Category Archives: Feature Corner

Feature corner – A conversation on informal research

We feature Mr. Dorai Thodla in our monthly Feature corner, He’s Founder of Technology Strategies LLC and iMorph Innovation Center Pvt. Ltd. this month. He has co-founded 4 software companies – two in India and two in the USA. Mr. J. S. Sridharan, our Co-Founder, and Senior Vice President ask a few questions to Mr. Dorai Thodla. You may follow Dorai on Twitter (@dorait) and LinkedIn.

He helps organizations leverage emerging technologies for building skills and creating innovative products. His companies build Information Assistants, a collection of tools for gathering and analyzing information.

He considers the ability to do informal research and tracking technology trends as important skills for the future of work.  

Sridharan – With reference to the informal research process, could you tell us more about which areas this would apply to?

Dorai – There are a few areas where quick informal research can help.

1. A product team trying to validate a set of ideas. They may need to know what already exists and whether they should build a product.

2. A startup with reasons similar to product groups, but may need additional information about the market size, how the market is growing, current players, and whether there is a viable business model.

3. Finding gaps and opportunities in emerging technology trends. Inferring latent needs from discussions and analyzing competitive market spaces. 

Sridharan – Why do you think organizations would undertake informal research and at what level? 

Dorai – It depends on the size of the organization. Small to medium organizations may use it at a tactical level. For example, a small product group may use it to do competitive analysis, opportunity analysis for product extensions, etc. 

Sridharan –  We know that we can leverage Twitter as a tool for any informal research.  Can you share insights on how twitter hashtags will help organizations in this research?  How can we leverage LinkedIn for this? What are some other tools we could use for doing this informal research?

Dorai – Hashtag’s usage is not uniform. Some of the most influential tweeters (like Paul Graham) do not use hashtags at all. An analysis of hashtags shows that they are used when you want to join conversations or when you are in a conference or other similar events.

Linkedin

Twitter is a broad platform serving a wide variety of people. You can see authors, journalists, news organizations, technology people from the small, medium, and large organizations. LinkedIn is different. It started serving the needs of HR and recruiters. For a while, now LinkedIn is broadening the base but the type and quality of conversations are very different. It is easier to find influencers and people from large organizations. The profiles on LinkedIn are much richer.

The major problem with LinkedIn is a highly restricted API. So gathering information using automated tools is a bigger challenge. 

With respect to other tools,  If you cannot afford to subscribe to expensive research resources from experts in the field (students and startups have this problem), there are many tools you can cobble together to do your own research. You can call this DIY Research or Informal research. These tools include:

– Search engines

– Blogs and syndicated feeds

– News sources (both raw and aggregated)

– Websites and Portals

– Social media tools like Twitter but also LinkedIn and to some extent Facebook

– Wikis including Wikipedia and its properties

– Trade and Research publications

– Data and Scholarly search engines

Sridharan – How can startups leverage social media for tracking technology trends?

Dorai – If you want to speak the language of your customers, you need to use the vocabulary of your market place. These include idioms, phrases, and memes in those places. For example, tl;dr (too long did not read) is a common term among software developers. Other terms include “code smells”, “tech debt” etc. You can gather tweets of developers, do a frequency analysis of the terms to understand the terms of the group. 

The importance of understanding the vocabulary of users may be much fold. If you want to use content marketing, you may want to use that language in your content. 

There are a few ways. The more open the social media, the better. That is why Search and Twitter are our primary tools of choice.

1. Google, Facebook, LinkedIn, Twitter will provide you data about the size of a specific niche audience.

2. The search parts of Google, Twitter, and others will provide discovery tools. I prefer tools that provide good APIs. These tools make data-driven decisions easier. For example, on Twitter, I can type a search, find popular hashtags, lists, and mentions, and can mine them for more information. This is not so easy to do for example in LinkedIn (despite their having an API) and Quora. So people resort to using scrapers and RPA (robotic process automation) tools.

3. Blog searches or Feed Searches

4. Job searches –  for certain types of research jobs are a leading indicator

5. Conferences, meetups, and discussion forums are useful tools for identifying watch signals.

6. Funding patterns, especially Angel-funding are leading indicators. You can also track other rounds of funding and private equity to understand the market space. 

