February 2020 - Page 2 of 4

5 Stages to a Successful Cloud-based SaaS Application Migration

Posted on 18/02/202015/06/2021 by Tushar

A digital transformation can deliver improved flexibility, faster speed-to-market and reduced costs, but only if you go about things in the right way. One path to a successful digital transformation is to move traditional applications to cloud-based software-as-a-service (SaaS) applications, a migration that requires a data-driven approach and using technology in strategic, new ways.

Traditionally, most business application migrations start with business process reengineering, whiteboard sessions, offsite process walk-throughs, process mapping and so on. All these are fine, but to achieve success, you need to take a fresh, data-driven approach that focuses on fact-based views of current processes and lays out non-biased options.

A data-driven approach that uses advanced technologies, such as machine learning and predictive intelligence, can provide opportunities to reduce costs, improve quality and boost innovation. These five steps can help you successfully migrate legacy applications to a cloud-based SaaS environment:

1. Establish a digital baseline. Before implementing SaaS, you should deploy data-discovery tools to identify the current state of business processes and build a digital blueprint of all baseline activities. Tools such as Hadoop, Spark and Google TensorFlow can be used to construct machine-generated process maps, automated metrics calculations, and intelligent “hot spot” analysis. This will show what process area need to be fixed and where the fixes should be applied.

2. Simplify and standardize. Once the digital baseline is in place, the next step is to simplify and standardize. This can be done analyzing each process area that has been customized and comparing them with modern best practices, while leveraging modern technologies including cloud, mobile, analytics, social, Internet of Things, and big data. This helps you visualize future state processes, identify process-improvement opportunities, and mitigate risks with the right organizational change management approaches and training strategies.

3. Deploy diverse migration tools. The path to SaaS migration relies on process discovery, rapid deployments and automation. Enterprises should deploy a wide array of migration and testing tools to perform extracts, upload setups and master data. Once deployed, you should engage in end-to-end automated functional testing of applications and other critical tasks.

4. Closely monitor the migration. Be prepared to generate detailed reports and dashboards that allow you to review configuration uploads to ensure they are all loaded, verifying that they are correct and supported. Testing is also key – you should establish a test repository with assets such as scenario descriptions, test scripts and user-configurable workbooks, and provision testing-as-a-service (TaaS) to reduce testing time and costs.

5. Automate and optimize. After your migration is complete, your focus should turn to automation and optimization. For example, you can use data from pre- and post-migration to identify candidates for automation to make sure your digital workforce (bots) is executing each automation step as planned. Also, your organization can drive continuous innovation and improvement via lean methods to optimize workflows and team performance.

Successfully migrating to cloud-based SaaS applications involves changing your business, your processes and even your people across the enterprise. A data-driven approach is effective only when technology, people, and talent – business and IT, along with leadership – are integrated with the right balance to execute cohesively with a clearly defined end goal in mind.

How learning aids in the development of an agile workforce

Posted on 17/02/202015/06/2021 by Tushar

While forward-thinking companies are rapidly investing in their technology resources, they’re doing the same for their human resources.

That’s because these organizations know that the workforce of the future isn’t made up solely of robots. Instead, the workforce of the future is an “augmented” one, where humans and AI work together to drive greater efficiency, innovation, and business success.

Humans and AI? Better together

As AI capabilities take over repetitive, more routine functions, human workers will be needed more than ever to not only manage and supervise technology but engage in higher cognitive functions, such as creativity, innovation, persuasion, and decision making.

Mercer describes this new, blended workforce as one that is “human-led and technology-enabled.” This idea of augmentation means that humans still matter in the workforce, despite fears to the contrary. While experts predict that 52% of existing human tasks will be performed by robots by 2025, technology will create 133 million new jobs by 2022.

According to Mark Sears, founder and CEO of CloudFactory, as quoted in Robotics Business Review, “People will always play important roles in the workplace of the future. Even in highly automated environments, people must continue to develop, train, and iterate machine learning models to facilitate robotic automation. The feedback provided by the humans in the loop—even in highly automated environments—is critical to the success of automation.”

Building an effective augmented workforce starts with learning

A high-functioning augmented workforce depends on effectively managing its two core elements: technology and people. But while the AI piece of augmentation depends on choosing the right technology, managing the human element isn’t as straightforward. The skills shortage means that organizations can’t simply hire their way to an agile, upskilled workforce. Instead, organizations must begin focusing on creating the workforce they need by engaging and retaining existing employees.

A successful augmented workforce requires highly trained, agile, continuously upskilled employees—employees who are both eager to learn and simultaneously given the opportunity to learn by their employers. HR has long known that learning is key to both employee engagement and retention.

In the age of digital transformation, learning has an additional, equally significant benefit: organizations that provide ongoing learning are anticipated to outperform those who don’t. Putting learning at the center of the organization enables the creation of a workforce that is always ready to work with and adapt to changing technology and the market, thus making the organization far more flexible and agile in an era of constant disruption.

The six keys to getting started

Building an augmented workforce begins with building a learning organization, one that enables employees to learn continuously, quickly upskill as needed, and drive their own careers paths.

For organizations accustomed to providing an employee stipend for a college course or offering one-day workshops, this new approach– learning at the center of the organization rather than at its periphery—can seem overwhelming. But while becoming a learning organization is crucial to remaining successful amid constant change, the transformation doesn’t have to happen overnight.

Organizations can begin with six manageable steps, efforts that help prioritize development and nurture learning for the long term:

1. Establish a learning culture. Start Thinking of learning as something that happens every day, within every job, for every employee, from hire to retire. Enable employees to try new skills and fail. In the workplace of the future, failure is an opportunity to move forward.

2. Nurture curiosity by offering learning opportunities to everyone. Research shows that every generation (not just Millennials) wants to learn. Offer learning opportunities to those in entry-level and leadership roles–and every role in between.

