle management, and so on.

In response to this situation, some organizations are adopting Unified Data Management (UDM), a practice that holistically coordinates teams and integrates tools. Other common names for this practice include enterprise data management and enterprise information management.

Regardless of what you call it, the big picture that results from bringing diverse data disciplines together yields several benefits, such as cross-system data standards, cross-tool architectures, cross-team design and development synergies, leveraging data as an organizational asset, and assuring data’s integrity and lineage as it travels across multiple departments and technology platforms.

But unified data management is not purely an exercise in technology. Once data becomes an organizational asset, the ultimate goal of UDM becomes to achieve strategic, data-driven business objectives, such as fully informed operations and business intelligence, plus related goals in governance, compliance, business transformation, and business integration. In fact, the challenge of UDM is to balance its two important goals: uniting multiple data management practices and aligning them with business goals that depend on data for success.

What is UDM? It is the best practice for coordinating diverse data management disciplines, so that data is managed according to enterprise-wide goals that promote efficiency and support strategic, data-oriented business goals. UDM is unification of both technology practices and business management.

For UDM to be considered successful, it should satisfy and balance the following requirements:

1. UDM must coordinate diverse data management areas. This is mostly about coordinating the development efforts of data management teams and enabling greater inter-operability among their participants. UDM may also involve the sharing or unifying of technical infrastructure and data architecture components that are relevant to data management. There are different ways to describe the resulting practice, and users who have achieved UDM call it a holistic, coordinated, collaborative, integrated, or unified practice. The point is that UDM practices must be inherently holistic if you are to improve and leverage data on a broad enterprise scale.

2. UDM must support strategic business objectives. For this to happen, business managers must first know their business goals, then communicate data-oriented requirements to data management professionals and their management. Ideally, the corporate business plan should include requirements and milestones for data management. Hence, although UDM is initially about coordinating data management functions, it should eventually lead to better alignment between data management work and information-driven business goals of the enterprise. When UDM supports strategic business goals, UDM itself becomes strategic.

More Definitions

UDM is largely about best practices from a user’s standpoint. Most UDM work involves collaboration among data management professionals of different specialties (such as data integration, quality, master data, etc.). The collaboration fosters cross-solution data and development standards, inter-operability of multiple data management solutions, and a bigger concept of data and data management architecture.

UDM is not a single type of vendor-supplied tool. Even so, a few leading software vendors are shaping their offerings into UDM platforms. Such a platform consists of a portfolio of multiple tools for multiple data management disciplines, the most common being BI, data quality, data integration, master data management, and data governance.

For the platform to be truly unified, all tools in the portfolio should share a common graphical user interface (GUI) for development and administration, servers should inter-operate in deployment, and all tools should share key development artifacts (such as metadata, master data, data profiles, data models, etc.). Having all these conditions is ideal, but not an absolute requirement.

UDM often starts with pairs of practices. In other words, it’s unlikely that any organization would want or need to coordinate 100% of its data management work via UDM or anything similar. Instead, organizations select combinations of data management practices whose coordination and collaboration will yield appreciable benefits.

The most common combinations are pairs, as with data integration and data quality or data governance and master data management. Over time, an organization may extend the reach of UDM by combining these pairs and adding in secondary, supporting data management disciplines, such as metadata management, data modeling, and data profiling. Hence, the scope of UDM tends to broaden over time into a more comprehensive enterprise practice.

A variety of organizational structures can support UDM. It can be a standalone program or a subset of larger programs for IT centralization and consolidation, IT-to-business alignment, data as an enterprise asset, and various types of business integration and business transformations. UDM can be overseen by a competency center, a data governance committee, a data stewardship program, or some other data-oriented organizational structure.

UDM is often executed purely within the scope of a program for BI and data warehousing (DW), but it may also reach into some or all operational data management disciplines (such as database administration, operational data integration, and enterprise data architecture).

UDM unifies many things. As its name suggests, it unifies disparate data disciplines and their technical solutions. On an organizational level, it also unifies the teams that design and deploy such solutions. The unification may simply involve greater collaboration among technical teams, or it may involve the consolidation of teams, perhaps into a data management competency center.

