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.

Data Security

Data security should be a priority in your organization.

For hackers, large-scale data breaches such as Home Depot, Neiman Marcus, and Staples are gold mines. For businesses, keeping valuable customer data out of the hands of cyber-thieves is a constant battle. Companies need to safeguard against every possible vulnerability across their entire infrastructure.

In 2014, the total number of reported data breaches in the United States hit a record high of 783, averaging about 15 per week, based on information compiled by the Identity Theft Resource Center (ITRC).

Companies, on average, can expect to encounter 17 malicious codes, 12 sustained probes, and 10 unauthorized access incidents each month, according to research from the Ponemon Institute, a provider of independent research on privacy, data protection, and information security policy.

Despite the growing number of attacks, many companies are still not doing nearly enough to secure their customers' personal and financial information. For many companies, the wake-up call only comes after they have fallen victim to a large-scale, high-profile breach.

Forrester Research noted that outside of banking and national defense, many industries are "woefully immature" when it comes to making the necessary investments in data breach protection, detection, and response.

This prompted Forrester to conclude that most enterprises will not be able to respond to a data breach without undermining their customers' trust or dragging their own corporate reputations through the mud.

Companies need to prevent data breaches from happening. They need to have an incident response and crisis management plan in place. Efficient response to the breach and containment of the damage has been shown to reduce the cost of breaches significantly and goes a long way toward reassuring customers who might have been thrown into a panic.

The first step toward that goal is having a high-level company executive who is responsible for data security. The key to addressing information security is first understanding what customer information is stored in company databases. Create a data inventory and determine what data is sensitive. Then segment out the sensitive and nonsensitive data.

Systematically purge the data that your organization no longer needs.

Take an inventory of all of their IT assets and business processes and analyze them for vulnerabilities that could expose sensitive data, for example, cardholder data. The next step, would be to fix those vulnerabilities. This assessment should be performed at least once a year. Make sure that the company's data security program meets industry best practices, government regulations, and the company's business objectives.

Make sure your web site uses encryption for processing customer's data. Once your company no longer needs customer data, such as payment cards or any other personal information, it should be securely deleted.

It is crucial for companies to segment data so that a breach in one file does not open other data repositories.

Companies should use Internet firewalls at all times, keep their operating systems and other business software up to date, and install and maintain antivirus and anti-spyware programs. Because many companies allow employees to use their own mobile devices, including smartphones, tablets, and laptops for business, these devices should be protected in the same way. Limit some company applications and data so that employees can't access them from unsecured mobile devices.

It is extremely important that companies limit data access to those employees who need it setting up appropriate security permissions in your data systems. You can put data logging in place, with alarms for when something happens out of the ordinary. This way you will know when someone is doing something with the data that does not coincide with their job description.

Contact centers are vulnerable to hackers. They use interactive voice response (IVR) systems for surveillance and data-gathering as a precursor to phishing schemes with agents, who are unwittingly coaxed into giving out sensitive information to unauthorized callers. In most cases, the call center agents are tricked by skilled fraudsters who use a variety of social engineering techniques to get them to break normal security procedures. The only real defense is proper training and protocols.

As many as 35% of data breaches have started with basic human error, such as sending an email with personal information to the wrong person or storing company files on laptops or tablets that were lost or stolen.

Even worse than careless employees or outside hackers, though, are the contact center agents who knowingly engage in illegal activities, using their jobs to gain access to information that they can sell or use on their own.

To help contact centers deal with this threat, call center technology can completely prevent skimming by agents. At the point in the transaction where the agent needs to collect the credit card information, systems can automatically pause recordings. With other solutions, the call can be transferred to an IVR system. Agent-assisted solutions can allow agents to collect credit card information without ever seeing or hearing it. The agent remains on the phone and customers enter their credit card information directly into the system using their phones' keypads. The standard dual-tone multi-frequency tones are converted to monotones so the agent cannot recognize them and they cannot be recorded.

In this environment, contact center managers and other employees need to be trained to spot at-risk employee behaviors. Training alone, though, is not enough. Employees need to know that there will be serious repercussions for violations of company practices and security protocols. Companies need to have a clearly defined formal policy so that employees know if they violate it, there are consequences that they will have to face.

