How to transform customer experiences with cognitive call centers

Customer data and insights can help steer companies to new levels of innovation, engagement and profit. And, most organizations are sitting on a gold mine of customer data. But, it’s how customer data is used that matters. How is your organization collecting and using customer insights? Are you using it to create the most engaging customer interactions? And, are you creating a cognitive conversation with your customers? These are critical questions that not all companies are yet considering, but should.

Understanding the importance of cognitive conversations

A cognitive conversation takes advantage of data from external, internal, structured and unstructured voice and multichannel sources to deliver a customer response that is more conversational, relevant and personal. Organizations can meet changing customer preferences and behaviors by learning from every interaction. All parts of the organization can take advantage of data collected from different areas of the company to improve customer loyalty.

Businesses are recognizing that while customer interactions often begin on one channel, valuable insight and feedback is also being gathered from customers on other channels across the business. Unifying customer information across channels gives businesses a more complete context to resolve customer issues more fully and more quickly.

With the speed to which customer expectations are changing, it is conceivable that customers will prefer to manage their relationship with businesses without interacting with a live agent. Customers are using more channels to interact with businesses and are doing more research through websites and referrals before ever engaging.

A more conversational, personal, seamless and device-independent approach to customer engagement is critical. Customers are likely to commit to a brand or product after a satisfactory experience, so it is time the whole company focused on adopting capabilities to enable a more cognitive experience for clients and potentially surpass competitors.

Taking full advantage of the gold mine of customer data

Every department in your organization, from marketing to sales to customer service, has data on customer behavior. That collective data can be used to meet and even exceed the demands of customers. An organization’s brand, website, notifications and certain self-service channels, whether voice or chat, demonstrate commitment to the customer.

It may seem overwhelming to gather all this information across your organization at every entry point of interaction and also deliver a seamless, consistent and cognitive experience. But, it doesn’t need to be.

If you can tap into cognitive capabilities, they will turbocharge interactions across channels. This combination can transform traditional self-service brand engagement into a more relevant and relational experience for customers. Customers can use both traditional interactive voice response (IVR) features with cognitive capabilities, which can be integrated with an existing contact center environment, as well as other third-party applications such as computer-telephone integration (CTI). Combining these capabilities is a unique approach that transforms the customer experience to a more conversational and cognitive interaction. As a result, resolutions can be achieved more quickly than interactions handled by traditional IVR systems alone.

Transforming the customer experience

Changes in the customer experience journey are happening fast. There is no slowing down or stopping the convergence of technology and increasing demand for quick, relevant and personalized customer interactions. The stakes are high when it comes to the customer experience, and it’s not just the contact center that needs to take notice. The customer experience is a whole-company issue.

Customer experience is at a crossroads of change and transformation, adding a new level of engagement across the organization. Taking advantage of the forces pressuring organizations to evaluate their strategy, technology and general understanding of customer behavior will set the pace for the cognitive revolution.

Four key factors are making transforming the customer experience a hot topic:

1. Increased value of customer experience as a market differentiator
2. Speed of changing customer demands
3. Cognitive capabilities make collecting, learning and understanding data in near real time a reality
4. Market leaders figuring out how to combine knowledge with technology to magnify the customer experience

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6 common DevOps myths

There are a lot of DevOps myths floating around the IT world. That’s not surprising, given how much hype the term — a combination of “development” and “operations” —  has built up in the past few decades.

DevOps is more than worthy of the hype. When done properly, the DevOps approach can deliver massive positive impact for businesses. It can reduce costs, improve performance and break down silos between teams.

To understand the power of this approach, however, it’s important to know what DevOps is and what it is not. Let’s start by correcting six common DevOps myths.

1. DevOps is only for shops born on the web.

It’s true that DevOps mostly started at companies that were born on the web. Maybe that’s why people get the idea that this methodology will only work at internet firms such as Netflix or Etsy. That idea turns out to be a myth.

Large enterprises have been successfully using DevOps principles to deliver software for decades.

2. DevOps only matters to engineering and operations.

The name DevOps clearly reveals the origin of the approach. DevOps started as a better way for operations and development teams to work together.

Today, the approach can empower the entire organization. Everyone involved in the delivery of software has a stake in this methodology.

3. DevOps can’t work for regulated industries.

Regulated industries have an overarching need for checks and balances, as well as approvals from stakeholders. This doesn’t mean DevOps is a problem, however.

