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2021 was a pivotal year for India’s start-up ecosystem, where VC funding in India grew 3.8x over 2020 and reached $38.5 billion. A total of 44 unicorns were minted in India.

India is home to prolific levels of innovation, and that’s one huge success story out there!

The past couple of years have been a period of unparalleled growth for the Indian technology ecosystem. Now, Indian start-ups are going global, and into new business areas. India offers an excellent environment for both the birth and growth of unicorns, especially in consumer tech and B2C segments. However, while start-up growth has centered on consumer services until now, the prevailing narrative is set for an intriguing twist. Look beyond the fast-growing fintech and e-commerce ventures, and it becomes evident that entrepreneurial B2B tech companies are rising to the fore. Bolstering recent trends, some analysts anticipate another surge through 2022 in areas such as life sciences, gaming and, of course, enterprise and SaaS technology.

The story until now: The numbers say it all …

2021 was a pivotal year for India’s start-up ecosystem, where VC funding in India grew 3.8x over 2020 and reached $38.5 billion. A total of 44 unicorns were minted in India, and for the first time, 14 sectors received over $1 billion investment each. Trade body NASSCOM’s recent report says, more than 2,250 technology start-ups were founded in 2021, taking the total number to between 25,000 and 26,000. The report indicates Indian start-ups raised $24.1 billion in funding — a 3x increase over the total funding generated in 2020. Per NASSCOM, Indian SaaS companies are expected to increase aggregate revenue six times by 2025, reaching a record $13 billion to $15 billion — and a significant portion of this revenue will come from global markets.

Start-ups that focused on the growing needs of the domestic market and consumer-oriented services have been a big success story. While demonetization and the Covid pandemic were important push factors, most start-ups were already thriving before these events. These include micropayments (CRED), e-commerce (Flipkart), transportation (Ola Cabs), and online learning (Vedantu). Many B2B technology players gravitated to support this consumer-oriented growth, and distribution and e-logistics in India became the fastest-growing market globally. While B2B technology continues to transform industries such as transportation, logistics and distribution, Indian start-ups continue to support consumer-oriented growth such as food-delivery platforms Zomato and Swiggy. Such is the level of growth that industry estimates suggest e-logistics in India has become the fastest-growing market globally.

Tracking a global path on the start-up journey

What’s notable now is that enterprise tech start-ups are eyeing the global market. The SaaS landscape is witnessing an interesting twist. Start-ups built on global customer bases witnessed a healthy 2.9x growth resulting in investors writing large cheques, with 12 start-ups raising $100+ million (Chargebee, Postman, Gupshup, and HighRadius). There is a distinct upswing in deal volume in emerging areas of B2B commerce and Web 3.0 / crypto as new subsegments and business models have evolved in these sectors. KPIs used to monitor SaaS companies such as Annual Recurring Revenue, Net Revenue Retention, Churn Rate, etc., are now being used in other businesses, which indicates the SaaS-ification of all businesses.

Indian start-ups are building innovative solutions for global clients with India as the base, implying increased focus on globally distributed sales and client relationship management teams, and a deep enterprise-focused product strategy. These start-ups are adopting rapid innovation by creating new categories (e.g., API management, testing platforms) and development of products addressing complexities for enterprises (e.g., subscription management, billing, security) and SMBs (e.g., accounting).

India has also seen the emergence of enabling entities that help bridge the gap between a start-up’s need for access to global markets, and markets that have that need. Principle among these is The Indus Entrepreneurs (TiE) founded in 1992 and still very active; through its global points of presence, it plays a seminal role in connecting the world with Indian start-ups and vice versa. Many foreign governmental outfits have also set up bases in India to enable deep collaboration, like La French Tech, Business Finland, Swissnex, Indo-Netherlands Business Circle, etc. They run frequent programs to connect the start-ups and prospective clients and partners from both sides, running meetups and hackathons, and engaging with the Indian government through agencies like MeitY.

Overcoming the challenges … and blazing a trail

While analyzing India’s start-up ecosystem with an impact globally, it is pertinent to discuss some challenges surrounding it. Apart from concerns like government regulations on labor laws or dispute resolution, other challenges include systemic delays or those surrounding the lack of transparency. While a major concern among investors is the issue of IP rights enforcement, challenges accompany the start-up system anywhere, and India is no exception. It would help to incorporate global best practices and remove any barriers around IP protection, like reducing time for approvals, for instance, but it is pertinent to know that India’s IP legislation covers every aspect around its governance in line with global norms.