Data security, therefore, has to be a business-wide endeavor. IT professionals, company executives, and employees at every level must work together to protect critical data assets from internal and external threats. Companies need to foster a security-aware culture in which protecting data is a normal and natural part of everyone's job.

Data security is also a constant game of what-ifs. The only certainty is that cyber-criminals will never stop learning and sharing information that will help them to get into high-profile targets. They will never stop trying to break into corporate databases. The information is just too valuable on the black market. The key is to make sure that you are not leaving the front door open for hackers to get in.

Galaxy Consulting has 16 years experience protecting organizations' data. We have done it for many companies. We can do the same for you! Contact us today for a free consultation!

Successful Self-Service Strategy

When it comes to customer service, simplicity is critical. Companies can improve customer experiences primarily by limiting the amount of effort it takes for customers to find answers to their questions and accomplish their tasks. Here lies the appeal of Web self-service, which for many consumers has become the preferred communication channel.

Instantly available, 24/7 online customer self-service portals are gaining ground over conventional agent-assisted support, marking a significant shift in consumer attitudes toward the technology. And, contrary to popular belief, interest in Web self-service technologies is not just coming from younger consumers. The technology is changing the behavior of consumers of all generations. In fact, a recent study by Forrester Research found that 72% of consumers, regardless of age, prefer self-service to picking up the phone or sending an email when it comes to resolving support issues. This certainly is welcome news for organizations looking to cut customer service costs and maximize revenue.

There are several elements to consider for successful self-service strategy.

The success of Web self-service depends on the quality and quantity of the information available and the ease with which it can be accessed. Online customers are extremely impatient and information-hungry, so the material available to customers through self-service needs to be succinct and direct, even in response to queries that are not.

The self-service option has to be easy to find on the Web site. To call more attention to the portal, organizations can prominently place a link to the self-service portal on the homepage and other common support pages that feature company, product, and services information. And, since a self-service portal is an extension of a company's Web site, it should have the same look and feel as the rest of the site.

Once on the portal, 80/20 rule applies which means that you assume that 80% of site visitors are looking for about 20% of the content, so that 20% should be easy to find.

As for the content itself, it should be clear, to the point, and easy to understand. This can be achieved by including graphic elements, such as diagrams, charts, and bullet points. When doing so, make sure the graphics are optimized for the Web. If they're not, the Web site could take too long to load, which might cause some customers to abandon it for a more costly agent-assisted channel. Consider keeping content to an eighth-grade reading level, so the average 13- or 14-year-old can make sense of it.

Ensuring accessibility also means that the site should support a variety of Internet browsers, operating systems, assistive technologies for the blind, and, of course, mobile platforms. The latter is becoming more important, especially when one considers that almost a third of all Web traffic today comes from mobile devices.

To make a self-service section even more effective, it can be combined with an automated guidance system that enables site visitors to enter questions and then takes them to specific responses without forcing them to scan an entire database for the answer they need.

One such system is marketed by WalkMe, a San Francisco start-up that enables Web site owners to enhance their online self-service options with interactive on-screen step-by-step instructions displayed as pop-up balloons. The balloons can be programmed to appear automatically when the site visitor rolls his cursor over certain items or when he clicks on a help button.

Customers who can't find answers on their own in a self-help knowledge base might be inclined to call a customer service line, but they are more likely to type their question into a Google search bar, and companies have no control over the results that the Google search returns. This presents a number of problems for a company. Not only has the visitor left your site, but he can find information that you may not want him to see.

Virtual agents are another option companies can use to help customers find what they're looking for. IntelliResponse's Virtual Agent technology simplifies its Web self-service options. The software helps site visitors to find the single right answer to their questions. To keep information current and relevant, it strips outdated FAQ entries, learns over time how to group and respond to questions, and captures data about customer service queries to find precisely what customers need so your organization can fine-tune how it presents information on its Web site.

Companies can also use Web chat to help customers through the self-service maze. It's a tool that's already widely accepted by consumers and businesses alike. LiveWebAssist chat enables agents to push prepared content such as photos, graphics, or Web link, to customers on the site with a single click.