3. Enable learners to drive their own learning. Let employees follow their curiosity. Offer a wide range of accessible, ongoing, and free opportunities.

4. Develop learning agility. Learning agility isn’t an innate skill. To drive efficient upskilling, vanguard organizations are teaching employees how to learn effectively.

5. Create career paths that are not so much traditional as transformational. See beyond traditional hierarchies and realize that the careers of the future will be very different than those of today. By offering a wide variety of developmental experiences, organizations can inspire employees to define their own career paths (and simultaneously encourage them to stay).

6. Offer empowerment, not entanglement. Offering employees ongoing learning doesn’t guarantee they’ll stay. However, by offering learning portability, organizations create an environment of trust and employee empowerment. And as more organizations do the same, employees will be free to find the right work “home” and thus be more productive and engaged.

The convergence of learning and work in the digital era.

Who wins in the digital era? Organizations that use both people and technology resources efficiently—the augmented workforce. While the use of AI for routine tasks is on the rise, the abilities unique to human beings—the cognitive, the creative, the empathetic—are still crucial to an organization’s long-term success. How then can organizations help the human workforce make this shift, from manual tasks to cognitive and creative roles? By making ongoing learning central to work and placing development opportunities at the core of every role. Practically, this means creating an employee-centric learning environment, such as a multi-platform, agile methodology where the learner’s experience is the focus.

Learn these 5 scaling RPA secrets to transform your organization

Posted on 16/02/202016/06/2021 by Anteelo Master

With robotic process automation (RPA) pilots almost everywhere, creating industrial scale has emerged as the new challenge for IT departments and shared service centres (SSCs) alike. The organisation, processes, tooling and infrastructure required to quickly develop a few in-house robots cannot simply be incremented at scale. Enterprises need to re-design their entire approach.According to a survey by HFS Research, the biggest gap in RPA services capabilities is not in RPA planning and implementation, but rather in post-implementation.

Here are five ways to meet the key challenges we hear from both IT and SSC executives about scaling robotics:

Begin with the end in mind and start looking at an operating model strategy that supports the bots where they will eventually be running. However, whether you manage the bots from a centralised production environment or on agents’ desktops, there is no way around the IT department once you’ve decided to scale robots. IT departments have to make technical resources and support staff available; manage the configuration, software distribution and robot scripts; provide and maintain security access; plus track and respond to incidents. Unfortunately, it takes time and effort to configure such processes, and IT can have more pressing priorities, but presenting a clear operating strategy can help spur them on.
If a business continuity plan has not yet been devised, it needs to be. If systems go down, the bots need to be re-started along with the entire software stack. Some organisations create mirrored environments they can switch to in case of extended system failures.
Leverage the cloud. Cloud is generally acknowledged as the way forward for large-scale RPA operations. Cloud makes it possible to provision extra bots with one click, for example, to address sudden peaks in transactions. Cloud also enables efficient, consumption-based models. However, some large enterprises have ring-fenced clouds due to regulations in critical industries, such as in defence or banking, and this needs to be considered.
Bring corporate security policies into force. Can hundreds of robots running in parallel access all corporate systems that require a human being’s credentials? They do not have an address, ID badge, a manager, an office, or a birth date – which may be mandatory to comply with existing corporate security policies. Corporate security policies need to reflect the new complexities.
Realize that constant change is a rule, not the exception, for bots. Some companies leave the technical changes to IT, but manage the functional changes in the business units (finance, human resources, etc.) that own the business process, and this approach does provide more speed to resolution. For the same reason, the relevant business units can also maintain re-usable libraries of standard information. Things become more complex, though, when third parties are in the picture, like tool vendors, RPA consultants and/or business process outsourcing (BPO) providers. In fact, governance is most often cited by IT and SSC leaders as a key challenge here. It is common that RPA investments do not progress past development and test phases due to governance roadblocks. A preferred approach tends to be establishing a centre of excellence — typically within the enterprise SSC organisation — with responsibility over the policies, governance and tool/vendor selection for RPA. Still, once bots take a significant share of workload from human agents, does it make sense to keep the SSC and IT under separate organisations? And also, what is the impact on the human workforce? As we train bots to act as humans, businesses need to train and acclimatize their human workforce to co-operate with bots, understand how they operate and where to intervene.

In summary, RPA is a very hot topic currently and whilst a lot of the hype these days is around enabling technologies to accelerate the development of robots, the real challenge in scaling up your RPA digital workforce lies in better operating model design, a flexible cloud-based platform and of course, better appreciation of human nature.

Supervised Stack Ensemble: Speeding Customer Service

Posted on 16/02/202015/06/2021 by Tushar

In the age of social media, companies are conscious about the reviews that are posted online. Any act of dissatisfaction can be meted out by way of tart sentiments on these platforms. And so enterprises strive hard to give 100% positive experience, by doing all that they can to address customer grievances and queries. But like they say, there are slips between the cup and the lip – not all grievances can be handled amicably.

Let’s take the specific case of call centers here. Their Service Level Agreement mentions terms like number of calls answered at a certain time of the day, percentage of calls answered within a specific waiting time, etc. Ensuring customer satisfaction and retention requires a far deeper, more holistic view of interaction between customer care representative (agent) and caller. There are other KPIs such as what causes a customer to be dissatisfied and number of escalations. But these seldom find a place in the SLA.

In this article, we will talk about identifying drivers of (dis)satisfaction and come up with ways to improve it. In the course, we will touch up on the solution design that can scale and institutionalize real-time decision making.

Introduction

We’ve all done it, dialing the call center for any issue encountered. We are surely an expressive bunch when it comes down to rattling our emotions and spitting out our dissatisfaction. And if that is not enough, we threaten to let our dissatisfaction be known to the rest of the world – through social media, not to mention #CustomerExperience.

