Data and Knowledge

Our view of data often doesn't extend further than numbers. When you think about it, data means a percentage, a total, or something to which those numbers are attached. Furthermore, we want to act on those numbers with familiar math.

We don't act on data, we act on information, and we only act on information when it creates knowledge in our minds that enables us to make informed decisions. We might call this customer insight, but in the reality it is - data, information, and knowledge.

Too often, we lose the sight of the need to add together multiple data points to arrive at information that creates useful knowledge. It leads us to think that managing data is an end goal, when the primary objective should be asking how we can make something valuable out of it.

Knowledge is a property of a human mind, so you might consider it information in motion. Knowledge is what makes people to complete their work. A person's name is data, and information might include additional data like job title and company, while knowledge is information extended by understanding the person's objectives for the year ahead. We sometimes interchange these terms.

The role of customer service in organizations has probably never been as important or as difficult as it is now. Competition has spread globally, product life cycles have been reduced, and customization has become more common. The end result is that products and services have become quite complex, and, in response, firms have generated mountains of documents outlining how their products operate as well as policies and procedures detailing how to support them. Companies are trying to consolidate that information and present relevant data to users on an as-needed basis.

The idea of managing knowledge is both vital and perplexing. It is perplexing because knowledge has always been something that we make out of information. How does one manage knowledge in any systematic way? Knowledge management is the grouping of tools, technologies, and processes that constantly and consistently make the right information available to decision-makers.

When it comes to data, organizations continue to struggle with two conflicting goals. On one hand, they want to collect and consolidate information to streamline their operations. On the other hand, data repositories often sprout up in an ad hoc fashion, so it becomes difficult, and in some cases impossible to make sense of an organization's millions and even billions of records.

To solve this problem, organizations first have to uncover the whereabouts of all of their data, which usually is scattered randomly throughout the organization. Next they need to determine how to integrate their various information sources. Finally, they have to find funding for the project. If the integration is achieved, which could be a multi-year process in large enterprises, the potential benefits are great: streamlined operations, lower service costs, and improved customer satisfaction.

Galaxy Consulting has 16 years experience solving this problem. We have helped many organization to organize their knowledge and thus increase their efficiency and productivity, improve compliance, and save cost. We can do the same for you. Contact us today for a free, no obligation consultation!

Analytics for Big Data

Companies are just now beginning to harness the power of big data for the purposes of information security and fraud prevention.

Only 50% of companies currently use some form of analytics for fraud prevention, forensics, and network traffic analysis.

Less than 20% of companies use big data analytics to identify information, predict hardware failures, ensure data integrity, or check data classification, despite the fact that by doing so, companies are able to improve their balance of risk versus reward and be in a better position to predict potential risks and incidents.

Banks, insurance, and other financial institutions use big data analytics to support their core businesses. Large volumes of transactions are analyzed to detect fraudulent transactions and money laundering. These, in turn, are built into profiles that further enhance the analysis. Some insurance companies, for example, share and analyze insurance claims data to detect patterns that can point to the same fraudulent activities against multiple companies. Healthcare is another area in which data analysis can be used for information security.

Big data can arise from internal and external sources, spanning social media, blogs, video, GPS logs, mobile devices, email, voice, and network data. It's estimated that 90% of the data in the world today has been created in the past two years, and some 2.5 million terabytes of data are created every day.

Although many companies already use data warehousing, visualization, and other forms of analytics to tap into this high-volume data, using that data to prevent future attacks or breaches remains relatively uncharted territory. This is changing and will continue to do so as security increasingly moves from being a technical to a business issue.

To balance the business benefits of big data analytics with the cost of storage, organizations need to regularly review the data they are collecting, determine why and for how long they need it, and where and how they should store it.

The Human Element of the Big Data Equation

Because data volumes grow considerably every day, deciphering all the information requires both technology and people-driven processes. People often find patterns that a computer can pass over. Some other steps organizations can take to analyze big data for information security purposes include the following:

• Identify the business issue;
• construct a hypothesis to be tested;
• select the relevant data sources and provide subject matter expertise about them;
• determine the analyses to be performed;
• interpret the results.

Most companies struggle to find value from their customer analytics efforts. Lack of data management, integration, and quality are the biggest inhibitors to making better use of customer analytics. 54% of surveyed companies have difficulty managing and integrating data from the many varied sources, while 50% are concerned about consistent data quality.

Companies also struggle with assembling the right type of analytics professionals, communicating the results of the analysis to relevant colleagues, performing real-time analytics and making insights available during customer interactions, protecting data and addressing privacy concerns, and keeping pace with the velocity of data generation.

While key drivers of adoption include increasing customer satisfaction, retention, and loyalty, analytics use skews largely toward acquisition of new customers. 90% of surveyed companies use analytics for this purpose.

