Tuesday 28 January 2020

Building an app for Business (1/5) – Starting right with Ideation

Building a successful Mobile App for Business

Welcome to another series on PsiberTech Solutions blog. In this series we will be discussing building an app for business. Be it retail, sales or inhouse app that’s going to increase your workforce’s efficiency these pointers will help navigating the mobile app journey.

The mobile app has taken over all the online interactions in the last 5 years. Customers want efficient and easy to use interface directly from their handheld devices instead of going to a desktop to complete a task or transaction. Even websites are made responsive so they can address the needs of mobile users. Including mobile in your service offering is definitely a good idea.

Every mobile app is a creation of idea and the challenge is to get that right. Before we talk about Ideation lets have an outline of what a mobile app creation process looks like.

  1. Discovery
  2. UX Design
  3. UI Design
  4. Development Handoff
  5. Technical Design Front End
  6. Technical Design Back End
  7. Integration and Testing
  8. Deployment
  9. Monitoring
  10. Improvement

The intention of the series is to not cover all these elements in detail ( I am sure there are enough materials out there that already speaks volume of these steps) , but to create useful pointers and identifying pitfall of Mobile App development. Now let’s talk about Ideation which is at the discovery step.


Ideation is a process of generating ideas. When you are creating the mobile app for your business, it could be one for your customers or for your internal use. Some examples of mobile app idea could be:

1. You that you already have a problem to solve

2. New service to provide to your customer/employees

3. Automating or improving the existing service to your customers/employees

4. Improving efficiency of your workforce

In essence, the idea can be a solution that you provide for your end users. Hence, there are no good or bad solutions, there are just solutions. However, we need to validate whether this is THE solution that solves THE problem.

In development terminology, this is also referred as “Brainstorming”. Brainstorming can be done with your end user groups with or without tech company involved. However, including your tech partner can be a good idea for this phase. You can use either NUF (New/Useful/Feasible) test approach or design thinking approach to evaluate your ideas

Here is a checklist that will help you to evaluate your App idea when working with tech partner

1. Understand business goal: List out the ways the app is addresses the business goal set out by the organization.

2. Understand the technology: The tech partner should help you with all the technologies ( not just the code) considered in the app.

3. Understand the cost: Understand all the cost involved in developing and maintaining the app. Your Tech partner should be able to help you with this as well. The clearer the requirement the close the accurate the cost of the app.

4. Understand the rewards: : Quantify using measurable such as engagement, improve efficiency 30% etc.)?

5. Understand the risks: What are the best- and worst-case scenarios of the app idea? What are the potential obstacles or situations?

Building an app for your business is a big decision, why not take time to evaluate it well before proceeding to the next stage? If you are unsure of the process talk to tech partners like PsiberTech solution who can guide you through every stage of your app development process.

Srivatsan Aravamudan - Sri
Senior Solution Consultant

Tuesday 21 January 2020

Regulating Artificial Intelligence

Regulating AI

“Companies such as ours cannot just build promising new technology and let market forces decide how it will be used,” writes Pichai. “It is equally incumbent on us to make sure that technology is harnessed for good and available to everyone.” The Alphabet CEO, who is heading perhaps the most prominent company in Artificial Intelligence has called for new regulations of AI. There is just one problem, how?

A quick flashback to our past article on Human-centric AI, we have discussed how the Singapore government is taking steps to regulate AI. It comes in two parts such as

Decisions made by or with the assistance of AI are explainable, transparent and fair to consumers

Ensure artificial intelligence solutions they deploy are human-centric

There are also concerns over facial recognition and deep fakes. The regulations in the EU and the US for AI regulations seem to be diverging. While the EU is considering the 5-year ban on facial recognition, the White House is advocating for Light-touch regulations that avoid overreach.

There are two factors in general that challenges the regulation of AI. Reach and speed of its advancement.


AI’s reach has exploded into different verticals. The possibility of AI driving our cars, flying drones, diagnosing patients, predicting weather, improving factories efficiency literally managing everything from home to how we conduct war. Here is the problem, there is no industrial model run regulation that governs all of it. While AI is being all persuasive the public sector that needs to regulate such technology isn’t.

Speed of advancement

Any advancement takes some time to spread giving enough buffer for the organizations involved in advancement can come to terms with it and regulate it as well. AI’s growth is exponential, and we are now confronted with dealing with its consequences rather than preparing for its regulations.

Innovators such as Alphabet, Amazon are making the rules about AI and sooner are later we will need to find grounds for regulating the AI. While AI poses a lot of panic over its use, we also need to take a step back here. Every technological revolution also bought in such fears in the past. So, is it wise to focus on technology’s effect rather than focusing on technology-based fears? Let's keep this conversation going, write to me or comment here about your thoughts on regulating AI.