Adopting DevOps actually improves compliance, if it’s done properly. Automating process flows and using tools that have built-in capability to capture audit trails can help.

Of course, organizations in regulated industries will always have manual checkpoints or gates, but these elements can be compatible with DevOps.

4. You can’t have DevOps without cloud.

When many people think of DevOps they think of cloud. There is a good reason for this. Cloud technology provides the ability to dynamically provision infrastructure resources for developers and testers to rapidly obtain test environments without waiting for a manual request to be fulfilled.

That doesn’t mean cloud is necessary to adopt DevOps practices, though. As long as an organization has efficient processes for obtaining resources to deploy and test application changes, it can adopt a DevOps approach.

Virtualization itself is optional.

5. DevOps means operations learning to code.

Operations teams have a long history of writing scripts to manage environments and repetitive tasks. With the evolution of infrastructure as code, operations teams saw a need to manage these large amounts of code with software engineering practices such as versioning code, check-in/check-out, branching and merging.

Today, operations teams can create a new version of an environment by creating a new version of the code that defines it. This doesn’t mean, however, that operations teams ,must learn how to code in Java or C#. Most infrastructure-as-code technologies use languages such as Ruby, which is relatively easy to pick up for people who have scripting experience.

6. DevOps doesn’t work for large, complex systems.

This myth is totally off-base. The opposite is actually true: complex systems often require the discipline and collaboration that DevOps provides. Large systems typically have multiple software or hardware components, each of which has its own delivery cycles and timelines. DevOps facilitates better coordination of these delivery cycles and system-level release planning.

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Social Learning in Practice at IBM

What is social learning and how can it help drive engagement and develop a culture of learning?

The social learning theory of Bandura emphasizes the importance of observing and modeling the behaviors, attitudes, and emotional reactions of others. Bandura (1977) states: “Learning would be exceedingly laborious, not to mention hazardous, if people had to rely solely on the effects of their own actions to inform them what to do. Fortunately, most human behavior is learned observationally through modeling: from observing others one forms an idea of how new behaviors are performed, and on later occasions this coded information serves as a guide for action.” Basically, Bandura’s theory is that human beings can learn by example.

Why does social learning matter?

Research states that most people only recall 10% of information learned within just 72 hours in typical training environments. Social learning can reverse this curve. In fact, research shows retention rates as high as 70% when social learning approaches are employed. Rather than relying on typical training environments with low recollection rates, social learning allows learning to happen in the working environment. Learners can pull knowledge from experts within the organization rather than have it pushed on them. Learning becomes a part of the organization culture.

An example of social learning at IBM

The Data Analytics Center Of Excellence (COE) at IBM continuously provides Data Science training for our employees and decided to pilot the use of the recently IBM Data Science Professional Certificate on Coursera. They identified 2 different controlled study groups 1) A group of individuals who would have otherwise gone through a 5-day full time face to face bootcamp and 2) A group of instructors who would typically teach this bootcamp. One of the biggest problems of using MOOCs for enablement is the high dropout rate, research shows that approx ONLY 5% of the total learners complete a course. Here are a few ways in which we are keeping this group of learners engaged:

FAQs and Forum

A dedicated SLACK channel has been established with the pilot participants in which employees can pose questions and receive answers from within the group. This promotes collaborative learning as individuals can learn from their peers and also learn from questions posed by others. Apart from the pilot, there is also a large IBM Data Science Community  that hosts events on a regular basis and has plenty of enriching forums with discussions.

Organization Wikis

The participants are encouraged to blog about their experience. Bernard Freund, STSM – Data Analytics CoE writes a blog post at the end of each week as he completes a course. This post not only provides user with a thorough review of the course, but also highlights some issues along with workarounds which has been extremely useful for other learners attempting the course later.

Utilize expert knowledge

Besides the SLACK channel, we have also instituted check-point calls with the Coursera and course development team. Not everyone attends these calls, but it does give the participants an opportunity to get some 1:1 time with the SMEs to overcome any obstacles that may be preventing them from completing the program.

Gamification and rewards

You can’t force people to learn but you can give them the right tools and incentives to make sure they don’t waste opportunities. IBM does this through the Open Badge program. The program awards badges upon the completion of each of the 9 courses and a certificate upon program completion. These badges provide a way for the administrators and users to track their learning progress.

