Improving User Adoption

Many organizations that deployed a content management system have gone through phases of deployment, development and upgrades without leveraging common practices around information architecture and usability. 

In some cases, a well-intentioned IT department holds user requirements sessions, only to implement the technical features without truly understanding core principles of usability. In other situations, a particular process will be enabled and user tested with good design principles but employing the “build it and they will come” deployment plan. 

In other words, let users just start using the system. In rare cases, organizations do get those elements right but then after the deployment is completed, there is no organizational design to maintain the system, continue to train users, and update design and functionality as user needs change.

The reasons for a lack of user acceptance break down into numerous categories ranging from lack of user involvement in the development process to inadequate content.

For these reasons, many users of content management systems are frustrated and long for a well-designed, maintained, highly functional system with well-organized information and search that gives them what they need when they need it. They blame the technology rather than the way that technology has been configured and managed.

The challenge is that everyone wants everything to be user friendly and intuitive. Users want tools that help them do their jobs without requiring that they jump through hoops to upload and access information. If the system is awkward and poorly designed, users do not want to spend the time to learn how to get the most from the system. However, even when the tools are sophisticated and well designed, fluency is still necessary to leverage them effectively.

When adoption is poor, it is difficult for an organization to get the majority of users needed to achieve the good collaboration, where the knowledge is producing real value and triggering successful cycles of participation and contribution. So moving to a new platform, rather than solving core issues, seems to be the preferred approach that many organizations take, though that will lead to a recurrence of the core challenges. It is best to get to the root of the problems and address them.

Even with a perfectly configured system and design that is user tested, validated, refined, tested some more and validated again, there is no guarantee that the system will be adopted and embraced. Taking an intentional approach to the system requirements and design will go a long way toward increasing the likelihood of user adoption. User adoption requires a thoughtful, intentional approach to a number of areas.

Here are some ways to maximize the chances for success of user adoption.

In many cases, users don’t have a voice in the design decisions and are not sufficiently kept in the loop through ongoing communications from leadership. Involve users in the development process. Socialization should be part of a project from the beginning and continue throughout the life of the project.

Perform user acceptance testing. It is very important to give users a chance to test the system before asking them to use it.

Create realistic expectations for how intuitive the system can be. No matter how user friendly the system is, it may never be completely intuitive to all. The nature of work processes and the information to support those processes can be complex. 

The nature of the task might require understanding terminology that is not part of everyone’s vocabulary. If the job itself requires training and skill development, the information may also require a degree of socialization. Some systems can be very complex.

Allow users time to develop a mental model. When learning to use an application of any sort, users need time to grasp the big picture and become fluent in the details. This means that it would be better to show users the details over time as opposed to in a one-shot training. Doing that at the scale of any enterprise requires planning and development of just-in-time learning that people can move through to get the big picture and can access in the context of their work processes. 

Provide users with the consistency they need. A consistent taxonomy and information architecture will help improve usability in the first place but also increase the learnability of the system. Once users learn about one part of an information structure, they can more quickly understand and internalize other areas if the same terminology is used.

Update functionality often enough to keep up with changes in user requirements. No information environment is static, so ongoing feedback that drives new functionality and capabilities is required. It is important to keep users updated on features in each new release. 

Without updates to functionality, continued testing and adjustments, the delta between what users need and what the application provides will get larger and lead to greater dissatisfaction.

Provide high-quality content. A system deployment should begin with value for the user. That means populating repositories with curated, tagged quality content that they will find valuable. Too often there is a “lift-and-load” migration in which poorly organized content filled with redundant, outdated and trivial content is presented to the user in a new environment. No matter how good the design is, the content will not be viable if it does not meet the users’ work requirements, and it will not be accessible if it is not tagged and organized.

User acceptance of a system will be improved when the right information is available for the tasks and the right processes are reflected in the application.

Offer users an easy way to contribute content. Another barrier to acceptance is a difficult process for uploading content. Too many metadata fields, long lists of choices or fields that don’t apply to the content will keep people from content uploading. The process for uploading content should be as painless as possible. Frequently the best answer is machine-assisted tagging where an auto-classifier tuned to the content and taxonomies appropriate for the process presents the user with suggested values, and the user either accepts them or selects a different value.

Establish a robust governance process. A content management system lives in an ecosystem that is continually changing. There are multiple upstream and downstream processes, and resources need to be allocated with a view to the larger picture of the information environment. 

The system owners and sponsors must make decisions in that context as well as within the context of the system environment. Therefore, they should have a seat at the table in the enterprise information governance decisions and the institution of controls, standards and compliance processes all the way down to the level of content repositories. If sites and content do not have ownership, they will quickly become outdated. If policy decisions are made without compliance mechanisms, they will not be implemented.

