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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