Products built by aerospace and defense companies are highly engineered and sophisticated, which means they’re often complex. That’s not a bad thing. But they’re also complex in ways that are undesirable. Products and their constituent parts are tracked in dozens of systems –from design to manufacturing to maintenance — which can result in an average of 26 different reference numbers for each part.
The drive to digital transformation is helping A&D companies recognize that situations like this, which arise from a lack of governance and the absence of an enterprise-wide data strategy, have created substantial costs and risks that have to be addressed to realize the full benefits of digital transformation.
That’s especially true for companies that want to establish a “digital thread” for products and parts throughout their systems. The ability to follow any part throughout the A&D value chain (design, manufacture, service) by following a single digital ID will help A&D companies recognize tremendous cost savings. A digital thread also provides a glass pane for status, reduces rework and errors, improves security, and helps manage compliance and regulatory issues with greater efficiency.
That sounds great. But the key question for many companies remains: How do you get started with an endeavor like that? Many organizations fail to prioritize defining a data strategy on the grounds that it’s either a case of “boiling the ocean” or else an “infinity project” that will deliver little value.
A few key points can help your company move toward a data strategy that allows you to pursue the rest of your digital transformation agenda.
1. Make an affirmative decision to manage your data.
All companies make decisions about how they engage with, operate on and leverage their data — whether at an enterprise or project level. Even if a company has no formal data management policy, that in itself is a decision, albeit one that leads to the situation many companies find themselves in today. On the other hand, companies that form a holistic point of view in adopting an enterprise-grade data strategy are well positioned to optimize their technology investments and lower their costs.
2. Establish executive sponsorship and governance.
Sustaining a successful data strategy requires alignment with corporate objectives and enforced adherence. As corporate objectives evolve, so should the data strategy — keeping up not only with how the business is operating, but also with how supporting technologies and related innovations are maturing. This means including representatives from all the domains that are involved. It also means assigning someone with the authority to resolve conflicts between groups. This is a key element to helping federate data across silos and moving to a data hub approach, thus eliminating the need to maintain 26 different part numbers for a single item.
Sustaining a data strategy also means making a specific investment in personnel. Companies that embrace the constructs of a data strategy often define dedicated roles to own these strategies and policies. This ranges from augmenting executive and IT staff with roles such as chief data officer and chief data strategist, respectively, to expanding the responsibilities of traditional enterprise data architects.
3. Get started by instituting good data management practices in smaller programs.
Success demonstrate the value that data management can deliver at a small scale and what it could potentially deliver at the enterprise level. Applying an Agile methodology, which continually demonstrates short bursts of success, will help gain momentum (like a snowball rolling down a hill) and organizational acceptance.
As with any business or technical process, a data strategy has its own lifecycle of continual evolution, maturity, change and scale. But the benefits it makes possible—for example, the ability to construct a digital thread for products and parts—will far outweigh the investment that’s required.
For a thorough view of the process that’s involved in setting digital strategy, read the whitepaper Defining a data strategy by my colleagues, Aleksey Gurevich and Srijani Dey. It offers a concise view of the components of a winning data strategy as well as the steps needed to implement, maintain and evolve it.
The world that we’re living in today, is almost entirely technological. And it is evolving every day with the wonders of Artificial Intelligence (AI), Business Intelligence, BlockChains (like NFT), etc. In order to make people’s lives easier, these smart technologies use complex algorithms and theories to quickly compute and summarize data. This data is then provided to users in the name of ‘useful information’. But a question that needs to be asked here is that what is the point of this data if the users can’t understand it? And if so, they might not even find it beneficial for themselves or their purpose. Hence, this is one of the most important reasons as to why dashboard design has grown significant over the last few years.
One of the first things to understand is that as much as UX of the dashboard is important, so is the UI of the dashboard. They are both equally essential. Before jumping into our main motive, let’s take a quick sneak peak into the most prominent features of a great dashboard:
A good dashboard allows its users to interpret, analyse and present the key pointers, or say insights.
What an efficient dashboard do is present the user with useful information that can be put to use. As well as compile a visual representation of the otherwise complex set of data.
The desired dashboard will always be customizable and it generally is quite intuitive.
As an additional point, they are always organized when it comes to use of space. There might be a lot of data on the dashboard but it won’t seem like a mess that is cluttered all in one little space. Rather it looks neat.
NOW WE’RE AT THE HERE’S HOW SECTION
CONSISTENCY MAKES IT EASY
The dashboard should always be consistent. Considering that the users access the dashboard through different devices that also vary in screen size. It is important that the design looks consistent, whether it’s with respect to the colours, font, style of charts or navigation. The user should not get distracted because of the inconsistency in the dashboard design and so sheer attention should be paid to how the design dashboard looks on multiple screen sizes. The key is to start with smallest screen size and then move up along the way. This way of approaching the design is a golden rule, this helps in creating a visually appealing dashboard, which gives both a great UX and a great UI.
SIGNIFICANT INFORMATION APPEARS FIRST
Let’s talk about the heart of designing a dashboard, ‘content hierarchy’. Although this goal is achieved at the UX stage of designing, but is essentially through visual design that conveys the most important information in the most efficient way. This not only guides the layout but also the design guidelines. A couple of things to note here is that; (a) Centre alignment is the most recommended as found out from the users’ behaviour pattern on the dashboard. They acquire that part of the information in the first place ; (b) Another thing is that 18 pixel font is the maximum to be employed and information should be highlighted through the use of distinctive colours.
COMFORTABLE CUSTOMIZATION
Never use the ‘one size fits all’ approach. There are different users with varying business needs and requirements. Designs should be scalable, so that the user experiences customized dashboard. Now this in turn will empower the users, which is the designers’ ultimate goal. Users should feel that they can adjust their fit in consideration to their individual business requirements. This can be as simple as adding or deleting a column, or even adding a whole new table or sheet. The things to remember here are: (a) Always allow for easy modifications like addition/deletion of modules; (b) Give different yet comfortably usable options for viewing and deleting data separately; (c) And lastly, try using easy drag and drop interaction for the users to experience customizable dashboard design.
CHARTS? LESS IS MORE!
When it comes to presenting data visually, i.e., via pie charts, bar graphs etc., they should always be put in simpler form. Cluttering the charts with heavy tools and representation techniques only hinders the user from understanding and using the said data. Instead of using 3D graphs and animation, using flat-laid charts and graphs makes it a lot more comfortable for the users. Colour differentiation although, is sheerly appreciated, since it allows the users to understand the separate sets of data clearly.