7. Books and articles are mostly lagging indicators but they are good sources to track the popularity of certain technologies. 

Sridharan – How can start-ups and organizations leverage this informal research for new product markets and finding new opportunities?

Dorai – There are several stages for a product startup. Let us look at the needs at each stage.

1. You come up with an idea for a product. You need to find out whether products similar to yours are there already and what are the pros and cons of each product (an analysis of alternative solutions). Having some competition validates that there are a problem and the need for a solution. But a lot depends on how established the competition is. At the end of this research, you identify a niche and some potential early adopters.

2. You build a prototype or a minimum viable product. You do this in stages. Once you have a proof-of-concept prototype or a functional, usable prototype, you need to validate that it solves the problem. You need research to identify these users, ways to reach them, and interact with them.

3. Once you have a few users using your product, you need to expand the number and also identify paying customers. You need research to go from 100s of users to thousands or tens of thousands of users.

4. Once you have sufficient users to feel comfortable with your validation, you need to price the product. If similar products exist, you need to research their pricing models. If there are no similar products but the market is being served by services, you need to research those pricing models.

As the number of users and types of users grows, you need more research to get to the next stage. Most of this research is not available in the market. Even if available, it may not be current. 

Sridharan – Could you elaborate on some new research methodologies that can help startups?

Dorai – Startups have several free and paid resources for product discovery, content discovery, keyword discovery, and competitive analysis. Informal research strings together available tools to create your own Research Assistants. I see this as a multi-stage process.

– Info tools – tools to gather data from a variety of sources – Web, RSS feeds, Tweets, Posts, Discussion boards, Forums, and Blogs.

– Analyzers – Analyze information gathered via info tools – segment/cluster, discover topics, mine entities, derive the vocabulary of conversations, etc. The analyzers use Machine Learning, Deep Learning, Natural Language Processing, semantic tagging, and other emerging techniques. 

Sridharan – With reference to businesses tracking technology trends, how do you think digital analytics help track the trends?

Dorai – Data gatherers will bring in a lot of data in various digital forms – text, images, speech (podcasts), videos. You need analytics for different levels of filtering. The first level simple analytics are early filters to separate signal from noise. Next level analytics provide good inputs for inference and prediction.

Sridharan – To understand customers and market size, which tools or platforms to look for?

Dorai – We can start with some of the available free tools. These include search engines, Twitter, LinkedIn, Facebook. They help you understand the market sizes since they need to know it to guide you in advertising. For example, using Facebook or Google ad products you can understand the size of a reachable market.

Using product hunt, beta list, and other similar services you can identify similar products. But nothing beats search. You can come up with a list of key terms that describe your product and try searches and analyze the results. Google provides a search API that you can use to automate this process.

You can use services like angel list to locate startups. You can use search to locate directories, professional associations in your product or technology space.

Twitter is becoming a great resource for discovery. Twitter APIs allow you to automate searches. Using Twitter you can find companies, products, trends, research reports, influencers, and discussions. You can create lists to manage the vast amount of information, retrieve tweets and links, and analyze the results.

Sridharan – How does text analytics help organizations in the changing business landscape?

Dorai – The outcome of searches (web search, Twitter search, Blog Search, Product Search, etc.) is short documents.

You need to text analysis to mine useful information from documents. Let us take an example of a blog comparing several products. You can use topic and keyword extraction techniques to derive useful information. You can use entity extraction to identify companies, products, and events mentioned in the article.

Feature Corner – Supercharging Businesses with Artificial Intelligence

Blog pat

With huge quantities of data generated every day, and sophisticated algorithms fuelling data modeling, we look at the scope of the expanding AI market in North America.  In our monthly Feature corner, our Co-Founder and Senior Vice President, Mr. J. S. Sridharan asks a few questions to Mr. Pat Krishnan, a seasoned C Level executive, from Bay Area, California, who has worked with leading organizations with disruptive technologies and has also provided strategic direction and leadership to various organizations in India and the US.

