Navigating Big Data

Big Data is an ever-evolving term which is used to describe the vast amount of unstructured data. Published reports have indicated that 90% of the world’s data was created during the past two years alone.

Whether it’s coming from social media sites such as Twitter, Instagram, or Facebook, or from countless other Web sites, mobile devices, laptops, or desktops, data is being generated at an astonishing rate. Making use of Big Data has gone from a desire to a necessity. The business demands require its use.

Big Data can serve organizations in many ways. Ironically, though, with such a wealth of information at a company's disposal, the possibilities border on the limitless, and that can be a problem. Data is not going to automatically bend to a company's will. On the contrary, it has the potential to stir up organizations from within if not used correctly. If a company doesn't set some ground rules and figure out how to choose the appropriate data to work with, as well as how to make it align with the organization's goals, it's unlikely to get anything worthy out of it.

There are three layers of Big Data analytics, two of which lead to insights. The first of these, and the most basic, is descriptive analytics, which simply summarize the state of a situation. They can be presented in the form of dashboards, and they tell a person what's going on, but they don't predict what will happen as a result. Predictive analytics forecast what will likely happen, prescriptive analytics guide users to action. Predictive and prescriptive analytics provide insights.

Presenting the analytics on a clean, readable user interface is vital but sometimes is ignored. Users get frustrated when they see content that they can't decipher. A canned dashboard does not work for users. They need to know what action they have to take. Users demand a sophisticated alert engine that will tell them very contextually what actions to take.

Using such analytics, ZestFinance was able to glean this insight: those who failed to properly use uppercase and lowercase letters while filling out loan applications were more likely to default on them later on. Knowing this helped them identify a way to improve on traditional underwriting methods, pushing them to incorporate updated models that took this correlation into consideration. As a result, the company was able to reduce the loan default rate by 40% and increase market share by 25%.

Unfortunately, insights have a shelf life. They must be interpretable, relevant, and novel. Once an insight has been incorporated into a strategy, it's no longer an insight, and the benefits it generates will cease to make a noticeable difference over time.

Getting the Right Data

To get the right data leading to truly beneficial insights, a company must employ a sophisticated plan for its collection. Having a business case around the usage of data is the first important step. A company should figure out what goals it would like to meet, how and why data is crucial to reaching them, and how this effort can help increase revenue and decrease costs.

Data relevance is the key and what is important to a company is determined by the problems it is trying to solve. There is useful data and not useful data. It is important to distinguish them and weed out not useful data. Collecting more than what is useful and needed is impractical.

Often data is accumulating before a set of goals has been outlined by stakeholders. It is being collected irrespective of any specific problem, question, or purpose. Data warehouses and processing tools such as Hadoop, NoSQL, InfoGrid, Impala, and Storm make it especially easy for companies to quickly attain large amounts of data. Companies are also at liberty to add on third-party data sources to enrich the profiles they already have, from companies such as Dun & Bradstreet. Unfortunately, most of the data, inevitably, is irrelevant. The key is to find data that pertains to the problem.

Big Data is nothing if not available, and it takes minimal effort to collect it. But unfortunately, it will not be of use to anyone if it’s not molded to meet the particular demands of those using it. Some people are under the impression that they are going to get a lot of information simply from having data. But businesses don’t really need Big Data - information and insight are what they need. While a vast amount of data matter might be floating around in the physical and digital universes, the information it contains may be considerably less substantial.

While it might seem advisable to collect as much information as possible, some of that information just might not be relevant. Relevant insights, on the other hand, allow companies to act on information and create beneficial changes.

It is a good idea to set parameters for data collection by identifying the right sources early on. It could be a combination of internal and external data sources. Determine some metrics that you monitor on an ongoing basis. Having the key performance indicators (KPIs) in place will help companies identify the right data sources, the types of data sources that can help solve their problems.

Technology plays a key role in harnessing Big Data. Companies should figure out what kinds of technology make sense for them. Choice of technology should be based on company's requirements.

Data collection is an ongoing process that can be adjusted over time. As the business needs change, newer data sources are integrated, and newer business groups or lines of businesses are brought in as stakeholders, the dynamics and qualities of data collection will change. So this needs to be treated not as a one-time initiative, but as an ongoing program in which you continually enrich and enhance your data quality.

