Understanding Data Insights and Analytics: The Foundation for a Data-Informed Organization

Improving the customer experience and creating impact with products centered around human values require organizations to move beyond traditional data and analytics strategies to lay the foundation for insights-driven change. To build an organization that maximizes the values of multidisciplinary teams, diverse data sources and omni-channel support, it’s important to strip the insight-generation process to its bare bones and understand the very basic concepts: data, analytics and insights.

What is Data?

Data is individual facts or pieces of information, generally expressed in their most simplest form. For example, a health insurance company collects the following data about its members: first name, last name, and phone number. Individually, each data point is not very useful, but collectively they provide a lot of information about an entity; in this case, a patient.

In the technology industry, we categorize this type of data as structured data, because it can easily be tagged, organized and stored in a structured format such as an Excel workbook or a SQL database. Collecting data is the first step in building a data-driven organization.

What is Big Data?

Big data refers to very large sets of data that are generated in exponential amounts, and come from a variety of data sources and formats – some unstructured.

A health insurance company usually collects vast amounts of data including claims information, emails, images, web analytics and patient history. To extract valuable insights that solve complex business problems and have a real impact on people, it is important to tap into diverse sources of data.

What is Data Analytics?

Data analytics and traditional business intelligence derive correlations, analyze trends, and answer specific business questions. Data analysts produce bar graphs, lines graphs, pie charts and scatter plots to allow business leaders to visualize data and extrapolate information.

A health insurance company will use data analytics to analyze claims in different market segments, identify trends in patient conditions and yearly medical spending. It would employ advanced analytics techniques to calculate costs for different coverage plans based on these trends.

What Are Data Insights?

Data insights are the knowledge and information that result from data analysis and experience. Deriving insights requires judgement and discernment over quantitative and often qualitative data analysis. The organization substantiates insights with an understanding of the customer through data, as well as empathy. Data insights are often referred to as actionable insights, since they form the basis for strategic plans and roadmaps.

A health insurance company will leverage its data and insights to design services that improve patient health.

Where Does Data Science and Machine Learning Fit in?

Traditional business intelligence and data analytics are founded on statistical methods and rely on programmers define all the input data points and rules to produce a specific result. Machine learning models extend this capabilities by providing the infrastructure to process large amounts of unstructured data and the continuously “learn” and adapt with new data and changing circumstances. Data science allows analysts to uncover associations and correlations that would be missed in traditional analysis, an indispensable asset when building an insights-driven organization.

A health insurance company is likely to implement data science models to predict customer churn. The insurance provider can feed demographic, claims, call center and complaints data to a model to produce a highly accurate list of customers that are at risk of canceling their service.

How Do You Build an Insights-Driven Organization?

In order to create an insights-driven organization you must first ensure it is a data-driven organization. According to a Forrester report, Now Tech: Insights Providers, although business leaders recognize the value and potential of data, most still rely heavily on past experience and intuition to drive decisions. The following are the key steps in building a data-driven organization:

  • Collect data. Think beyond the data you currently collect. Are you doing enough surveys? Do you do A/B testing when releasing products? Are you engaging with your customers in social media? Collect data that matters and that can ultimately drive better insights and business outcomes.
  • Make data accessible. Important data should be made accessible throughout the organization via flexible platforms and protocols such as APIs. Otherwise, collecting data is a useless endeavor and a waste of resources.
  • Use data. If the data is accessible, use it, Clean it, scrub it and use it to make informed decisions, to identify bottlenecks, prioritize activities or to trigger new thinking. If people don’t use data, then it may stop being collected, and if you decide later that you need it, it may be too late.

Building an insights-driven organization requires going beyond an academic analysis of data and focusing on what really matters. You can easily get absorbed and overwhelmed with never-ending data analysis and large amounts of data visualizations, unless the analysis is focused on getting right to the heart of a problem.