Along with chat and virtual agents, companies can use assisted browsing, or cobrowsing, to move self-service interactions along. This functionality lets the agent—or possibly the virtual agent—temporarily take control of a customer's computer screen. Not only does this improve the self-service experience, but, when interactions move to the contact center through either phone or chat, co-browsing can reduce the average handling time.

It is important to measure response time. Perhaps the most effective measure is the number of customer questions that are submitted and get a response. This can apply to those questions where the customer finds the answer on her own as well as those that are answered through a social community or by a representative of the company. Consider these elements:

• the number of issues resolved per month through social communities. This includes the number of new questions posed to and answered by the community, the percentage of issues resolved by members of the community rather than company employees, and the number of "this article helped me" votes received.
• the number of issues resolved every month through FAQs and company knowledge bases. This includes the number of page views that both receive per month.
• the average cost to resolve issues through channels that involve a company employee. These include phone, email, and chat.

And then, as with any customer service channel, it's important to collect user feedback about the self-help experience. As with any other customer service channel, this can be done through customer surveys, Web analytics and search logs, customer interviews and focus groups, usability testing, and collaborative design processes.

For self-service to be done right, it should be in the interest of the customer. You do not want customers to use self-service because they are forced to. You want them to use it because it serves their needs.

Galaxy Consulting has 16 years experience in optimizing self-service on companies web sites. We can do the same for you. Contact us today for a free consultation!

The Power of Knowledge

Your contact center agents must be available and equipped with the knowledge they need to handle customer issues quickly and efficiently.

However, with the explosion of new channels such as Internet, social media, and mobile computing, many companies lack the tools and processes required to empower their employees to deliver great customer experience.

Organizations struggle with static, siloed knowledge systems that not only provide redundant, often inaccurate information, but are costly to maintain.

Companies that have invested in creating a Powerful State of Knowledge are delivering great customer experiences, which translate into sustainable growth and profitability.

To achieve powerful state of knowledge, companies must be able to:

1. Establish a single knowledge base. Consolidate your knowledge into one single source of truth and make it available to agents and customers across your web site, mobile, and social channels. Tie knowledge to analytics and key performance indicators (KPIs) to present valuable content and address information gaps. This new level of visibility makes it easy for agents to:

• Update knowledge
• Identify potential customer issues
• Provide fast, accurate resolution

If you become driven by market demand for enhanced self-help services and internal demand for efficient productivity improvements, you can transform your customer and employee support systems, taking your existing separate knowledge repositories and establishing one central cross-channel knowledge base. This solution will help to raise efficiency and reduce the cost-per-call of your agents, and it will also improve the quality of the customer support you provide to your customers.

2. Social media has evolved knowledge management from static data residing in a structured database to dynamic, unstructured data created in every social interaction. As a result, you must monitor customers’ social conversations on Facebook, Twitter, and other sites to analyze sentiment and prioritize and respond to service issues.

3. Not many organizations are using traditional knowledge base technology. Instead, many are attempting to embrace the chaos that Big Data, social media, and the move to the cloud create, yet they still face challenges bringing it all together to make the most out of the information.

Unified indexing and insight technology enables just that - tapping into full knowledge ecosystems and providing support agents, employees and customers with contextually relevant information. This unprecedented access to actionable insight has helped companies achieve dramatic results, such as a 30%+ reduction in case resolution time, 10%+ increase in customer self-service satisfaction and more.

The need to make the most of organizational knowledge, to get as much value from it as possible is greater now than ever before. Organizations of all sizes are finding themselves with overwhelming amounts of information, often locked away in silos--different systems, different departments, different geographies and different data types, making it impossible to connect the dots and make sense of critical business information.

Traditional KM initiatives have considered knowledge a transferable commodity that can be stored in a system of record and used mechanically. Yet, in reality, knowledge goes beyond data and information, and is personal and contextual.

Data is factual information measurements, statistics, or facts. In and of itself, data provides limited value. It must be organized into information before it can be interpreted. Information is data in context organized, categorized or condensed. Knowledge is a human capability to process information to make decisions and take action.