Currently, we are 1 month into the 3 month pilot and the learners seem very engaged and vested in their progress. On an average most participants have completed at least 2 of the 9 courses which does put them on track for completing the certificate within the pilot timeline. Stay tuned as we report further results in the coming months.

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Get a health check for your SAP HANA on IBM Power Systems

Are you confident your SAP HANA on IBM Power Systems are getting optimal performance?

SAP HANA has been available on IBM Power Systems for a few years, and many organizations have migrated to it, bringing numerous advantages such as flexibility, efficient resource utilization, server consolidation and reduction in costs. As a Tailored Data Center Integration (TDI) model, an SAP-certified person is required to install and configure HANA. During deployment, a certified HANA engineer sets up the system following IBM Power server and SAP HANA best practices and runs an SAP HANA Hardware Configuration Check Tool (HWCCT), which ensures the environment has been configured for HANA prerequisites and for hardware performance to meet HANA KPIs.

After deployment, however, organizations will eventually need to make changes to their workloads and infrastructure. The monitoring tools you use might not capture deviations from best practices. Some components of your system might require periodic checks like firmware updates, patches, backups, cluster operations and so on. Hence the need arises for a periodic health check for SAP HANA on Power Systems. Without periodic health checks, you might not be getting the best availability and performance from your systems, and you could be at greater risk for an unplanned outage.

What is an SAP HANA on Power Systems health check?

A health check involves inspecting your system in several key areas, such as:

◈ Ensuring up-to-date software levels
◈ Examining the adequacy of hardware resources
◈ Looking at system tuning based on your current workload pattern
◈ Doing checks for best practices in virtualization
◈ Checking the feasibility of adopting newly released features in the Power server/OS/HANA

Your HANA configuration, error logs, high availability and backup policies are also validated.

Minimum checks that needs to be carried out as a part of an SAP HANA on Power Systems health check

The following chart shows a list of the minimum checks that must be covered as a part of an SAP HANA on Power Systems health check. This is only a high-level list; additional checks based on your results may be needed.

Benefits of an SAP HANA system health check

An SAP HANA on Power Systems health check offers numerous benefits:

◈ Helps you identify any single point of failure and fix it

◈ Helps prepare you for handling unexpected downtime

◈ Demonstrates current hardware utilization and growth trends, thus helping you plan for future growth or release a portion of your hardware for other workloads, thus saving on budget for any additional workloads

◈ Helps you get better support by staying up-to-date with software versions

◈ Helps you better manage your IT budget by knowing growth trends

◈ Helps you know new technologies that could be applied to your environment

◈ Improves productivity, improves your confidence and may reduce the cost of acquiring additional hardware for new workloads

Who can perform an SAP HANA on Power Systems health check?

An SAP HANA on Power Systems health check can be done by anyone who has good knowledge of IBM Power Systems, Linux and HANA. You may do it yourself or engage a team of experienced consultants like IBM Systems Lab Services. Lab Services helps organizations build and optimize SAP HANA solutions with Linux on Power Systems with a tailored data center infrastructure strategy, and health checks are among the many services we offer to help clients optimize their SAP HANA environments.

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IBM X-Force Red Security Team takes on security challenges with the help of IBM Cloud

Unless you live under a rock, you’ve likely seen a recent top news headline with the words “security breach” somewhere in there. This is not the type of press companies want to be recognized for, and it is even worse for the millions of customers who are left out in the cold when their unauthorized information is made public.

High-profile security breaches are becoming more common every year as cyber criminals are becoming more sophisticated in finding new security vulnerabilities to penetrate to access protected data. These hackers aren’t planning to ease up on businesses anytime soon, either. With that in mind, the best course of action for organizations is to rapidly test, identify and fix where they are most vulnerable.

Security penetration testing to better manage vulnerable data

IBM recognized this need two years ago when it launched IBM X-Force Red, a team of security professionals and ethical hackers whose goal is to help businesses discover vulnerabilities in their computer networks, hardware and software applications before cybercriminals find those same vulnerable areas. The security testing expertise that IBM X-Force Red brings to the table spans multiple industries including healthcare, financial services, retail, manufacturing, government and the public sector.