In terms of deployed solutions, unification means a certain amount of inter-operability among servers, and possibly integration of developer tool GUIs. Technology aside, UDM also forces a certain amount of unification among business people, as they come together to better define strategic business goals and their data requirements. When all goes well, a mature UDM effort unifies both technical and business teams through IT-to-business alignment.

Why Care About UDM?

There are many reasons why organizations need to step up their efforts with UDM:

Technology drivers. From a technology viewpoint, the lack of coordination among data management disciplines leads to redundant staffing and limited developer productivity. Even worse, competing data management solutions can inhibit data’s quality, integrity, consistency, standards, scalability, architecture, and so on. On the upside, UDM fosters greater developer productivity, cross-system data standards, cross-tool architectures, cross-team design and development synergies, and assuring data’s integrity and lineage as it travels across multiple organizations and technology platforms.

Business drivers. From a business viewpoint, data-driven business initiatives (including BI, CRM, regulatory compliance, and business operations) suffer due to low data quality and incomplete information, inconsistent data definitions, non-compliant data, and uncontrolled data usage. UDM helps avoid these problems, plus it enables big picture data-driven business methods such as data governance, data security and privacy, operational excellence, better decision making, and leveraging data as an organizational asset.

To be successful, an organization needs a data strategy that integrates a multitude of sources, case studies, deployment technologies, regulatory and best practices guidelines, and any other operating parameters.

Many organizations have begun to think about the contributions of customer information in a more holistic way as opposed to taking a more fragmented approach. In the past, these efforts were undertaken mainly by organizations traditionally reliant on data for their model (like finance and publishing), but today, as more and more businesses recognize the benefits of a more comprehensive approach, these strategies are gaining much wider deployment.

What should a good strategy look like?

First, identify all data assets according to their background and potential contribution to the enterprise.

Second, outline a set of data use cases that will show how information will support any of a variety of customer marketing functions.

Next, create rules and guidelines for responsible use and access, making sure that the process is flexible and transparent. Keep in mind that not all data should be treated the same way; rather, it should be managed according to its sensitivity and need.

Finally, make sure that this process is ongoing so that tactics can be evaluated and adjusted as needed.

Such a strategy combines the best practices with responsible data governance and smart organization. Everyone wins - the employees who gain quick access to essential information, the enterprise that is running more smoothly; and of course, the customers who are served by a resource-rich organization!

Content Categorization Role in Content Management

An ability to find content in a content management system is crucial. One of main goals of having a content management system is to make content easy to find, so you can take an action, make a business decision, do research and development work, etc.

The main challenge to findability is anticipating how users might look for information. That's where categorization comes into play. The quality of the categorization of each piece of content makes or breaks its findability. Theoretically, good tagging will last the lifetime of the content. You would think that if you do it well initially, then you can forget about it until it is time to retire that content. But reality can be very different.

Durable Categorization

Many issues complicate content categorization. They include:

• the sheer volume, velocity, and variety of internal and external-facing content which needs management;
• evolving/emerging regulations and compliance issues, some of which need to be retroactively applied; 
• the need to limit the company's exposure and to support the strength of its position in any legal activity.

Some organizations face the added challenge of integrating content from acquisitions or mergers, which most likely use content management structure, categorization, and methodologies that are incompatible and of inconsistent quality.

Considering these issues, the success factor for good content categorization are the automatic categorization techniques and processes.

Traditionally, keywords, dictionaries, and thesauri are used to categorize content. This type of categorization model poses several problems:

• taxonomy quality - it depends on the initial vision and attention to detail, and whether it has been kept current;
• term creep - initial categorization will not always accommodate where and how the content will be used over time, or predict relevancy beyond its original focus;
• policy evolution - it can't easily apply new or evolving policies, regulations, compliance requirements, etc.;
• cost and complexity - it is difficult and costly, if not practically impossible, to retroactively expand the original categorization of the existing content if big amount of content is added.

Automatic Categorization

Using technology to automatically categorize content is a solution. It applies the rules more consistently than people do. It does it faster. It frees people from having to do the task, and therefore has less costs. And, it can actively or retroactively categorize batches or whole collections of documents.