In fact, the success of India’s start-ups is largely the result of state policy. Initiatives like Start-up India and Digital India have provided start-ups a boost to compete globally. And the results are all around us. With a large internet user base driving an economy enabled by digital products and services, key sectors like education, banking and finance, trade and commerce, and government and agriculture, have been transformed by a mix of start-up innovation backed by state policy.

The net result is that India has become the third-largest start-up ecosystem in the world after the US and China. The country has the world’s largest pool of software developers (1.5x times that of US), and the Indian SaaS segment now has the attention of prominent global VCs, enabling them to raise significant capital. With the continued momentum, and growth-focused policies by the government, it’s only a matter of time that India will be home to the next generation of global SaaS start-ups.

– By Rajan Venkatesan, Chief Financial Officer, LatentView Analytics

Source: Financialexpress

The fundamental question that all scientists — from elementary science students to NASA engineers and PhDs — aim to answer has evolved little since the early philosophers began to question the world around them. Evident in the ongoing babble of toddlers exploring their environment with new eyes, it is human nature to want to know “why.”

This curiosity doesn’t leave us as we grow up; rather it morphs and evolves as the scope of our problems changes. In business, we don’t ask our teams why the sky is blue, but we do ask why a certain combination of strategies is the best approach to achieve our desired goals. We start with “why,” plot the best course of action, track and analyze KPIs and adjust based on the insights we find, before we do it all over again. In our ever-faster-moving business environment, executive leaders strive for a clear understanding of their business data, and to digest it quickly and execute strategies without slowing innovation. But this process cannot happen without support from data-savvy teams.

As businesses mature in their analytics journeys, their teams should evolve to present data in succinct ways that make sense for the context and message of the information being conveyed. In order to help business practitioners understand when it is appropriate to use which type of data visualization, we will break down each data visualization type. We will also explain when is the best time to implement it as you build a dashboard and strengthen your visual vocabulary — all in the context of distinguishing between decision boards and dashboards.

This practice is not limited to data science-heavy industries and verticals. CIOs, CFOs, CMOs and even Chief Data Officers can benefit from improving the way their teams present and how they interpret data.


chart 1

To understand how to work toward implementing decision boards we have to understand where we started: dashboards. By now, we are all too familiar with analytics dashboards, which include the default integrated reporting platforms of the digital tools we know and love, such as Google Analytics and Hubspot. They are effective at providing a high-level snapshot of performance broken down by category (day of the week, location, age, gender), and they are visually appealing but require a presenter who puts the data in context to answer the fundamental question: Why does this matter?

Decision boards, on the other hand, are fluid. They aggregate the data from cross-organizational channels to paint a clear, easy-to-follow picture that goes beyond descriptive metrics. These are often custom builds designed for an organization’s specific needs. Varying by the level of analytics maturity and design resources, decision boards can also illustrate diagnostic metrics, or why something happened; predictive metrics, or what is likely to happen; and prescriptive metrics, or what needs to happen next. Making the jump from dashboards to decision boards requires basic knowledge of design thinking, which when integrated into an organization’s culture can advance its analytics and reporting capabilities.

chart 2


The most effective decision boards are created when we implement design thinking. Loved by corporate powerhouses like Google and Apple, and legacy academic institutions like Harvard, design thinking’s methodical process means we get to the heart of the problem quickly, every time. It is efficient and built around the people who will use it — two staples of the insights we are trying to build. As part of design thinking, teams can assess which of the four major metric types (or combinations thereof) are needed to build a decision board.