Users don’t hate content management systems. They hate poorly designed applications. In reality what they don’t like is the lack of functionality, the poorly constructed taxonomies, confusing navigation, endless fields to fill out and poor-quality content. With the correct approach to design and deployment and with adequate training and ongoing updates, people like and in many cases like a content management system. It helps them do their jobs, makes tasks easier to accomplish, improves efficiency and lets workers redirect their efforts to the more challenging and fulfilling parts of their jobs.

Challenges of Records Management

Records management is very important for companies. There are many electronic records management systems that can optimize the process of records management. However, the huge amount of data is raising new challenges about how records management should be handled. 

A few of the ongoing issues include big data, master data management (MDM) and how to deal with unstructured data and records in unusual formats such as graph databases.

Records are kept for e-discovery, compliance purposes, for their business value, and sometimes because no process has been implemented for systematically removing them. This might be a double-edged sword: getting rid of data makes IT nervous, but there are times when records should be dispositioned.

Data stored in data lakes is largely uncontrolled and typically has not had data clean up processes applied to it. Data quality for big data repositories is usually not applied until someone actually wants to use the data.

Quality assurance might include making sure that duplicate records are dealt with appropriately, that inaccurate information is excluded or annotated and that data from multiple sources is being mapped accurately to the destination database or record. In traditional data warehouses, data is typically extracted, transformed and loaded (ETL). With a data lake, data is extracted (or acquired), loaded and then not transformed until required for a specific need (ELT).

MDM is a method for improving data quality by reconciling inconsistencies across multiple data sources to create a single, consistent and comprehensive view of critical business data. The master file is recognized as the best that is available and ideally is used enterprise-wide for analytics and decision making. But from records management perspective, questions arise, such as what would happen if the original source data reached the end of its retention schedule.

As a practical matter, a record is information that is used to make a business decision, and it can be either an original set of data or a derivative record based on master data.  Therefore the “golden record” that constitutes the best and most accurate information can become a persistent piece of data within records management system.

Unstructured data challenge

A large percentage of records management efforts are oriented toward being ready for e-discovery. 

There is the more of a problem in the case of unstructured data than in MDM. MDM has gone well beyond the narrow structure of relational databases and is entering the realm of big data, but its roots are still in the world of structured databases with well-defined metadata classifications, which makes records management for such records a more straightforward process.

The challenge with unstructured data is to build out the semantics so that the content management or records management and data management components can work together. In the case of a contract, for example, the document might have many pieces of master data. It contains transactional data with certain values, such as product or customer information, and a specialist data steward or data librarian might be needed to tag and classify what data values are represented within that contract. 

With both the content and the data classified using a consistent semantic, it would be much simpler bringing intelligent parsing into the picture to bridge the gap between unstructured and structured data. Auto-classification of records can assist, although human intervention remains an essential element.

Redundant, obsolete and trivial information constitutes a large portion of stored information in many organizations, up to 80%.  The information generated by organizations needs to be under control whether it consists of official records or non-record documents with business value. Otherwise, it will accumulate and become completely unmanageable. On the other hand, if organizations aggressively delete documents, they run the risk of employees creating underground archives of information they don’t want to relinquish, which can pose significant risks. Companies need to approach this with a well thought out strategy.

The system should allow employees to easily save documents using built-in classification instead of a lot of manual tagging. It is important to make the system intuitive enough for any employee to use with just a few seconds of time and a few clicks of the mouse. 

The value of good records management needs to be communicated in such a way so that employees understand that it can actually help them with their work rather than being a burden. A well-designed system hides the complexity from users and puts it in the back end. 

Studies of records management consistently show that only a minority of organizations have a retention schedule in place that would be considered legally acceptable and that some organizations have no retention schedule at all. Even if a schedule is in place, compliance is often poor.

A strategy should be developed to reconcile dilemma between keeping everything forever in order to extract business value from it and using records and information management to effectively get rid of as much information as soon as possible.

From a business perspective, the potential upside of retaining corporate records so they can be used to gain insights into customer behavior, for example, may outweigh the apparent risks that result from non-compliance. 

The highest value is within records management framework for understanding and classifying information so that its business value can be utilized. 

If organizations view records management as a resource rather than a burden, it can contribute to their success. In many respects, the management of enterprise information is already becoming more integrated and less siloed. For example, most enterprise content management (ECM) systems now have records management functionality. The same classification technology used for e-discovery is also used for classification of enterprise content. Seeing records management as part of that environment and recognizing its ability to enrich the understanding of business content as well as ensuring compliance can support that combination.