While standard surveys exist to capture the sentiments of customers, the percentage of people filling these surveys is very low. This compounds the problem of effectively addressing customer needs.

Automating the task of predicting customer satisfaction requires a balanced mixture of text mining, audio mining, and machine learning. The resulting solution needs to:

Scale and be deployable
Identify the drivers of dissatisfaction
Generate actionable insights and generalize well to the population

Modeling Pipeline

Modeling pipeline includes all the components (data ingestors, model builder, model scorer) that are involved in model building and prediction. It is mandatory for the modeling pipeline to seamlessly integrate all the components for it to be scalable and deployable – production worthy. These components vary depending on the problem, available architecture, tools used, scale of the solution and turnaround time. The following pipeline was built in Google cloud to solve the problem of dissatisfaction in call centers.

Modeling (actual work – driver identification)

In the above problem, the satisfaction survey showed good internal consistency. Calls, emails and chats had sufficient discriminatory power to model customer satisfaction. Exploration of the data showed that the patterns were non-linear. However, like other psychometric models, the satisfaction model was plagued by three major issues which threatened its external consistency: shortage of data, variance and instability. These problems were addressed in the following manner:

First, the issue of data shortage was solved using resampling (bootstrapping). Second, the challenge of model instability was resolved using k-fold cross validation for tuning hyperparameters of different models. This was followed by model averaging. Finally, the issue of model variance was solved using stack ensemble approach on bootstrap samples. Several classification algorithms were used to build the first layer of the stack. Logistic regression was used to predict the outcome by combining the results from the first layer. The accuracy thus obtained was superior to that of any individual model in the first layer of the stack.

Driver Analysis

Only two types of classification models are directly interpretable: logistic regression and decision tree. Interpretation of other Machine Learning techniques such as regularized regression and regression splines require knowledge of calculus, geometry and optimization. Machine Learning models such as support vector machine and neural networks are considered black box techniques because of the high dimensionality, which is difficult for the human brain to comprehend.

Standard measures of variable importance exist for commonly used black box techniques such as SVM and neural networks. Simple weighted average method is used to calculate the importance of variables in the stack ensemble, with the weights being determined by the logistic layer. However, it is important to note that the final importance is not a measure of linear dependence of satisfaction on the independent variables. The importance metrics need to be combined with business intuition and actionability to provide recommendations for improving customer satisfaction.

Consumption

A call center manager would like to track customer satisfaction level along with several KPIs that are critical to operation. Information related to utilization of customer care representatives is provided to the manager in real-time. Model prediction is run in semi-real-time to reduce the required computational power. The manager is provided with options to deep dive into historical data based on variables that are drivers of dissatisfaction. For example, calls can be redirected to customer care representatives by existing ERP systems based on their history and subject matter expertise. This reduces the number of escalations and enables near real-time actionability without significantly affecting other KPIs.

The problem of customer dissatisfaction in call centers can be solved using audio mining, text mining and machine learning. Intelligent systems greatly reduce the stress on customer care representatives by automating majority of the processes. These cloud-based systems can be seamlessly integrated with existing ERP systems to provide highly actionable insights about dissatisfaction without significantly affecting other critical KPIs that are related to call center operations.

How to Pitch Design Ideas to Clients like a Pro!

Posted on 15/02/202021/06/2021 by Anteelo Master

Effective design is the best sales pitch! Design is good when it serves a purpose and turns a few heads, but it becomes phenomenal when it can twirl your client by the pixel. And this is where most designers face a roadblock. The only problem is, they somehow fail to associate “selling” with designing. And for those who don’t fall into that category, are most probably doing it wrong.We, designers come across a wide variety of clients to appease. Some of them turn out to be quite friendly and supportive, who hands over the liberty to the project in a barrel with other important stuff you might need to know. But some are more specific about their requirements and prefer to keep the freedom under a leash. Whoever we work with, the bottom-line remains the same: ideas don’t sell themselves. The key is to adapt to the ‘sales strategy’ to suit the customer. These are soft skills every designer must have!

Playing the role of an effective virtual tour guide isn’t a cakewalk, but I have for you, a few valuable and time-tested skills to help you add muscle to your selling.

Here are some pointers that you can mobilize to sell the design to your clients.

1. Know your Client : Get Talking

The number one rule of sales is getting to know your customer. This is where all the magic happens. It always starts with a string of conversations. The trick is to not let the thread go cold. At the start of a project, gather as much information about the client as possible. This will serve you well in the future in navigating through what actually matters to your client. You can ask about their city, (a classic conversation starter), the weather may be, or about their likes and so on. And if you hit the right buttons, you would be amazed at what a simple conversation can uncork about your client’s design preferences, unless of course, you are Sherlock Holmes. Here’s what happens when you get talking:

You would get a clearer picture of what your client would prefer in your design.
A friendly conversation establishes trust. And once your client begins to trust you, the restrictions fall apart giving way for a fair amount of liberty on the projects you’re handling.
Once clients feel comfortable working with you, 80% of your pitching is done. They would start taking your designs more seriously and who knows, their next string of projects might have your name on them.
Establishing a relationship with the client is a fundamental precursor to pitching design ideas to them. It always gets them listening and responding more positively to your ideas.

2. DO YOUR HOMEWORK: GAIN CREDIBILITY

Decision making in design can be a bit challenging. It is not like throwing in variables in a formula to get to the right answer. Therefore, there’s always room for error. And this is why you need to have an answer for everything you do because rest assured there will be questions!

The business of design dictates that there exists logical reasoning for every UI/ UX move you make. There needs to be a reason for your chosen palette of colors or, your one-page layout preference. Backing your ideas up with concrete statistics is the way to go. A little bit of research goes a long way. It is always advisable to have complete knowledge of the amazing solution you are about to present since this dramatically reduces the chances of skewing up the thought process. This way, you can let the data talk for itself. And clients seldom argue with data.