Other factors driving the use of analytics include reacting to competitive pressures, reducing marketing budgets, and addressing regulatory issues.

The use of predictive analytics as a growing trend, with 40% of organizations use it. 70% have been using descriptive analytics and business intelligence reporting for more than 10 years.

Organizations that have already mastered basic analytics methodologies and gained efficiencies in aggregate analysis are now looking to adopt advanced ways to do real-time, future-looking analysis.

Additionally, companies would like to start using social data as a viable source of customer analytics. This was cited as a long-term goal by 17% of the companies in the survey.

Organizations should look beyond social media for unstructured data. While many marketers have embraced social media as an effective way to engage customers, from an analytics standpoint, they have only scratched the surface in how other data sources, such as call center data and voice-of-the-customer data, can feed traditional customer analytics processes.

Analytics can also be used to improve customer engagements. Customer engagement features at the bottom of the list of metrics. This is a missed opportunity for customer analytics practitioners to gain deeper insight into how individual customers interact with content, offers, and messaging across various touchpoints.

Customer analytics practitioners do a number of things right, including focusing on the right types of analytics and methodologies to achieve a basic understanding of who their customers are, their propensity to buy, how to target them effectively, and how best to experiment with content, features, and offers. But despite this, companies should develop a holistic customer analytics solution framework.

Although individual customer analytics techniques answer specific business questions, they fail to deliver efficiency in generating insights at an aggregate level.

Organizations should look outside their own four walls and connect with partners who are knowledgeable in analytics technology, analytical services, and data mining to explore the next steps for customer analytics. It is not just about buying an analytics tool, it is also about employing the professional services to make sense of the data.

Galaxy Consulting has 16 years experience in the area of analytics. Contact us today for a free consultation and let's get started!

Gamification for Content and Knowledge Management

To successfully manage content and knowledge in an organization it is very important to promote a culture among the organization's employees to collaborate working on documents, share and document knowledge, comply with document control and information governance procedures.

This gets difficult when employees are disengaged from this aspect of their jobs. A recent Gallup poll found that more than 70% of all U.S. workers are either actively or passively disengaged from their work. It is a particularly problematic situation for contact centers, where employee turnover is much higher than in most other industries. It is imperative to ensure that employees are properly engaged in their job so that content and knowledge management initiatives could succeed.

One way this can be done is by using gamification which is borrowing from video games the principles of virtual challenges, contests, and quests for the purpose of racking up points, advancing to higher levels, or earning rewards. Gamification can be used as a means to get employees more passionately involved in collaboration on working with documents, to document tacit knowledge, to follow document control and information governance procedures.

People get excited with possibilities for rewards, status, achievement, and competition. Gartner predicted that by 2016, more than 40% of the top companies would be using gamification to transform their business operations.

What Can be Gamified?

Companies can use gamification to reward incremental improvements in knowledge sharing such as documenting processes they work with, in content management such using a content management system to create, update, and approve documents, in document control such as using a CMS workflow to approve documents, etc.

Employees can be recognized for improvement in content and knowledge management procedures, for increasing their knowledge of these processes through training, for properly using social media or email channels as far as company information is concerned or for driving traffic to company knowledge bases or online portals.

Anything that can be measured in content and knowledge management can also be gamified. But you have to tell your employees what you want them to do and why.

In the contact center, for example, gamification can be applied to many things, from entering information in the knowledge base to logging and handling more phone calls, chats, or email. The most basic contests can involve reducing average call handling time and increasing first-call resolutions, updating knowledge base entries.

Gamification can be used as a long-term strategy or implemented for shorter duration when managers see a need for improvements. In the long term, gamification ensures that processes and workflows do not end up getting monotonous over time.

Set Clear Improvement Goals

One of the trickiest parts of an implementation could be determining the targets to be achieved. While setting goals might appear to be a good motivator, employees will react negatively to unrealistic goals. Ideally, a target goal should stretch employees to achieve a higher level of performance, but still be based in reality, using established industry best practices. The goal must be consistent for all employees and across all customer interactions and then it must be clearly communicated to all employees.

When managers notice a slip in one area, it is a good idea to implement contests to bring that number up again. Here, managers need to determine what percentage improvement is needed to close the gap between the current level and the benchmark.

Other special contests can be held monthly, quarterly, or yearly as needed or desired. You can implement any contest quickly, and you can easily change your reward one week to the next.

However, contests can lose their motivational power over time without personalization, transparency, and immediate feedback.

You can use recognition and virtual rewards, for example you can put achievers' names on top of a leader-board as well as financial incentives such as gift cards. Other common rewards include posting an employee of the month photo on a board in the break room, online badges, titles or access to privileges like special parking spots or free lunches.

You reward people for what they are doing and make it clear what they need to do next to advance in the game.

For gamification to be an effective motivator, companies need to make all of the results public so team members can see where they stand compared to their colleagues. It also adds transparency and trust.