You can experience these benefits by using concept-based categorization driven by an analytics engine integrated into the content management system. These systems mathematically analyze example documents you provide to calculate concepts that can be used to categorize other documents. Identifying hundreds of keywords per term, they are able to distinguish relevance that escapes keyword and other traditional taxonomy approaches. They are even highly likely to make connections that a person would miss.

Consider 3D printers as an example. These are also known as "materials printers", "fabbers", "3D fabbers", and as "additive manufacturing". If all of those terms are not in the taxonomy, then relevant documents that use one or more of them, but not 3D printer, would not be optimally categorized.

People looking for information about 3D printers who are not aware of the alternative terms would miss related documents of potential significance. This particularly impacts  external facing websites that sell products on their websites. Their business depends on fast and easy delivery of accurate and complete information to their customers, even when the customer doesn't know all of the various terms used to describe the product they are looking for.

In contrast, through example-based mathematical analysis and comparison along multiple keywords, conceptual analytics systems understand that these documents are all related. They would be automatically categorized and tagged as relevant to 3D printing.

Another difference is that taxonomy systems require someone to enter the newly developed or discovered terms. In conceptual analytics, it is simply a matter of providing additional example documents that automatically add to the system's conceptual understanding.

The days of keeping everything "just in case" are long gone. From cost and risk exposure concerns, organizations need to keep only what is necessary, particularly as the volume and variety of content continue to grow. Good categorization and tagging systems are essential to good content management and to controlling expense and exposure.

Outdated and draft documents unnecessary expand every company's content repositories. Multiple copies of the same or very similar content are scattered throughout the organization. By some estimates, these compose upwards of 20% or more of a company's content.

Efficiently weeding out that content means 20% less active and backup storage, bandwidth, cloud storage for offsite disaster recovery, and archive volume. Effective and thorough tagging can identify such elements to reduce these costs, and simultaneously reduce the company's cost and exposure related to legal or regulatory requirements.

The Value Beyond Cost Savings

An effectively managed content delivers better cost of content management and reduced exposure to risk. While this alone is reason to implement improvements in categorization, there are other reasons.

Superior categorization through conceptual analysis also affects operational efficiency by enabling fast, accurate, and complete content gathering. A significant benefit for any enterprise is that it allows more time for actual work by reducing the time it takes to find necessary information. It is of critical importance for companies whose revenue depends on their customers quickly and easily finding quality information.

Conceptual analytics systems deliver two other advantages over traditional taxonomy methods and manual categorization. It creates a mathematical index, so it is useless to anyone trying to discover private information or clues about the company. Also, it is deterministic and repeatable. It will give the same result every time and so it is very valuable in legal or regulatory activities.

Concept-based analysis makes content findable and actionable, regardless of language, by automatically categorizing it based on understanding developed from example documents you provide. Both internally and externally, the company becomes more competitive with one of its most important assets which is unstructured information.

Video Instant Messaging for Collaboration and Knowledge Management

Due to falling costs, high-availability data connections, smart mobile devices, and the growth of cloud computing, knowledge management and enterprise collaboration are undergoing something of a rebirth. Most enterprise collaboration remains centered on documents. There is an opportunity to extend beyond document-centric tasks to other collaborative, linear, process-oriented work. Simple to use video instant messaging (VIM) technology may be very useful in this much needed shift.

Today, most companies use video for:

• team conferencing;
• customer interaction/service support;
• pre-recorded-corporate communications;
• an alternative to text for training/education.

Beyond those areas, VIM functionality is just starting to arrive to companies. There are different opinions about VIM. like it and feel it helps tocreate a better "human connection," and others don't like it and claim that it invades their privacy. In fact, the most common arguments in favor of and against the use of video are surprisingly similar to one another and suggest a cultural split that can be difficult to bridge or even manage.

Video instant messaging, as its name makes clear, is potentially the most invasive use of video in companies. For example, it can be images of unexpected video calls on an active laptop coming from an angry boss, with the recipient of the call being unprepared. One-to one, face-to-face discussions are the most optimal use of such real-time technology. There are valuable ways of using the technology.