Although there are unique security vulnerabilities in each industry, password security issues remain among the top areas of concern for every enterprise, no matter the industry. It only takes one weak password for a cybercriminal to breach an entire business. The need for greater password security has given rise to an entire segment of “password auditing” solutions that test for password weaknesses within an enterprise, particularly among website applications.

Password auditing, or password cracking, is the act of running plain text through an algorithm to generate a hash, then matching the plain text to hashes. When a match occurs, the hash is considered cracked. Once the hash is cracked, so is the password. This assumes there hasn’t been anything added to the password before hashing — referred to as password “salt” — which is added to slow down hackers.

Hacking anything to secure everything

In the world of password auditing, there is little that the IBM X-Force Red team doesn’t know. The team put this on full display recently at the Black Hat Security Conference in Las Vegas, Nevada. However, as the team prepared for the security event, members realized that, to rapidly test all aspects of an organization’s password security vulnerabilities, they would need a strong compute foundation to run their tests at scale.

Dustin Heywood, also known as EvilMog, from the IBM X-Force Red team and a member of Team Hashcat — a group of password security researchers and the contest team for the open source Hashcat project — led both teams, first in a demo of their “Cracken” password cracking application, then in the Black Hat “Crack me if you can” password cracking contest. He decided to turn to IBM Cloud infrastructure as a service (IaaS) for high-computing performance and scalability. In preparation for both the demo and the contest, Heywood and his team provisioned and tested a complex, 32-server virtual server environment with 64 NVIDIA Tesla P100 graphical processing units (GPUs) all in under a day. In the words of one Hashcat team member, “it was a little like bringing a nuke to a gunfight.”

Big results

The IBM Cloud environment provided a fivefold increase over the existing IBM X-Force Red 16-server GPU-based infrastructure to fuel the “Cracken” password cracking application and demonstrate real-time, eight-character password cracking in an average of two to three minutes, a feat that would normally take the X-Force Red GPU-based infrastructure alone about eight to 12 hours per password to accomplish.

The IBM X-Force Red team didn’t stop there. With the DEF CON 26 conference coming hot on the heels of Black Hat, EvilMog used the same IBM Cloud and Cracken combined infrastructure to tackle the “Crack Me If You Can” contest, which is essentially, the World Series of password cracking contests. Over a two-day period, Team Hashcat cracked more passwords than any other team.

The team’s performance shows that the IBM Cloud is an ideal environment to consider for quickly running complex, compute-intensive applications.

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PowerAI for systems integrators

Business owners for enterprises of all sizes are struggling to find the next generation of solutions that will unlock the hidden patterns and value from their data. Many organizations are turning to artificial intelligence (AI), machine learning (ML) and deep learning (DL) to provide higher levels of value and increased accuracy from a broader range of data than ever before. They are looking to AI to provide the basis for the next generation of transformative business applications that span hundreds of use cases across a variety of Industry verticals.

AI, ML and DL have become hot topics with global IT clients. They are driven by the confluence of next-generation ML and DL algorithms, new accelerated hardware and more efficient tools to store, process and extract value from vast and diverse data sources that ensure high levels of AI accuracy. However, AI client initiatives are complex and often require specialized skills, ability, hardware and software that is often not readily available.

Trusted advisors such as systems integrators (SIs) are building the next generation of AI solutions for clients. SIs are being called upon to integrate best of breed parts to accelerate AI projects, driving the need to rapidly ramp up their own internal skills, capabilities and thought leadership around the multiple components of AI solutions. In addition, clients rely heavily on trusted SIs to clarify and demonstrate how business problems can truly benefit from today’s AI solutions and what is ‘not quite there yet’.  Taking a new AI project, with a broad suite of AI models, through the entire “AI lifecycle” of design, development, proof of concept, deployment and production, as well as integrating the new AI functionality into existing client transactional systems, is the ‘sweet spot’ for SIs.

As SIs move from the build-up phase to ramping up their AI skills, experience and IP associated with integrated and complex solutions, it becomes very important for SIs to leverage highly skilled partners such as IBM. IBM provides a broad range of industry-leading AI solutions. It includes both the software and the hardware infrastructure that are deeply optimized for a complete production AI system. Partnering with IBM’s AI offering teams sets the stage for SIs to establish their AI leadership by enabling delivery of an entire suite of brand new AI assets.