Governance can be a unifying technique that provides a framework to encompass any type of information as it is created and managed. Governance is a set of multidisciplinary structures, policies and procedures to manage enterprise information in a way that supports an organization’s short term and long term operational and legal requirements. It is important to consider the impact of all forms of information, from big data to graph data. Within a comprehensive strategy of governance, records management is successful.

GDPR Compliance

GDPR compliance is being enforced. The GDPR has already garnered international attention, with similar legislation in the works in countries like China, Japan, India, Brazil, and New Zealand. Attention around the GDPR has been mounting in US. Beyond the United States and the other countries already mentioned, most experts predict that an even wider rollout of consumer data protections is inevitable.

Since GDPR took effect, Google was fined nearly $57 million for processing personal data for advertising purposes without obtaining the required consumer permissions. Google also failed to adequately inform consumers about how their data would be used, nor did it provide enough information about its data consent policies.

The GDPR requires companies doing business in EU member countries to get consumers' consent via an explicit opt-in process before collecting and sharing information about them; to provide a way for consumers to correct, update, and delete the data that companies hold about them; to fully disclose what information is being collected and how it will be used; and to properly notify all parties involved when there is a data breach.

Most companies are certainly pushing to improve their processes by updating older software solutions and processes where parts of their responsibilities are clear, and others are still in a murky world of gray and uncertainty. Many companies are still looking at their obligations under the legislation, trying to determine what is applicable to them and their portions of processing an individual's data.

In a recent survey from the International Association of Privacy Professionals, less than half of respondents said they were fully compliant with the GDPR, and nearly a fifth said they believed full compliance with the GDPR would be impossible.

One of the biggest shortfalls for businesses right now concerns the GDPR provisions requiring a full accounting of all the information organizations hold on consumers upon request within one month.

Companies should simply assume that all aspects of the GDPR apply to them.

Experts and insiders concede that the GDPR has been successful in one key area: Consumers now have more of an interest in what happens with their personal information. GDPR has made it simple for consumers to understand the important details about their data, such as how it is being used, where it is being stored, etc. Because of the GDPR, consumers are asking more questions and reading companies' privacy policies more closely. And that will ultimately lead to greater accountability.

The GDPR has also changed the entire dialogue between companies and customers.

Whether it was a stated goal of the GDPR or an unforeseen consequence, companies are beginning to self-regulate, knowing that regardless of the form, there is increased need to give consumers greater transparency and control over their data.

Because of the penalties and other negative ramifications of ignoring GDPR, companies have to take GDPR seriously with internal programs to organize their data better. Companies need to provide transparency about the data they capture, as well as a mechanism for consumers to choose which information can be captured and how it can be used.

For companies that have come into compliance, the GDPR has resulted in finely tuned databases and distribution lists, and streamlining email communication has made outreach more impactful with higher-than-before engagement rates.

If GDPR compliance is done right, companies will have the ability to create a master record of customer data on one platform.

That master record could contain all of the customer's allowed permissions, revoked permissions, or any changed notification settings, as well as a unified customer profile that combines details about their behaviors, interests, preferences, purchases, and other information from any engagement system or data source.

GDPR continues to require an investment of time and resources, but it is a worthwhile investment.

When all aspects of the GDPR are carried out fully, companies are able to deepen relationships and profitably grow revenue, consumers are able to gain transparency and control over their data, and regulators are able to safeguard commerce and consumer rights.

Galaxy Consulting has over 15 years in helping companies to achieve compliance in different areas, and since GDPR was released, we are helping companies to achieve compliance with GDPR. Please contact us for a free consultation.

Building Blocks for Digital Transformation

69% of decision makers use social media for purchase decisions. 90% of buyers trust peer recommendations. 94% of B2B buyers conduct online research before making a purchase. Companies like Amazon and Alibaba continue to raise the bar, forcing every company to rethink its digital strategy. Companies such as Airbnb and VRBO continue to wreak havoc in the hotel industry and threaten to disintermediate additional industries. Uber and Lyft have transformed the taxi industry using powerful digital tools.

89% of executives say that digitization will disrupt their businesses. Yet less than one-third of these executives believe that their digital strategies are correct, and only 21 % believe that the right people are setting their digital strategies. What is causing this disconnect, and why are so many digital transformation projects underperforming or failing?

Executives are still not sure how best to tackle digital transformation. They do not have the right road map to drive digital transformation success. And they are falling short in one or more of these five building blocks:

CRM

At the core of every successful digital transformation are holistic customer profiles that get leveraged at each step of the transformation. Most companies need to spend more time, money, and effort to create truly holistic customer profiles that integrate transactional, CRM, and third-party data and that integrate both offline and online customer information using identity resolution tools. 