  • Understand the customer. Building empathy with the customer base will be key to a qualitative analysis that can complement the quantitative to generate insights. Making continuous efforts to understand customers is the first step to solving a problem that matters to them.
  • Embed throughout the organization. Insights should be used in every department across the company, from the top down: to define company strategy, design new products, create marketing campaigns, identify process improvements and measure the impact of every activity.
  • Augment human capabilities with AI. Companies can take the power of their data to the next level with artificial intelligence. They can their workforce’s capabilities by automating business processes or drive value to the customer experience with smart personalized algorithms.
  • Embrace iteration. Insights change as people grow and evolve. Companies should embrace an iterative process where they engage in continuous learning and improvement from insights. This allows them to evolve alongside the people they serve.
  • Lead organizational change. Employees should be encouraged to collect data, search for insights and change the way the business works. It requires leadership and change management to create a positive company culture around data-driven insights and decisions.
Understanding Data, Analytics and Insights

Figure 1: Understanding Data, Analytics and Insights

How Can an Insights Service Provider Help?

Building an insights-driven organization may sound like the latest buzz word to businesses that face the following common barriers:

  • Silos in the organization. Business that have multiple departments often work in silos. Data collection efforts are duplicated across departments and rarely shared. Bureaucracy creates communication barriers that make people gravitate toward silos.
  • Data is not accessible. Data that is collected is rarely readily accessible to people outside the department through APIs or shared platforms. The need to request data delays access to the latest and most reliable information. Making data accessible requires a robust data governance and infrastructure strategy.
  • Technologies evolve at a rapid pace. For most businesses without sophisticated IT departments, keeping up with all the new technologies is nearly impossible. Specialized talent is scarce, abut it is critical to creating digital solutions as the business grows and customer needs and expectations evolve.
  • There is simply not enough time for all of this. Meeting the needs of the core business leaves no time to learn new technologies, implement new business practices or take on new strategic projects. Executing a strategic vision often requires help from technology partners.

Insights service providers and digital transformation consulting firms like Wovenware can help to provide specialized talent to help your business evolve into a data and insights-driven organization. From building a data strategy around business opportunities, to creating sophisticated artificial intelligence models, bringing in an expert will help drive organizational change, set a robust data infrastructure, and accelerate the time to extract value from data insights.

Best Practices for Addressing Digital Transformation Challenges

One of the most popular IT – and business – goals today is to achieve digital transformation. Everyone’s talking about it, but how many organizations are actually doing it? As with many buzzwords, maybe it has become a catch-all phrase encompassing all kinds of digital initiatives. It is helpful, however, to clear up the confusion and set the boundaries between what’s truly business transforming and what is simply a digital upgrade.

When companies upgrade or even vastly improve their technology, they may refer to it as digital transformation, although what they’re really doing is digital modernization – more of the same, only better. Digital transformation, however is just that, using automation to totally transform the business you are in to create a radically new revenue stream – it’s a game changer.

There are several well-known examples of this. Take Amazon for example. It started as an online bookstore. But savvy executives realized that they were doing more than just moving a brick-and-mortar store online; they created a technology platform enabling vendors to sell anything online and manage fast deliveries. Amazon’s value proposition to sellers was a huge marketplace to sell their goods, and for customers it was convenience in a one-stop shop. But Amazon didn’t stop there. After they developed the tech infrastructure and servers for business operations, they realized they had another new revenue stream – selling cloud storage and its developer platform, Amazon AWS – disrupting its business once again through technology.

On a smaller scale, consider a towing company that developed its own software to help it manage its fleet of roadside assistance vehicles. It realized its software provided critical value that could be monetized, providing value for other companies as well. In this way, the company morphed from a towing company to a software provider. The company is essentially making money while the team sleeps – the holy grail of SaaS companies.

Digital transformation is the brass ring

There’s no question that being able to improve workflow and processes is absolutely critical for the customer experience and overall business success, and it might even be a great first step, however, it’s not enough. It’s important to take a good hard look at digital transformation and consider ways that you can exponentially move your business forward. If you don’t do it, a competitor might, and then you run the risk of becoming an industry laggard or worse, obsolete.

Digital transformation is rarely an easy task and it takes time. Heavy investment in legacy systems, which can be mission-critical, can bog organizations down with technical debt. Further, Companies may be grappling with competing priorities or lack the executive and stakeholder buy-in to support the initiative.

