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!