IBM’s new PowerAI Enterprise is a unique solution which makes DL and ML more accessible to clients. PowerAI Enterprise is a complete environment for “data science as a service”, enabling SIs to accelerate the build of more accurate AI applications for clients. It also accelerates the performance of those applications when running in production. PowerAI Enterprise combines popular open source DL frameworks and efficient AI development tools, and enables AI applications to run on accelerated IBM Power Systems™ servers.

PowerAI’s focus is to provide the comprehensive hardware and software infrastructure required to support new and demanding client AI applications, what IBM calls ‘The AI Infrastructure Stack’ (see diagram below). This stack spans components from servers all the way to ML/DL software. Since modern AI methods usually use GPU-acceleration, IBM has built a server optimized for AI, the IBM Power Systems AC922 server with POWER9 architecture, that has a high-speed connection between the POWER9 CPUs and the NVIDIA GPUs.

PowerAI running on Power Systems, combined with NVDIA GPUs and NVLink technology, enables SIs to rapidly deploy a fully optimized AI platform that delivers blazing performance, proven dependability and resilience, and it is fully supported by IBM. SIs can easily add their own unique incremental value on top of PowerAI, resulting in their own competitive advantage in the rapidly growing AI systems integrator marketplace.

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It’s Time for IBM Datacap Design

It is no secret that IBM Datacap is a robust and highly powerful imaging platform. Using Datacap, one is able to build just about any imaging application imaginable. This can be anything from the simplest of straight forward form capture solutions to the more complex AP, sales orders, medical claims and EOB’s (Explanation of Benefits Form). The power of Datacap is rooted in many ways it can be configured and customized and therefore the need to follow a well-defined process is paramount.

Since MagicLamp Software began implementing IBM Datacap, we have completed just north of eighty-seven successful projects. Our approach is solid, our team is dedicated and we are guided by one of Datacap’s finest, Tom Stuart, as our Vice President of Development.

What makes a Datacap Project Successful?

Every Datacap Project that MagicLamp undertakes is based around a solid approach of the System Development Lifecycle (SDLC) methodology and a significant effort placed on requirements / analysis, design and proper UAT. Because Datacap is capable of addressing complex business requirements close attention is needed at all levels of the lifecycle.

How Should a Datacap Project Work?

MagicLamp’s core values of LISTENING, UNDERSTANDING, BUILDING and TRANSFORMING begin at the sales cycle and never really end. Over time MagicLamp has been able to create project accelerators in the areas of

◈ Accounts Payable,
◈ Sales Orders,
◈ Medical Claims and
◈ Explanation of Benefits.

An accelerator is best described as a base Datacap application that contains features and lessons learned from all of our previous engagements in that particular space. The value of an accelerator is that it is designed to lower the overall project cost and timelines of a project from inception to production.

During our time MagicLamp has also learnt a few key tips to success along the way and are outlined below:

◈ Manage Expectations: the client must understand exactly what they are going to receive once the project is over.

◈ Well Defined Requirements: Well-defined requirements are also important to any successful project. Even though some clients may provide a BRD (Business Requirements Document) during the project onset, a due diligence exercise must be undertaken just to ensure the requirements do make sense. This task should take at minimum 120 hours to complete consisting of onsite workshops, document writing / review / updates and signoff

◈ Detailed Design: Next to UAT design is probably the most important part of a project. During design architects must focus on the following items:

• Ensuring that the Datacap DCO is comprehensive
• Trying to ensure that every Field in the DCO contains at minimum one Clean and one Validation Action
• All business logic must be evaluated through Datacap’s Automated path and its Manual Verification path. Reuse should be the goal during this process knowing that it is not always possible
• The actions of each Datacap process Scan, Page ID, Profiler, Verify, Export & Audit must be laid out in bullet form. Datacap Developers already know their craft therefore bullet form is just fine
• A well-defined Audit process ensures that both the DCO is complete and that all of the business logic has been considered in order to ensure that the Audit information can be accounted for. This task should take at minimum 120 hours to complete consisting of design work, document review / updates based on feedback and signoff

◈ Implementation & Configuration: The implementation of any Datacap project should be straightforward at this point. All developers and testers should be following the requirements and design document as roadmaps. Architects must perform periodic code reviews just to ensure that everything is being implemented correctly as outlined in the design document. Testers should also be creating a confirming their test case library against the requirements document to ensure that nothing has been overlooked.