Srivatsan Aravamudan - Sri
Senior Solution Consultant

What Netflix’s Circle have taught me about Networking

Let me start with a confession first, I allow myself to Netflix at times just to get away from the never-ending task list. As my psychology professor, Jerry would put it, sometimes you got to love the space between productivity.

If you don’t know what “Circle” is about, let me try and explain it without making it sound boring. It’s a reality show from Netflix where 8 contestants live in an apartment building in different units. They can never see or hear each other. They can only communicate via a social media app called “Circle”. Here is the twist, the contestants can either be themselves or can pretend to be someone else in the Circle. Everyone rates others based on their social media communications. The most liked contestant or the influencer gets to vote on who gets to go home from the competition. The winner gets 100,000$ at the end.

It's surprisingly riveting! Its a reality check and a true reflection on our influencers and social media. Aside from that, it really struck a deeper chord in me about our communication over social media platforms. In Circle, the top 3 contestants rose to the top, just by being themselves period. They kept the communications, honest, easy and worked on making real connections / allies.  “Duh! That’s the basics”, I can almost hear you say that, however, trust me it really takes a lot of courage and determination to remain real in the conversations we do via social media channels.

I am putting this to test in my communications, both in person and in social media channels.  I am already feeling the difference. Those awkward conversations on the networking events have turned into liberating ones, as I am not trying to be somebody I am not. The after conversation via social media channels are interesting and engaging. Most importantly I am making a real connection to the person on the other end of the line. So let's get back to the basics, the technologies/channels can change but we are ALWAYS working with people.  

Srivatsan Aravamudan - Sri
Senior Solution Consultant

Tuesday 14 January 2020

Business Intelligence for Business (5/5): BI vs Business Analytics

Business Intelligence vs Business Analytics
Business Intelligence vs Business Analytics
Welcome to the last in the series of Business Intelligence for Business. If you have not caught up on the rest of the series, here are the links.

Business Intelligence For Business (4/5): Trends In BI For 2020

For the last installment on this series, I want to bring your attention to the most basic question, how does BI fit in the era that’s run by Data and Analytics. For any business to thrive, some of the most important questions for the management is, what is working, what is not and how to improve it. Due to the vast amount of data available from the systems, this becomes the tricky part of the data-driven era. 

BI and BA both provide methods and tools for handling data and help in making sense of it. So how do they differ from each other?

To understand the difference, we must define what BI first. BI uses a structured form of data such as the ones from ERP or financial systems. BI Tools like Power BI has grown more intuitive and user-friendly over the years. There are also customized BI tools such as Voyager BI which works well with custom-developed systems. Analytics goes beyond this and offers additional features such as prescriptive analytics and predictive analytics. Analytics is the process of exploring data and reports in order to extract meaningful insights, which can be used to better understand and improve business performance. Analytics takes what of the data and provides answers to why and how.

BI is the gateway to Analytics

Depending on the kind of user you ask, Data analyst or otherwise people categories BI either as a subset of Analytics or vice versa. To me, BI is the gateway to Analytics and enables more to the business requirements. There are few tangible differences we have with BI and Analytics. BI is often used to run the existing business operations whereas Analytics is used to build on/ change the existing business operations and improve productivity. Analytics uses data repositories, data lake, modern data warehouse, and BI uses only a subset of these.

In summary Business Intelligence and Analytics differs by,

BI uses past and current data whereas Analytics uses past data to extract insights and run the business operations that drive the customer needs and increase productivity.

BI mostly concentrates on reporting the analyzed data whereas Analytics concentrates on multiple tools that perform different operational applications using different tools.

Business Intelligence is the way of analyzing the existing data whereas Business Analytics will have Business Intelligence reports acts as inputs for the analytics to process the extracted information in a more sophisticated way to visualize the analyzed data.

Both approaches are valuable, just in different ways. It’s important to know whether you are more in need of descriptive analysis, predictive analysis, or both before you invest in a platform.

For example, it’s great to have a way to generate predictions about future growth, but if you can’t drill down into the underlying data to understand the basis for these predictions or tweak your dashboards to give you exactly the insights you need, you may be limited in your business planning.

It is vital to focus on what you need the system to do, and who will use it. How detailed your insights need to be? How tech-savvy are the people who would be using the system? How much control and visibility do you need? Are you more interested in understanding how you got here (present) or getting an idea of where you’ll go next(future)? Not sure about any of these? Have a chat with us and figure out which would be an ideal way to go about BI for your business.