  • Descriptive Metrics: Though not inherently valuable for decision-making, descriptive metrics give a snapshot of what has happened or is currently happening. They are a real-time glance at how multiple variables work together. Graphs and charts that illustrate descriptive metrics include:
    • Distribution (box plots, histograms, dot plots)
    • Part-to-whole (pie charts, waterfalls, stacked column charts)
    • Correlation (scatter plots, XY heatmaps, bubble charts)
  • Diagnostic Metrics: Diagnostic charts allow decision-makers to ladder down from the descriptive metrics to the “why.” In decision boards, diagnostic charts are linked to their correlating descriptive metrics, so that users can logically draw conclusions when they click on the data. Displaying diagnostic information is more about the flow of data than the structure of the chart. When choosing what graph to use, it is important to evaluate what specific questions you are trying to answer. The following structures are most often used for diagnostic charts:
    • Flow (chord diagrams, networks, Sankey charts)
    • Distribution (barcode plots, cumulative curves, population pyramids)
  • Predictive Metrics: Perhaps the simplest to understand, predictive charts forecast what will happen based on the existing dataset. These metrics are critical in making the transition from dashboards to decision boards and, when done correctly, should chart a clear path to the next steps.
    • Correlation (line+column, scatterplot, bubble chart)
    • Change Over Time (line chart, connected scatterplot, area)
    • Deviation (diverging bar, surplus/deficit)
  • Prescriptive Metrics: The divergence into prescriptive metrics tips the scale from dashboards to true decision boards. These displays of data indicate the next steps for business leaders. Requiring the most advanced data science knowledge, these charts use AI and ML to optimize performance.

As you build decision boards, focus on flow. Think about how your information will be digested and aim to create the most logical structure for your boards. This is where the basic principles of UX/UI design will benefit your teams the most.

The learning curve for building charts can be difficult, but not so difficult that a general business user can’t get the hang of it with time. To help with the construction of your decision board, LatentView has created a Visual Vocabulary, which is an open-source guide to building custom charts in Tableau. Periodically, LatentView will release step-by-step tutorials that walk users through employing Tableau filters. The first installment covers data source and extract filters.

chart 3 for newsroom

As your company progresses on its data analytics journey, there are a few key pillars to remember. First, make your decision boards easily accessible to the right stakeholders. Done well, these boards serve as an ongoing resource that is meant to be accessed regularly rather than presented at quarterly meetings. This is the primary reason decision boards are a more effective tool than previous iterations of data visualization.

Second, continue to ask for feedback and refine the structure of your decision boards. The composition of your boards will evolve as your business needs do.

Finally, be relentless in your pursuit of the “why.” It will make your predictive charts stronger, more intuitive and more sustainable in the long run. And by the way … the sky is blue because the gases of our atmosphere refract white light from the sun, scattering blue light waves (the shortest and quickest of the color spectrum) across the daytime sky.

Chart types index

Descriptive metrics

Bubble chart: Gives us a glimpse of the current state of the business. This chart provides an overview of sales (on the y-axis) against profit (on the x-axis) for varying subcategories. The size of the bubble is proportional to the size of the sale and the color represents the respective category that each subcategory belongs to. A quick glance shows that the subcategory “Tables” is on the lower side of profit despite a reasonable number of sales.

image4 1

Waterfall chart: Another way to exhibit positive and negative factors that affect the total profit, broken down by subcategories. Using the key as a guide, the sample below shows that the “Bookcases” and “Tables” subcategories are largely responsible for profit loss.


Diagnostic metrics

Sankey chart: Visualizes the flow of data. In the waterfall chart example above, we observed that both sales and profit for categories that fall under “technology” were higher as compared to other office supplies. To understand the major contributors to this category, the next chart clearly shows that phones and machines are responsible for the majority of sales. (Note: The width of the arrows represents the magnitude of the metric under discussion.)



Predictive metrics

In the below snapshot, the quarterly sales show an exponentially increasing trend over several years. It is also good to know what the future trend could look like. Hence, the forecast chart plays an invaluable role in certain cases. The prediction of sales will help businesses estimate factors like allocation of resources or expanding markets.

image1 2

Prescriptive metrics

Cluster chart: Helps us understand different types of clusters formed based on Tableau’s backend k-means algorithm. With sales vs. profit illustrated below, cluster 1 depicts low profit and low sales typically with the most number of data points; cluster 2 depicts moderate sales and profit; and cluster 3 depicts maximum profit and sales. Further drill-down analysis of the cluster 1 data would lead to clarity for further action needed, like how to improve marketing strategy or financial management.

image2 2

Author: Boobesh Ramadurai is the director of data and analytics at LatentView Analytics.

Source: Venturebeat

Venkat Vishwanathan, Founder & Chairman, LatentView Analytics Ltd.