The building blocks of knowledge are everywhere, fragmented, complex, unstructured, and often outside the systems of record (in the cloud, in social media, etc.). The key is to bring it all together, and presenting it in context to users.

Unified indexing and insight technology is the way that forward thinking companies access knowledge and experts. The technology brings content into context--assembling fragments of structured and unstructured information on demand and presenting it, in context, to users.

Designed for the enterprise, unified indexing and insight technology is built to bring together data from heterogeneous systems (e.g. email, databases, CRM, ERP, social media, etc.), locations (cloud and on-premise), and varied data formats of business today, It securely crawls those sources, unifies the information in a central index, normalizes information and performs mash-ups on demand.

The technology can be context-aware, relying on the situation of the user to anticipate and proactively offer enriched, usable content directly related to the situation at hand such as solutions, articles, experts, etc. from across the vast and growing ecosystem.

Best Practices for a Higher Return on Knowledge

Bringing relevant content to your agents and customers will increase productivity, create happier employees and drive higher customer satisfaction. Follow these best practices to achieve a higher return on knowledge:

1. Consolidate the knowledge ecosystem. Bring together information from enterprise systems, data sources, employee and customer social networks, social media, etc. Connect overwhelming amount of enterprise and social information.

2. Connect people to knowledge in context. Connect users to the information they need, no matter where it resides, within their context and in real-time.

3. Connect people to experts in context. Connect the people associated with the contextually relevant content to assist in solving a case, answer a key challenge or provide additional insight to a particular situation.

4. Personalize information access. Present employees and customers with information and people connections that are relevant, no matter where they are, and no matter what they are working on.

Investing in the creation of a powerful state of knowledge builds a defensible advantage in delivering great customer experiences. Those experiences lead to sustainable growth and profitability by driving customer acquisition, customer retention, and operational efficiency.

Service and support agents can solve cases faster. No longer do agents need to search across multiple systems or waste time trying to find the right answer or someone who knows the answer. They will have relevant information about the customer or case at hand right at their fingertips: suggested solutions, recommended knowledge base articles, similar cases, experts who can help, virtual communication timelines, etc.

Customers can solve complex challenges on their own. Logging in to customer self-service, customers will see a personalized and relevant view of information form the entire knowledge ecosystem (from inside or outside your company) intuitively presented so that they can solve their own challenges.

Employees can stop reinventing the wheel. When every employee can access relevant information, locate experts across the enterprise, and know what does and does not exist, they can finally stop reinventing the wheel.

Galaxy Consulting has 16 years experience in this area. We have done this for few companies and we can do the same for you.

Personalization in Content Management

Content personalization in content management makes your users' experience more rewarding. Content personalization targets specific content to specific people. One simple example is showing code samples to developers and whitepapers to business users.

Segment Your Users

The first step to delivering a personalized customer experience is to segment your visitors so you can present them with what’s most relevant to them.

Any good personalization strategy starts with a fundamental understanding of your customer’s behavior, needs and goals. Upfront research goes a long way to building out the personas and having the insight from which to develop an approach to personalization. This may already be gathered through ongoing customer insight or voice of the customer programs, or be more ad hoc and project based. Regardless of the approach, be sure that any approach to personalization is grounded in a solid understanding of your users.

The next step in the process is to define the audience goals and objectives so you can know if the personalization efforts are successful. These may include top-line key performance indicators such as conversion rate or online sales, or be more specific to the personalization scenarios (i.e. landing page bounce rate). Try to be specific as possible and ensure that your measures of success directly relate to the areas of focus for your personalization efforts impact.

Personalize Your Content

In order to provide personalized content, it is necessary to determine which content is most effective for each audience segment. This content mapping process can be done alongside the audience segmentation model to ensure you have the right content for the right user at the right stage. If we use the business users and developers example from above, we can personalize the home page for the developers segment to talk about things related to the technology and how it can be extended while we serve business users with information related to how they can achieve their goals using this solution.

The biggest mistake organizations make with personalization is thinking too big and getting overwhelmed before they even start. It is exhausting to even start thinking about how to deliver the right message to the right person at every single interaction. Starting with a few specific personalization scenarios can help you more rapidly adopt the processes and technology and see what works on a small scale before expanding.