The shortfall is not the technology component: Most CRM software vendors have the tools, including artificial intelligence (AI) and process automation tools, to create these profiles. The shortfall is in leveraging a structured business process to create these profiles, i.e., what information really needs to be collected and to keep these profiles clean and useful over time.

Data and analytics

Data-driven decision making has become a requirement for effective digital transformation. Successful companies perpetually data-mine their holistic customer profiles to gain customer insights. They also leverage data and analytics processes and tools to enhance customer profiling and segmentation, to achieve insights into customer life cycles and journey maps, to target lead scoring and routing, to achieve better forecasting and cross-selling, to model customer behaviors for more effective marketing campaigns, and more.

Social Media

Customers expect to be able to communicate with organizations digitally. They expect 24/7 customer support. Social media communities address these requirements by helping to maintain and increase the kind of customer engagement and interaction that drives customer acquisition and retention. 

They provide members with an online, private platform with a corporate URL, accessible from work and available 24/7, helping to drive customer satisfaction. They reinforce product/industry leadership and expertise, which creates long-term competitive advantage. They are a company’s best lead nurturing tool. Most importantly, social media communities allow a company to listen to the voice of the customer, which is a key component of successful digital transformation.

Customer Engagement

Customer engagement, especially cross-channel customer journey mapping, omnichannel management, customer experience management, and customer success programs are very important. Effective customer engagement shortens sales cycles, increases customer spending, lowers customer churn, increases brand awareness, and secures higher customer loyalty and advocacy. 

To achieve these benefits and to secure digital customer engagement, companies increasingly are using videos, content sharing, chatbots with conversational AI, and robotic process automation tools in their digital transformation efforts.

Emerging technologies

The list of emerging technologies is long and growing all the time, and it currently includes these: mobile apps/technology, identity resolution, virtual and augmented reality, AI and machine learning, personalized digital videos, digital portals, wearables, addressable TV, the Internet of Things, and blockchain. 

These digital technologies provide new ways to capture customer knowledge and insight, enhance data integration and dissemination across channels, digitally connect and collaborate with customers, create better products and services, help shorten the sale cycle, drive down operational costs, and stay one step ahead of the competition. A sound digital transformation includes multiple emerging-technology pilots.

Every company’s digital transformation needs to be based on an integrated framework where individual projects connect and feed each other, e.g., leveraging data and analytics as a foundational platform to analyze and provide insights used in social media communities, CRM, and customer engagement; leveraging customer journey mapping and customer experience surveys to feed holistic customer profiles; leveraging emerging technologies like AI in CRM systems to provide next-best-action recommendations for individual clients, and so on.

In other words, an effective digital transformation strategy pulls together all of these components. Successful companies tackle digital transformation by implementing these components in bite-size chunks, supported by a long-term road map that focuses as much on people and process issues as technology.

The result of a successful digital transformation strategy? More satisfied, engaged, and loyal customers who purchase and then advocate for your company’s products and services, which provides the type of sustainable competitive differentiation that companies like Amazon, Airbnb, and Uber thrive on. 

Is your company’s digital transformation ready for prime time? If not, please contact us for a free consultation.

Knowledge Management to Increase Efficiency and Productivity

Knowledge management (KM) has become both an important topic driven by a number of industry trends, foremost among them the strong and growing interest in artificial intelligence (AI). A knowledge base (KB) can serve as the centralized source of knowledge for an organization, providing the data needed to feed an AI solution. 

Interest in KM is also being driven by its ability to help companies achieve many of their top enterprise servicing goals: improving productivity, increasing the use of self-service, decreasing customer effort, reducing operating costs, improving cross-departmental coordination, increasing customer and staff engagement, and delivering a better, more personalized customer experience.

This is a major and long overdue turnaround for the KM, which has taken many years to catch the attention of organizations. The question that organizations are now asking is whether KM solutions are able to meet their needs in the era of digital transformation.

KM Awakening

The new generation of KM solutions, many of which are relatively new market entrants, are either up to the digital challenge or are benefiting from investments to get them there. These solutions are built to run in the cloud (although many can also be placed in a private cloud or on premises); use the newest database technology; incorporate responsive design techniques to allow delivery of content to many groups of internal and external users in a variety of channels; depend on highly sophisticated and fast-search software to speed the delivery of information; and embed content management functionality to enable the collection and preparation of all types of data from an unlimited number of sources. 

Many of these solutions also incorporate a KM framework such as knowledge center support to help users roll out and apply their solutions effectively.