Sridharan – Having worked in AI, cybersecurity, analytics, and various other innovative ideas, please tell us how the pre-COVID and future priorities are changing for organizations in terms of adopting technologies like AI.

Pat Krishnan – AI as one knows is the “mantra” for every organization and every domain such as healthcare, cybersecurity, retail, financial to name a few. I think the market has been growing regardless of the COVID situation as far as AI goes.  With COVID-19 and scientific community still trying to understand the nature of this RNA genome, AI and mathematical models come in very handy to provide more insights into this Coronavirus. The models can get very advanced once they decipher the pattern and I personally think that we will be able to predict the future mutations of this SARS COV 2. This is a prediction with some level of certainty that is only possible with  AI models.

Sridharan – What are the problem areas you think AI can solve in the coming 3 to 5 years for these organizations?

Pat Krishnan – With COVID 19, new normal is happening and it will only be time before we get adapted to this. That said, Pharma (drug development), healthcare, retail, financial, insurance, real estate, entertainment, and food industries will go through a massive transformation and their reliance on AI and machine learning models will increase monumentally to an extent where vital decisions will be taken using data and technology.  Problem areas AI can solve are InSilico drug modeling and profiling, Telemedicine, Disease mgmt such as Diabetes (CGM) and terminal illness, Money movement and payments, Insurance Risk and underwriting, Food deliveries, and Online Retail merchandise (from groceries to apparel and the likes).

Sridharan – With more conversational agents like Alexa and Echo, and while reports state that 70% of the total US smart speaker owners will continue to use an Amazon Echo device, tell us more about the adoption of conversation AI and how conversational AI is seen as the next paradigm shift in the AI technology in the North American market?

Pat Krishnan – I consider the consumer-driven lifestyle type applications such as Alexa, Ok Google is the guinea pig for the Conversational AI technology which is about to invade into enterprises. Increasing working patterns like WFH have pushed the envelope to adopt Conversational AI in call centers and contact centers. According to reports, Conversational AI can save the industry $150 billion by eliminating the drudgery of communication and enhancing the customer experience in leaps which will benefit the business.  The next 5 years will see a lot of growth in conversational AI and related technologies in enterprises.

Sridharan – What according to you, are the areas organizations -small and big can supercharge customer engagement with the help of virtual agents and intelligent automation tools?

Pat Krishnan – Telehealth and continuous monitoring use cases, disease management are plum for engaging virtual agents and tools. Where there is a need for continuous engagement with the customer, automation tools will come in handy. The beauty of virtual agents stems from the fact that the back end Ai engine does the heavy lifting converting the customer queries and intent to tangible actions. The day will not be far off when most of the invasive questions are answered by virtual agents themselves.

Sridharan – How do you think organizations can leverage AI for higher levels of stakeholder engagement?

Pat Krishnan – Stakeholders live and die by making decisions that are important for the company be it operational or strategic level. AI is so democratic and objective that it does not differentiate any area of specialization. Where there is data, there possibly could be a need. Stakeholders should engage with AI service providers and work closely to close the gap that exists between science and technology. It will become increasingly necessary for stakeholders to come up to speed on the science aspect of AI which can help implement effective AI systems and platforms in the organization. Operationally the IT organization in the company should get aligned with the stakeholder and service provider.

Sridharan – What do stakeholders need to do to jumpstart their conversational AI journey?

Pat Krishnan – Stakeholders should start engaging with AI vendors and get the problem statement clearly laid out. This cannot happen in a silo and AI vendors should handhold the execs and help refine the problem. This is key to the successful implementation of an AI solution. This calls for stakeholders like the C suite giving enough time to go thru the implementation process which alone will pay rich dividends in the future.

Connect to us at info@genisights.com if you are looking at jumpstarting your AI journey







Feature Corner- Transforming the Digital Banking Landscape

Digital technologies have been helping banks and financial institutions to seize new markets, grow the business, and cut costs by providing a competitive advantage.  We see tremendous changes in the banking and financial services industry impact businesses and in an attempt to seek insights from business leaders from the industry, our Co-Founder and SVP Mr. Sridharan J. S has a conversation with Mr. Palani Balasubramanyam Nama, Head of Open Innovation and Digital Transformation, Societe Generale, who has over 21 years of experience working with Innovation & IT divisions of Investment Banks, Financial Institutions and European/American Multinationals, about the state of AI and other technologies in banking, in our exclusive ‘Feature Corner’.  As the Head of Trade Execution, Palani is also accountable for the IT & OPS team ensuring on-time and secure trade execution across global exchanges, efficiently from India.