ICONS CATCH THE EYES
What is generally ignored in the process of designing a dashboard UI is the ‘icons’. The best dashboard designs always include icons that are familiar to the users. For example, the pen/pencil icon is usually the edit tool. Likewise, the trash icon is for the option of deleting anything. This way, the user would have less time wasted in processing what the icon means and would have more utilized time in wrapping up their work. Thus, familiar icons help create great UX and UI.
RIGHT FONT REMAINS IMPORTANT
Employing the right style of the font and its size is equally important in both the UX and UI designs. It is advisable that while designing, not more than two fonts shall be employed. This is done in order to maintain a clean dashboard design. Creating a visual hierarchy by employing the inverted pyramid style in all the written content is quite significant. This can be done by putting the most important sets of information in the largest and the not-so-important sets of information in the smaller font, along with a hint of colour differentiation.
FILL COLOURS IN DESIGN AS MUCH AS IN LIVE
The concept of colours is such that, it might seem simple, but it also has a complex side. Different colours signify different things. Just like red colour is usually employed to denote danger and green colour is used to denote ease and agreement. Now, imagine if their use is reversed, won’t it be confusing? Therefore, designers are required to be really mindful whilst working on the colour palette of their designs. Besides, it is best not to go overboard with the colours, even if you’re tempted to do so. A good start would be picking two colours in the initial stage, that too something like contrasting contemporary colours.
MAKING THE FINAL POINTS
All of this would be worthless if you don’t know your users’ preferences. Whatever the studying concept maybe, if you don’t have the emotional connection with your users and if you don’t know their choices and requirements, then the whole work would just remain pointless. Therefore, besides knowing the tips and right ways of employing the tools to create a great dashboard, you should also know your users. Research, understand, analyse and imply your methodologies and tools.
Taking your leave with,
“Graphic design will save the world right after rock and roll does.”
Last year when one of our healthcare partners (we refer to our clients as partners) was looking to build a conversational AI chatbot, I was apprehensive about guiding them. I had only worked on the level 2 (out of the 5 levels of conversational AI) type of bots. But this time our partner wanted to build a contextual/consultative AI-powered chatbot assistant.
I was concerned about how the bot would understand end users’ problems. What features can we build to make it more humanistic? Would it be successful in replacing human care and compassion? Would it replicate the same emotions of empathy, compassion, and care?
And even if we managed to do everything, how would we know if the conversational AI chatbot is working the way we designed it? How would we define the ‘success’ of our initiative?
My apprehensions became real when I read a Forbes article about chatbots killing customer service with their clumsy conversations and limited learning capabilities. After reading the below paragraph, I realized the problem-
“The AI didn’t always get it, which was frustrating. Even more irritating — the company using the chatbot seemed to shrug the problem off. I detailed my own experience using Skyscanner’s chatbot, which often misunderstood my requests. Some of the companies I mentioned in the column appeared to shrug off my concerns.”
The problem is with organizations/management who choose to look away and see the importance of data analytics in chatbots for healthcare. They think that understanding the users’ behavior, what disappoints them, what makes them happy, is beyond the scope of their work. Because of this mindset, chatbots are still a lost cause.
Is there a solution in sight?
Yes, indeed there is. We’re at a very interesting place where we hold the future of chatbots in our hands. To make chatbots more welcoming and user-friendly, we not only need to make its software side–engineering, UX design, security– more robust. Rather, we should strive to make data analysis a part of the development process– i.e. we must constantly monitor chatbot’s effectiveness and improve features as per users’ needs.
How can we measure a chatbot’s efficiency?
Building a good conversational AI healthcare chatbot is a daunting task. Even after launching it as a service, one can’t be sure of its success. That’s why it’s crucial to measure every interaction with the end users.
There are certain indicators that one can track to see if the chatbot is serving its purpose.
Botanalytics
If you’re looking for a tool that gives you an overview of the user’s lifecycle, then Botanalytics is for you. It’s a great tool for identifying bottlenecks in the user’s journey. You can deep dive into every conversation (transcripts are available for each conversation) and see where your bot failed to respond.
You can set various goals and categorize chats into conversation paths. This is a great feature as it helps you examine which conversation attained its goal and which didn’t.
For example- if your goal is to make users download your mobile app through the link provided in the chat, then this tool will show you how many conversations ended up in accomplishing that goal.
You can also set conversation paths and check how many conversations were successfully handled by the chatbot.
Grafana
Grafana is not a bot-analytics tool. Rather, it’s an open source platform that can be used to monitor applications, websites, and even custom data sources. We integrated it with our platform to use it as a chatbot analytics tool.
One of the advantages of using Grafana is that it’s very easy to customize and you can tweak its dashboard to suit your needs.
If you have a chatbot where there is a lot of data to understand, analyze, and dissect, then you must explore Grafana. Icing on the cake? It’s free. And like I mentioned earlier, highly customizable. You can create dashboards, add panels, change visualizations as per the need of evaluators and stakeholders.
Chatbase
Chatbase is a free cloud-based tool that allows you to integrate your bot into the analytics platform. One of the best features of Chatbase is that it helps you both analyze and optimize your bots.
In the analytics part, Chatbase has every possible feature that you can imagine- session flows, creating funnels, the grouping of not-handled messages, chat transcripts, and so on. The UI of the dashboard is quite similar to Google Analytics. So if you’re a GA user, you’ll find it easier to use it.
In the optimization part, Chatbase provides insights to understand your users by tracking how they behave and what works (or not) for them. This is especially helpful when you want to target a specific audience and you want to improve your messaging and promotions according to specific inputs from the analytics tool.
When it comes to building chatbots, including analytics in the strategy is often sidelined. It’s considered an additional responsibility, something that can be easily avoided. However, measuring the performance metrics of a chatbot must be included in the development strategy because it’s the only way to define if your chatbot is working as you imagined it to be.
I hope you learn to integrate these tools and use analytics to enhance the experience of the chatbot for your end-user. In case you begin to feel that it’s a sponsored post, let me tell you it’s not. All these recommendations are personal and I have learned through trial and error. I hope you find the best tool suited to your needs.
Designing conversational UI is a challenging task. I say this from my experience of designing a conversational AI chatbot in healthcare. From the moment I began working on it, I knew it wouldn’t be an easy feat. Questions like what kind of visual elements would I use, how can I reduce the user’s cognitive load in effort-intensive activities, were always on my mind. Prior to this, I had worked on UI design of many web and mobile apps. But none of them was as challenging as this one.The most challenging part for me was designing to handle the human-machine interaction. Each user is different. Unlike in websites/apps, where users can simply browse and leave, the chatbot users open the chat window to interact. They come with all sorts of questions- vague /smart /genuine /rogue /irrelevant and (sometimes) absurd. When they type a query, they expect the conversational UI to adapt to their needs–digest questions and construct intelligent answers/follow up questions.
