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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.

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.

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.

The growing popularity of low-code/no-code application development platforms

Posted on 09/02/202014/06/2021 by Tushar

“Software is eating the world.” That was the bold proclamation renowned innovator and venture capitalist Marc Andreessen expressed in an article he wrote for the Wall Street Journal. More and more businesses are being run by software, he argued, or are differentiating themselves and disrupting their competitors and industries using the same. Today, that article is regarded as one of the seminal works in shaping how people think about digital transformation—or using digital technologies to bring about great change in the way individuals and organizations think, operate, communicate or collaborate.

Often, at the heart of many digital transformation efforts is the desire to enable the organization to be more agile or responsive to change. This requires looking for ways to dramatically reduce the time needed to develop and deploy software, and simplify and optimize the processes around the maintenance of software so it can be deployed quickly and with greater efficiency.

Another key outcome that is part of many digital transformation efforts is enabling the organization to be more innovative — finding ways to transform how the organization operates and realize dramatic improvements in efficiency or effectiveness; or creating new value by either delivering new products and services or creating new business models.

For organizations using conventional approaches to developing software, this can be a tall order. Developing new applications can take too long or require very specialized and expensive skills that are in short supply or hard to retain. Maintaining existing programs can be daunting as well, as they struggle with increasing complexity and the weight of mounting technical debt.

Enter “low-code” or “no-code” application development platforms. This emerging category of software provides organizations with an easier to understand — often visual — declarative style of software deployment, augmented by a simpler maintenance and deployment model.

Essentially these tools allow developers, or even non-developers, to build applications quickly, easily, and rapidly on an on-going basis. Unlike Rapid Application Development (RAD) tools of the past, they are often offered-as-a-service and accessed via the cloud, with ready integrations to various data sources and other applications (often via RESTful APIs) available out of the box. They also come with integrated tools for application lifecycle management, such as versioning, testing, and deployment.

With these new platforms, organizations can realize three things:

1. Faster time to value

The more intuitive nature of these platforms allows organizations to quickly get started and create functional prototypes without having to code from scratch. Pre-built and reusable templates of common application patterns are often provided, allowing developers to create new applications in hours or days, rather than weeks or months. When coupled with agile development approaches, these platforms allow developers to move though the process of ideating , prototyping, testing, releasing and refining more quickly than they would otherwise do with conventional application development approaches.

2. Greater efficiency at scale

Low-code/no-code application development platforms allow developers to focus on building the unique or differentiating functionality of their applications and not worry about basic underlying services/functionality such as authentication, user management, data retrieval and manipulation, integration, reporting, device-specific optimization, and others.

These platforms also provide tools for developers to easily manage the user interface, data model, business rules and definitions, making on-going management easy and straightforward. So easy in fact that even less experienced developers can do it themselves, lessening the need for costly or hard-to-find expert developers. These tools also insulate the need for the developer and operations folks to keep updating the frameworks, infrastructure and other underlying technology behind the application, as the platform provider manages these themselves.

3. Innovative Thinking

Software development is a highly creative and iterative process. Using low-code or no-code development platforms, in combination with user-centric approaches such as design thinking, organizations can rapidly bring an idea to pilot in order to get early user feedback or market validation without spending too much time and effort (so-called “Minimum Viable product” as coined by Eric Ries in his book “The Lean Startup”).

Not only that, because these platforms make it easy to get started, even non-professional developers or “citizen developers,” who more likely than not have a deeper or more intimate understanding of the business and end user or customer needs, can develop the MVP themselves. This allows the organization to translate ideas to action much faster and innovate on a wider scale.

While offering a lot of benefits, low-code/no-code application development platforms are certainly not a wholesale replacement to conventional application development methods (at least not yet). There are still situations where full control of the technology stack can benefit the organization—especially if it’s the anchor or foundation of the business, the source of differentiation, or source of competitive advantage. However, in most cases organizations will benefit from having these types of platforms as part of their toolbox, especially as they embark on any digital transformation journey.

Designing systems for machines rather than people: Latest Tech Trend.

Posted on 08/02/202014/06/2021 by Tushar

For businesses to be agile and respond quickly to changing market conditions, they need to provide business users with real-time and near-time operational data. That means harnessing data from devices and tackling the latency challenge. In 2020, we will see more organisations shift their design thinking from services and systems for people to services and systems for machines. The move to machine-to-machine (M2M) systems also means processing is moving to the network edge, where the data is.