Here are a few example rules-based scenarios for an insurance company:

• If a user in a specific region of the United States visits the site, show them regionally specific rates and agent information.
• If a user has shown a specific interest in a vehicle, show images and offers that include that vehicle.
• If a user is an existing customer (as identified through specific site actions or e-mail campaigns) feature tools and content that help them maintain their relationship with you.
• If a user has already subscribed to the newsletter, replace the subscribe to newsletter call-out with a different offer or high value piece of content.

As you begin to think about the overall customer journey and digital experience, this list of scenarios is going to be far more detailed. However, it should not be more complicated than is necessary to accomplish the organizational goal of making it easier for audience segments to achieve their objectives while having the best possible user experience.

The process of content mapping and scenario planning will inevitably surface holes in the inventory of your existing content. Obviously, they will need to be filled. This will require some combination of recreating existing content for different audiences in addition to generating some which is completely new. Not to mention the ongoing process of updating and managing these content variations based on what’s working and what’s not.

Personalization in CMS

It would help to develop a content model and taxonomy for your CMS that is aligned to your audience segmentation approach. By tagging content appropriately you can often automate many areas of personalization. For example, display all white papers from a specific vertical industry.

Regardless of what tool is used to manage all of this complexity, it will require custom configuration. Some systems are naturally more user friendly than others but none of them come out of the box knowing your audience segments, content mapping, and scenarios. All of this information, once determined and defined, will need to be entered to the system.

Rules-based configuration is the most common type of work you’ll do with a CMS which is literally going through a series of "If, Then" statements to tell the CMS what content to show to what users. It’s important to have someone inside your organization or agency partner that owns the product strategy for personalization and can ensure it is consistently applied and within the best practices for that specific platform.

Sitefinity content management system has a simple interface for defining segments through various criteria such as where the visitor came from, what they searched for, their location, duration of their visit, etc. You can define custom criteria and have any combination of AND/OR criteria to define your segments.

Testing Your Personalization

Once your audience and content plans are sorted out and the technology is configured, it is time to test the experience from the perspective of each segment and scenarios within segments. You should test each variation on multiple browsers and mobile devices.

Some CMS allow to impersonate to test your results. For example, Sitefinity allows you to impersonate any segment and preview the customer experience on any device with the help of the mobile device emulators. This way you can be sure how your website looks like for every audience on any device.

Measure the Results

After you’ve segmented your audiences, personalized their experience and checked how your website/portal/CMS is presented for different audiences on different devices you should see the results of your work. They can be measured by the conversions and other website KPIs for the different segments compared to the default presentation for non-segmented visitors or to the KPIs prior to the personalization. Measuring will help you iterate and improve the results further.

Going forward it will be possible to revise previous assumptions with new information which is substantially more valid. Using the built-in analytics within your CMS or third party analytics, you’ll be able to watch how each segment interacts with the personalized content and if it was effective.

Galaxy Consulting successfully implemented content personalization for few clients. We can do the same for you. Contact us today for a free consultation.

Is Your Web Site Optimized for Mobile Devices?

Many people are highly dependent of their mobile devices for every day interactions, including mobile commerce. Our society is becoming highly mobile and connected. In the latest Shop.org and Forrester Research Mobile Commerce Survey, it's estimated that U.S. smartphone commerce will grow to $31 billion by 2016.

Those organizations that can best serve mobile customers will have an advantage in the competition. With a surge in mobile traffic comes the added potential to connect with and sell to customers through mobile commerce. Having a concrete mobile infrastructure plan and strategy is no longer an option, as it had been in recent years, but rather a must to compete in any customer-facing situation.

But despite this upward trajectory, retailers and other consumer-oriented companies still express some hesitancy about investing in multi-device environments. There is still some apprehension by companies, when it comes to moving forward with mobile planning. Companies still struggle to maintain uniformity across multiple device experiences when there are various screen sizes, operating systems, hardware specifications, and loading speeds to consider. One fear is that of the unknown, but security, data management, and simply proving a use case and subsequent return on investment are concerns as well.