◈ Solution Playback: The value of a playback session is that it presents the opportunity for client feedback. Items can be evaluated and discussed during the playback and should a change be required this is truly the best time to do so.

◈ UAT: UAT is the most important yet understated task within the entire process. UAT should be the longest task in the lifecycle and MagicLamp recommends nothing less then 4 weeks for UAT.

◈ Test Cases: The client is ultimately responsible for generating test cases. A set of test cases must be created for the implementation developers to use, which should be a subset of the greater test case library and cover all of the different scenarios that the solution needs to address.

◈ Go Live: Lastly is Go Live. The biggest tip for “Go Live” is to ensure that there is a “Go Live” checklist. In most enterprise environments there are a large number of moving pieces and because of this it is very important to ensure nothing is missed. So follow the checklist to the letter and all should be fine.

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Three Charts That Display How Aviation Professionals Think

Some of the most prominent aviation professionals convened at the Aviation Festival 2017 to discuss the future of the industry. While we were there, IBM surveyed participants regarding their predictions and their struggles. Here’s what we discovered…

Personalization is their biggest digital experience challenge.

When asked to select their top priority when it comes to customer experience, almost two out of three respondents confidently leaned towards personalizing the experience more.

They consider post-flight to be the hardest time to connect.

Airlines seem to be more confident with their booking experience compared to other parts of the journey. When polled, our respondents were almost split between which area was hardest to reach passengers: in transit or post flight.

They’re most interested in customer spending habits.

A few participants remarked that they were “not surprised” at the results of this question. Customer spending is the information that professionals are most compelled to have.

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How content analytics helps manufacturers improve product safety and save lives

Manufacturing problems can have a serious impact on businesses. This is especially true when these problems manifest themselves as product safety issues causing injury, or even death. Whether it’s a car or a children’s toy or an advanced medical device, product safety issues don’t just result in negative publicity about your products and services, it can also lead to millions of dollars in lawsuits and liability.

Manufacturers have to leverage all their data and external data to identify issues as quickly as possible to get ahead of avoid negative press, expensive product recalls, huge penalties by industry regulators, millions in legal liability, and most importantly, they need to protect the safety of their customers. Identifying emerging issues before they escalate into full-blown product recalls helps protect reputations, customer loyalty and money.

It’s critical for manufacturers to identify reactions to new products early, especially negative feedback and to effectively address customer perceptions and concerns as soon as possible by making sense of ALL the data, structured and unstructured, that’s available to them. And yet, most manufacturers are struggling to gain deep insight into developing trends and potential issues hidden in unstructured data like customer emails, social media posts, warranty claims, surveys, complaints to regulatory agencies, blogs posts, engineering reports, quality assurance tests, or call center recordings and transcripts.

How automobile manufacturers are proactively identifying issues

The data and tools needed to do this are more accessible than ever before. In this video demonstration, I illustrate a real-world example of how an automobile manufacturer uses IBM Watson Explorer and its Natural Language Processing (NLP) capabilities to identify leading-edge indicators for a serious vehicle safety problem, way before it escalated.

Structured data often exposes the “who,” “what” and “when” of a problem. But the “how” and “why” — often the root causes — are buried in unstructured content. Here’s an example of how manufacturers can quickly and accurately reveal the “how” and “why.”

This video shows how the automobile company is using Watson Explorer to:

◈ "Read” and analyze thousands of consumer complaints

◈ Identify statistically significant trends in this data

◈ Find “language” that is highly correlated to this trend, which helps identify the root cause for the problem. (Watson does this without any presupposed hypothesis of what the problem could be and without bias as to probable cause.)

The video shows how an automobile manufacturer can effectively harness text analytics on vehicle safety data to diagnose recall issues through publicly available data. The video also demonstrates how Natural Language Processing models can be created by subject matter experts at companies (not just programmers or data scientists), to effectively dimensionalize abstract concepts. This allows more teams and employees to ask questions of the data that wasn’t possible before as standard text analytics and search technology couldn’t deal with the variability in natural language text.

Auto manufacturers can now isolate and pinpoint the cause of safety issues through data from the National Highway Traffic Safety Administration (NHTSA) through basic out-of-the-box analysis tools. The same concepts can be applied to other industries and issues where unstructured or text-based data is available to manufacturers.