Action at the edge

Organisations are experimenting with extending data clusters to the edge to reduce latency, gain operational efficiencies and improve products and services. Fast food company Chick-fil-A is running Kubernetes on 6,000 kitchen devices in all 2,000 of its restaurants. This is part of the chain’s internet of things (IoT) strategy to collect and analyze more data to improve throughput, operational efficiency and, most of all, customer service.

The move to M2M dovetails with the notion of catering to local markets using local data. This will foster new design architectures that take into account privacy, security and regulatory concerns regarding the “frame of reference” of data — i.e., the notion of localised data being more valuable than global data in terms of its usability and mining. When organisations consider modernizing their IT operations and changing the way microservices are deployed — especially large multinationals with a wide global reach — it becomes critical for them to consider the advantages of targeting a geography locally, and realizing that there are new tools and ways of doing this.

Another facet driving the change in design thinking is the need to make maximum use of computing resources. Whereas the decision frequency of a person ranges between 1-15 hertz, which means that people can decipher information and make a decision in about ½-1 second, today’s microprocessors can operate at gigahertz and process information in nanoseconds. If these processors are not operating as fast as they can, they are just space eaters. Organisations want to keep their processors as busy as possible, which means designing for billions of decisions or operations per second. Otherwise, they may end up paying for unused capacity.

Signs of M2M

Two examples of M2M architecture are SAP Leonardo and KubeEdge. Through SAP Leonardo intelligent technologies and capabilities, SAP is integrating its ERP applications with IoT platforms, combining traditional IT services with M2M capabilities. Importantly, SAP can address broader markets than niche IoT platforms.

KubeEdge, built on Kubernetes, is an open source platform for building edge computing solutions that extend to the cloud. The platform supports network, application deployment and metadata synchronization between the cloud and edge. It extends the Kubernetes ecosystem from cloud to edge and provides benefits such as lower latency, low resource consumption and applications at the edge that can run in offline mode.

The road ahead

We are starting to observe a shift in IT design from IT for humans to IT for machines. These design patterns deliver richer experiences because they enable substantially more processing in the same experience time. Design shifts will lead to changes in batch processing and stream processing architectures, which are constantly being updated and reimagined with better M2M capabilities. Data and analytics will continue moving to the edge where the machines are, to analyze the massive influx of IoT data and provide maximum throughput with minimum latency. Rapid deployments of these transformational architectures may not be immediate, but over time these new architectures will be a forcing function for IT modernization.

Teams, not individuals, are the greatest achievers in Technology.

Posted on 07/02/202014/06/2021 by Tushar

Developing high-performing teams will be the focus of many enterprises in 2020. Companies will confront the fallacy that pace is what unlocks the company’s full potential and recognize that how the company organizes its people and information flow is what determines performance.

Organizing for a dynamic and complex environment requires a much different structure than the traditional command-and-control pyramid. Talent acquisition and development strategies must be built on a team-of-teams approach consisting of multidimensional individuals, rather than traditional siloed teams consisting of single subject matter experts. Put differently, the focus shifts from developing so-called 10x individuals to developing 20x teams.

Lessons from the battlefield

The transformation of the battlefield and the U.S. military’s adaptation offers an excellent case study, specifically the experience in Ramadi, Iraq. The unpredictability and dynamic conditions of the battlefield are similar to the current business environment: combating insurgents who utilize asymmetrical tactics and are well equipped with the latest technology, while having to maintain and grow the customer base (citizenry).

The “clear, hold and build” strategy that worked in Ramadi revealed that capabilities such as raids had been elevated to the level of strategy. Modern business has done something similar by elevating the increase in pace, agile and DevOps capabilities to strategy. However, merely holding meetings more frequently and in a different manner falls short of desired results. What companies need, as the military learned, is more flexible joint power from multidimensional teams that provide multiple options in the face of volatility, rather than the limited options of a traditional pyramid of teams.

As retired U.S. Army General Stanley McChrystal explains in Team of Teams, the military in Iraq needed to be not only efficient but also adaptable. It became a priority to focus on reconfiguring to be able to deal with volatility quickly. Simply deploying more resources and putting more people to work, to become more efficient in the current operating model, was not enough. As McChrystal points out, the traditional pyramid constrains team productivity due to choke points, ineffective communication channels, stifled creativity and inadequate response time.