The uncertainty of the usage makes the design process complex. Unlike websites/applications, there are no specific UI design principles for designing conversational interfaces. It might appear as a small thing but the limited knowledge on the UI design patterns for healthcare chatbots ultimately affects the customer experience.
So, what can a designer do to make sure that the conversational AI solution caters to most, if not every, user persona? I can share a few suggestions. Since I worked on a healthcare chatbot, most of my suggestions would be about best design practices for healthcare conversational user interface.
Chatbots are now making their way into healthcare solutions like patient engagement solutions, handling emergency situations or first aid, medication management, and so on. To create a great user experience, it’s crucial to pay attention to the process of designing the chatbot. One of the ways to do this is streamlining the process of UI design through UX research.
When I started working on UX research, I borrowed a few tried-and-tested methods applied in web and mobile apps. However, healthcare is a complex domain where data security is a major concern. Therefore, I modified a few of them to suit my needs.
Let’s talk about a few UX research methods which are important to conduct before deep-diving into UI design of a healthcare chatbot.
Discover users’ pain points
To discover users’ pain points, you must first know who your users are. So, the first step to any good UX research is defining your user persona. Your user persona should include demographics profiles, health profiles and task profiles.
The persona diagram must include needs, difficulties, frustrations, motivations, aspirations of your end users. For instance, a 56-year old woman’s persona should include pain points like– “I’m an old woman, I don’t have the patience to repeat the same thing over and over again” or “I am ageing towards dyslexia, I forget conversations I had 15 minutes ago.”
After you’ve defined your user persona, the next step is to figure out how they will interact with the chatbot. For that you can invest in any of the below attitudinal approaches-
Gather inputs by rolling out surveys to your target audience and asking them related questions.
Conduct interviews and listen to what users say.
Depending on the kind of healthcare chatbot, your choice of the method will change.
For example- if you’re doing UX research for a chatbot that guides people towards better mental health, then you can do anonymous email surveys as people don’t openly talk about issues like depression and anxiety.
Observe users in their natural environment
Direct observation (a primary research method) is the key to understanding user needs and preferences for a new product. During observations, record verbal comments and the time spent on various tasks.
For example, if you’re designing a chatbot for surgeons, then observe them when they plan the pre and post surgical procedure. If the patient has a lot of complications, what dietary guidance do they offer? How do they perform the surgery? What instruments they use and how its usage differs from surgery to surgery?
By conducting interviews with people while they perform tasks, you can collect valuable inputs from them and use it to recreate a similar experience in a conversational chatbot.
You can also create interactive mock-ups to show artificial interactions. This gives sufficient scope to test the chatbot with users without requiring the engineers to actually build it. This helps in quick iterations with the users after validating the user experience and understanding their perception of the chatbot.
Research through complementary data gathering techniques
A quick and rewarding UX research method is secondary research. This is done by doing competitor analysis to see how others are solving the same problem. This approach isn’t useful if you’re developing something unique. But, if it’s something you are trying to improve, this method is easy and gives quick results.
You can experience real conversations, access failure threads and use data to improve your chatbot’s experience. Secondary research also includes searching through customer service logs, FAQs, online reviews or comments on blogs.
For example- if you’re building a patient registration chatbot, look at other chatbots available in the market. What is the tone and personality of chatbot? How much time does it take to book an appointment on the chatbot? What are the demographics that the chatbot covers?
Study the chatbot as a user and find out things that they are doing right or things where the experience could be improved. You will find some unsolved problems that could become a major feature in your chatbot.
Get help from stakeholders
There may be situations where you wouldn’t get time to do surveys and interviews with end users. In such cases, ask for help from people who want to build this chatbot. Get to the behind-the-scenes of the problem. Go to their sales and marketing calls. Understand the ‘why’ behind building a chatbot and what problems they are trying to solve.
Talk to the sales team to understand what kind of customer support calls/tickets they receive frequently. Interview their marketing team to understand what vision they have for their end users. What motivates their users? What do they need in order to be happy? What’s their idea of a good chatbot?
Gather real quotes/statements from the end users and use them to draw an empathy map and user journeys. This will help you identify major pitfalls and crucial moments.
The UX research phase is a crucial part of developing a great customer experience. When it’s given its fair share of time and effort, UX research helps discover important insights and reduces the number of iterations required to build the chatbot.
However, there’s no surety that your research findings will translate into a flawless user experience. All the research findings must be implemented and tested in real-time for you to discover if your research was right or not. Sometimes, a wrong method of research or the timing of the research may give you erroneous information.
I hope you figure out the right UX research method for your chatbot. If you have worked on a healthcare chatbot, I would love to hear the UX research methods you used.
Imagining Artificial Intelligence in situations and use cases where there are a massive number of data in picture makes perfect sense. But what happens when the situation is entirely based on human discretion? Will an artificial intelligence user experience design would also be able to do what AI did to several other industry verticals?
Designing, almost in all its different forms is driven by keeping the human part of process at a much higher ground than the analytical and data driven side. While there are some domains like CAD design or Product Design that leaves some space open for machine learning to enter, when the design form in question is mobile app design, the gap seems to become negligible.
However, Artificial Intelligence, like a number of other industries have found a place in the Mobile App Design vertical as well, giving birth to the concept of artificial intelligence user interface design. A concept that is ought to bring a new level to the relationship between artificial intelligence and customer experience.
While, the answer to whether machine would replace designers is next to impossible, there are ways that the designer community has started taking AI user experience together in their journey to designing memorable mobile apps in multiple ways, like –
Getting time-taking manual works like image resizing automated
Making designs localized by taking help of AI based translation
Bring system consistency between users and products
Give insights into which elements are users interacting with, which needs attention
This participation that the deisgning industry is witnessing coming in from the AI driven UI domain is something that is showing to have a huge impact on the industry’s present, while paving the way to a world where AI and the future of design is much better linked.
Now that we have seen the impact that AI carries on Mobile app Design and how it is soon becoming one of the proven tips to enhance mobile app design, the next step is to look at the principles that guide their unison in the domain of designing AI experiences
The Guiding Principles that Combine Mobile App Design with Machine Learning
Develop a Shared Language
Elements like user experience review, product vision, and business goals are something that needs to be understood and shared by the complete team. You would only be able to create a meaningful and truly intelligent user experience if the mobile app design and machine learning development methods complement each other through shared concepts and common language. The machine learning experts and user experience designers should come together to develop a common blueprint which includes data pipelines and user interfaces, with the aim to set a blueprint that grounds the team’s product planning with the users’ reality.