Differentiating between KM, search, and content management software has always been a challenge. In fact, a good KM solution depends on content management techniques to enable it to capture, structure, and properly store data. 

KM ensures that the right components of the data are delivered in a manner appropriate for each group (agents, IT staff, back-office employees, executives, customers, partners) and in a format appropriate for each channel (live agent, web self-service, voice self-service, email, chat, SMS, video, social media). When it comes to data sharing, a KM solution is the heart, and it pumps knowledge out to where it is needed, when it is needed, to keep an organization running properly.

Changing KM's Perception and Value Proposition

Major technical innovations during the past few decades are enabling a new generation of KM solutions. But this is only a small part of the developments that are altering the perception of KM. 

In the past, KM solutions were sold to customer service, contact centers, technical support, field service, and other departments that were dependent upon having a source of information to address customer inquiries. 

The value proposition was that a KM solution could replace or lessen the need for staff training and reduce the average handling time with customers. Essentially, KM solutions were sold to enhance productivity and reduce operating costs while improving service quality and first-contact resolution (FCR).

The problem was that employees did not like using many of the KM solutions because the solutions slowed them down; instead of reducing the average handling time of inquiries and improving FCR, the opposite occurred, and agents were penalized. The solutions came with poorly designed interfaces, and the search capabilities were ineffective. 

In addition, agents learned not to rely on a KM solution’s answers because much of the information residing there was either out of date or inaccurate, and the process of keeping knowledge current was cumbersome, time consuming, and costly.

The situation is different today. Companies are anticipating much broader uses for their knowledge bases. Executives have bought into the concept of having a single version of the truth for organization's knowledge, particularly when the information can be rendered appropriately for each group of users. 

As a result, the number of potential KM users has increased, which is a significant game changer. Customers are also making it known that they prefer to use self-service over speaking to live agents, making it necessary to have a clean, accurate, and easy-to-update KB. 

Additionally, Millennial agents, who are now the primary employee demographic throughout  organizations, are wired to look up answers and are happy to use a KM solution, as long as it can quickly give them the accurate information they need. In other words, the current generation of KM solutions is delivering on its promise and has a proven and quantifiable value proposition, when supported by the right enterprise framework and culture.

The KM Competitive Landscape

The fundamental KM concepts still stand, but how they are addressed varies by vendor. Each solution is unique, with an assortment of underlying technology and approaches. Vendors are entering the KM market from many IT sectors, including AI, customer relationship management (CRM), enterprise resource planning (ERP), IT service management (ITSM), workforce optimization (WFO), contact center infrastructure, professional services, and others. 

Some vendors sell only a KM solution; many others offer a KM capability as part of a suite of products, but do not offer it on a stand-alone basis.

The market is in the early stages of transformation, and a great deal more change is expected in the next few years. KM has remained more or less the same for decades, but this is expected to change as organizations get serious about creating a single source of knowledge. The opportunities are great for disruptive solutions to enter and transform this sector.

KM Needs a Framework and Best Practices

While the KM offerings have improved substantially, the primary challenge confronting this sector remains the acquisition, maintenance, and delivery of content. A KM solution is effective only if the underlying data is correct; if the data is inaccurate, it doesn’t matter how well organized or how fast and easy to deliver it is. 

Moreover, for a KM solution to work, a company needs to create an operating environment where all employees support the concept and practice of KM. It’s more than building a KM culture. An organization must institute a framework supported by internal infrastructure that facilitates the processes. It’s not about rewarding employees for authoring articles and using the KM solution. Instead, KM needs to become an inherent and essential component of what employees do on a daily basis.

Final Thoughts on KM

It’s taken a few decades, but KM is finally in the spotlight. AI is helping to push the KM agenda, and companies are getting on board with the idea of creating a single repository of enterprise knowledge, formal and “tribal”, as they consider its broad benefits for the organization, employees, partners, and customers. 

While it’s challenging to implement a KM solution, this is actually the easy part of the effort. More challenging is to set up the organization and processes to succeed with the transformation.

We have 20 years experience in KM. Please contact us today for a free consultation.

Digital Trust

While consumers have happily shared personal data on social platforms in return for greater connectivity and shared experiences, recent news about data harvesting has caused alarm. Many companies that rely on consumer insight are rethinking how to build digital trust and make it sustainable.

A study of 25,000 consumers across 33 countries, the majority of  92 % of which are U.S. consumers say it’s extremely important that companies protect their personal information. Another 79 % say it’s frustrating to realize that some cannot be trusted to use it appropriately. Lack of trust is one of the biggest reasons consumers switch companies.