Strategies to support digital transformation

With careful planning and strategic approaches, these challenges can be overcome and companies can begin the process of evaluating digital transformation initiatives to determine if they are appropriate.

Here are four ways to address digital transformation challenges:

  • Engage executives and stakeholders. As with other major initiatives, getting buy-in from executives requires making the business case. With the potential for new revenue streams and competitive advantage, it can be a compelling case. Once you have buy-in, make sure you keep executives and stakeholders engaged throughout the process. By increasing collaboration, you can be sure that all needs are being considered and generate better ideas.
  • Take a design thinking approach. Design thinking is a human-centric way of looking at problems, challenging assumptions and brainstorming new solutions and approaches. By speaking with customers and end-users and understanding their unique business challenges – including their pain points, frustrations and wants — you can find your way to the digital transformation initiative that will resonate. The design thinking process includes creative problem-solving workshops to engage participants and encourage them to think beyond the status quo to whole new ways of doing business.
  • Use innovation sprints. Instead of committing a lot of resources into a digital transformation initiative, you might want to test the waters to make sure it is an appropriate undertaking. An effective way to do this is to undertake an innovation sprint, or condensed proof-of-concept exploration into whether a larger initiative makes sense for your organization. The sprint, which conducts design, prototyping and idea testing in a matter of weeks, can rather quickly deliver the validity for moving forward.
  • Begin with modernization. The road to digital transformation begins with modernization. Modernizing traditional applications or legacy systems can be a great first step to realizing the benefits of digitalization and carrying them forward in an incremental approach.

While there’s a reason digital transformation is often called disruptive, the results are impossible to ignore. It can bring in new streams of revenue and deliver critical competitive advantage. The key, however, is not only to discern between digital transformation and digital modernization, but also to decide to go bold. Only through true digital transformation can you radically evolve what you are offering to the marketplace and discover new opportunities that will help future-proof your business.

Wovenware Think Club: Fostering a Culture of Discovery and Innovation

One of the biggest lessons we have learned from the pandemic is the capability to adapt quickly to sudden changes as well as the importance of resiliency. Nevertheless, it is that mindset of creativity, innovation and openness for uncertainty that allow us to go with the flow, adapt effortlessly and be ready for the pivots in business and life we can’t control. While highly structured, rigid companies had to go into survival mode, innovative companies have seized the adversity, transforming it into new opportunities.

Organizations have their own ways of fostering a culture of innovation. In some, innovation comes from the leadership team where founders and officers lead the way and are the main voice. At Wovenware, our culture of innovation is driven by everyone. We have created the environment for this culture to grow organically giving employees the forum to present their ideas and own their critical thinking. This culture causes a rippling effect across our teams.

One of the initiatives I nurtured in house two years ago was the AI Think Club. The club was a space where the data science team met to discuss topics and debate ideas. Every Friday afternoon, one team member prepared and presented a topic of his/her interest related to AI. That space has sparked valuable debates. The fact that the club met at the end of the week had an effect on the team as they would go home infused with energy and curiosity. On Mondays, the morning coffee break sometimes was related to weekend discoveries inspired by the session.

The idea for the club came from the famous “Journal Club” where Berkeley Labs scientists would meet for two hours a week to discuss ideas during the era of the verge of big science. The members of these meetings included Nobel Laurates like Ernest Lawrence, Glenn Seaborg, Robert Oppenheimer and even Enrico Fermi. In our case, we have not yet made a science- changing discovery but the club has been the space where ideas have turned into concepts which eventually turned into valuable software assets for the company. Most importantly, that space gave us a bubble to be crazy, creative and embrace the idea that it’s good to be one of the crazy ones.

Today we are incorporating human-centric design practices into our project life cycles, since they are crucial components to our ability to grow that spirit of innovation in our culture and relationships with clients. This moment in time is the right one for us to extend the intention, curiosity and energy that flourished from the AI Think Club across the entire enterprise.