However, where data falls short, big players come in handy. Another way to gain credibility is by making examples out of well-recognized names in the market. Think of this as a simple hack to the path of least resistance. If your idea coincides with Google’s, to some extent, then that should definitely be a part of your pitching strategy. This little information can open up doors you never thought existed. The bottom line is, clients will have a lot of queries, and you need to have all the answers ready to make for a smooth design selling work-day!

3. KNOW THE TRENDS: DESIGN FOR THE FUTURE

Don’t just be a great designer, be a smart one. We happen to live in a world where nothing is constant, except change. And when it comes to design, change is what pulls the wagon.

The next time you have a design intervention, do quick trend research. Make yourself aware of the big trends in the market and find out the ones that will stick. You can incorporate those in your designs and make it work. Thinking out of the box is a gift, but thinking smart is an acquired taste. Whatever you do, keep in mind that there’s a difference between an unprecedented risk and well-thought-out-and-researched one. You would definitely need to avoid the former.

If you look closely, you would find that there exist two broad kinds of designers, the trend-setters and the trend-followers. Who do you want to be?

4. PRESENTATION, PRESENTATION, PRESENTATION

Even the best, path-breaking, award-winning ideas need a good presentation to get them out of the shed. Which is why, in order to sell your design ideas effectively, you will need more than a few sketches or words.

Consider making a pitch desk that communicates your ideas in a way that catches the client’s imagination. Make sure that they get the bigger picture. While addressing the client, make sure that you put everything in context. Use mockup templates, distribute design samples, go the extra mile. This will help the client visualize what the final design will emulate. The closer your working prototype comes to the real-life design functionality, the closer you will be to sealing the deal.

5. DON’T UNDERESTIMATE YOUR CLIENT: ACCEPT CRITICISM

In a profession without absolutes, criticism comes in bountiful. Your work might be your territory, but you need to keep in mind that you have been hired to solve a problem. And how effectively you do it, measures the conversion rate. Your clients may not have all the design know-hows, but they know exactly what they want and how they want it. So it’s best to always stay on top of your game and pitch your design ideas without getting too defensive.

Your clients need to know that you are distilling their design ideas and steering them to the best possible fruition and not taking it as a challenge. So treading with a touch of finesse would be a great idea. Instead of responding “I don’t think this change is required”, you could tone it down to a “While the changes you have suggested are completely do-able, you might find that it already satisfies these requirements, if you re-examine the one I have submitted.”

The manner of accepting the feedback on the design is critical to its final acceptance. You will find yourself in situations, where a positive attitude, attention to detail and an inane ability to address all the pain-points will ensure that the client is more receptive to your version of the final design that otherwise.

Quick Tip: You are on the same side as your client, stop taking it as a challenge.

Summary

With design, you need to keep two things in mind:

Less is more
It is always better to show than to tell

Even though the thought of “selling” might make you cringe, it is a milestone to achieve to make your designs see the light of the day. Having said that, you need to believe in your pixels and your instincts to see you through the worst because at the end of the day, you are what you present. Figuring out the art of presenting your design ideas, pitching them articulating them proficiently and ‘closing’ the design sale are important skills that will come in handy rather regularly over the course of your career.

Industry 4.0 / Digital Transformation

Posted on 15/02/202015/06/2021 by Tushar

Digital Transformation / Industry 4.0 is on everyone’s mind. Investors are happy to hear from organizations that they are embarking upon a complete Digital Transformation / industry 4.0 journey. Investors love it, leaders advocate for it, directors have to make it a reality, managers have to design for it, but few understand what all it means in the grand scheme.

Hopefully, we can simplify this world for you.

What is Digital Transformation? Let’s keep it simple.

The simplest way to describe Digital Transformation is “Using Digital technology, innovation and intelligence to find better ways to do various things that organizations do today. It’s not about creating something new, but more about improving effectiveness and efficiency of existing processes for better business outcomes.”

Digital Transformation started as Industry 4.0 in some places. However, the idea remains the same. While Industry 4.0 started with the intention of transforming the manufacturing processes using Digital Technology, the principles of Digital Transformation now apply to all functions across the organization.

How does this theory apply in practice? Let’s study an example:

Step 1 – Current State

Map out the current process to uncover gaps that can be filled with better technology or intelligence.

Consider a global paper products manufacturing company. The manufacturing industry team is constantly trying to find opportunities to improve efficiency and productivity and reduce costs.

Energy consumption is a big area of focus for the manufacturing team. Currently, manufacturing industry reports and energy dashboards are used to track the consumption of energy across a few important machine parts.
Operators use these dashboards to identify sections of machines that are in green/red (good/bad) zones in terms of energy consumption and adjust the settings to optimize energy consumption.
These dashboards only track a limited set of machine parts that influence energy consumption.

Step 2 – Future State

Outline what the future should look like, after Digital Transformation.

Energy consumption of machines at the mill (specific reference to Tissue Machines) can be reduced by finding the key driving factors of energy consumption, determining their optimal settings while factoring in for the production constraints in terms of time, quantity and quality.

The following challenges will have to be addressed to get to the future state

There are a few hundred variables in a tissue machine that determine the energy consumption. These machine variables have to be studied comprehensively to identify the key influential factors for energy consumption. Relationships between these variables also need to be considered.
A detailed and statistically robust mechanism is created to generate insights/correlations across all relevant machine variables, to take proactive steps to minimize energy consumption.
Study the process characteristics that influence energy consumption and optimize them. E.g. machine speed, maintenance schedule, aging of parts.

Step 3 – Establish how technology, data and analytics can bridge this gap.