What is Video Conferencing

Video conferencing is when multiple people, typically four or more, in more than one geographic location use audio and video technology to connect via a virtual conference room environment to conduct a pre-arranged meeting.

Web conferencing (or webinar) is where multiple users watch a single remote screen; this method is typically used for one-to-many presentations.

Video instant messaging (VIM) is distinct from the other two in that the interactions are typically one-to-one and are also most typically not pre-arranged, but rather ad hoc.

Opportunities

VIM is not particularly new, but its use (and misuse) has until recently been largely confined to the consumer world. Some knowledge management vendors such as Citrix, TIBCO and Teambox are adding VIM to their product suites because VIM functionality is particularly relevant in the mobile era: almost all handheld devices contain cameras. This is key to understanding the value of VIM, since it's not so much the video as the mobile camera that has much potential.

Future use cases for VIM technology in knowledge management will be situations where a physical object or environment needs to be collaboratively viewed. For example, in a service situation, a technician is looking at a complex wiring structure and using a mobile device to stream video of the situation to a remote expert, enabling them to share visuals to resolve the situation. Similarly, designers and creative people can discuss and view samples and compare options in real time. In situations like these, the ability to share a clear real-time moving image of the in real environment via a handheld device can be invaluable and a highly efficient use of collaboration technology.

Pros and Cons of Using Video

Pros

• Seeing the other attendees builds a deeper connection.
• It reduces travel costs and time on the road.
• Meetings can be recorded.
• Processes can be more efficient.
• Collaboration can be enhanced.

Cons

• People are self-conscious on camera.
• Audio/video quality varies.
• A poor or lost connection means no conference at all.
• It is not as good as face-to-face meetings.
• Signal delays can ruin the meeting.

New Horizons

For traditional users, there is a somewhat parallel use case in that they often want to share a document, drawing or whiteboard in real time with a colleague. It is about seeing the same thing and collaborating on it. VIM can have a valuable role in supporting collaborative processes in areas like law enforcement, healthcare, and engineering. The established use for video are users who create a lot of text-based communications and need to occasionally see one another from their home offices.

Moving from a fixed camera position to a fully mobile situation opens the technology to many more uses. Video stream can be used for all the key management and productivity functions, such as calendaring, expertise location, file access edit and view, knowledge sharing, and team and project management.

Three Priority Objectives

On a more tactical level, it will be important to use these objectives in VIM use.

First, focus on tying collaboration and knowledge sharing back to clearly defined business processes that involve human interaction. Take existing technology and configure it to meet your organization needs, and also use VIM to engage with work objects and environments, rather than simply using it as a face-to-face conversation tool.

In an organization situations, communication can't simply stop if someone hangs up. Whether with video or text or voice, the trail needs to be kept traceable, and the files and data created should be contextualized and made relevant. Documents need to be retained intact until a decision has been made to remove or destroy them, depending on governance, housekeeping and compliance requirements. This option should be available for video and other forms of interaction. Information lifecycle needs to be considered.

Third, if VIM is to be used for collaboration, then still-image functionality that freezes and captures views in HD should be provided within the VIM frame. This enables a detailed visual examination of a specific point in time, and also creates a file of record. Shifting between moving and still images in a collaborative engagement, allowing a free-flowing interaction and the capture of specific elements, would be of great value to many industry and process-specific collaborative situations.

Future

VIM has a key role to play in taking enterprise collaboration to the next level. This role is not so much about being able to see the face of the other person as it is about viewing and capturing in real time what the other party is seeing.Refining the use cases and collaborative process that leverage VIM will take some effort. Over time, VIM will prove to be a value in the growth of enterprise collaboration. It will be especially useful for industries such as healthcare, law enforcement, engineering, and maintenance. Enterprise collaboration tools and suites already have many of the key pieces of the puzzle; now they need to put them together into a coherent, practical whole.

Search Applications - Vivisimo

Vivisimo was a privately held technology company that worked on the development of computer search engines. The company product Velocity provides federated search and document clustering. Vivisimo's public web search engine Clusty was a metasearch engine with document clustering; it was sold to Yippy, Inc. in 2010.