Venkat Viswanathan is chairman and founder of LatentView Analytics, a global digital analytics consulting and solutions firm. Venkat’s vision came to life in 2006 when he founded the company, well before the world caught on about the complexity and power of using large data sets to influence critical business decisions. This is when he noticed an emerging opportunity in the analytics industry and decided to venture into entrepreneurship. Before launching LatentView, Venkat was a successful Manager of Business Development at Cognizant Technology Solutions.

From 2006 to 2014, Venkat served as LatentView’s CEO and was a pioneer in the field of analytics in India, paving the way for other companies that followed in LatentView’s footsteps. Under his leadership, LatentView grew to become one of the most respected, largest, and fastest-growing data analytics firms globally. During the 2008 global recession, Venkat’s traditional and conservative approach to business influenced his decision to keep the company bootstrapped, not seeking external funding. Despite the economic environment, LatentView continued to grow across diversified markets and acquired its first international client during this time.

Although LatentView experienced some challenges related to the global pandemic, the company achieved growth with a significant market expansion in 2020-21, as a portion of its clients’ businesses rapidly accelerated their digital transformation. In November of 2021, LatentView created history as it became the highest subscribed Indian IPO. In 2022, in his role as Chairman, Venkat continues to play a strategic role in the growth and success of the organization.

Pramadh Jandhyala, Co-Founder, LatentView Analytics Ltd.

Pramad is an accomplished entrepreneur, speaker, and analytics thought-leader. At Latentview Analytics, Pramad focuses on building a strong platform for growth and is responsible for finance, talent management, strategic planning and CSR.

She has over two decades of experience working with corporate finance and credit teams in global firms such as IBM, Ford, Moody’s ICRA and Kotak Mahindra Bank. She has a diverse background, and has worked in consumer lending, corporate finance, project finance, capital markets and credit analysis.

Under Pramad’s strategic guidance and execution, LatentView Analytics created Indian Capital Markets history as the most subscribed IPO at over 338 times, collecting over 1.13 lakh crores.

In her spare time, Pramad nurtures an avid interest in digital photography and spends many hours with her Nikon capturing Chennai’s lively beaches and colourful temples. She graduated with a B.E. in Computer Science from BITS Pilani, and also has an MBA from IIM, Calcutta.

With what mission LatentView Analytics launched?

Vision: Inspire and transform businesses to excel in the digital world by harnessing the power of data and analytics.


  • Help clients win by creating holistic and sustainable impact powered by data.
  • Become a talent magnet by empowering employees through a culture of fun, collaboration and learning.
  • Drive excellence through thought leadership by ingraining innovation and insight into our DNA.

Purpose: For enterprises ready to solve complex business problems with data and move up the analytics maturity curve, LatentView is an established thought and execution leader in business intelligence (BI) consulting, data engineering, data analytics, and data science. Differentiated from traditional pure-play analytics companies or consultancies that focus either exclusively on analytics strategy or delivery, LatentView is an end-to-end analytics partner that provides bespoke analytics strategy, optimization, and implementation. LatentView has deep functional expertise across multiple industries and relationships with industry leaders in cloud, data visualization, data engineering, and customer data platforms.

USP of LatentView Analytics

We have a strong understanding of the digital-native space and the technology practice, which is also our largest practice. We deliver our services through a combination of mathematics, domain knowledge and technology understanding which make our solutions sustainable. Problem-solving is our core strength – our ability to understand the business and be a consulting partner to help guide businesses along the journey to the future is what drives our teams. We have strong & specific value propositions backed by strong assets and accelerators to deliver them.

Future Plans of the company

With the current trajectory that the industry is taking, especially from the point of view of post-COVID-19 recovery, we certainly see a big role for analytics and AI services to play in both predictive and pre-emptive ways across various industries and functions. To this effect, we have invested in creating multiple Centers of Excellence, one being in the Data Science Consulting Services space, through which we see ourselves play the role of an advisory partner over and above that of an implementation partner for several of our clients. Within our consulting practice, we have been able to create a number of unique value propositions with our strong industry and sub-sector understanding as well as our value chain understanding and functional expertise. We are strongly invested in an industry-aligned recruitment for our growth teams across sales, pre-sales, demand generation and marketing. To bolster our existing capability areas and develop new ones, we are taking a horizontal-focussed hiring approach, for example, in Graph ML and Image Analytics.