There are few technology solutions for gamification. Most gamification solutions offer a leader-board feature. These tools provide robust analytics and expert reports that can provide insight into what motivates employees and to which challenges they respond the best.

Freshdesk Arcade, for example, enables companies to display a leader-board of top performers in specific categories. Bunchball's Nitro solution also offers custom leader-boards that can be displayed publicly on dedicated monitors or TV screens. Or, with a single click on the user console, users can see their current point totals, how many points they need to reach the next level, and the rewards toward which they are working.

Badgeville has a platform for Behavior Platform, a suite of products that includes Game Mechanics for creating gamelike activities, Reputation Mechanics for promoting status in an online community, and Social Mechanics for using social networking techniques.

Recent product enhancements for LevelEleven's flagship gamification platform, Compete, include real-time feedback, an updated user interface, newly designed leader-boards, and real-time breaking news bursts for LeaderTV. The company also recently announced a mobile application and strategic integration with several top cloud-computing providers. Web portals with real-time dashboards cost nothing to install and operate and can be just as effective.

With most solutions now available in the cloud, the gamification applications can be very affordable, on-boarding can be accomplished more quickly, and ROI can be realized in much less time.

Products from Bunchball, Badgeville, FreshDesk, and other vendors are software-as-a-service platforms rather than individual applications. As such, they can be easily customized to fit the individual needs of each company and enable the businesses to track behavior and activities across their Web and mobile properties. This also comes in handy, since every deployment will likely have different audiences and goals.

Gamification can really help to achieve positive results in content and knowledge management initiatives. Galaxy Consulting is on the top of developments in this relatively new field.

What is Ontology?

Ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that exist in a particular domain of information. Ontologies are created to limit complexity and to organize information. Ontologies are considered one of the pillars of the Semantic Web.

The term ontology has its origin in philosophy and has been applied in many different ways. The word element onto- comes from the Greek "being", "that which is". The meaning within information management is a model for describing information that consists of a set of types, properties, and relationship types. Ontologies share many structural similarities, regardless of the language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations.

The most common ontology visualization techniques are indented tree and graph.

Ontology Components

Common components of ontologies include:

• Individuals: instances or objects (the basic or "ground level" objects).
• Classes: sets, collections, concepts, classes in programming, types of objects, or kinds of things.
• Attributes: aspects, properties, features, characteristics, or parameters that objects and classes can have.
• Relations: ways in which classes and individuals can be related to one another.
• Function terms: complex structures formed from certain relations that can be used in place of an individual term in a statement.
• Restrictions: formally stated descriptions of what must be true in order for some assertion to be accepted as input.
• Rules: statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form.
• Axioms: assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application.
• Events: the changing of attributes or relations.

Ontologies are commonly encoded using ontology languages.

Ontology Types

Domain Ontology

A domain ontology (or domain-specific ontology) represents concepts which belong to a certain term. Particular meanings of terms applied to that domain are provided by domain ontology. For example, the word "card" has many different meanings. An ontology about the domain of "poker" would model the "playing card" meaning of the word.

Since domain ontologies represent concepts in very specific and often eclectic ways, they are often incompatible. As systems that rely on domain ontologies expand, the need comes to merge domain ontologies into a more general representation. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.).

Upper Ontology

An upper ontology (or foundation ontology) is a model of the common objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that contains the terms and associated object descriptions as they are used in various relevant domain sets.

There are several standardized upper ontologies available for use such as Dublin Core, for example.

Hybrid Ontology

Hybrid ontology is a combination of upper and domain ontology.

Ontology Languages

Ontology languages are formal languages used to construct ontologies. They allow the encoding of knowledge about specific domains and often include reasoning rules that support the processing of that knowledge. The most commonly used ontology languages are Web Ontology Language (OWL), Resource Description Framework (RDF), RDF Schema (RDFS), Ontology Inference Layer (OIL).

Ontology Editors

Ontology editors are applications designed to assist in the creation or manipulation of ontologies. They often express ontologies in one of many ontology languages. Some provide export to other ontology languages.

Among the most relevant criteria for choosing an ontology editor are the degree to which the editor abstracts from the actual ontology representation language used for persistence and the visual navigation possibilities within the knowledge model. Also important features are built-in inference engines and information extraction facilities, and the support of meta-ontologies such as OWL-S, Dublin Core, etc. Another important feature is the ability to import & export foreign knowledge representation languages for ontology matching. Ontologies are developed for a specific purpose and application.

Ontology Learning

Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting a domain's terms from natural language text. As building ontologies manually is labor-intensive and time consuming process, there is a need to automate the process. Information extraction and text mining methods have been explored to automatically link ontologies to documents.

Galaxy Consulting has 16 years experience working with ontologies. Please contact us for a free consultation.