Companies should continually monitor the success of their data usage and implementation to ensure they're getting what they need out of it. There should be a constant feedback stream so that a company knows where it stands in relation to certain key metrics it has outlined.

Risks

Companies must always be aware of the risks involved in using data. Companies shouldn't use prescriptive analytics when there is significant room for error. It takes good judgment, of course, to determine when the payoffs outweigh the potential risks. Unfortunately, it's not always possible to get a prescriptive read on a situation. There are certain limitations. For one thing, collecting hard data from the future is impossible.

People and Processes

Big Data adoption often becomes a change management issue and companies often steer clear of it. When a company implements something that's more data-driven, there's a lot of resistance to it.

Like most initiatives that propose technology as a central asset, Big Data adoption can create conflicts among the various departments of an organization. People struggle to accept data, but people also aren’t willing to give it up. To avoid such clashes, companies should make it clear from the outset which department owns the data. Putting the owner in charge of the data, having this person or department outline the business rules and how they should be applied to customers would be helpful to overcome this issue.

These are two good tips to follow: Give credit where credit is due and don't dehumanize the job. Don’t attribute the success to the data, but to the person who does something with the data. Remember that change can't just come from the top down. Big Data adoption requires more than executive support. It needs buy-in from everyone.

Successful Change Management in Content and Knowledge Management

It is getting more unlikely to find paper documents in filing cabinets or electronic documents in shared network drives. Filing cabinets and shared network drives have been replaced by content management systems, knowledge base application, and collaboration tools in majority of organizations.

At a certain point, it's inevitable that organizations have to make adjustments to keep up with the times. users must constantly adapt to the tools of an evolving world. After all, if customers are using advanced technology, it makes sense that companies should be interacting with them using tools that are up to date as well.

If technology adoption is to have an effect on an organization, users' commitment becomes a required element. But getting that kind of cooperation is not always a simple task. Users might not immediately take to the new processes without some resistance.

Though it's counter-intuitive that anyone would resist technology designed to make their job easier, the resistance is unavoidable element of content and knowledge management initiatives. Organizations should anticipate a number of challenges and do their best to ease their users's resistance through the transition and change management.

Drawing from our 16 years of experience successfully managing user adoption and change management in content and knowledge management initiatives, these are our guidelines for organizations to overcome challenges in these areas.

1. Communicate the Goals

There may be myriad practical reasons for why the change in how your organization manages its content needs to be put in place. Before proposing any major change, establish clear reasons for why the change is being proposed, and how it is going to enhance users' experience.

For users to understand how technology is going to help them, they need to understand what their future will look like with this technology in place. What it amounts to is if you can't articulate the benefits of making the change, you have no business of making this change.

It is very important to create a consistent narrative that instills confidence in users as well as the language you use to deliver this narrative to users. Avoid using the term "change management". The reason is that employees hear "change management" as "Whatever you have done until now is wrong, and now we are going to put you on the right track." That is not a good message.

You may want to use the term "cause management" which attributes any need for adjustment within a company to a cause. Under this approach, organizations would make an effort to craft a story that communicates the idea that this is the outcome that will best benefit the company.

Highlighting what is not going to be changing can be a source of encouragement for users. This way you are introducing a consistency while asking users to evolve.

2. Fear of Change is not Necessarily Fear of Technology

Technology itself is not usually the reason that employees are resistant to change. People are becoming less resistant to using technology. Problems begin to surface when employees are not given enough notice about what technology they are expected to use.

Even before it's been decided which technology organizations have settled on, organizations should give their employees an outline of the problems they are trying to fix. This would give them the opportunity to provide input and make suggestions about what types of processes they would like to see streamlined and how they envision their ideal work environment. Though organizations might not always have the budget for what the employees have in mind, they will at least be involving them and making them feel as though they are part of the equation from the outset.

Also important is that workers are given the time to develop the kinds of skills necessary to make full use of technology. It takes employees some conditioning to see how new technology and procedures can be of aid to them. If you can be proactive about teaching people these new skills and how to use the technology in small segments, this definitely can accelerate the change.