The key issue in smartphone shopping continues to be the form factor, which can make navigation more difficult for customers. In addition to slower page load times on smartphones, some customers are concerned about the security of the transaction or simply complain that the experience just is not the same.

A successful mobile experience, like many other customer experiences, is about fulfilling customers' needs. First-time users of a mobile site or app tend to be less satisfied with their mobile experiences than frequent users because of their lack of familiarity with layouts, navigation, and functionality according to the survey of the mobile users. Knowing the different kinds of mobile devices customers use is critical. It is pertinent to develop a strategy that encompasses all types of customer scenarios.

Before embarking on any one mobile strategy, it is important to learn how your company's customers most likely would use their mobile devices. In addition to enabling customers to interact how they wish, any company looking to optimize its mobile presence must naturally consider the effects on the business as well, and how mobile usage will impact other lines of business and cross-channel marketing efforts.

In addition to justifying a use case and ROI for mobile, companies that wish to get into the mobile side of business must be aware of its limitations. Under ideal circumstances, companies want to engage with their customers and cultivate a one-to-one relationship while taking into consideration CANSPAM and privacy regulations. It is very important to adjust taxonomy and information architecture for the mobile experience. A lot of searches are made using mobile devices, so search also has to be optimized.

Optimizing your mobile site or developing a native application is no simple task. There are security considerations, as well as device-specific functions, to consider. Don't take a cookie-cutter approach. Some companies make the mistake of simply cloning online information without considering that consumer behavior on the mobile phone is dramatically different. Justify mobile ROI with consumer insight.

Consider security. Create a military-grade security infrastructure, while maintaining user-friendly design. Hire the best user interaction designer to design the security setup interaction.

Utilize mobile wisely. Once someone has discovered your brand through search, referral, or a marketing message, and they download the app, this may indicate a loyal customer. The app can be a great way to maximize and monetize that loyal relationship because it's in a controlled environment.

Galaxy Consulting has experience optimizing information architecture and search for mobile devices. Contact us today for a free consultation.

Data Lake

A data lake is a large storage repository and processing engine. Data lakes focus on storing disparate data and ignore how or why data is used, governed, defined and secured.

Benefits

The data lake concept hopes to solve information silos. Rather than having dozens of independently managed collections of data, you can combine these sources in the unmanaged data lake. The consolidation theoretically results in increased information use and sharing, while cutting costs through server and license reduction.

Data lakes can help resolve the nagging problem of accessibility and data integration. Using big data infrastructures, enterprises are starting to pull together increasing data volumes for analytics or simply to store for undetermined future use. Enterprises that must use enormous volumes and myriad varieties of data to respond to regulatory and competitive pressures are adopting data lakes. Data lakes are an emerging and powerful approach to the challenges of data integration as enterprises increase their exposure to mobile and cloud-based applications, the sensor-driven Internet of Things, and other aspects.

Currently the only viable example of a data lake is Apache Hadoop. Many companies also use cloud storage services such as Amazon S3 along with other open source tools such as Docker as a data lake. There is a gradual academic interest in the concept of data lakes.

Previous approaches to broad-based data integration have forced all users into a common predetermined schema, or data model. Unlike this monolithic view of a single enterprise-wide data model, the data lake relaxes standardization and defers modeling, resulting in a nearly unlimited potential for operational insight and data discovery. As data volumes, data variety, and metadata richness grow, so does the benefit.

Data lake is helping companies to collaboratively create models or views of the data and then manage incremental improvements to the metadata. Data scientists and business analysts using the newest lineage tracking tools such as Revelytix Loom or Apache Falcon to follow each other’s purpose-built data schemas. The lineage tracking metadata also is placed in the Hadoop Distributed File System (HDFS) which stores pieces of files across a distributed cluster of servers in the cloud where the metadata is accessible and can be collaboratively refined. Analytics drawn from the data lake become increasingly valuable as the metadata describing different views of the data accumulates.

Every industry has a potential data lake use case. A data lake can be a way to gain more visibility or to put an end to data silos. Many companies see data lakes as an opportunity to capture a 360-degree view of their customers or to analyze social media trends.