And with the General Data Protection Regulation (GDPR), a regulation intended to strengthen data protection for EU citizens and let individuals decide which brands can use their personal data, good data stewardship is becoming critical to the success of every business globally.

The Importance of Insight

The ability to process personal data is critical to business in the digital age. Data-driven organizations rely on customer insights to help inform the development and design of products and services, the overall customer experience, and marketing strategy. From demographics to personal preferences, customer data allows companies to deliver hyper-relevant products, services, and experiences.

Some companies have built entire business models around the sale of anonymized personal data. Technology is creating opportunities for businesses to understand their customers on a deeper level and monetize this knowledge. Biometric, visual, genomic, and device data can allow ever-increasing degrees of personalization.

Personal data is a currency no business can afford to risk.

Earning Digital Trust

To earn digital trust, organizations' leaders have to eliminate anything that jeopardizes it. Companies looking to future-proof their customer data supply should take these measures:

• Deliver on their commitments. 83 % of U.S. consumers say it’s extremely frustrating when companies promise one thing but deliver another. An organization’s commitment to delivering promised experiences and meeting customers’ expectations is paramount to earning trust. Successful companies understand their baseline level of trust and eliminate issues or offers that detract from the trust. Otherwise they must reset their parameters.

• Establish rigorous governance. The only way trust can become sustainable is by establishing a rigorous process and a robust, cross-functional governance structure to continuously measure trust and hyper-relevant effectiveness and acting on the findings. Please see our posts on Information governance.

• Give customers full control over their data. As customers demand greater control over how companies use their personal information, organizations must become more transparent. Customers must be given full access to, and control over their data, which will demonstrate responsible stewardship and ethics. Furthermore, they must ensure that the appropriate safeguards are in place to protect it.

Some companies may look to adjust their profit models and potentially charge for services (i.e., “pay for privacy”) so customers are explicitly aware of the value being exchanged. That way companies could make money on direct interactions with customers as opposed to the derivatives of those interactions (i.e., selling insights or advertising). Or they could move from an information exchange relationship to a more classic view of understanding what customers need and having them pay for it.

More companies will undoubtedly assess their existing propositions and the economic viability of new models. But the question remains as to whether the underlying information and experience will become something that is merely expected, rather than something that customers would be willing to pay for.

The Path Forward

Digital trust is only sustainable when companies establish a rigorous process and governance structure. Most importantly, digital trust must be managed as the critical growth enabler it is. Companies will inevitably look to capture new categories of customer data such as biometric, geolocation, even genomic data in their drive for greater relevance. Customers' concerns will inevitably rise, so it’s critical that companies have strong data security and privacy measures in place, give customers full control over their data, and, crucially, are transparent with how they use it.

We have successfully implemented data security and data privacy in many organizations. Please contact us today for a free consultation.

Mastering Fractured Data

Data complexity in companies can be a big obstacle to achieve efficient operations and excellent customer service.

Companies are broken down into various departments. They have hundreds, thousands, or even hundreds of thousands of employees performing various tasks. Adding to the complexity, customer information is stored in so many different applications that wide gaps exist among data sources. Bridging those gaps so every employee in the organization has a consistent view of data is possible and necessary.

Various applications collect customer information in different ways. For example, CRM solutions focus on process management and not on data management.

Consequently, customer data is entered into numerous autonomous systems that were not designed to talk to one another. Client data is housed one way in a sales application, another way in an inventory system, and yet another way in contact center systems.

Other organizational factors further splinter the data, which can vary depending on the products in which a customer is interested, where the product resides, and who (the company or a partner) delivers it.

In addition, information is entered in various ways, including manually, either by the customer or an employee, or via voice recognition. And applications store the information in unique ways. One system might limit the field for customers’ last names to 16 characters while another could allow for 64 characters.

The challenge is further exacerbated by software design and vendors’ focus. CRM vendors concentrate on adding application features and do not spend as much time on data quality.

Customers can input their personal information 10 different ways. Most applications do not check for duplication when new customer information is entered.

Human error creates additional problems. Employees are often quite busy, move frequently and quickly from one task to the next, and, consequently, sometimes do not follow best practices fully.

Data becomes very fractured and there appear different versions of truth. The data features a tremendous amount of duplication, inconsistencies, and inefficiencies.

The inconsistencies exist because fixing such problems is a monumental task, one that requires companies to tackle both technical and organizational issues. Master data management (MDM) solutions, which have been sold for decades, are designed to address the technical issues. They are built to clean up the various inconsistencies, a process dubbed data cleansing.