Value proposition “ConnectedView” helping the CPG Industry

On Shelf Availability of products is one of the most important north metrics for CPG companies. Availability in CPG is the Right Product for the Right Customer with the Right Quantity in the Right Place on the shelf (physical or digital) at the Right Time at the Right Price with the Right Quality. CPG companies have reported close to 25 percentage point drop in their On Shelf Availability & Service levels – which typically translates to a 6-7% loss in revenue.

As we at LatentView have tread deeper into the potential causes for this drop and gone beyond the external factors impacting modern supply chains, we have seen 2 Efficiency factors emerge : 1) Lack of Visibility, 2) Sub-optimal supply chain processes. The root cause for this is a Disconnected Supply Chain – a supply chain ecosystem where processes have standardized operations and technologies have integrated systems, but the intrinsic connections & inter-relationships between processes and their data points havent been clearly established. Therefore, supply chain personas at the decision making moment of truth are unable to take optimal decisions to drive improvements in On Shelf Availability. This is the very mission of ConnectedView.

ConnectedView is an On Shelf Availability improvement philosophy that enables supply chain personas with better and faster decisions through Connected Visibility to the supply chain and  Connected Optimization of decisions delivered through a core ConnectedView framework, AI-powered accelerators, and LatentView’s analytics delivery capabilities.

Role of Data & Analytics, creating impacts in the Logistics & Supply Chain industry

It is an interesting paradox in the CPG industry today.

Data is ubiquitous and growing in volume and velocity at a breakneck speed. Everything from a picker’s mod and a truck driver’s tab to a drone’s flying path and an autonomous guided vehicle’s routing path is a data point. And this is being generated not by the month or the week, but by the minute and sometimes even more granular.

And on the other side, in our conversations with supply chain personas on ground, we learn that despite all that data, it takes 50% of their day to make sense of all that volume of data to arrive at the insight that can aid them in their decision making.

Data Analytics has a key role to play in simplifying the decision making journey for personas by detecting anomalies in the supply chain, finding the causal drivers, delivering actionable insights, and collecting feedback on action taken to continuously improve decisions.

Can you mention a few of the challenges in the Supply Chain industry and how it can be overcome with big data analytics?

The broad industry challenge is the 25 percentage point loss in On Shelf Availability that has resulted in a 6-7% loss of revenue opportunities for major CPG and Retail players. The 2 big drivers of this drop are:

Lack of Visibility – the sub challenges in this include that personas on ground are typically having to navigate 6-7 systems to arrive at decisions, most data points are Hindsights, not foresights; most systems don’t have the ability to identify and alert on risks

Sub Optimal processes – including Poor demand forecast accuracy, Long supply lead times, Poor suppli quality, Poor manufacturing throughout, Inefficient Logistics and last mile delivery operations.

With an AI-powered framework and solution like ConnectedView, organization’s can improve forecasting accuracy by bringing together internal as well as external data to understand drivers of demand, they can use modern AI/ML techniques and cloud technologies to predict manufacturing failures at scale, and they can drive improved visibility and orchestration of the supply chain by bringing together data from multiple systems and establishing connections.

How can data analytics be applied in supply chain management to improve operations and customer satisfaction?

The goal and ambition of analytics in supply chain has to be to drive better on shelf availability and customer service levels i.e. getting the 7 Rights of supply chain in place. That in itself is the road to improved customer service level and satisfaction.

In November last year, Chennai-based LatentView Analytics made a stellar stock market debut listing at a premium of 169 per cent. With the first anniversary of its public issue weeks away, businesslike caught up with LatentView Analytics CEO Rajan Sethuraman to understand what changed since going public, prospects for data analytics, the company’s growth and expansion plans and more. Edited excerpts:

You had a blockbuster IPO around the same time last year. How has the journey been since then?

The IPO itself was a significant milestone for us. We have been a quiet company and have been doing good work for the last 15 years for some of the marquee clients on the Fortune 500 list. Data analytics is still an emerging area. You don’t see massive $100 million deals in this space yet. A lot of organisations are still building it, like transaction processing systems, ERP and custom development. In these, they generate a lot of data, but making sense of it, and using it for optimisation and decision purposes is still a holy grail. That’s where data analytics and new technologies like artificial intelligence come in.