Srivatsan Aravamudan - Sri
Senior Solution Consultant

Thursday 9 January 2020

IIoT is disrupting manufacturing industry, for good

IIoT is disrupting manufacturing industry, for good
Custom Dashboard with data collected from IoT

2020 is a year of smart things. We have a critical mass of smart devices from home to industrial use. Wireless sensors are mapping the physical world to the virtual network of interconnected devices that send and receive information for actionable outcomes. I have been to the OPG showcase in Toronto and I witnessed firsthand some of the amazing sensors that are used in the Industrial Internet of Things (IIoT).

The IIoT can transform traditional, linear manufacturing supply chains into dynamic, interconnected systems also known as the digital supply network (DSN). By enabling DSN IIoT can have impact on the way the products are manufactured, such as efficiency, safety and cost reduction.

According to a recent estimation by McKinsey, the potential economic impact of IoT applications in 2025 is between US$ 3.9 and $11.1 trillion, of which $1.2 to $3.7 trillion is allotted to IoT applications within the factory environment. Also known as smart manufacturing, these are fully networked manufacturing ecosystems driven by the IoT.

Here are some ways IIoT is disrupting manufacturing industry

Predictive Maintenance

If you are new to predictive maintenance here is a great article on it by Rootquotient. The idea of predictive maintenance systems is to build accurate probability predictions on the data, rather than simply reporting it. The predictive system could prevent downtime and reduce the cost of buying a backup component without a need for it. The predictive system could predict when a component was going to fail and place an order for the replacement part so that it arrives in time for the maintenance crew to replace the component during a scheduled maintenance, virtually eliminating downtime.

Decentralized management

Using IIoT technologies the management has gone from local to global. Using IIoT we can connect the devices on a large scale without the need for human intervention. A decentralized IoT network often has an organizational structure, meaning that nodes are clustered in smaller networks with so-called super-nodes which are the central processing point for sub-networks and facilitate the communication between constrained devices.

Improved internal collaboration

Often departments in manufacturing companies operate in silos, stifling collaboration and data access. However, using IIoT Technology organizations are maximizing the collaboration and efficiency of their control center. Data from each silo can be analyzed to create processes improving efficiency not just within the departments but also organization overall.

Manufacturing organizations often have goals going into a financial year, to reduce downtime, increase efficiency, etc. Having a custom build dashboard that collects, consolidates and makes sense of the data collected from IoT would be an ideal. PsiberTech works with manufacturing giants like 3M to optimize processes and improve efficiency. Contact us to know how you can benefit from IIoT from PsiberTech Solutions.

Srivatsan Aravamudan - Sri
Senior Solution Consultant

Tuesday 7 January 2020

Rethinking Custom Software Development in AI era

Custom software development serves the niche area where traditional off the shelf products can’t satisfy the needs of the customers. How does this fare in the AI era? when customized solutions of Machine learning in the AI domain are taking over every field from Healthcare to Energy efficiency.

Remember the game Pokémon go? The popularity of the game was high and the use of AI technology in the game is what made it stood out in the crowd. The Wall Street Journal estimates that AI-enabled tools are projected to pull $2.9 billion in business revenue by 2021.

Traditionally, developing a custom software program requires the developer to specify in advance exactly the system need to do. Encoding many tasks in an explicit way is possible, as computers before the advent of AI were still quite powerful. However, there was a bit of a problem in this approach, often the programmers couldn’t specify all the possible outcomes or paths the computer must take to provide the desired result. Sometimes the programs are upgraded in regular intervals to ensure it covers all the newly identified scenarios that were not been thought of in the first place.

Even an activity as seemingly simple as identifying whether a photo or video on the internet is of a dog is beyond the reach of traditional software development. Given the vast possible permutations that dog photos can take, no team of engineers can possibly enumerate all the rules that would reliably recognize dog vs. all the other possible objects that can appear in media.

With AI the engineer does not code all the rules on how to make decisions and take actions. Instead, domain-specific data is curated and fed into the learning algorithm which is iteratively and training for continuous improvement. The Machine learning model can deduce the patterns and features from the data and understand them without the engineer explicitly coding this knowledge.

Over time, these systems have become incredibly complex, requiring multiple dependencies and integrations as well as layers upon layers of functionality and interfaces. All these components must be manually managed and updated by humans, leading to inconsistencies and unresolvable bugs. Software 2.0 is code written in the form of “neural network weights” not by humans but by machine learning methods.

However, the code written by AI is just a fraction and software development still can benefit a lot from AI. AI can help in Rapid prototyping, Intelligent program assistance, and automatic error handling. Automatic bug detection and self-repair tools are already been in the market and getting boost from AI. Traditional custom software development needs to adapt to the ways of AI to stay relevant and efficient in handling the requirements of the business.

Srivatsan Aravamudan - Sri
Senior Solution Consultant
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