Next month we will start the new the WOC, Wovenware Think Club beginning new sessions enterprise wide to cover topics that converge disciplines and lead to solving problems and new challenges that are around today. By nurturing a culture of innovation and discovery the possibilities become endless.

Look for future blogs, as we will continue to write about our practices, culture and how these have been augmented with service design.

Debugging Quarantine Fatigue and Anxiety From the Perspective of a Tech Guy

These have been strange and tough times. Each and every one of us has been affected in one way or another by the current, global pandemic and it’s during these times that we are forced to be our strongest, most resilient selves. That’s not always an easy task to accomplish, but to quote one of my favorite Beatles’ songs, “I’m going to try with a little help from my friends.” Because, when we help each other, we are capable of great things.

So, for my Wovenware family, friends, peers, and those who’ve listened and shared their experiences in these trying times, I wanted to share how I’ve debugged my quarantine fatigue and anxiety over the past seven months to help put my best foot forward each day.


Earlier this year, I tried to stop drinking coffee, not for any specific reason, probably just as a challenge to myself. I switched from coffee to all different kinds of flavors of tea. But, ultimately, it didn’t work out for me, so I won’t go much into that. I will just say that peppermint tea is amazing.

By the start of the pandemic, I was back to drinking two, quite generous, cups of coffee a day. Those magnificently balanced cups, of equal parts almond milk mixed with Puerto Rico’s most delicious coffee and a sprinkle of brown sugar, not only warmed my bones, but also gave me a burst of focused energy that I could channel into coding, debugging, designing or maybe (just maybe) even doing some compiling. It quickly became part of my daily ritual again and little did I know what was in-store for me.

The thing is, drinking that amount of coffee wasn’t an issue while working from the Wovenware campus. Since the place is so big, we get plenty of walking done daily, but now that everyone’s staying safe in lockdown, my coffee ritual started working against me. Fast-forward to a couple of months, where being cooped up in the apartment became the new normal and two cups of coffee meant having all this extra energy that I wasn’t expending. It was making me feel uneasy.

So, the first thing I thought to do was to cut back on the coffee. I didn’t eliminate it all together, but it still was quite a challenge. The concept of drinking coffee is a thing of ancestral pride here on the Island, a staple, if you will. Generally, Puerto Ricans tend to own a large moka pot, or what we colloquially call a “greca,” to prepare coffee for the family, guests or, in my case, for having a nice tall mug of “café con leche” just for myself. I always ended up having too much. Then, as if some sort of alignment of the stars occurred in my favor, I was given the perfect solution to my situation. I received a smaller-sized “greca,” compliments of Wovenware and Gustos Coffee Co. It was part of a “Lockdown Care Package” sent to us employees that included the insulated mug, Gustos Coffee grounds, and a tote bag. Now, I can limit myself to a small cup o’ brew a day without sacrificing the delicious taste and boost of energy in the morning.

Wovenware Care Package to Help Ease Quarantine Fatigue

After my cutting-back-on-coffee experiment I felt some improvement, but still felt tense most days. I was having upper back and wrist aches that eventually brought me back to feeling stressed, anxious, and uneasy. It took some time and conversations with friends and coworkers for me to realize that all this was just the quarantine fatigue and anxiety getting to me. It also is a side effect of sitting down for prolonged periods of time, adopting weird postures in my desk chair throughout the day, and not being able to move about freely. The more sedentary lifestyle also meant that I read too many news-headlines about the pandemic and politics, and worried about friends and family. I was doing less exercise and not sleeping as well as I used to. I knew then that there were a couple more things that I needed to try to get back into the swing of things.


I realized during this time that working from home is great and super convenient, but it also puts you in a weird position – Are you at home or work? You have distractions left and right, ranging from family matters, the dog, the neighbor mowing his lawn, and, not to mention, your house chores and hobbies calling out to you. You can use a pair of headphones and listen to music to block out the noise, but then you have to fight the urge to sing or dance along to your music, making you lose track of your workflow. I quickly found an effective way to remedy this, in the form of a 24/7 YouTube channel broadcast: Beats to Relax/Study to. Listening to the playlist can help me stay focused and relaxed, but when I get tired of that, I’ve found that the most soothing thing I can listen to, that also helps me absorb information, is a playlist titled, 24/7 Relaxation/Meditation Music.