3. New Technology Can Bruise the Ego

All employees are proud of their work. They like to feel as though they possess an innate talent, and that there's a reason they're doing what they've chosen to dedicate so much of their time to. Regardless of age or experience level, there are certain natural emotions that might come into play when companies are proposing changes. If employees are led to believe that so much of what they spent a great deal of time mastering can be transferred to anyone with an ease, they might resent it on an emotional level that they might not even share.

Thus, it would be a good idea to communicate how the technology is going to help them work together and be more connected.

4. Technology is not Only for Managers

It goes without saying that technology should never force people to do more work than they are already doing. If you force people to use a system that is making their jobs worse, they' are going to do everything they can to avoid it.

Employees should never feel as though technology is being deployed solely for the benefit of the managers. Granted, content management system provides managers with more visibility into work processes, but the central message managers should be sending is that the technology is there to help employees do their jobs better.

It is helpful to illustrate that higher management is using the technology as well, for the sake of driving home the idea that the technology is being universally adopted by the organization.

5. Deploy Gradually

When it comes to deploying the systems that employees are going to be using regularly over an extended period of time, it is a good idea to steer clear of an abrupt implementation in favor of a more gradual one.

Use Pilot Periods. During these periods, a small subset of the company is selected to test the technology and share its experiences with the others. Keeping employees updated via email, meetings, or through other internal communication channels can be helpful, as it also lets people know what to expect. Likewise, getting user testimonials and videos in which those who have piloted the product attest to its benefits could prove useful.

However, it's important to be all-inclusive when deciding who is going to be participating in such trial periods. While it might be tempting to recruit the most enthusiastic and vocal representatives of a company to test the materials, it might be a better idea to go for a mix to act as testers. Use a subset of users that will represent those who are ultimately going to be expected to use the new technology. Of course, asking volunteers to step forward is advised, but testers should also be drawn from a segment of those who are not as keen on trying it.

Including people who are not technology experts is a good idea, because it helps drive home the point that anyone can use the solution effectively. It also reinforces the idea that there will be support and training opportunities available.

If the right group of people is selected for the pilot program, they can generate excitement about the system and show how the program has helped them do their jobs.

One small factor to keep in mind about the pilot period, however, is the capacity of the system. Since the entire program will eventually be inhabited by more users, the experience that the small subset reports might differ from the one that is waiting further down the line. For example, a system that works fine when you have ten users on it may not work as quickly when there are 200,000 users connected to it. You need to be able to account for things like that.

6. Maintain the Change

Change management is not as simple as preparing employees for the transition that is about to be introduced. It has just as much to do with ensuring that employees don't revert to outdated and inefficient methods as it does with ensuring that people begin to use it. Managing resistance is a process, not a series of events.

Because it's a process, managers should be very careful to communicate the fact that the improvements might not come all at once, but rather in small increments. Incentives can also act as fruitful aids in encouraging adoption. For this very reason, gamification applications have been gaining popularity because they allow employees to compete against one another and display to the rest of the company how well they have done by showing off their achievements.

It is important to build employees confidence and a positive environment. Set specific event days to encourage the use of the new technology. Typically held once a month, these are known as blitz days. The idea is to set aside a time period during which everybody is forced to use the technology in a fun environment. At the end of the day, the users share those results. The goal is to say that if this can be done on one particular day, why can't it be done every day? Over time, the benefits of these events could be substantial.

Change is ongoing. As time goes on, the window for change for technology is becoming much narrower than it used to be, with updates occurring far more frequently. For some people, it might seem that just as they are getting used to one change, another one is on the way. Organizations need to create an infrastructure that better supports that.

Characteristics for Driving Change

• Be outwardly focused - avoid being locked into one area of the company. Look for ways to make an impact across organizations.
• Be Persuasive - be clever and persuasive enough to gain the support of users.
• Be Persistent - do not give up. Constantly work through the channels of the organization to ensure that new systems and processes are factored into organizations' way of work.

Better Business Operations with Better Data

Businesses today understand that data is an important enterprise asset, relied on by employees to deliver on their customers' needs, among other uses of data such as making business decisions and many others.