The best way Digital Transformation approach for this example would be:

Select a machine, in a market, which can be managed and monitored easily. Maturity in terms of capturing data, and the groundwork that has already been achieved for manufacturing systems and lean energy dashboards provides an immediate feasibility in terms of execution and adoption.
Build a Driver Model to understand key influential variables and determine the energy consumption profile.
1. Identify Key Variables –
  1. There are ~ 600 machine parts that drive the consumption of energy of a tissue machine. First, shortlist the top contenders and eliminate the non-influencer variables, using inputs from technical teams and plant operators.
  2. Identify primary drivers among the selected machine variables using variable reduction techniques of Machine Learning.
2. Driver Model –
  1. Building multivariate regression models to understand the impact of top drivers of energy consumption using techniques like linear regression, RIDGE/LASSO regression, Elastic Nets.
3. Optimize the engine to lower energy consumption.
  1. Optimize energy consumption by identifying the right combination of drivers under the given production constraints – time, quantity and quality.
  2. Create a mechanism to provide guidance during the actual production hours (In-line monitoring).
    1. Track energy consumption of the machine parts and their active energy consumption states. Identify deviation from the standards.
    2. In case of deviation, provide guidance to machine operators to bring the energy consumption to within defined limits.
4. Adoption
  1. Real-time dashboards, refreshed weekly, provide charts on energy consumption, recommendations, and improvements achieved through proactive measures.
  2. Post-live support to operations teams to enable adoption.
5. Scaling
  Determine phased roll-out to other machines using
  1. Strategic initiatives.
  2. Machines or mills which utilize higher amounts of energy to target higher ROI.
  3. Similarity in process and parts characteristics of tissue machines.
  4. Data availability and Quality.
  5. Readiness and groundwork for adoption by plant operators and energy management teams.

4 key stages in Digital Transformation

How should you, as a leader in an organization, look at Digital Transformation? Organizations should consider the 4 key stages of Digital Transformation, in order to create a sustainable impact on their organization. To make Digital Transformation a reality, all these steps cannot work independently. The philosophies of Design Thinking are embedded in the framework’s interconnected elements.

DEVELOPMENT PHASE:

Focus is on identifying the key areas and prioritizing the Digital Transformation efforts

Stage 1 – Discovery

Identify the key areas of opportunity or risk and related key stakeholders. Detail out the gaps in process, data, insights or technology, fixing which would help capture opportunities or mitigate risks.

Stage 2 – Design

Rapid iterations on design and implementation of prototypes helps reach optimal solutions faster. Build out Proofs of Concepts (PoC) to establish the theoretical validity of the approach. Validate the practical validity of the approach through a Proof of Value (PoV).

IMPLEMENTATION PHASE:

Implementation needs to account for limitations arising from human behavior and scale of the operations.

Stage 3 – Adoption

Building solutions that keep the user at the center of the design, is key to adoption. This means that users must be included in the design and feedback early on. In addition, there should be support for users post design, in the form of FAQs, training videos, chatbots etc.

Stage 4 – Scalability

If we can’t solve a problem at scale, then the solution does not solve organizational problems. The issues that we anticipate at scale, should be accounted into the design in the Development phase. This means considering the technology used, the infrastructure required, process automation possible / required and how to manage future developments.

Like Design Thinking would dictate, the Development phase of the Digital Transformation processes have to always consider the Implementation aspects.

Digital Transformation is no longer just optional.

Every organization is transforming the way they do business. Numerous organizations like BASF, Mondelez, KLM airlines, Aptar group, PepsiCo etc. are already making massive strides in this area.

If you want to zip past your competition, or even stay competitive, it’s about time you started thinking about how to transform the way to do business. After all, there’s no growth in comfort.

Advantages of Developing an iPhone App for Your Company

Posted on 14/02/202015/06/2021 by Tushar

Apple has a tradition; every January, they disclose statistics to prove how well the App Store and iOS apps performed that year. This year’s announcements are an indicator of the staggering popularity and penetration of iPhone apps. Take a look…

There are around 2.2M apps in App Store.
On average, around 2,540 apps are released on App Store every day.
The App Store revenue recorded in Q2 2019 is around $$25.5B, which is nearly 80 percent more than what Google Play Store earned.

These and the plethora of other App Store statistics gives the same message: Apple’s business is thriving and iPhone apps are a people’s favorite. Businesses looking to capture market share in apps would be making a lucrative deal by investing in iPhone application development for businesses.

Apple’s closest rival, Android, also has great numbers to show. But iPhone’s reach and penetration are unsurpassed. In fact, according to BGR, there is a user defection trend among Android users.

Nearly 18 percent of Android users have looked into the advantages of the iPhone and switched to the iOS platform. These advantages included the iPhone is one of the oldest smartphones. The way the iPhone managed to maintain its iron grip on users by offering consistent performance and innovation.

iPhone apps are known to be high quality and revenue-generating. Both startups and established brands are investing in iPhone app development in Virginia to meet their business needs and make a quick return on their investment.

Let us look at some of the perks of investing in iOS app development services and how iPad app development can expand business growth.

Benefits of iOS App Development

1. Better App Revenue

iPhone apps have a greater ROI than Android apps. A fact that adds on to the benefits of iOS App development, to a huge extent.

To get the best of the revenue generation opportunities from your iOS application development process, keeping an eye on the mistakes, tips and tricks, and other related information would be a big advantage. Refer to our mobile app development guide formulated from the experiences of our experts to get an idea of everything worth knowing

2. Security of Enterprise Data

Intrusion into a business’s sensitive enterprise data lodged in apps is a big risk with Android apps. on the other hand, when you compare the iPhone vs Android on the basis on security, iPhone apps, protect firmware and software through stringent security measures such as :

Integrated data handling systems.
Measures to prevent duplication of data.
Measures against loss of security due to data encryption.

iPhone users are cushioned against hacking and malware and this again counts under the advantages of iPhone over Android.