The work sounds straightforward, but it is time-consuming and excruciatingly complex. The company has to audit all of its applications and determine what is stored where and how it is formatted. In many cases, companies work with terabytes and petabytes of information. Usually, they find many more sources than initially anticipated because cloud and other recent changes enable departments to set up their own data lakes.

Cleansing Process

Cleansing starts with mundane tasks, like identifying and fixing typos. The MDM solution might also identify where necessary information is missing.

To start the process, companies need to normalize fields and field values and develop standard naming conventions.  The data clean-up process can be streamlined in a few ways. If a company chooses only one vendor to supply all of its applications, the chances of data having a more consistent format increase. Typically, vendors use the same formats for all of their solutions. In some cases, they include add-on modules to help customers harmonize their data.

But that is not typically the case. Most companies purchase software from different suppliers, and data cleaning has largely been done in an ad hoc fashion, with companies harmonizing information application by application. Recognizing the need for better integration, suppliers sometimes include MDM links to popular systems, like Salesforce Sales Cloud, Microsoft Dynamics, and Marketo.

Artificial intelligence and machine learning are emerging to help companies grapple with such issues, but the work is still in the very early stages of development.

Still other challenges stem from internal company policies—or a lack thereof—and corporate politics. Businesses need to step back from their traditional departmental views of data and create an enterprise-wide architecture. They must understand data hierarchies and dependencies; develop a data governance policy; ensure that all departments understand and follow that policy; and assign data stewards to promote it.

The relationship between company departments and IT has sometimes been strained. The latter’s objectives to keep infrastructure costs low and to put central policies in place to create data consistency often conflict with the company departments' drivers. And while departments have taken more control over the data, they often lack the technical skills to manage it on their own.

It is a good idea to start with small area and then expand to other areas.

Having clean and organized data would make company's operations much more effective and would enable to optimize customer service. They can take steps to improve their data quality.

Please contact us for more information or for a free consultation.

Digital Transformation

Digital technology is drastically changing how companies do their business and companies' relationship with their customers. While customers gain the power of information and choice, digital technology dramatically improves the economics of business. The rules of business are being rewritten nearly every day with new digital technologies. Every company has a unique digital transformation opportunity.

However, doing so involves far more than merely converting paper processes to electronic ones. Companies undergoing a digital transformation also need to make sure that all of their digital processes are interconnected. Even more important, though, digital transformation requires a company-wide culture transformation.

For successful digital transformation, first we need to understand that digital transformation is more than simply a technology change or software adoption. It requires a cultural shift and a change in how a business behaves, given changes in customer demands. The shift and change require complete support within the company, from top managers to rank-and-file personnel.

A well-timed adoption and utilization of technology and software can support this bridge by enabling seamless flow of information between the company and the customer.

Digital transformation is also about being focused on the company's customers. It is about you enabling them to be intelligent and self-educated and to go along their own journey in a self-guided manner, and then you figuring out where you need to intersperse human touchpoints along that journey to add value to the digital touchpoints.

The best practice is a mix of digital content and human interaction that is orchestrated around customers and how they want to learn about and experience the company and its brands.

Another critical element of digital transformation is the interconnection of all company's data. Companies are broken down into various departments. They have hundreds, thousands, or even hundreds of thousands of employees performing various tasks. Adding to the complexity, customer information is stored in so many different applications that wide gaps exist among data sources. Bridging those gaps so every employee in the organization has a consistent view of data is possible and necessary.

But the task requires large investments of money and manpower and sweeping process changes, steps that most organizations have not been willing to make thus far.

It’s not an easy task, but it is getting simpler, particularly as a wide and growing variety of applications emerge. Vendors are now building solutions to streamline workflows for employees inputting data or responding to various triggers, like customers calling in with a problem.

Please contact us today for more information and for a free consultation.

Electronic Signature and Content Management

 At this time of digital transformation, it is difficult to talk about managing content management without talking about using electronic signatures. E-signatures make it possible to create digital workflows, help to maximize ROI from content management, and enhance productivity, compliance, security, and analytics.

Quite a few content management tools include e-signature implementation such as SharePoint, Box, and other content management systems (CMS).

Electronic signatures, digital business, and content management are interdependent. Without e-signature capability, documents continue to be printed for signing, then photocopied, shipped, corrected, imaged back into the system, archived, and shredded. 90% of the time and cost of labor dedicated to managing paper can be saved by using e-signatures. There are also other benefits of using e-signatures such as faster decision making, shorter sales cycles, and improved customer experience.

In the last few years, financial services, insurance, healthcare, and government have embraced digital transformation. A major driver is compliance and risk. Many organizations are concerned about legal risk or they struggle with the constantly changing regulatory landscape in their industries, in part because manual processing is very prone to errors.