So the military created linkages between teams, put people from different service branches and agencies on the same team, and shared information widely so everyone understood the larger mission and could make decisions accordingly. McChrystal understood that “technology had changed in such a way that management had become a limfac [limiting factor].”

The same is true in today’s business environment, where an explosion of technological progress — improved capabilities to track, measure and predict via big data and advanced analytics; moonshot projects; and unconventional business models — has created a more interdependent, fast-paced and complex business environment. In this environment, companies need interconnected multidimensional teams that can adapt and scale.

The new teams

The new interconnected teams have diverse skills that capitalize on the team’s collective intelligence and increase a team’s productivity. The shared sense of purpose and mentoring that occurs within the team not only strengthens the relationships and performance but also mitigates knowledge gaps between senior and junior employees.

Populate these teams with double-deep personnel and now you have the ability to scale across the company, purposefully cross training your people to fill gaps. This creates a natural talent pipeline. Rotating people through different teams means employees advance through project-based promotions and team assignments rather than the traditional subjective requirements.

In order to be effective and maximize the potential of these multifaceted teams, they need to be empowered to make decisions. This does not mean they operate totally on their own, but they “have implicit trust that their senior leaders will back their decisions,” as Navy SEALs Jocko Willink and Leif Babin point out in Extreme Ownership. With proper decentralized command, teams that are closest to the situation can execute in a manner that supports the overarching goal without having to ask for permission.

In 2020 leaders will determine how to, in the words of McChrystal, “scale the fluidity of teams across entire organizations” amidst the chaos of ongoing business transformation. Ultimately, building better teams will build better individuals and enterprises. The ability to develop and lead a network of high-performing teams will be key to business success in the post-digital age.

Workday Payroll dashboards: How to Get the Most Out of Them

Posted on 06/02/202014/06/2021 by Tushar

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The future of work is bringing new challenges for Human Capital Management (HCM) practitioners, especially as the skills shortage becomes a day-to-day reality. In the near future, the people responsible for people in your organization — from CHROs to payroll specialists, will need more time to strategize, analyze, and innovate. With truly innovative HCM technology, not only will employees have more time, employee productivity and engagement will increase.

Workday Payroll is designed to help employees increase efficiencies, eliminate errors, and anticipate changes, particularly around compliance or tax related items—a competitive advantage in a fast-changing regulatory environment. Plus, Workday Payroll not only saves employees time, but perhaps most critically, reduces questions to the payroll department and increases insight around the complex process of compensation.

Workday Payroll best practices

Our Workday experts at Anteelo recommend starting with the following simple steps to drive user engagement and adoption to get the most out of Workday Payroll dashboards:

Share—and share often.

For employees involved in the day-to-day work of payroll (or any HCM work), new technology requires that management does their due diligence in communicating the change. But we’re all bombarded with hundreds of messages every day; so this requires creating messaging that has meaning:

Empathize with the difficulties of change (even positive change is stressful!).
Make it personal: Share how Workday Payroll dashboards will benefit both employees and administrators in day-to-day work. For example, teach employees how to easily compare pay periods and make updates, such as tax elections, from one place.
Show payroll partners and administrators how the dashboard gives them instant visibility into the status of each payroll, retro differences to be paid, and employees affected by regulatory changes (such as tax rates).

Follow-up with non-users.

It’s simple: Ask employees who aren’t using Workday Payroll dashboards why they aren’t. Engage employees by providing more training, enabling one-on-ones with power users, giving employees extra time to learn about the dashboards, and/or scheduling frequent check-ins to answer questions and provide encouragement.

Invite existing users to share why they love Workday Payroll dashboards.

Ask employees who are using Workday Payroll dashboards what they love about it. And then ask them to share this with their peers. Happy users are the best advocates for new technology. Product evangelists—your employees who love payroll dashboards—can greatly influence others to get on board.

Make sure updates go smoothly.

Build a repeatable test plan for updates and always communicate any upcoming changes to Workday Payroll dashboards far in advance. If you need help, our experts can help you develop a solid testing plan that is both repeatable and not overly taxing on resources.