Some companies have built big data sandboxes for analysis by data scientists. Such sandboxes are somewhat similar to data lakes, albeit narrower in scope and purpose.

Relational data warehouses and their big price tags have long dominated complex analytics, reporting, and operations. However, their slow-changing data models and rigid field-to-field integration mappings are too brittle to support big data volume and variety. The vast majority of these systems also leave business users dependent on IT for even the smallest enhancements, due mostly to inelastic design, unmanageable system complexity, and low system tolerance for human error. The data lake approach helps to solve these problems.

Approach

Step number one in a data lake project is to pull all data together into one repository while giving minimal attention to creating schemas that define integration points between disparate data sets. This approach facilitates access, but the work required to turn that data into actionable insights is a substantial challenge. While integrating the data takes place at the Hadoop layer, contextualizing the metadata takes place at schema creation time.

Integrating data involves fewer steps because data lakes don’t enforce a rigid metadata schema as do relational data warehouses. Instead, data lakes support a concept known as late binding, or schema on read, in which users build custom schema into their queries. Data is bound to a dynamic schema created upon query execution. The late-binding principle shifts the data modeling from centralized data warehousing teams and database administrators, who are often remote from data sources, to localized teams of business analysts and data scientists, who can help create flexible, domain-specific context. For those accustomed to SQL, this shift opens a whole new world.

In this approach, the more is known about the metadata, the easier it is to query. Pre-tagged data, such as Extensible Markup Language (XML), JavaScript Object Notation (JSON), or Resource Description Framework (RDF), offers a starting point and is highly useful in implementations with limited data variety. In most cases, however, pre-tagged data is a small portion of incoming data formats.

Lessons Learned

Some data lake initiatives have not succeeded, producing instead more silos or empty sandboxes. Given the risk, everyone is proceeding cautiously. There are companies who create big data graveyards, dumping everything into them and hoping to do something with it down the road.

Companies would avoid creating big data graveyards by developing and executing a solid strategic plan that applies the right technology and methods to the problem. Hadoop and the NoSQL (Not only SQL) category of databases have potential, especially when they can enable a single enterprise-wide repository and provide access to data previously trapped in silos. The main challenge is not creating a data lake, but taking advantage of the opportunities it presents. A means of creating, enriching, and managing semantic metadata incrementally is essential.

Data Flow in the Data Lake

The data lake loads extracts, irrespective of its format, into a big data store. Metadata is decoupled from its underlying data and stored independently. This enables flexibility for multiple end-user perspectives and maturing semantics.

How a Data Lake Matures

Sourcing new data into the lake can occur gradually and will not impact existing models. The lake starts with raw data, and it matures as more data flows in, as users and machines build up metadata, and as user adoption broadens. Ambiguous and competing terms eventually converge into a shared understanding (that is, semantics) within and across business domains. Data maturity results as a natural outgrowth of the ongoing user interaction and feedback at the metadata management layer, interaction that continually refines the lake and enhances discovery.

With the data lake, users can take what is relevant and leave the rest. Individual business domains can mature independently and gradually. Perfect data classification is not required. Users throughout the enterprise can see across all disciplines, not limited by organizational silos or rigid schema.

Data Lake Maturity

The data lake foundation includes a big data repository, metadata management, and an application framework to capture and contextualize end-user feedback. The increasing value of analytics is then directly correlated in increase in user adoption across the enterprise.

Risks

Data lakes therefore carry risks. The most important is the inability to determine data quality or the lineage of findings by other analysts or users that have found value, previously, in using the same data in the lake. By its definition, a data lake accepts any data, without oversight or governance. Without descriptive metadata and a mechanism to maintain it, the data lake risks turning into a data swamp. And without metadata, every subsequent use of data means analysts start from scratch.

Another risk is security and access control. Data can be placed into the data lake with no oversight of the contents. Many data lakes are being used for data whose privacy and regulatory requirements are likely to represent risk exposure. The security capabilities of central data lake technologies are still in the beginning stage.

Finally, performance aspects should not be overlooked. Tools and data interfaces simply cannot perform at the same level against a general-purpose store as they can against optimized and purpose-built infrastructure.

Careful planning and organization of data lake strategy is required to make this project a success.