3. High-Quality Standards

iPhone app development for business stays incomplete until the apps are built to pass the high-quality standards of Apple’s Play Store before they are made available to the market. When a user downloads an iPhone app, he can be assured of flawless performance and amazing experience. This trust and goodwill on Apple’s legacy have managed to garner a large and loyal consumer base for iPhone apps.

Android app developers dig into the open-source libraries and follow a non-standardized development approach. This results in apps that offer good user experience on some devices and less-than-satisfactory experience on others. iPhone users are welcomed by the same scintillating UX across all devices.

Plus, since the development of Android apps is slower, cost of Android app development is much higher than that of iPhone development. Businesses are forced to outsource android projects to the lowest bidder who can deliver substandard products. This is the reason why Apple play store personally vets each and every app before publishing and releasing it to the market.

4. Apps for all business needs

Brands of all scales and sizes have recognized the advantages of having mobile apps for business. Consumers expect mobility and responsiveness from any business they deal with – something that comes attached with the process of iPhone app development for business. Mobile applications are the perfect gateway to reach consumers on the go and remain connected with them every moment.

iPhone apps, custom-built or off-the-shelf, are available to suit all business needs. Despite the fact that the cost of iPhone app development is somewhat higher than other options available, iPhone apps are found to be more lucrative for businesses due to their assured market reach and better-paying clients.

5. Established Customer Base

The established user base is the one Apple USP which is also one of the prime benefits of iOS application development. Apple is a pioneer in technology and applications. Although 75% app users are Android users, Apple has a well-established niche of clients that swear by Apple’s quality and performance. In fact, it is said that once a smartphone user experiences iOS, they will never be satisfied by any other OS. Android apps have to battle stiff competition as there are too many apps crowding this segment.

6. Exemplary User Experience

iPhone apps delight users with an excellent user experience supported fully by the inherent capabilities of Apple’s iOS. Total cohesion between software and hardware amounts to the great performance of iPhone applications. To top it all, comprehensive customer support and maintenance results in improved satisfaction among users through an app’s lifecycle. Something that again adds to the advantages of preferring iOS app development.

7. Tech-Ready Audience

iPhone users are found to be tech-savvy and open-minded towards innovation. This presents businesses with numerous opportunities to craft challenging applications that can create disruption in markets. Businesses that opt for iPhone app development can become market leaders and stride ahead of competitors.

8. Low Fragmentation and Ease of Testing

Apple typically develops just one updation on its existing OS every year. Also, the number of Apple devices are lesser than Android-based ones. Hence, Android apps have to be tested comprehensively to work well on all the versions of Android OS. iPhone apps just have to meet testing criteria of the prevalent iOS versions. This considerably reduces testing time and guarantees a quick time to market for iOS apps.

Android market is seriously fragmented. Only 10.4% of Android users are using the latest OS version and the majority of users are still using three-year-old versions. This presents a grave problem to businesses. They have to spend in developing apps that are suited to all popular versions of the Android OS. Obviously, all this translates to a higher cost of app development.

Apple’s market is a consolidated one. Nearly 89.8% iPhone users are using iOS 12, the latest version of OS. This is a good chunk of a user base and practically viable for any business to reach. Something that triggers them to turn towards a reputed iOS app development company.

Besides, Low fragmentation in iOS also results in more scalable apps. And the newly introduced features keep on upscaling existing apps.

9. High Market Penetration

Apple has a huge presence in developed markets such as US and UK. For a business to spread its wings and penetrate these markets, iOS apps can be a winning proposition. Apple’s legacy and quality are well-established enough to guarantee good success for your iOS apps. Google’s legacy in app domain is zilch. Android apps have to struggle with millions of new apps released to the market. Even after investing heftly in top rated android app development companies, it’s highly uncertain if the app will make sufficient revenue to recover initial investment, let alone make any profit.

10. Less Development Time

When it comes to how long does it take to build an app, iOS apps take nearly 28% less time than Android apps of the same specifications. This is because Android apps have to be tested across at least 20 devices with varying resolutions, screen sizes, and OS versions. Naturally,

Android app’s development cycle is longer and payment for development apps depend on the location like charge of android app development in Florida might differ from that of Texas, California, and so forth.

iPhone apps guarantee market visibility, profitability, and customer loyalty. This added to the low iPhone app development services time, low production costs and affordable maintenance, makes the mobile app development process very beneficial for the businesses. Businesses can piggyback on Apple’s legacy to secure a prominent place in the competitive app market. What more can you ask for!

Frequently Asked Questions

1. What is the advantage of iPhone over Android?

When it comes to mobile app development, there are various benefits of choosing iPhone over Android. This included higher app revenue, lower development time, more loyal customers, upgraded security, and more.

2. Is Android or iOS better?

iOS is a better option over Android in terms of developing an application because:-

It has better presence in developed countries, like the USA and UK.
Apps developed on iOS platform are of high-quality, have more innovative features and security than what is found in Android apps.
The platform offers better revenue when compared to Android.
It needs less time and cost to develop an iPhone app than what is associated with Android application.

AI Applications in Documents

Posted on 13/02/202015/06/2021 by Tushar

“We are drowning in information, but starved for knowledge”

This is a famous quote by John Naisbitt which shows the key difference between information and knowledge. Advancement in data engineering techniques and cloud computing have made it easy to generate data from multiple sources but making sense of this data and getting insights is still a huge challenge. The data volumes have now increased exponentially and along with the traditional structured data, data can now reside in different formats like unstructured social media text, log files, audio/video files, streaming sensor data etc.

Applying manual methods to process this diverse data is not only time consuming and expensive but is also prone to errors. Hence the need of the hour is to use Artificial Intelligence (AI) based automated solutions that can deliver reliable insights and also give a competitive advantage to customers. Here are few examples of how customers across industries can benefit from AI driven solutions.