Sridharan – Being in the banking industry for more than a decade, tell us how banks fostered innovation before COVID and how the perspective has changed during this situation? 

Palani – Pre COVID-19, innovation in banking was more focused on internal optimization, focused increasing digital footprint for efficiency, or improving customer journeys.

COVID has compelled to accelerate the adoption of contactless service delivery for customers, at a rapid pace. This constraint forced banks to partner with external eco-system for rapid transformation, to provide neo banks equivalent digital experience for customers. Apart from service channels, there is quite a bit of investment being done on employee experience, like enabling them to work from home, across multiple secure digital channels and collaboration tools.

Sridharan – Approximately what percentage of spend, do you know of, that banks spend on IT costs annually? 

Palani – Large banks spend between 5 to 10%, small & medium banks spend between 20 to 30% of their total spend on IT costs.

Sridharan – Approximately What percentage of banks’ transactions or what areas are automated?

Palani – Above 70% of P2P transactions are automated, however, B2B transactions hover between 30 to 40%. B2B transaction STP % is catching up in the recent past with the evolution of Machine Learning & Smart Automation.

Sridharan – As more devices connect to the internet and the concept of the Internet of Things gaining rapid momentum, what are the preparations banks are involved in the rapid increase in equipping themselves with the huge amount of data generated from the devices?

Palani – BFSI has been slow in the adoption of IoT before COVID, given security threats and central bank regulations. However, post-pandemic, banks are more open to adopting digital channels to deliver contactless & remote client experience.

This unprecedented disruption has increased data volumes multifold. Most of the banks have established internal data analytics teams to address basic data processing needs. Despite interest in partnering with external partners, there are roadblocks to be addressed before it can become reality, especially around secure data sharing, multiparty computation & adherence to regulatory norms (GDPR).

Sridharan – Data, being the most important factor for the digital transformation for banks, what technologies are used to leverage data to meaningful insights for smarter decisions?

Palani – Large BFSI enterprises have multiple tech stacks based on needs. However, they have well defined IS strategies guiding the usage of tech stacks based on problem patterns being addressed.

While exploring partnerships with tech giants like ORACLE, Teradata, or IBM, there is an intrinsic push to adopt opensource tech stacks, to ensure open standards and cost optimization.

Sridharan – Do you think banks are struggling with the rapidly changing technological landscape in terms of which technologies to adopt and which ones not to? 

Palani – It is a challenge to keep up with fast-paced Technology landscape evolution for any organization. It is more complex in BFSI given constraints around tech choices to be made keeping in mind the security and reliability of services. In BFSI, business capabilities and security take precedence over technology grandeur. 

There are few instances of large banks partnering with external partners to disruptive technology transformation, however, results so far haven’t been very encouraging. BFSI transformations demand as business acumen and technology expertise. External partners tend to be good at technology expertise, however most of the time they lack business acumen.

Sridharan – AI has been long seen as the next big thing in the banking and financial industry be it in the area of planning, cost-saving, customer experience, fraud prevention, or anti-money laundering.  What do you see as the future of banking given the exponential growth in IoT devices, cloud computing with Artificial intelligence capabilities?

Palani – AI, so far has been capable of helping in raising proactive alerts, simplifying decision making, and processing of large datasets. however, it is far from autonomous process execution or decision making, which limits the full-scale unsupervised deployment of AI-driven process flows, which in turn would help increase the straight-through processing rate.

Necessity is the mother of invention. Pandemic has increased BFSI’s appetite to adopt technologies like IoT, Cloud computing, and AI. The exponential growth of these technologies should be complemented by the rapid hardening of enterprise cybersecurity strategies, without which, banks may be hesitant to leverage the full potential of emerging technologies.