Focus on Use Case
The important thing when developing a consumer facing app, as the top software designers would tell you, is not the technology that backs it but the business goal and the user experience that you plan on achieving. And so, it is extremely important that you crystallize the use case. With a separate focus on the use case, you can then put your intricate attention on the user flow, which then allows the team to identify the main points where machine learning can be added to enhance the experience.
A clear understanding of the use case also enable teams of the mobile app design company to determine the right KPI for the development of user experience program, which in turn is aligned with machine learning metrics.
Mix Quantitative and Qualitative Data
In order to understand the true impact of combining the machine learning solution and user experience design, it is important that both qualitative and quantitative data is considered. You should make use of qualitative research methods like questionnaires, interviews, etc to measure how the users are experiencing your app.
The reason why we are emphasizing on using a combination of quantitative and qualitative data is because when designing a new app, it is possible that you meet unexpected factors that affect machine learning developmant and user experience. Factors like: Effectiveness of feedback loop, ability of data point capturing intention and user behaviour, which are must to know parts of Artificial Intelligence app design can best be answered only after a deep consideration of both the data types.
Bring Your Combined Data to Real Life Setting
How do you make sure that machine learning is actually used to develop comprehensible and fluent user experience? By setting up an end to end solution that shows how machine learning and user experience fit together in real world. An MVP that includes the working data pipeline along with the machine learning models makes it easy to iterate the AI assisted design together and helps in getting a direct feedback from the users via beta or user testing.
When both UX designers and Machine Learning experts of your partnered AI app development company share the understanding of product design issues, iteration is productive and fast. While on the other hand, user experience designers become aware of possibilities that surrounds machine learning: when it can be used to improve the user experience and how.
Be Transparent About Collecting Data
Designing for AI and with it, needs a constant effort and for it to be absolutely on point, it is important that you give a special focus to the data you have collected. It is very important to consider the end user side in this cycle of collect data – convert data into information – iterate design. Tell users that their data is being used to feed the AI and give them the option to alter the collected information in a way that the best context comes through. In addition to giving users the option to change what data is collected by the AI, you should also give them the option to change what the AI learns – to ensure that the predictions are what the users desire.
While these principles that we just saw help in giving some clarity into how the combined AI and UX design should function, let us look at how some of the famous designing and editing tools that are backed by the developers community across the globe are using the technology to offer better mobile app user experience.
Tools That Use Artificial Intelligence for Design
Tailor Brands
The Tailor Brands logo maker is a famous product used by businesses to get professional logo in a small budget. The AI designs are built upon with your input coming in form of information that would be entered in logo.
Adobe Photoshop
The Select Subject functionality that Photoshop offers make use of AI for memorizing the shape, and then shifting, changing, and editing them with much ease. The tool works on an internal AI system known as Sensei that enables changing backgroungds by recognizing the different subjects in the image.
Prisma and Deepart
Both the famous image editing tools/AI design software make use of artificial intelligence for identify the different aspects of your video and photo and transforming them in a style of your choosing. They give you the option to work around filters and colours among other things.
Let’s Enhance
One of the most frequently arising issues in the designing industry is low quality images. Let’s enhance, powered by AI improves the quality of images using three filters. Anti-JPEF filter converts image to high quality PNG while Boring filter scales up image to around 4 times without any compromise on the image quality. Magic, the third filter allows you to add detailing inside the image. Making Artificial Intelligence a primary part of the Mobile App Design process is something that comes packaged with several add on factors that have to be considered to ensure that that User Interface and User Experience is intact.
And this in turn is not an easy process.
Packaging your app’s user experience with Artificial Intelligence in a way that the whole process gets translated into Artificial Intelligence design patterns calls for a lot of homework, which in itself is heavily dependent on the information that the users provide with consent.
If you are just starting with making your designs smarter, there are some UI patterns that would help you start on the intelligent journey.
A. Criteria Sliders
A number of apps use machine learning algorithms to predict an outcome or pass recommendations. A criteria slider comes in handy here for it helps userss adjust and then fine tune recommendations on the basis of criteria that is meaningful to them. Here, you will have to ensure that the criteria that the users are manipulating with is mapped correctly to data which the machine is using in algorithms.
B. Like and Dislike Button
A simple like and dislike button help better the user experience that someone shares inside the application. Wwhen you ask users to feed in their experience even through a simple like and dislike button, you give them the option to not just build upon the recommendation system but also give feedback on what they don’t like and why.
C. Confidence Inducing Tips
More often than not, users not just not know how the whole prediction and artificial system works, but also they don’t know how much confidence they can place in the system. When you ask users to feed in their data or answer questions in return of something – better matched clothes choice, next show to follow option, etc. The confidence quotient increases even more when you give users the result and let them approve or disapprove it. Doing this makes your users in charge of the charge – something that automatically instills confidence in the app.
D. Give them an In and Out Option
Not all users would want to feed in data for you to fetch and feed in the artificial intelligent system or even want to take the smart route. So, give them the option to opt in and out of the smart options as and when it suits them. Doing this, they would not just have a more positive outlook towards your app but also, knowing that they have an out option, they will be more willing to add in their data in the future.
Now that you have seen the ways AI powered UX is impacting the app design industry, the guiding principles of designing for AI, tools that are already using AI, and the UI patterns that you should add in your design manifesto to make your users open to the idea of AI, there is only one last thing left to do.
And that last thing is to make AI an active part of your mobile app design process. Let our team of UI/UX designers help you with that.
It is a known fact that human is a social animal. A human needs to interact with his fellow humans to express himself. Interaction gives a human a sense of comfort and understanding. This fact is well utilized by Chatbots, which has been a revolution in the market in a short time. The Chatbot technology has taken a giant leap and has emerged as a ground-breaking trend. All the businesses are eager to keep up with the updating trends and utilize the Chatbot technology to interact with their customer base and influence the market. Within the brief time-period, Chatbots have taken over the market. They are seen to be rocking the sectors of Customer Support, Customer behavior Analysis, Customer Service & Management, DevOps Management, Branding, and other Qualitative tasks.
What is a Chatbot?
In the most simple layman language, A Chatbot is an automated computer program that talks back to you. It interacts with the users as a human would and attends the customers round the clock without needing any break or having any geographical limitations. The conversation can be either text-based or voice-based, depending upon the requirement of the business. It is a virtual assistant that aids businesses to get closer to their customers and broaden customer engagement. With the introduction of ultra-modern technologies like speech recognition, machine learning, artificial intelligence, and NLP (Natural Language Processing), Chatbots have advanced so much that they can simulate a human conversation very closely.
Broadly, Chatbots can be divided into two categories:
1.Command Based Chatbots:
These Chatbots are based on the “if-else” algorithm. These Chatbots do not learn from the conversations they have; instead, they can only answer a limited number of predefined questions. It has a set of pre-written questions with specific answers. In these situations, users do not usually type or converse with the bot; instead, they select one of the many options provided to them. They have a fixed database so they can not answer the questions which are not listed in their database and can not function outside their code.