Microsoft Azure based AI solution

In 2017, more than 34,000 documents related to John F Kennedy’s assassination were released. The data volume was huge, and data existed in different formats like reference documents, scanned PDF files, hand written notes and images. It would take researchers months to read through this information and hence manually reviewing this data was not the most optimal solution. Microsoft Azure team applied AI based Cognitive Search solution to extract data from these diverse sources and gained insights. Technical architecture for this use case was built using Azure Cognitive Services components like Computer Vision, Face Detection, OCR, Handwriting Recognition, Search and core Azure components like Blob Storage, Azure ML, Azure Functions and Cosmos Database. This solution also annotated text using custom CIA Cryptonyms.

Hospitals usually deal with a lot of patient data which could reside in electronic medical records (EMR), handwritten prescriptions, diagnostic reports and scanned images. AI based Azure Cognitive Search could be an ideal solution to efficiently manage patient’s medical records and create personalized treatment plan. Many downstream use cases like Digital Consultations, Virtual Nurses and Precision Medication can be built once the patient data is optimally stored.

Google Cloud Platform (GCP) based AI solution

GCP introduced Document Understanding AI (beta) in Cloud Next 19. This is a serverless platform that can automate document processing workflows by processing data stored in different formats and building relationships between them. This solution uses GCP’s vision API, AutoML, machine learning based classification, OCR to process image data and custom knowledge graph to store and visualize the results. Customers can easily integrate this solution with downstream applications like chatbot, voice assistants and traditional BI to better understand their data.

Customers who deal with Contract Management data like Mortgages are usually faced with a lot of manual tasks to ensure that the contracts are complete and accurate. This could mean processing contracts in different formats/languages, reviewing the supporting documents, ensuring that the details are accurate and complies with regulatory standards across documents. By using Document Understanding AI and integrating it with a well-designed RPA framework, customers will be able to efficiently process Mortgage applications, Contracts, Invoices/Receipts, Claims, Underwriting and Credit Reports.

Use cases from other industries

Document Management AI solution can also be applied to diverse use cases from other industries like processing claims related to damages to shipped products by e-commerce companies, handling know your customer (KYC) process in the banking industry, invoice data processing by Finance teams, fraud detection during document processing etc.

As more and more companies embrace the digitization wave, they will be faced with different variations of data/document management challenges. Based on the current trend, number of use cases are only going to increase and an AI driven solution is probably the most efficient way to solve this problem as it can reduce manual work, save cost and deliver reliable insights. This will ensure that companies can spend more time on building their business and less time on manually processing documents and data preparation.

The New Customer Satisfaction Era

Posted on 12/02/202015/06/2021 by Tushar

Let us start with an oft repeated question,” What do you know about your customer’s preferences”?

The answer could be any of the standard responses which talk about their tastes in your merchandise based on past transactional records. It could be also one of the slightly more personalised answers which talk about the customer’s likes and dislikes basis whatever they have filled in their surveys and feedback forms. Does this tell you all you need to know about your customers? Does this help you make the customer experience of that customer something which he/she will remember? Something that gets ingrained into the sub-conscious decision-making component of their minds. That is the holy grail which most CX organisations are after.

Where does data come into the picture?

With 91 properties around the world, in a wide variety of locations, the Ritz-Carlton has a particularly strong need to ensure their best practices are spread companywide. If, for example, an employee from their Stockholm hotel comes up with a more effective way to manage front desk staffing for busiest check-in times, it only makes sense to consider that approach when the same challenge comes up at a hotel in Tokyo. This is where the hotel group’s innovation database comes in. The Ritz-Carlton’s employees must use this system to share tried and tested ideas that improve customer experience. Properties can submit ideas and implement suggestions from other locations facing similar challenges. The database currently includes over 1,000 innovative practices, each of them tested on a property before contributing to the system. Ritz-Carlton is widely considered to be a global leader in CX practises and companies like Apple have designed their CX philosophy after studying how Ritz Carlton operate.

What does this tell you- Use your Data wisely!

The next question that may pop up is, “but there is so much data. It is like noise”. This is where programmatic approaches to analysing data pop up. Analytics and data sciences firms across the globe have refined the art of deriving insights out of seemingly unconnected data to a nicety. What you can get out of this is in addition to analysing customer footprint in your business place, you get to analyse the customer footprint across various other channels and social media platforms.

This aims to profile the customers who are most susceptible to local deals/rewards/coupons basis their buying patterns.

How is this done? The answer is rather simple. Customer segmentation algorithms (both supervised and unsupervised) enable you to piece together random pieces of information about the customer and analyse the effect they have on a target event. You will be surprised at the insights that get thrown out of this exercise. Obviously caution needs to be exercised to ensure that the marketeer doesn’t get carried away by random events which are purely driven by chance.

Okay- so I have made some sense out of my data. But this is a rather cumbersome process which does not make any difference to the way I deal with my customer on a day-to-day basis.

“How do I get this information on a real-time basis so that I can actually make some decisions to improve my customer’s experience as and when it is applicable?”

This takes into the newest and most relevant trend into making data sciences a mainstream part of decision making. How do we integrate this insight deriving platform into the client’s CRM system so that the client can make efficient decisions on a real time basis?

In Anteelo, for one of our leading technology clients, we have built an AI-based orchestration platform which derives the actionable insights from past customer data and integrates this into the customer’s CRM system so this becomes readily available to all marketeers as and when they attempt to send out a communication to their customers.

What does this entail? This entails using the right technology stack to build a system which can delver insights from the data science modules at scale. I prefer calling it out as a synergy of both data sciences and software development. Every decision that a marketeer is trying to make must be processed through a system which will invoke the DS algorithms in-built on a real time through the relevant cloud computing platforms. Insights will be delivered immediately, and suitable recommendations will also be made on a real-time basis.

This is the final step in ensuring that personalised recommendations being made to every customer are truly personalised. We in Anteelo call it “The Last Mile adoption”. This development is still in its nascent phase. However, companies would be wise to integrate this methodology as a part of their data science integrated decision making since it is very unlikely that they will hit the holy grail of customer satisfaction without delivering real-time personalised recommendations.