Getting ahead of problems by mining text for indicators

Truly understanding and managing the perceived quality of your products and getting ahead of problems requires a data-driven approach to connecting and analyzing social media, governmental and internal and external data sources to mine text for indicators, sentiment and red flags. This leads to faster issue detection, problem resolution, competitive advantages and improved product design.

Want to harness the power of all the data available to your team to identify issues earlier, resolve them faster, reduce recall and PR costs and increase sales? Learn more about how Watson Explorer can help you get started. Watson Explorer is a content analytics platform that connects and analyzes your structured and unstructured content, across systems and silos and surfaces critical insights, trends and patterns. Watson Explorer combines enterprise search with cognitive capabilities to help you explore, analyze and interpret information to improve decision-making and business outcomes across your organization.

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How to Design An Effective Work Environment

Optimal working conditions, like many other things in life, depend on the individual needs, team needs, objectives, and resources available. Thinking there is one tool, one way, one method, to solve all problems leaves powerful opportunities for success on the table and lowers effectiveness down to the lowest common denominator.

Since it usually all depends, that means it is critical we take the time to understand the…

◈ individuals
◈ teams
◈ objectives
◈ organizations

…that we are serving when we take on the challenge of designing and implementing solutions to achieve optimal working conditions.

How we think about our challenges directs our ideas and approaches for solving a problem. When scaling is the goal, our minds fast forward to serving all people with our solutions. While there is nothing wrong with a bold vision, it is critical we show some results, even small ones. In order to do this, we need to think small at first. Small assumptions, small experiments, small implementations, small impact.

Whom we decide to serve can also direct our ideas and approaches for solving a problem. One simple way to begin making this decision is by segmenting your audience. When you segment the market, you have the opportunity to intentionally select one group of people, start small, and fully address their problems before moving on to the next or scaling that particular solution.

As you embark on designing and implementing solutions that contribute to an optimal work environment, consider applying the following process.

Segment the Audience

There are several ways you can segment your audience. Clayton Christensen, Harvard Business School professor, suggests segmenting by the “jobs” people or teams are attempting to perform. Ultimately, solutions help people do jobs more effectively, so focus on jobs as the primary method of segmentation. Here are examples of some jobs people or teams try to complete in a company:

◈ Learn new skills
◈ Complete heads-down work (state of flow)
◈ Meet with individuals (one-on-ones)
◈ Meet with groups of people (meetings of 3+)
◈ Schedule and complete group working sessions
◈ Rest, reset, break
◈ Eat
◈ Network (internally & externally)
◈ Obtain feedback
◈ Get promoted
◈ Contribute to the organization’s success
◈ Understand how their work contributes to success

The list can go on and on. When we segment our audience this way, we can begin addressing specific and clearly-defined situations that can be solved more effectively and thoroughly.

To begin this segmentation exercise, list as many “jobs” as you can, and then decide what segment you will serve first. Within the segment you choose, start with people or teams that have this job in common and then further filter this group down to those you can access easily. Keep the group small. Don’t go for the kill (i.e. take on too many) on your first attempt, because if you miss, it will cost you a great deal. Take several experimental jabs at your problem first.

Study the Segment Experience

With a job segment and group of people selected, go talk to people in your target groups so that you can investigate this journey they undergo to complete the job in question. For instance, learn about everything related to how people in your organization go about learning. You mission here is to obsess over this problem because then and only then will you be positioned to identify and deliver the most innovative and effective solutions. Consider the following steps:

◈ Observe how individuals and teams engage in the job you are studying. In the case of learning, you might observe people attending a company class or people at a company training event. You could also ask a few people to complete a specific task related to finding learning opportunities and watch them look for this. All the while, you are taking notes on their experiences, processes, successes, and pain points.
◈ After learning from observation, you can begin talking to people

about their experiences in learning and development; listen carefully for pain points. In these conversations, ask mostly open-ended questions (i.e. who, what, when, where, why, and how). When you hear a pain point, note it, and when the time is appropriate, repeat it to them to be sure you understood clearly and ask follow-up questions.

◈ Ask questions about their most successful experiences in learning and development so that you can understand the good that already exists. This will provide you with an existing foundation to build from – no sense in reinventing the wheel.

◈ Ask questions about their least successful, most painful, and failed attempts at learning and development. These questions will illuminate the pain points, ineffective processes, and possible misunderstandings. There always stands the possibility that pain point is nothing more than a misunderstanding in the current processes that could easily be resolved with minimal effort.