Rather than react to regulatory pressure with additional people, manual controls, and process complexity, organizations that adopt e-signatures have these benefits:

• Leverage workflow rules to execute transactions correctly and consistently.
• Capture a full audit trail and electronic evidence.
• Minimize exposure to risk due to misplaced or lost documents.
• Make the process of e-discovery easier, more reliable, and less expensive.
• Demonstrate compliance and reduce legal risk through the ability to playback the exact process that was used to capture signatures.

Let's look at this example: the VP of compliance is asking for transaction records from 5 years ago. How helpful would it be to quickly produce all signed records, in good order and replay the entire web-based signing process for context.

According to Forrester Research, organizations and customers now recognize that e-signature is an important enabler of digital business.

Today, the business is digital and e-signature is a foundational technology enabling end-to-end digitization. Let's look at this example: a customer filled out an insurance application. When the package is ready to be signed by the customer, traditionally it would revert to paper. Instead, documents are handed off to the electronic signature solution. This solution would manage every aspect of the e-sign process, including notifying and authenticating signers, presenting documents for review, capturing intent, securing documents, collecting evidence, etc.

Once e-signed, the documents can be downloaded in PDF format and stored in any archiving system. The e-signature audit trail and the security travels seamlessly with the document, ensuring the record can be verified independently or the e-signature service.

A document centric approach to embedding e-signatures within signed records allows for greater portability and easier long term storage in an CMS solution. Additional metadata related to the e-sign transaction can be handed off to the CMS as well for analytics purpose.

Adopting electronic signatures is quick and easy and does require IT or programming resources. Companies who are looking for a more integrated automated workflow, e-signature plugins for SharePoint, Salesforce, Box are available.

Organizations can quickly and easily enhance approval workflows with a more robust e-signature solution than a checkbox on an approval routing sheet, while also automating archival.

Metadata Driven Solutuions

Metadata is data that provides information about other data. Many distinct types of metadata exist, including descriptive metadata, structural metadata, administrative metadata, reference metadata, and statistical metadata.

• Descriptive metadata is descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords.
• Structural metadata is metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships and other characteristics of digital materials.
• Administrative metadata is information to help manage a resource, like resource type, permissions, and when and how it was created.
• Reference metadata is information about the contents and quality of statistical data.
• Statistical metadata, also called process data, may describe processes that collect, process, or produce statistical data.

Metadata, properly managed, is the powerful tool to make things happen. We can have processes and solutions which are driven by metadata.

In application building process which is metadata driven, instead of building the desired application directly we define the application’s specifications. These specifications are fed to an engine which builds the application for us by using predefined rules.

Instead of building a package to create a dimension, for example, we can provide the dimension description (metadata) to a package generating engine. This engine is then responsible for creating the defined package. Once the package is executed, it will create and maintain the prescribed dimension.

Why is metadata driven so much more efficient than traditional methods?

• Creating a definition of a process is much faster than creating a process. A metadata driven approach results in building the same asset in less time as compared to traditional methods.
• Quality standards are enforced. The rules engine becomes the gatekeeper by enforcing best practices.
• The rules engine becomes a growing knowledge base which all processes benefit from.
• Easily adapts to change & extension. Simply edit the definition and submit to the engine for a build. Need to inject a custom process? No problem, create a package the old fashioned way.
• Enables agile data warehousing. Agile becomes possible due to greatly increased speed of development and reduced rework required by change.

The ongoing proliferation of devices joined with the distributed nature of data sources has created an indispensable role for metadata. Metadata provides knowledge such as location of the device and nature of the data, which facilitates integration of data regardless of its origin or structure.

Enterprises are incorporating data quality and data governance functions as part of data integration flows. Embedding these processes in the integration pipeline necessitates sharing of metadata between the integration tools, and the quality and governance tools.

Metadata also facilitates performance optimization in integration scenarios by providing information on the characteristics of underlying sources in support of dynamic optimization strategies.

In content management, folder-less approach allows you to search for and access files however you want – by client, project type, date, status or other criteria. It's completely dynamic, enabling you organize and display information how you need it, without the limitations of antiquated, static folder structure.

All you do is save to files and tag the file with the properties you need, and you are done. No more wandering through complex, hard-to-navigate folder structures, trying to guess where to save a file. With metadata you just quickly describe what it is you are looking for.

Metadata management software provides context and information for data assets stored across the enterprise. ... Metadata management tools include data catalogs, or assemblages of data organized into datasets (e.g. searchable tables or other arrangements, facilitating exploration).