Yet too few organizations realize that addressing data quality is necessary to improve customer satisfaction. A recent Forrester survey shows that fewer than 20% of companies see data management as a factor in improving customer relationships. This is very troubling number.

Not paying attention to data quality can have a big impact both on companies and the customers they serve. Following are just two examples:

Garbage in/garbage out erodes customer satisfaction. Customer service agents need to have the right data about their customers, their purchases, and prior service history presented to them at the right point in the service cycle to deliver answers. When their tool sets pull data from low-quality data sources, decision quality suffers, leading to significant rework and customers frustration.

Lack of trust in data has a negative impact on employees productivity. Employees begin to question the validity of underlying data when data inconsistencies and quality issues are left unchecked. This means employees will often ask a customer to validate product, service, and customer data during an interaction which makes the interaction less personal, increases call times, and instills in the customer a lack of trust in the company.

The bottom line: high-quality customer data is required to support every point in the customer journey and ultimately deliver the best possible customer experience to increase loyalty and revenue. So how can organizations most effectively manage their data quality?

While content management systems (CMS) can play a role in this process, they can't solve the data-quality issue by themselves. A common challenge in organizations in their content management initiatives is the inability to obtain a complete trusted view of the content. To get started on the data-quality journey, consider this five-step process:

1. Don't view poor data quality as a disease. Instead, it is often a symptom of broken processes. Using data-quality solutions to fix data without addressing changes in a CMS will yield limited results. CMS users will find a work-around and create other data-quality issues. Balance new data-quality services with user experience testing to stem any business processes that are causing data-quality issues.

2. Be specific about bad data's impact on business effectiveness. Business stakeholders have enough of data-quality frustrations. Often, they will describe poor data as "missing," "inaccurate," or "duplicate" data. Step beyond these adjectives to find out why these data-quality issues affect business processes and engagement with customers. These stories provide the foundation for business cases, highlight what data to focus on, and show how to prioritize data-quality efforts.

3. Scope the data-quality problem. Many data-quality programs begin with a broad profiling of data conditions. Get ahead of bottom-up approaches that are disconnected from CMS processes. Assess data conditions in the context of business processes to determine the size of the issue in terms of bad data and its impact at each decision point or step in a business process. This links data closely to business-process efficiency and effectiveness, often measured through key performance indicators in operations and at executive levels.

4. Pick the business process to support. For every business process supported by CMS, different data and customer views can be created and used. Use the scoping analysis to educate CMS stakeholders on business processes most affected and the dependencies between processes on commonly used data. Include business executives in the discussion as a way to get commitment and a decision on where to start.

5. Define recognizable success by improving data quality. Data-quality efforts are a key component of data governance that should be treated as a sustainable program, not a technology project. The goal is always to achieve better business outcomes. Identify qualitative and quantitative factors that demonstrate business success and operational success. Take a snapshot of today's CMS and data-quality conditions and continuously monitor and assess them over time. This will validate efforts as effective and create a platform to expand data-quality programs and maintain ongoing support from business stakeholders and executives.

Galaxy Consulting has over 16 years experience helping organizations to make the best use of their data and improve it. Please contact us today for a free consultation!

Hadoop Adoption

True to its iconic logo, Hadoop is still very much the elephant in the room. Many organizations heard of it, yet relatively few can say they have a firm grasp on what the technology can do for their business, and even fewer have actually implemented it successfully at their organization.

Forrester Research predicted that Hadoop will become a cornerstone of the business technology agenda at most organizations.

Scalability, affordability, and flexibility make Hadoop uniquely suited to change the big data scene. An open-source software framework, Hadoop allows for the processing of big data sets across clusters on commodity hardware either on-premises or in the cloud.

At roughly one-thirtieth the cost of traditional data storage and processing, Hadoop makes it realistic and cost effective to analyze all data instead of just a data sample. Its open-source architecture enables data scientists and developers to build on top of it to form customized connectors or integrations.

Typically, data analysis requires some level of data preparation, such as data cleansing and eliminating errors, outside of traditional data warehouses. Once the data is prepared, it is transferred to a high-performance analytics tool, such as a Teradata data warehouse. With data stored in Hadoop, however, users can see "instant ROI" by moving the data workloads off of Teradata and running analytics right where the data resides.