2. Artificial Intelligence Based Chatbots:
These Chatbots learn from the conversations they have with users and better themselves over time. They are contextual Chatbots and utilize high-end technologies like Artificial Intelligence and Machine Learning and appear quite sci-fi at the initial glance. They can understand natural questions and reply with the most appropriate answers. They remember the details about their customers and improvise their communication with them accordingly.
Now, if you are asking yourself, “Do I really need a Chatbot for my business?”, the question itself is problematic. The market is ever-growing, and the competition is getting stronger every day. The inability of a business to keep up with such a trend, which is woo-ing the market with its exceptional capabilities, could be a fall-behind for and your business. NOW is the ideal time to introduce your business with this cutting-edge technology and watch your sales and business fly high. Your business/company might be in a need of Chatbot Development if you relate to any of the below-mentioned points:
If you own an E-Commerce Business:
As per a recent study, it has been established that E-Commerce is one sector that is expected to make the most benefit out of the Chatbot Technology. The Chatbots can turn the monotonous shopping process into an exciting one by encouraging more user engagement. A Chatbot can keep an account of customer history, preferences, location, address, and navigation and, based on that, personalize the whole shopping experience for the customer. The Chatbots can recommend customers new products according to their preferences and also provide them with personalized vouchers and coupon codes, which can boost up customer engagement, resulting in an extremely positive impact on the sales. For example, if you have are a Pizza delivery App, a Chatbot can remember the previous orders made by the customer, additional topping requests along with their address and contact details so that they need not fill in the information again while placing an order. This makes the ordering process seamless and attracts more customers. This also increases the loyalty of the customer towards the brand and ensure that the customer revisits your app/website.
If you have customers/queries coming in round-the-clock:
Chatbots are not bound by and physical or geographical limitations. They can work day in and out without getting tired or needing a break. So if you are a business that deals with customers from different time zones, or if you like to keep your services open each and every day, every time, Chatbots can be a blessing for you. This takes off the burden of this redundant job from the shoulders of a human employee and allows his energy to be utilized for something more crucial.
If you need to give repetitive/similar information:
Most of the businesses need to answer an almost identical set of questions all the time. Customers usually inquire regarding the timings, address, or prices. A significant portion of human effort can be saved by automating this process with the help of Chatbots.
If scaling your business is requisite:
Unlike humans who can handle just 3-4 customers (at max) at a time, there is no such limit for the Chatbots. Chatbots can be assigned as the initial point of contact for the customers. In this manner, they can help to filter or segregate the customers according to their issues and then raise the issue to a human representative if required. This way, the tedious and redundant part of recurrent question-answering is handled by these new-age virtual assistants. They increase your productivity and efficiency, thereby improving your ranking and reputation in the market.
If you are low on time and budget:
No matter how fancy and sci-fi these Chatbots sound, the cost of building one won’t be leaving any holes in your pocket. The cost of developing a Chatbot is often similar to or sometimes even less than developing an application. Additionally, Chatbots aren’t just economical but are worth the price you pay for them as they bring in a commendable amount of business along with them. They are like a one-time investment that can take your business to a new height.
One Chatbot is equal to an army of employees. It saves the time of your team, which might otherwise go into redundant tasks or hiring a team to do the Chatbot’s jobs.
If you are targeting social media:
The integration of Chatbots with various social media websites and apps and their capability to engage the audience on the same has swept off the market. In this age of social media and smart devices, people prefer having apps that are multi-functional. Therefore, it is crucial for businesses not to leave any potential area to grab the customer base. Facebook, Instagram, Whatsapp, etc. have become the greatest markets, and Chatbots can help you acquire them by interacting with your customers on the same.
If you need to analyze customer behavior:
The Chatbots can be made to ask for feedback from the customers regarding their experience on your website/app or about product or service. This feedback can be a great help while making the marketing strategy and targeting a more precise clientele. The data acquired by the Chatbots can be used to provide a personalized experience to the customers, which can significantly impact the engagement on your platform.
Conclusion
A Chatbot is the need of the hour for every business, whether big or small. No matter which industry you belong to, Chatbots can definitely scale up your business and put you on the front foot, a step ahead of your contemporaries. The customer is always looking for a smoother experience, and with a Chatbot, you can ensure that your customer gets exactly what they want.
From a time when an old school telegraph was considered to be the hottest medium of establishing communication to now when Facebook has doubled down its effort on the development of a mind-reading device, social media has already crossed several stages of evolution. Stages that have established it as a sector that is here to stay and grow. One of the pivotal factors behind the unprecedented growth that the domain is witnessing both in terms of users and scope of constant growth is the technology used in social media.
These number projections are the direct result of the direct influence of technology on social media. The constant inclusion of communication and content creation, distribution aiding technologies like native mobile apps – getting business access to users’ camera and GPS – geotagging, AI for image recognition, etc. have helped shape the current stature of the social media domain on a global scale.
In this article, we will be briefly revisiting how far social media technology innovation has come since Six Degrees, the platform that ruled 1997 followed by a look into the new technologies for social media.
The Evolution of Social Media – A Timeline and Key Events
The future of social media technology began in 1997 with SixDegrees.com, the first social media site. The platform enabled you to create a profile page, curate a list of connections, and send messages to your network. At its peak, the website was used by over one million users before being bought over for $125 million and facing an ultimate demise in 2001.
The failure of Six Degrees, like its success, was followed by a number of social media networks. Around the time and early 2000s a number of new social media platforms like Friendster, AmIHotorNot.com, MySpace emerged and witnessed their decline in the domain.
However, there were a few that, at the back of their strong business model ready for growth from day one. Here are some of the social media platforms that continue to come on top of the social media landscape in terms of user count –
In addition to these, there were some other key names in the social media technology evolution landscape – LinkedIn, foursquare. Grindr, Pinterest, Snapchat, amongst others.
An unvalidated fact behind the growth and sustainability of the selected social media platforms can be seen in the incorporation of the right technology in the domain. Let us delve into it deeper.
The Importance of Technology in Social Media
The role of technology in social media evolution, although starts with the advent of smartphones and laptops on a precise level, begins with mobile apps. In 2019 alone, it was estimated by a Lyfemarketing report that over 91% of all social media users use social channels through mobile devices.
There can be a number of reasons behind the rise in mobile application adoption for social media usage:
Convenience in terms of not having to open a laptop and opening the application within three clicks.
Integration with mobile in-built features like camera, location, microphone, etc.