The company was acquired by IBM in 2012 and Vivisimo Velocity Platform is now IBM InfoSphere Data Explorer. It stays true to its heritage of providing federated navigation, discovery and search over a broad range of enterprise content. It covers broad range of data sources and types, both inside and outside an organization.

In addition to the core indexing, discovery, navigation and search engine the software includes a framework for developing information-rich applications that deliver a comprehensive, contextually-relevant view of any topic for business users, data scientists, and a variety of targeted business functions.

InfoSphere Data Explorer solutions improve return on all types of information, including structured data in databases and data warehouses, unstructured content such as documents and web pages, and semi-structured information such as XML.

InfoSphere Data Explorer provides analytics on text and metadata that can be accessed through its search capabilities. Its focus on scalable but secure search is part of why it became one of the leaders in enterprise search. The software’s security features are critical, as organizations do not want to make it faster for unauthorized users to access information.

Also key is the platform’s flexibility at integrating sources across the enterprise. It also supports mobile technologies such as smart phones to make it simpler to get to and access information from any platform.

Features and benefits

1. Secure, federated discovery, navigation and search over a broad range of applications, data sources and data formats.

• Provides access to data stored a wide variety of applications and data sources, both inside and outside the enterprise, including: content management, customer relationship management, supply chain management, email, relational database management systems, web pages, networked file systems, data warehouses, Hadoop-based data stores, columnar databases, cloud and external web services.
• Includes federated access to non-indexed systems such as premium information services, supplier or partner portals and legacy applications through the InfoSphere Data Explorer Query Routing feature.
• Relevance model accommodates diverse document sizes and formats while delivering more consistent search and navigation results. Relevance parameters can be tuned by the system administrator.
• Security framework provides user authentication and observes and enforces the access permissions of each item at the document, section, row and field level to ensure that users can only view information they are authorized to view in the source systems.
• Provides rich analytics and natural language processing capabilities such as clustering, categorization, entity and metadata extraction, faceted navigation, conceptual search, name matching and document de-duplication.

2. Rapid development and deployment framework to enable creation of information-rich applications that deliver a comprehensive view of any topic.

• InfoSphere Data Explorer Application Builder enables rapid deployment of information-centric applications that combine information and analytics from multiple sources for a comprehensive, contextually-relevant view of any topic, such as a customer, product or physical asset.
• Widget-based framework enables users to select the information sources and create a personalized view of information needed to perform their jobs.
• Entity pages enable presentation of information and analytics about people, customers, products and any other topic or entity from multiple sources in a single view.
• Activity Feed enables users to "follow" any topics such as a person, company or subject and receive the most current information, as well as post comments and view comments posted by other users.
• Comprehensive set of Application Programming Interfaces (APIs) enables programmatic access to key capabilities as well as rapid application development and deployment options.

3.Distributed, highly scalable architecture to support large-scale deployments and big data projects.

• Compact, position-based index structure includes features such as rapid refresh, real-time searching and field-level updates.
• Updates can be written to indices without taking them offline or re-writing the entire index, and are instantly available for searching.
• Provides highly elastic, fault-tolerant, vertical and horizontal scalability, master-master replication and “shared nothing“ deployment.

4. Flexible data fusion capabilities to enable presentation of information from multiple sources.

• Information from multiple sources can be combined into “virtual documents“ which contain information from multiple sources.
• Large documents can be automatically divided into separate objects or sub-documents that remain related to a master document for easier navigation and comprehension by users.
• Enables creation of dynamic "entity pages" that allow users to browse a comprehensive, 360-degree view of a customer, product or other item.

5. Collaboration features to support information-sharing and improved re-use of information throughout the organization.

• Users can tag, rate and comment on information.
• Tags, comments and ratings can be used in searching, navigation and relevance ranking to help users find the most relevant and important information.
• Users can create virtual folders to organize content for future use and optionally share folders with other users.
• Navigation and search results can return pointers to people to enable location of expertise within an organization and encourage collaboration.
• Shared Spaces allow users to collaborate about items and topics that appear in their individualized views.