I like to joke around that meditation music is cool because “it reminds me to breathe,” like anybody needs to be reminded to breathe, right? What I mean to say is that it reminds me to be conscious of my breath. Have you ever tried to pay attention to the rhythm of your breath for more than a minute or two? it’s a bit harder than it seems. You’d be surprised how much we take breathing for granted, such a natural thing, a sort of automatic mechanism that basically runs our entire body. I started using a meditation app called Headspace that offers a free trial. With this, I learned the basics of breathing exercises.

One of the most surprising things I noticed about breathing exercises and meditation is how hard it is to concentrate on your breathing rhythm. Try it; I dare you. Just try to count 1 on the inhale and 2 on the exhale, and so on up to 10 and then restart. See how far you can get until you realize you’ve actually been thinking about that email you have to write and have completely forgotten to pay attention to your breathing. Well, the point is that whenever I get overwhelmed by messages, deadlines or requests, I can always use my breath as my token, like in the movie Inception, to ground me back and help me regain my focus. Practicing meditation has really helped me improve quarantine fatigue and anxiety.


Once the pandemic hit, it surprised me how quickly I could get used to not going out. I’ve never really been big on going to the gym and preferred playing basketball or running as ways of keeping active. And, a couple of years back, I got into swimming and fell in love with it and it’s been my go-to sport ever since, but now that pools are closed and going out is risky, I got myself a kettlebell weight and I’ve been training with it whenever I need to let off some steam. It’s quite convenient that I can get a full body and cardio workout right in my living room with two videos I found from Keith Weber: Beginner to Advanced and No time? No Problem. Note that kettlebell workouts can be dangerous and it’s very important to start-off slow.


Yoga is my favorite habit of all, simply because it’s a mix of both meditation and exercise. Learning to implement it into my routine has benefited me in so many ways. It takes a little effort to put in the time and practice, but it’s well worth the investment to fight quarantine fatigue and anxiety.

I’ve become a loyal follower of the YouTube channel Yoga With Adrienne. I can’t remember how I found Adrienne, but I know I’m grateful for her cheerfulness, positive vibes and her willingness to share her passion. She provides a good alternative to those Tony Horton 10 Minute Trainer videos I used to do back in college. She also offers an enormous list of free yoga videos specifically made for different ailments, symptoms, practices and even professions. I’ll just share a couple of routines that have helped me throughout the lockdown.

Neck and Shoulder Relief
Many times I find myself with my shoulders shrugged up almost next to my ears when I’m too focused on the task at hand. This takes a toll on me later in the day, so this little session helps alleviate that upper back tension and neck pain from the inevitable bad posture I always fall into after a couple of hours of coding.

Upper Back Love
I tend to gather a lot of stress in my upper back and this helps. I know this thanks to Wovenware’s Wellness program and the wonderful Pipe, our wellness therapist, who brought attention to this tendency of mine.

Deep Stretch
Whenever I’m feeling sore, stiff or restless, this is the routine I turn to. It’s a little on the longer side, clocking in at around 45 minutes, and includes some intermediate yoga moves, but I always feel great after taking the time to stretch out the entire body.

For Gut Health and Hips and Lower Back 

I can’t confirm that this was the cause, but at the beginning of the quarantine I was using a certain cushion on my desk chair that I think may have been messing up my lower back and causing some stomach pain. It led me to try this video that involves using the abdomen, core muscles, and focuses on breathing. It’s so easy to get caught up in the maelstrom of news and social media about the pandemic, and at the same time, there are so many other issues we are facing on a daily basis. We are bombarded with so much information that it’s easy to get overly stressed out. This video also addresses all of that.


Having people to hear me out, share experiences with and attack problems together, has helped me the most during these trying times. Hanging out virtually with my friends, sharing funny memes, short greetings, or having healthy competitions on who can plate the best Ribeye steak (now that we’re all YouTube-trained home-chefs). We even put together a little music project at the beginning of the pandemic by combining the efforts and excitement of a lot of busy people. Word on the street is, there may be another one of those little projects currently in-the-works, but no one really knows for sure.