Ease of capturing and sharing content
We believe that up until this point you must have gathered the need for social media application development. However, the list of the impact of technology on social media doesn’t just end with one component. There are a number of other technologies like APIs, geotagging, QR codes, etc which have contributed to making social media where it stands today.
Technology incorporation makes social media accessible, safe, and real-time in addition to making the sector operate seamlessly with users’ experience through the mode of automation, integration with other social media applications, and eCommerce.
With the benefits of technology and social media peeked into, let us move on to the list of technologies that are helping social media app developers take the sector to its next evolution set.
Technologies Driving the Future of Social Media
1. RFID – Radio Frequency Identification Tags
RFID, in layman terms, means a small computer chip that can store information about an individual or object. Every chip comes with a unique serial number that can be tied to the information present on the chip. Let us give you a practical application of this technology through this example – Suppose you are at a music concert and you scan your RFID device with an RFID device that has social features integrated into it. By simply bringing your RFID device to the other one, you will be able to Like a band on Instagram or Facebook or download a couple of their music tracks on your device.
The growing popularity of RFID in the event and eCommerce domain (through the mode of NFC) has led to a number of social networking app development companies integrating RFID into their mobile applications.
2. Augmented Reality
AR and mixed reality are some of the most popular social media application features. There are a number of use cases that social media houses experiment with when integrating AR with their applications but the one that has witnessed mass popularity is the use of face filters. Popularized by Snapchat, AR-driven filters are used by both individuals and businesses to deliver engaging content.
Another example of business-level usage of AR in social media can be seen in social media advertisements. Last year, Snapchat created an AR-based app for Snap Original where Bhad Bhabie interacted with the users as if they were interacting in the real world.
3. Artificial Intelligence
Out of all the new-age technologies that you will read about impacting the social media sector, the one name which will be placed on the top is Artificial Intelligence.
AI is a prime component of every social platform active in the market today. This is the number one reason why the technology is now involved in the social media app development cost on a default note.
Facebook utilizes advanced machine learning for a number of tasks: recognizing faces in poss to targeting users for advertisements and even for strengthening their search functionality.
LinkedIn makes use of AI for offering job recommendations, suggesting people whom they’d like to connect, and sending them specific posts for their feed.
Snapchat uses the capability of computer vision for tracking physical features and overlaying filters that move with them in real-time.
These business examples are a validation of how AI is a crucial part of all the different genres of the social media domain.
4. Blockchain
Decentralized social platforms is one of the most up and coming genres of the social media sector. There are a number of use cases of social media and blockchain convergence which businesses from both sides are experimenting with. Here are some of them –
The social media networks depend on ad-based business models that share a common shortcoming: the creators are unequally compensated for their content on the platform. A smart contract can be put into use here for ensuring that the creators get the amount that their content is worth without any delay or unannounced deduction.
There are businesses working towards combating internet censorship. Usually, based on a distributed ledger, the individuals will be able to read and curate their own content with a surety that no entity will be able to block access to content.
5. IoT
The last in our list of social platform and technology trends is the Internet of Things. The technology is used heavily for social media monitoring and marketing purposes by some of the top names in the industry like N&W, Disney, and Tencent, etc.
Organizations are constantly on the lookout for an IoT skilled social media app development company that would help them create solutions around real-time monitoring of data and insights coming in from social media to help them make better business decisions.
Here were the five technologies which are taking the social media sector towards a new evolution era – one that will be a lot more open and transparent in nature. Want to be a part of the revolution? Contact our team of social media experts.
Before autonomous drones and machine learning came into foray, James Cameron enthralled the world with his dream project The Terminator in 1984 where he introduced ‘Skynet’, a futuristic artificial superintelligence network that wants to replace humans with machines. Much has been debated about the film franchise ever since as scientists passed it off as a fan service action series, yet the seed of technological brilliance was sown. Whether Artificial Intelligence will take over the world or not, it certainly has given businesses a means of revolution and to readers/debaters like us, food for thought.
Another important piece of disruptive technology that is equally changing lives is IoT which expands to the Internet of Things. Like AI, the IoT has come of age. Its utilities include not just making smart homes but also wearable devices, smart vehicles and smart cities. The role of Artificial Intelligence and IoT in business is currently at its epitome.
AI and IoT are redefining the way businesses used to perform. On one hand, AI with its powerful subset of machine learning, has paved the way for smarter task execution with real-time analysis and greater interaction between humans and machines; IoT, on the other, has upped the scale of communication between devices and humans via effective intelligent technology. The confluence of the Internet of Things and Artificial Intelligence makes each other’s applications more varied and powerful.
The merger: How AI and IoT joined forces
IoT accumulates large amounts of data through device connectivity via the internet and AI, especially through its powerful mechanism, Machine Learning helps in assimilating and evaluating this data. Machine learning in IoT devices helps to identify patterns and detect any faults in data collection through extremely advanced sensors. Intrinsic things such as stimulation to air, temperature, humidity, pollution, sound, vibrations, lights, etc. are derived with this technology over a period of time. Unlike traditional technology, IoT and machine learning make operational forecasts 20x faster with heightened accuracy. This is the reason why businesses that use AI technology sees a growth in their revenue numbers – a validation of which can be seen in the graph below
Revenues generated by businesses using AI from 2018 to 2025 (estimated)
AI’s role in IoT’s revolution has helped in a massive revenue boost which also means in the sale of more connected devices.
Below is a graph that shows how many IoT-powered devices were there in 2025 and the estimated curve predicts a huge nerve till 2025.
The demand for IoT is certainly going uphill. IoT, along with AI, are currently on demand by every business, whether it’s a Fortune 500 or a startup. Since there is no limit to either’s abilities, companies wish to use them to their full potential and unbridle their potentiality to the world. The following image draws a comparison among different technologies and shows which ones are the most trending.
As IoT keeps collecting data, AI takes the onus of converting it into meaningful and creative actions. Data exchange happens through sensors and in the process, a few of the following things happen:
Data insights are more accurately obtained, monitored and evaluated
The entire process becomes faster and more efficient
Surveillance against cyber-attacks is more defined and stronger
Advantages of AI and IoT in business
Together AI and IoT are unstoppable forces of technology. There are a lot of advantages which the two provide. The following elaborates the same:
Data collection, sharing and formulating user perceptions
Data collection is extremely vital for a business’ growth and development. A business with an IoT strategy knows how technology can transform data compulsion by offering greater access to consumer information. AI makes it easier to handle that information. IoT devices have this unique mechanism to track, record and observe patterns in a user and his/her interaction with the device(s). Businesses use the acquired data to devise better means to enhance consumer experiences.