Other use of Hadoop is for live archiving. Instead of backing up data and storing it in a data recovery system, such as Iron Mountain, users can store everything in Hadoop and easily pull it up whenever necessary.

The greatest power of Hadoop lies in its ability to house and process data that couldn't be analyzed in the past due to its volume and unstructured form. Hadoop can parse emails and other unstructured feedback to reveal similar insight.

The sheer volume of data that businesses can store on Hadoop changes the level of analytics and insight that users can expect. Because it allows users to analyze all data and not just a segment or sample, the results can better anticipate customer engagement. Hadoop is surpassing model analytics that can describe certain patterns and is now delivering full data set analytics that can predict future behavior.

There are few challenges.

Hadoop's ability to process massive amounts of data, for example, is both a blessing and a curse. Because it's designed to handle large data loads relatively quickly, the system runs in batch mode, meaning it processes massive amounts of data at once, rather than looking at smaller segments in real time. As a result, the system often forces users to choose between quantity and quality. At this point in Hadoop's life cycle, the focus is more on enormous data size than high-performance analytics.

Because of the large size of the data sets fed into Hadoop, the number-crunching doesn't take place in real time. This is problematic because as the time between when you input the data and the time at which you have to make a decision based on that data grows, the effectiveness of that data decreases.

The biggest problem of all is that Hadoop's seeming boundlessness instills a proclivity for data exploration in those who use it. Relying on Hadoop to deliver all the answers without asking the right questions is inefficient.

As companies begin to recognize Hadoop's potential, demand is increasing, and vendors are actively developing solutions that promise to painlessly transfer data onto Hadoop, improve its processing performance, and operationalize data to make it more business-ready.

Big data integration vendor Talend, for example, offers solutions that help organizations transition their data onto Hadoop in high volume. The company works with more than 800 connectors that link up to other data systems and warehouses to "pull data out, put it into Hadoop, and transform it into a shape that you can run analytics on.

While solutions such as those offered by Talend make the Hadoop migration more manageable for companies, vendors such as MapR tackle the batch-processing lag. MapR developed a solution that enhances the Hadoop data platform to make it behave like enterprise storage. It enables Hadoop to be accessed as easily as network-attached storage is accessed through the network file system; this means faster data management and system administration without having to move any data.

Veteran data solution vendors such as Oracle are innovating as well, developing platforms that make Hadoop easier to use and to incorporate into existing data infrastructures. Its latest updates revolved around allowing users to store and analyze structured and unstructured data together and giving users a set of tools to visualize data and find data patterns or problems.

RapidMiner's approach to Hadoop has been to simplify it, eliminate the need for end users to code, and do for Hadoop analytics what Wordpress did for Web site building. Once usable insights are collected, RapidMiner can connect the data platform to a marketing automation system or other digital experience management system to deploy campaigns or make changes based on data predictions.

Moving forward, analysts predict that leveraging Hadoop's potential will become a more attainable goal for companies. Because it's open-source, the possibilities are vast. Hadoop's ability to connect openly to other systems and solutions will increase adoption in the coming months and years.

Oracle Knowledge Management

As customer expectations rise, delivering personalized experiences that improve customer loyalty, increase customer acquisition and optimize efficiency is increasingly more challenging to achieve. It is very important to engage customers in their preferred channel and to minimize the overall effort of that engagement.

A key to minimizing the customer effort is to deploy a knowledge management platform that crosses all channels, presents accurate content from multiple sources, maintains, relevance, and captures feedback for continuous improvement. Oracle Knowledge is a complete knowledge management solution which provides personalized cross-channel service and support.

Knowledge Platform

Oracle Knowledge Platform is an integrated set of knowledge management capabilities including advanced natural language processing, search, flexible authoring and publishing, rich analytics, customizable self-service, and agent facing knowledge applications. Oracle Knowledge is built on a highly scalable J2EE architecture and on Oracle technologies including WebLogic, Oracle Data Integrator, and Oracle Business Intelligence.

Semantic Search

The Oracle Knowledge semantic search capabilities are built on the fundamental understanding of language. Core language dictionaries are available in 20 languages understanding everyday terminology. In addition, multilingual industry dictionaries are available for major industries including high tech, telecommunications, insurance, finance and automotive.