◈ Finally, ask them if there are any last thoughts or comments. Usually, after an effective interview, related ideas may have surfaced that would be of value to capture.

Brainstorm Solutions

Review your research. Regroup with your team and review the problems discovered during these sessions. Wherever possible, categorize them and identify themes. Should you find themes within one segment, you have the opportunity to prioritize the most significant themes first. Then, as you engage in other segments, you might find similar themes across segments. This is evidence of an opportunity to scale a solution beyond a single segment. This scaling opportunity is not the same as scaling for larger audiences, that will come later. [use image of segment jobs, then themes, then circle the overlapping themes]

Brainstorm with your team.Begin brainstorming solutions with your team around these validated pain points. For this activity, find a room with a white board, list your validated pain point themes and then, using one post-it per idea, stick up as many ideas as possible by each theme. It does not mean one solution cannot be scaled to another theme, this is just for the sake of keeping things as organized as possible.

Brainstorm with your clients. Repeat the same exercise with your clients. Invite them to a session and ask them to list their ideas, one per post-it, near the appropriate theme. By engaging the customer in the solution-building process, you will not only validate the ideas you came up with as aligned with clients’, but you will also stand the best possibility of having implemented solutions being met with the most support.

Select ideas for experimentation. With several ideas listed per theme, now comes the task of selecting which ones to experiment with. In order to reduce the list, first look for overlapping ideas and consolidate them. Next, look for product/market fit, that is, look for which solutions most closely fit the problem; look for ideas that meet no more, no less than (to a few degrees) the problem theme it is addressing. Some ideas will be too much firepower for a particular problem and others may not be enough to effectively address the scope of the problem. Find the right fit.

Experimenting and Measuring

Design a prototype. Now that you have a few customer-approved solutions in hand, begin designing low-cost experiments (i.e. minimal viable products) to test. This is the simplest and roughest prototype you can get away with and still deliver minimally acceptable value to the client. In other words, this is the absolute least someone would pay for.

Select your experimental group. Select a group of clients and work closely with them to set up and conduct the experiment. Find your baseline data, this will often come from your studies on the segment experience plus some analytics on the data you gathered. This is your control data. However, you can also select a blind control group that you will measure against after the experiment. Any group of people engaging in the “job” that were not part of your experiment will satisfy this control experiment. Always favor those you have easy access to. Blind studies are best because it reduces the risk of them being lead in any way. Before you conclude this step, decide on the metrics you will measure, qualitative or quantitative. You may not be able to measure everything, so do your best to track as much of the result as possible. This part will get easier as you find that other experiments may be measured by the same metrics. Thus the first few will be more challenging.

Measuring results. Once your experiments are set up, begin measuring the results. In order to make quick decisions, know what you are looking for, that is, decide what range qualifies as success, worthy of discussing, and simply ineffective. This will allow you to make quick decisions and move on to the next steps where you either pivot (i.e. adjust your approach) or persevere (continue down the current solution path).

Pivot or Persevere

Equipped with results from your experiments, you can now begin to review these results, decide which experiments were the most impactful, and invite your clients to review your conclusions with you. Your clients will help validate the data you captured as well as provide qualitative feedback you may not have been able to capture with metrics. In addition, including your clients in this process will help build support for the first phase of this implantation. As each successful implementation concludes, the team can commence subsequent implementations. However, as the audience grows, there will be a new challenge to address – scaling to large audiences.

Scaling

This is where large companies do best and they must because scaling is a necessity! Equipped with valuable lessons, validation, results, case studies, success stories, and most importantly, satisfied customers, you will have the best evidence in hand to make the strongest case possible for funding the larger phases of implementation.

Do keep in mind, scaling does not just mean duplicating this effort to go from 10 satisfied clients (teams) to 100 new teams. Scaling is a unique challenge of its own, made easier by having strong evidence and support for the particular solution you are attempting to scale. You are now going to encounter new clients, in different geographies, with different cultures, people, languages, ways of working, businesses, etc. These broader differences will present new challenges to your solution and the manner in which you apply them. A one-size-fits-all implementation strategy will likely not work. It will be necessary to segment your larger roll-out audience by categories that affect implementation. For instance, if your solution requires specific systems, start with those groups that already have access to and experience using those systems.

Essentially, when you are ready to scale, repeat this process, with scaling set as the new challenge.

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