I’ve been extremely lucky to have found some good activities to channel all of the emotions caused by everything going on today. Another good strategy has been to limit the amount and quality of information that I was absorbing. I uninstalled news apps and just stayed tuned to local weather, news and lock down announcements. This helped me focus on the things that directly impact me, at least for the time being. All of these activities have helped me lighten the mental load and ease quarantine fatigue and anxiety. Hopefully, some of what I’ve shared today can help you do the same.

AI Can’t Go Very Far without Good Data

The benefits of AI are all around us, but the real driver to its success is the data that fuels it. This is a topic I recently explored in the Forbes Technology Council blog. As I mentioned in it, the tendency to shortchange the data is a fairly universal problem that often results in misleading or incorrect results and poor business outcomes.

So, how can you make sure your data is going to properly fuel your AI projects? Consider the following five best practices that I shared:

  1. Go big or go home. When you are trying to solve a problem, you may not always know where you will find the answer, so try to gather as much data as possible. There’s really no such thing as too much data.
  2. Make sure the data is shipshape. After the right data is collected, it needs to be cleansed, validated and prepared to ensure that it is in good shape and ready for analysis. While this can be a time-consuming process, it can mean the difference between good and bad results.
  3. Put the data through a workout. A good way to know if the data is accurate is to test it, and find out if there is a problem before you are too far along in the process. You should divide your data into two parts and set one aside for testing and the other for feeding the algorithm.
  4. Use a data auditor. It pays to hire data auditors who can help you assess the data you have, conduct the tests and help you plan for future data training needs.
  5. Avoid biased data. It’s important that AI solutions that help organizations make important decisions operate fairly and equitably. To enable diverse AI-based decisions, the data shouldn’t focus on only one type of data source, but should encompass all scenarios. We’re all too aware of the problems encountered when AI makes decisions based on racial or gender profiling.

In the quest to be seen as a technology trailblazer, many organizations rush to deploy AI solutions to try to quickly realize their benefits. Yet, what they often don’t realize is that the best AI solution is useless without good data. By focusing on better data collection, cleansing, auditing and diversity, true AI success can be much achieved.

Chatbot Development Methodology: A Melting Pot of Diverse Teams and Frameworks

Artificial Intelligence is the driving force behind the creation of innovative products like autonomous vehicles and chatbots. Recent advancements in Natural Language Processing (NLP) have made chatbots, also referred to as virtual assistants, a great option for improving the customer experience. Answering frequently asked questions, filing claims, checking the status of an order and getting feedback from customers are among the most popular use cases for chatbots. Building a chatbot that offers a good experience to customers requires collaboration from an interdisciplinary team of business analysts, service designers, data scientists, machine learning engineers and software developers. The chatbot development methodology blends several modern frameworks and methodologies including design thinking, AI innovation sprints, and agile software development.

Defining the Chatbot’s Purpose and Managing Expectations

Like most artificial intelligence applications, the first step in developing a chatbot is clearly defining its purpose, the problem it is going to solve, and the value it is going to bring to the users. Chatbots should be designed to help users navigate through a very specific business scenario. One must avoid the trap of trying to create a jack of all trades (and master of none) chatbot. For example, some conversational applications are getting a lot of hype that can set unrealistic expectations and contribute to negative user experiences. Defining a narrow scope and a clear chatbot purpose will be key for managing the rest of the chatbot development process.

Understanding the Audience

As expected in any human-centric design approach, it’s important to understand the target audience.  Service designers will conduct interviews to gain an understanding of customers as human beings. Using design thinking techniques, we learn about their pain points, their likes and dislikes, how they communicate and what they value the most. Understanding the human needs of a customer is essential to designing a chatbot experience that delights them.

A Chatbot Design Experience is a great tool to have to guide the team in defining all the key components of a virtual assistant.