What Does Product Management Look Like in Data Science?

Posted on 11/02/202015/06/2021 by Tushar

The topic related to ‘Product Management’ has received several laurels in recent years. Several rounds of discussions have happened to create an analogy out of client’s stand point. As I heard more of these conversations, there was an uncomfortable ambiguity stemming from disbelief – is this another fad or is there meaning to it? Well, the initial rumblings were from the cool kids in the bay. But, why did grounded Midwest and shoot-from-the-hip south latch on? Must be something deeper, right?!

Product management has been around forever in the software, e-commerce world. But, today, mainstream IT and AI teams in fortune 500 companies are thinking of a product paradigm shift. Leading consulting firms are also developing products or beefing up their technology as an eventuality.

But, the question that begs attention here is – why products? What happened to software as a service, platform as a service, ML as a service? Do we need another paradigm shift? Or as the saying goes – Old wine in a new bottle?

IT teams are today being led by progressive Chief Digital Officers, Chief Data officers. Conventionally, CIOs have been leveraging their value by app dev teams, BI teams, infrastructure teams et al. While this may have become a table stake, it has been around for a while already. The question is – ‘How to deliver incremental value to business?’

So, what has changed?

Demand:

IT is today called upon to be a true business partner. And, given the rate at which business is facing change, the time to deliver value is compressed.

Glocal innovation:

For a fortune 500 firm operating globally, innovation is striking at its core from multiple directions. While the USA is still the biggest revenue (EBITDA generation engine), problem and solution innovation is happening in other markets faster than the USA. For starters, they have less legacy to deal with. The local markets are facing competition from nimbler players. VC money is flowing into firms in China, Israel, Korea, India which are encountering newer problems in e-commerce, voice commerce sectors. Other traditional revenue generating markets, individually facing slower growth, find it difficult to make a business case to invest in solutions led by such innovations.

Problem repeatability:

This is going to sound rhetorical. But, I must state it because it is relevant. Business problems in today’s enterprise are constantly changing. Few of them get recreated, and hence are not available in large volumes. Few others are becoming common across markets and thus moving into a constant state of being a tightly defined problem that can be applied globally. Repeatable.

A good indicator to this is AWS recent product launches – out of the box image, text, voice, reinforcement learning, forecasting. Common problems which are finding repeatable solutions.

The AI candy shop:

Today, nobody wants to use process automation tools that are not embedded in intelligence. Passé, inefficient. Wallstreet, investors and boards are lapping up the buzzwords – cognitive, AI, embedded ML.

Cloud enabling global scalability:

Cloud platforms such as Azure, AWS have ensured that once you have these AI capabilities developed, they can be deployed globally. The global-local adaptation is a key design criterion in this context.

Glocal solution adaptation…er,… maybe Glocal problem adaptation:

Each market has its secret sauce in terms of the market structure, drivers and customer nuances. Thus, before adapting a solution from one market to the other, it is essential to adapt the problem as well. For example, it is an interesting pursuit to adapt the problem structure from the modern trade Australia market to half way across the world in Brazil.

And, then adapt the solution.

So, who’s game is it anyway?

Given the above guard rails, it is quite evident that the business case should be developed by a country specific P&L or ROI measure. It must be a global mandate. IT is one of the few functions which is ideally poised to ride this wave. That, they own the data systems is coincidental. Or, well.. was that the whole plan! Go, Trojan..

Finally, after rambling about half the things in the world – we come to the initial topic of this article. Products. Why?

A product has a life – it evolves constantly. The focus is on continually making the best product for its end user, ever possible. It has a roadmap. In a world of multiple users, it needs a strong owner who plans and decides well. It has a clear value proposition in each update/release. It can be developed in a sprint like manner. It can be defined with a bounded scope and sold internally in enterprises, with greater ease. And, be defined, abstracted, customized for a global roll out.

Looks like a duck, walks like a duck, sounds like a… must be a duck. Yes, I guess it does look like a product.

But, how do we help organize people and teams to get the products rolled out?

While the below roles are common to a product-oriented firm, the thought process is different from conventional IT projects. Sharing of resources across projects being the biggest drawback. The smartest of each of the below folks will perhaps still fail, without an organizing framework. The roles to work in a closely integrated manner, dedicated to making a single product management successful.

Product Designer:

The role of a product designer is someone who can completely immerse himself in the shoes of the end user, without worrying about the AI or Tech related issues that may occur sometimes. Just solve the end user’s real problem and keep tracking the end user’s behaviour as the product usage evolves. In product management, there is a contradictory school of thought which mandates that the designer must appreciate “how” a product works. This, however, might dilute the designer’s objective of empathizing with the end user.

Product owner:

A functional expert of impact analysis who can connect the dots and identify the nuances of each problem. A great problem solver, with functional expertise, has the knack to see through the commonalities, and the uncommon aspects too. Prioritization between the must-haves, nice-to-haves and must-not-haves is a key skill required in the role.

Product BAs

Products are quite massive in terms of their scope today. Primarily, each product usually is broken down into sub products which are owned by individual product Bas.

The AI solution developer(s)

Usually, it is very difficult to get a product owner who really gets AI solution development. By and large, individual intelligence is anyways overrated. It is important to have a dedicated AI solutioning team which can translate the problem into a modular AI solution.

The AI deployment team

It is not enough to develop a modular AI solution. To be able to deploy it in globally scalable platforms requires seasoned IT big data engineering & testing capabilities. The plumbing and wiring required to take the AI concept to enterprise last mile reality is no mean task. It is a specialized function. Truly speaking, they give the product its real-life form.

Scrum & Program Managers

Last but not the least, you need the scrum team and program managers. Everyone benefits from their discipline and order amidst the chaos.