This core understanding of a user’s language is key to finding precise answers from multiple external sources including the knowledge base, web sites, file systems and other internal knowledge repositories. The most recent release of Oracle Knowledge continues to build on this foundation with widely expanded language and geography coverage, significantly increased performance and reduced footprint with faster search response times, faster content processing performance, and a reduced semantic index size, as well as learning-based search ranking for reduced incident handling time. These improved features deliver increased productivity and lower operation costs.

Authoring, Publishing, and Workflow

Oracle Knowledge is designed to help companies to develop a knowledge base as an integral part of a user’s job. Contact center agents and customers create content as a by-product of solving support issues using a powerful, web-based, WYSIWYG rich text editor. Product experts and contact center agents can collaborate with other users and customers to refine or expand the knowledge base.

Advanced editing capability such as global find and replace and replacement tokens improve the article accuracy while lowering operation and knowledge administration costs. Oracle Knowledge comes with valuable tools to manage the life-cycle of articles. Customers can create their own article templates and metadata. The software tracks all revisions of the articles and provides detailed history. Articles may be routed for approval through the use of a workflow. Providing users with the ability to attach files to forum posts allows them to provide additional information to explain their issues. These capabilities improve self-service rates, while expanding the knowledge base and the user community.

The user interface of the authoring system is available in 24 languages, but content can be created in nearly any language. Oracle Knowledge allows customers to manage the relationship of an article across different locales and languages, while providing authors with the ability to develop locale-specific content and metadata allowing fine-tuning of the customer experience.

Analytics

Analytics Dashboards are tailored to functional roles across the service organization. They harness the optimal value of company stakeholders by providing relevant insights at a glance to reduce operational costs, increase employee productivity, and strengthen customer relationships. With the configurable custom KPI wizard for creating KPI with thresholds and triggers, organizations can increase the efficiency of authoring content, increase answer relevancy, and improve the overall insight of knowledge activity.

InfoCenter: Self-Service Knowledge

InfoCenter provides a Knowledge portal for customers and employees with integrated browse and search functionality via a customizable user interface and knowledge widgets. InfoCenter surfaces the power of industry-based libraries, knowledge federation abilities, and natural language processing abilities of the platform to deliver true, intent-based best possible answer to customers. It transforms the self-service experience for customers by providing contextual, and relevant answers to their questions.

iConnect: Agent Knowledge

iConnect provides robust and scalable answer-delivery framework aiding the agent-facing service delivery model. The context-driven user interface simplifies and enhances the user experience and is tuned for increased performance. iConnect is available as an out-of-the box integration into Oracle Service, and Oracle Service Cloud. Open APIs allow for integration into most industry standard CRM applications.

AnswerFlow: Guided Knowledge

AnswerFlow provides consistent service resolution for agents and customers with the prescriptive delivery of knowledge. AnswerFlow combines decision trees with external data that leverages and increases the strategic value of Knowledge Platform across self and assisted service customer interaction channels. AnswerFlow enables to create and deploy automated interactive processes that guide users toward appropriate answers or solutions in cases where:

• answers are conditional, and can vary based on factors such as account status, location, or specific product or model;
• diagnosis is complex, and identifying the best response among many possible answers involves asking detailed questions and eliminating alternatives.

Galaxy Consulting has over 16 years experience in many knowledge base applications. We can help you to deploy Oracle Knowledge Management. Contact us today for a free consultation!

Use Knowledge Management to Increase CRM Value

Today's post focuses on how knowledge management adds an essential layer of value to customer facing systems, which ultimately drives improved customer experiences.

Two sweeping trends have emerged in recent years: the proliferation of customer channels and the resulting explosion in the amount of data produced. 

Customer relationship management has done a great job of providing a strong framework for these multi-channel interactions. Knowledge management (KM) has done an equally remarkable job by providing the brains behind the increasingly diverse network of customer contact points.

With the explosion of data from many different types of sources in the past several years, the tight integration of KM and CRM systems has become even more essential to offering customers and the agents who serve them the concise and timely information they need.

KM and CRM have a long history of ultimately serving the same goals of quickly and efficiently providing customers with information, whether it is through Web self-service, a call center agent, kiosk or mobile application.