Defining the Chatbot’s Personality

Techniques to define user personas should be adapted to create a “chatbot persona”, including its age group, interests, how it acts, how it communicates, its sense of humor and its limitations. Is the chatbot an advisor, an assistant, or a friend? A poorly designed chatbot personality is one of the top reasons why many chatbots fail. A chatbot with personality and empathy toward the customers can drive user engagement and create meaningful experiences. Robotic chatbots, however, have the completely opposite effect.

Designing the User Journey and Conversation

With user persona and chatbot personalities created, a service designer next should define in greater detail the human + machine interaction. He/she must design the user journey and script the actual conversations. What questions might a user answer? If the chatbot does not know an answer, do they redirect the query to a support representative? What information does the chatbot need to know in order to answer a question? The conversational and human-in-the-loop design are the most challenging parts of designing an optimal customer experience.

Developing the Chatbot

Once the chatbot design is complete, the development team can map out the features and create a technical design. Identifying if the chatbot requires artificial intelligence (AI) to fulfill its purpose will be important in defining the chatbot development methodology. Not all chatbots need AI to be fully functional, and if this is the case, the team will likely implement agile development methodologies in the traditional software development lifecycle (SDLC). Beyond building a rule-based chatbot, the software engineering team should define what background tasks and fallback mechanisms need to be carried out in order to properly integrate all the system’s components. Development can be completed through incremental deliveries, adapting design with feedback obtained from the end-users.

If a chatbot requires Natural Language Processing (NLP) components, agile software development and machine learning will need to be blended in a chatbot development methodology. The machine learning process is inherently different from rule-based systems. It requires exploration and experimentation when performing data collection, data preparation (cleaning, wrangling, and merging), feature engineering, model training, and model evaluation. The project team must account for the experimentation that will take part in the chatbot development process. Wovenware has developed a proprietary methodology, the Innovation Sprint, to integrate innovation and discipline in a goal-oriented experimentation process.

Integrating Natural Language Processing

The NLP components in chatbots are mainly used to recognize user intent and extract entities. Each intent can represent a task to be performed. A conversation flow will vary depending on the detected intent. How the chatbot handles intents will define its relationship with the user. Optional components, like sentiment analyzers and language translators, can add value and enhance chatbot experience. Sentiment analysis can help chatbots respond to users in a personalized manner by understanding what makes them happy, while language translation components can be used to give chatbots multilingual capabilities. Data scientists will optimize NLP components in order to provide information about intent, meaning and the context in which the chatbot needs to appropriately respond to a user’s query. Natural Language Processing is constantly evolving so these components must be updated regularly to keep the virtual assistant functioning in optimal conditions.


Once each system component has been designed and implemented, testing and evaluation should be carried out. For middleware software, unit testing should be performed to test individual components. Machine learning components need to be evaluated differently. Testing data is used to evaluate the trained models and success metrics must be clearly defined in order to measure model performance. Usability testing must be done before and after deployment. These tests will help to understand if the chatbot follows the expected conversational flows and error handling strategies by monitoring user interactions.

From Chatbot Rookie to MVP

Successful chatbots are designed to learn, making maintenance an integral part of the chatbot development methodology. Once the chatbot is interacting with real users it is important to analyze user feedback and sentiment, along with other insights the interactions may produce. Insight analysis may give us an understanding of possible usability issues or areas of improvement, but it also may provide us with possible market opportunities to implement more data-driven solutions. Analytics can help us understand bot performance, user engagement, sentiment and demographics. Fixing bugs and other software defects should be part of the software maintenance strategy, but with evolving customer expectations and technology advancements it is important to also keep the system updated and sustainable. Metrics for the machine learning components should always be monitored in order to address a possible decrease in performance. In the case of chatbots, the ever-evolving nature of natural language may cause the NLP components to worsen over time. Constant retraining, through active learning and evaluation of those components is vital during the maintenance and development process.

Chatbots move from rookie to MVP level through the collaboration of business leaders, service designers, data scientists, machine learning engineers and software developers. Each member of the team needs to stay at the top of his/her game to implement modern frameworks and novel technologies to continue to improve the customer experience. The team will always be on a journey to understand the user, to evolve the application around the things that matter to them the most and make the technology as human as possible, but this is what makes for great chatbots.