Plan ahead—and communicate frequently—about big events, such as audits.

One of the advantages of Workday Payroll dashboards is that they enable users to start the audit process early. Help employees understand that auditing early and often, using the tools in payroll dashboards, can make the end of the year far less stressful—and even reduce the need for overtime around the holidays.

Creating a positive, engaging user experience is the first step to getting the most out of Workday Payroll.

It’s imperative that employees doing the day-to-day payroll work need to know how Workday Payroll dashboards help them personally do their jobs better. Organizations can drive greater adoption and engagement by actively sharing the employee-centric benefits of payroll dashboards, encouraging existing users to advocate for the technology, and making it easier for non-users to become engaged users.

Business continuity with a remote workforce-Checklist

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

Whether dealing with the current coronavirus (COVID-19) crisis or anticipating a future natural disaster, organizations need to prepare for emergencies that require employees to suddenly switch from a corporate environment to a home office. Even once the crisis arrives, organizations might have to drastically adjust their plans to address the scale required. Continuity plans must encompass not only the technology required to keep the business up and running but also the human aspect — making sure employees are well-trained and prepared to work remotely. As the size of the remote workforce suddenly increases, here is a checklist for IT leaders to consider when tackling the technology piece of the puzzle:

Network capacity planning: Conduct a capacity analysis to determine whether internet bandwidth is sufficient to handle the increased WAN traffic that occurs when large numbers of employees access the network. Additional items on the capacity planning checklist include firewalls, VPNs, and other remote access-related technologies that might be overwhelmed by the increased volume of traffic coming from outside of corporate headquarters.

Security monitoring: Intensify activities designed to detect and prevent attacks. Hackers are likely to take this opportunity to increase malicious activities. “Secure the Human” training can be delivered remotely to ensure that employees remember the organization’s security practices.

Identity and access management: Beef up identity and access management for remote workers through methods such as multifactor authentication.

Data protection: Ensure that security is extended and corporate data is encrypted to prevent the unintentional or malicious exposure of sensitive data.

VPNs: Make sure that VPN agents are installed on every device that connects to the corporate network to provide secure remote access.
Devices: Prepare for a shortage of devices to support the growing number of remote workers. Have a contingency purchase plan as well as a template for quickly configuring the device and loading the appropriate software.
Bring your own device (BYOD): Consider BYOD as supply chains are strained and the ability to get the needed hardware to employees becomes increasingly difficult. Using a cloud-based portal, employees can self-register their devices and download VPNs and other security tools, with the ability to segregate corporate data from personal information.

Virtual desktop infrastructure (VDI): Consider deploying a thin client architecture for remote workers as another option. However, upfront planning is required to make sure the thin clients are available and that the back-end server infrastructure is in place to support the thin client model.

Collaboration: Provide strong communication and collaboration capabilities to help keep employees productive and engaged. Organizations may want to look at videoconferencing tools to compensate for the lack of face time to help employees feel more connected. Collaboration platforms such as Microsoft Office 365 with Teams activated and G Suite, as well as videoconferencing services such as Zoom, are providing new ways of working, connecting and collaborating. Training employees on how to conduct work in these virtual spaces is critical.

Omnichannel support: Be prepared for a significant volume increase for IT support. Some employees may have been abruptly moved to a remote access environment, while others may now find themselves with new devices, software or tools they have never used before. And as a remote working model typically provides increases in flexibility, spikes in support needs may not follow the traditional patterns of regular business hours. The IT department needs to be prepared to offer omnichannel and remote support, including video options for face-to-face communications. Of course, service desk employees may themselves be working remotely, so pay special attention to make sure they have the capacity and the tools to respond to service desk issues from their home offices. Proactive and predictive analytics tools, combined with an easy-to-use support portal, can drastically reduce the calls to a strained service desk for rudimentary problems.

As we all know, even the best-laid plans don’t always work perfectly. In the midst of a global crisis, organizations might find that their business continuity plans haven’t accounted for an unprecedented level of scale, urgency, capacity and employee needs. In these instances, organizations should stay in close contact with their preferred vendors. There is no need to go it alone when expert help and support are available.

Unexpected challenges can cause maximum disruption to people’s lives and to business. The way in which we mitigate risks to ease the strain on businesses and their employees will ensure an organization’s ability to continue into the future.