CRM and KM Synergy

CRM applications are systems of record that manage customer data. Knowledge Management (KM) systems, in the context of customer engagement, enable businesses to systematically capture knowledge from subject matter experts within the enterprise, and social knowledge from online communities, social networks, partners, etc. for use by customer-facing organizations and end-customers.

When integrated, KM helps expand the business value of CRM, delivering transformational benefits in enhanced customer experiences, contact center productivity, and improved customer acquisition, among other things. KM systems are also able to leverage existing content management systems by adding a layer of findability and know-how for content-enabled process automation.

How it Works

There are many use cases of how CRM and KM work in tandem to deliver business value. A common one is in the customer contact center where knowledge solution is often used in conjunction with CRM.

When customers call, agents use a CRM to open a case, enter the problem description, and click on a “solve” button. This, in turn, invokes a resolution path, for example, a set of search paths to find the right answer or next steps. Agents get to the resolution using the path of their choice, “accept” the resolution, communicate it to the customer, and close the case. The interaction, including the path to the answer and the knowledge base article that was used to solve the problem or sell a product, is recorded in both the CRM and KM systems.

Business Value

Many companies worldwide leverage the combined power of knowledge and CRM to drive business value. Adopting best practices can help make the business case, implement knowledge, and manage it for sustained business value. Here are some examples:

• Premier home appliance manufacturer: $50M in savings by eliminating unwarranted truck rolls through knowledge-powered resolution processes in the contact center and website.
• Semiconductor giant: 59% increase in web self-service adoption, 30% increase in First Contact Resolution.
• Global knowledge and legal services solutions provider: 70% deflection of calls and emails through knowledge-powered self-service, 30% reduction in content authoring time.
• Leading telco provider: 42% reduction in unwarranted handset returns through knowledge-powered resolution process in the contact center.
• Global bank: 88% reduction in agent training time and 70% increase in productivity through knowledge-powered account opening process in small business sector.

Quantify Value

Assessing expected and realized ROI before and after the deployment would help you to justify the initial investment as well as continuous improvement of the CRM - KM solution. Make sure the ROI metrics you use are aligned with business objectives. For instance, if your main business goal is to increase sales, reduction in average handle time will be a conflicting metric. As you assess ROI, keep in mind that KM delivers ROI across a broad range of business problems. Some examples are:

• deflection of requests for agent-assisted service through effective self-service;
• increase in first contact resolution and sales conversion;
• reduction in escalations, transfers, repeat calls, and average handle times;
• reduction in training time, unwarranted product returns, field visits, and staff wage premiums.

Start with Depth

Unfocused deployments almost always result in a shallow knowledge base that is full of gaps. If agents and customers can’t find answers, or receive inadequate or wrong information, they simply stop using the system. Focus first on depth rather than breadth. Start with common questions on common products or lines of business and expand out over time.

Knowledge-Centered Support (KCS)

Best practice frameworks have emerged over time in knowledge management. For example, the Knowledge-Centered Support (KCS) framework is a comprehensive methodology that helps to improve speed of resolution, optimize resources, and foster organizational learning. Adopting frameworks like KCS is a win-win-win for customers, contact center agents, and the organization alike. Implement the best practices in knowledge-centered customer support.

Maximize Findability

Users prefer different ways of searching for information, just as drivers prefer different ways of reaching their destination. A GPS-style approach with multiple options to find information dramatically improves knowledge base adoption. For example, new agents may find it difficult to wade through hundreds of keyword search results, but might fare better if they are guided through a step-by-step dialog, powered by technologies like Case-Based Reasoning (CBR).

Multiple search options such as FAQ, keyword and natural language search, topic-tree browsing, and guided help enable a broad range of users to quickly and easily find information. Make sure you leverage a unified multi-channel knowledge platform for consistent answers across customer touchpoints.

Implementing these best practices, while making sure that the KM and CRM solutions are tightly integrated, will help you deliver transformational customer service experience while generating breakthrough value for the business!

Galaxy Consulting has 16 years experience in this area. We can help you to integrate your knowledge base with your CRM. Contact us today for a free consultation!

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.