Elimination of downtime
Oil and gas manufacturing organizations use heavy machinery which can suffer unseen/unplanned breakdowns. This causes downtime that can incur huge losses. Having an AI-enabled IoT platform makes it possible for predictive maintenance. It helps in anticipating machinery failures and breakdowns in advance by utilizing the analytics so that you can plan a course of action beforehand and not let your operations get affected. A study by Deloitte led to the following conclusions-
20-50% reduction in time taken for maintenance planning
5-10% cutback in maintenance costs
10-20% increment in equipment availability and uptime
Strengthening security measures
With the current rise in data breaches and theft of confidential information, security and safety are the most concerning factors for a business. IoT powered by AI provides militant support to your private information and doesn’t allow third parties to intrude. Machine-to-machine communication is being facilitated by various organizations to detect incoming threats and give out automated responses to hackers. A common example could be in the banking sector where illicit activities in ATMs are picked up by IoT sensors and conveyed immediately to law enforcement bodies.
Automated operational efficiency
IoT deployment streamlines your business and helps in making accurate predictions, all of which are extremely crucial for improving the efficiency of the business. Placing your money on the Internet of Things investment is very necessary in today’s time as the technology also helps in giving you insights into redundant activities and the ones which are consuming a lot of time. A good example will be Google’s reduction in expenditure in cooling their data centres which they could do with AI and IoT. Like Google, you too can find out which of your operational activities need some fine-tuning so that efficiency is not neglected.
Helps in processing business analysis
There needs to be a fine balance between demand and supply. AI helps in improving inventory management and letting go of the pressure on the stock as it will help you to know in advance when you need to restock. This provides an important aid to retailers as they at times hoard too many products to find out later on that all of them cannot be sold. This proves how accurate it is than manual methods. There are IoT applications which help them in gathering the data and analytics for the maintenance of stock.
Better at Risk Management
Earlier we mentioned how AI and IoT help in maintaining cybersecurity. When it comes to risk management, which includes handling financial loss, personnel safety and cyber threats, the pair effortlessly deal with situations and give out prompt responses so that such situations do not arise. For example, Fujitsu, a Japanese IT equipment and service provider makes certain worker safety is maintained through data collected from wearable devices with the help of AI.
Scope for new and improved products and services
The Natural Language Processing (NLP) technology which aims to improve communication via speech, text or gestures has augmented the transmission of information between humans and devices. AI-powered drones and robots give a whole new meaning to monitoring and inspection which never existed previously. It helps to fetch data that a human may never be able to do physically. This proves how strong the IoT and AI future is. For commercial vehicles, it helps in fleet management by monitoring every measurable information. Rolls Royce is a great example of AI-powered IoT use cases. Plans to use AI technology to implement IoT-enabled aeroplane engine maintenance needs. This will help in creating perceptual patterns and help explore in-depth insights.
Examples where AI and IoT are showing brilliance
Now that you’re aware of how AI and IoT solutions help in leveraging business opportunities, let us mention a few examples from real-life instances to prove how the role of artificial intelligence and IoT is helping to create new business models and provide better user experiences. Many of these examples also make up for the most cutting-edge and futuristic trends to watch out for.
Wearables
By now you must have heard how wearables play a key role in the current IoT scenario. Fitness trackers, smartwatches, wearable panic buttons, remote monitoring systems, GPS trackers and music systems are some of the most popular examples of wearables which take up a large part in the IoT ecosystem. You need to simply download IoT applications in your smart devices to get the most precise information.
Robotics
The manufacturing industry was in dire need of adopting AI-focused IoT solutions. This helps in facial recognition, deep learning, big data analytics and especially robotics. Robots and robotics have always been the frontrunners of technology for decades and now, with the passage of time, they have become smarter, more reliable and more efficient. Through implanted sensors meticulous communication is facilitated. Using the fusion of AI and IoT, robots can learn and adapt to newer environments with precision. This makes the manufacturing process linear and saves time and money.
Smart Homes
The smart home ecosystem is growing and is currently valued at $91 million. It’s one of the most pleasing activities of technology where you do not have to get and go to a particular appliance and operate it. The AI-powered IoT technology enables the controlling of your light, fans, television, thermostat, ACs, etc. through your phone. Not just inside but even if you’re travelling outside, say to an outstation and you need to check whether an appliance has been wrongly set on, you can do it with a simple command. Or if you’re returning home after a tiresome day at work and need a bath, you can set the temperature of the water say 10 minutes before you reach home.
Self-driven vehicles
The thought of autonomous cars and vehicles seems exciting and thrilling at the same time. With powerful sensors, installed cameras and robust hardware and software integration, a car gathers monumental information roads, traffic, additional routes, navigation, weather conditions, consumer behaviour and whatnot. Self-driven cars are the perfect examples that will highlight the role of artificial intelligence in future technology. One major concerning factor is safety. Many will face apprehensions over their initial journeys in driverless cars, but that’s what the whole game is about. It has mindblowing learning abilities and high-powered AI mechanisms that will give priority to the passenger’s life at all costs.
Amazon Go
This is truly a masterstroke in AI technology use. To support its retail outlets, Amazon uses IoT to make the shopping experience more convenient for the user. With no cashier or even cash counters, the sensors present will optimise the entire process. For example, sensors are used to determine your activities. Like a supermarket or retail outlet, items are arranged and when you pick up any product, it automatically adds it to your cart and the moment you keep it back, it’s removed from your cart. It connects to your payment mode(s) so when you leave the store with the items, the total amount is debited from your account or online wallet. Just like self-driven cars, they used computer vision, deep learning algorithms and sensor fusion, procreating the ‘Just Walk Out’ technology.
Healthcare
This is currently the need of the hour. With the Coronavirus pandemic, everyone has become extra cautious with their health and technologies like AI and IoT are leveraging the entire healthcare system. The IoT applications and deployments powered by AI help in collecting data to provide preventive measures for a person/patient, early detection and providing drug administration. It draws data from internet-powered medical devices, medical records, fitness trackers, healthcare mobile apps, etc. Many healthcare companies around the globe are making IoT investments so that people stay safe under such hazardous conditions.
Smart Cities
This is the biggest example to show the prowess of the AI and IoT pair. If it is able to maintain civic decorum, then it speaks volumes about technology’s success. Things like smart traffic management, smart parking, smart waste management, smart policing, smart governance and many other factors are the components which constitute a smart city. The Internet of Things for smart cities changes the way cities operate and delivers amenities to the public which includes transportation, healthcare, lighting, etc. Smart cities are arguably a futuristic concept and have a lot of ground to cover. The above video explains how three cities have successfully implemented it.
There are many IoT application development company who have done a tremendous job of integrating the technology into various business types and creating something unconventional out of the banal scheme of things. AI is truly reinventing IoT along with other modern-day techs, and businesses that are vigorously using this technological emergence only have good things to say. No second thoughts need to be spared to explain — The future of IoT is AI and will remain so.
The insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums annually, providing fraudsters with huge opportunities to commit fraud using a growing number of schemes. Fraudsters are successful too often. According to FBI statistics, the total cost of non-health insurance fraud is estimated at more than $40 billion a year.
Fighting fraud is like aiming at a constantly moving target, since criminals constantly hone and change their strategies. As insurers offer customers additional ways to submit information, fraudsters find a way to exploit new channels, and detecting issues is increasingly challenging because threats and attacks are growing in sophistication. For example, organized crime has found a way to roboclaim insurers that set up electronic claims capabilities.
Advanced technologies such as artificial intelligence (AI) can help insurers keep one step ahead of perpetrators. IBM Watson, for instance, helps insurers fight fraud by learning from and adapting to changing business rules and emerging nefarious activities. Watson can learn on the fly, so insurers don’t have to program in changes to sufficiently protect against evolving fraud at all times.
Here are four compelling reasons insurers need to begin to address fraud with sophisticated AI systems and machine learning that can continuously monitor claims for fraud potential:
The aging workforce. There are many claims folks who are aging out and will soon retire, taking years of knowledge with them. Seasoned adjusters often rely on their gut instinct to detect fraud, knowing which claims just don’t seem right, based on years of experience. However, incoming claims staff don’t have the experience to know when a claim seems suspicious. Insurers need to seize and convert that knowledge, getting it into a software program or an AI program so that the technology can capture the experience.
Evolving fraud events and tactics. Even though claims people may have looked at fraud the same way for years, the environment surrounding claims is always changing, enabling new ways to commit fraud. Fraud detection tactics that may have worked 6 months ago might not be relevant today. For instance, several years ago when gas prices were through the roof, SUVs were reported stolen at an alarming rate. They weren’t really stolen however — they had just become too costly to operate. Now that gas prices have gone down, this fraud isn’t happening as often. If an insurer programs an expensive rule into the system, 6 months later economic factors may change and that problem may not be an issue anymore.
Digital transformation. Insurers are all striving to go digital and electronic. As they make claims reporting easier, more people are reporting claims electronically, stressing the systems. At the same time, claims staffing levels remain constant, so the same number of workers now have to detect fraud in a much higher claims volume.
Fighting fraud is not the claim handlers’ core job responsibility. The claim adjuster’s job is to adjudicate a claim, get it settled and make the customer happy. Finding fraud puts adjusters in an adversarial situation. Some are uncomfortable with looking for fraud because they don’t like conflict. A system that detects fraud enables adjusters to focus on their areas of expertise.
In the past, insurance organizations relied heavily on their experienced claims adjusters to identify potentially fraudulent claims. But since fraudsters are turning to technology to commit crimes against insurance companies, carriers need to turn to technology to help fight them. Humans will still be a critical component of any fraud detection strategy, however. Today, insurance organizations need a collaborative human-machine approach, since they can’t successfully fight fraud with just one tactic or one system. To fight fraud, humans need machines, and machines need human intervention
Data is all around us. It’s created with everything we do. For the life sciences industry, this means data is being collected faster and at a greater rate than ever before. Data takes the form of structured content — from clinical trials, regulatory filings, manufacturing and marketing, drug interactions and real-world evidence — with regard to how drugs are used in healthcare settings. It also is found in unstructured content from the internet of things (IoT), such as social media forums, blogs and so on.
But having massive quantities of data is useless without the regulatory intelligence to make sense of it. Let’s define what we mean by regulatory intelligence. This is about taking multiple data sources and feeding those into a regulatory system that can look at the data, analyze it, make use of it, collect information from it, then take that information to distribute it where it needs to go. This might be to the regulatory agencies requesting updates or information about the drug portfolio to satisfy compliance mandates, it might be to partners that you’re working with, such as trading partners, or it might be consumed internally.
Although referred to as regulatory intelligence, it encompasses many other areas of the product life cycle, including clinical research and development for detailed analysis and safety and pharmacovigilance for signal detection.
Life sciences companies can leverage these different types of data for real-time decision making to protect public safety, respond to supply shortages, protect the brand, advance the brand — for example, into new indications or new markets — and for many other purposes. In this blog, I’ll explore some of these uses of regulatory intelligence in greater depth.
Know your target
Since data is consumed across the life sciences in different ways by different people and different functions, getting to the point of intelligence first requires knowing the target and objective. If there is real-world data indicating adverse events that weren’t detected in clinical trials, having that intelligence early on allows companies to act accordingly — both to protect public safety and to safeguard brand reputation. What action the company takes will depend on what the data shows, as well as what the agencies require. For example, it might simply be to reinforce a message about avoiding other medications or foods while undergoing a specific treatment or it might require a broader response.
Another way data can be leveraged for real-time strategic decision making is to advance the brand. For example, IoT data or data held by the authorities might show weakness in a competitor’s product or weakness in the market — perhaps a gap in a region the company has begun targeting. By leveraging that intelligence, companies can take advantage of those gaps or competitor weaknesses and promote their brand as a better alternative or prepare a new market launch.
Regulatory intelligence might also shine light on other potential indications for your product. These insights might be gathered from IoT sources, such as physician blogs, or from positive side effects observed in clinical trials. The most famous example is Viagra, which initially was studied as a drug to lower blood pressure. As was the case here, not all side effects are negative, and during clinical studies an unexpected side effect led to the drug’s being studied and ultimately approved for erectile dysfunction. Having that regulatory intelligence available gives you the leverage to make the case for expanding clinical studies into new indications and extending therapeutic use.
From data to intelligence
Now that we have explored the definition of and some purposes for regulatory intelligence, we should also look at how you get from that point of data to intelligence. An important first step is to deploy the right analytical tool to sift through that data and pull out relevant information. It’s equally important to know how to make use of that data, and that requires knowing your end goal and narrowing the scope of your data search to eliminate extraneous data.
Time and resources can also be saved by leveraging automation to collect data for analysis. Since data is continuously being created, updated and pushed out, automated robotic processes make it possible to keep up to date with the latest findings and pull relevant data into your regulatory operational environment.
Regulatory intelligence is the key to real-time strategic decision making across all areas of research and development. Its importance to the organization can’t be overstated.
The most challenging part for me was designing to handle the human-machine interaction. Each user is different. Unlike in websites/apps, where users can simply browse and leave, the chatbot users open the chat window to interact. They come with all sorts of questions- vague /smart /genuine /rogue /irrelevant and (sometimes) absurd. When they type a query, they expect the conversational UI to adapt to their needs–digest questions and construct intelligent answers/follow up questions.