Is the awareness of data analytics solutions gaining momentum?

Data Analytics sits on top of the IT services stack, and companies are increasingly starting to look at it to help with their decision-making. As more investment is being made in the application from a transaction process system, they all generate tonnes of data. Unless one does something with the data and uses it for decision-making, one is not capitalising on the investment being made.

The transition is starting to happen. Data Analytics is still largely driven on the fringes by business sponsors and stakeholders. People who are responsible for making those decisions as opposed to being done by central CIOs and CTOs. That is where the big budgets are. In the last 18 months, we are witnessing data analytics moving from the fringes to the mainstream. There are still a lot of runways there when Data Analytics will really take off in a big way and become multi-million and multi-year spanning deals. We are in that evolving stage now.

Can you name a few clients who are working on such a transition?

We work with Adobe, leading in pivoting the entire organisation into a data-driven model. For every decision, there should be sound data with an analytical basis for that decision-making. Anybody who buys an Adobe product, subscribes to it, and uses it off the Cloud. The company can look at how the customer is using the product. They can tie consumer behaviour with the demographic data they already have and come up with a rich understanding of who needs what and what they should come up with in terms of products, services, marketing and packaging. We do a lot of work around the front end, like customer analytics and marketing analytics, to understand customers, customer segmentation, loyalty, cross-sell and upsell.

You also work for Uber, which should be generating tonnes of data. What do you do for them?

We started engaging with them around the pandemic when the mobility business took a big hit. They pivoted very quickly to Uber Eats, which became a mainstay during the pandemic, and that business became very important. We have been helping them on Uber Eats with what people are buying, and analytics help in coming up with the rightproposition and products. The data that’s generated can run into terabytes.

What’s your average deal size? Are they getting bigger?

Three years ago, our average deal size was in $150,000-$200,000 per the statement of work. This year, it is $600,000 to $700,000 as the initiatives are becoming more mainstream and large .

I am expecting that the size will get bigger. We recently closed a $2 million plus deal, and there is one that is potentially $3 million. Trend moving towards bigger deals.

Nearly 85 per cent of your business comes from repeat customers. Do you expect this to change?

We don’t expect this to change and for existing clients to spend more. Data Analytics budgets are expanding. We also believe that we can run aggressively on new business. The 15 per cent is becoming bigger with every passing year.

How mature is the Indian market in adapting to data analytics solutions?

This year, we have kicked off an initiative where we created a small core team for India, and we recently won our first engagement here. We had conversations with ten organisations, and there is a healthy pipeline of opportunities. We see a spectrum in India that is not very different from what we see in other markets like the US and Europe.

Most organisations have set up some IT stack or applications. So, there is no dearth of data, but the question is how advanced you want to become in bringing data-oriented decision-making.

There is too much buzz around Data Analytics. Do you see more players coming into this space?

Data analytics is a very hot area, and while we are the first company to be in the listed space in data analytics, there are several others like Mu Sigma, Fractal Analytics and Tiger Analytics. There has been good growth in new companies coming in and finding their niches and growing. This space is also witnessing huge demand, even from traditional players. Large IT services organisations like Infosys and TCS are also trying to build their analytics practices.

LatentView Analytics continues to be focused on the US market. What’s the progress of your European expansion?

There is very good progress in Europe. We have hired a new business head for the Europe region, and he is already building his team. We are adding people to the front-end sales and business development. We are also adding delivery and capability. There is good potential in the European market despite slowdown fears. We have already closed 2-3 engagements, and there are half-a-dozen more in the pipeline. So, we are expecting good traction in the coming quarters.

What’s your target for the European market?

We want Europe to contribute 15-20 per cent of our revenues in a 3-4 year time frame.

Are you also looking at inorganic growth opportunities?

Absolutely. In fact, inorganic growth is our objective stated in IPO filings. We have earmarked ₹450 crore of fundraising for inorganic growth, and coupled with cash balances and reserves, we have a sizeable kitty to pursue inorganic opportunities. We have already evaluated over 30 candidates but have not made any acquisitions so far.

There are 2-3 interesting opportunities on the table, and one of them is at an advanced stage.

What are your criteria for acquisition? Is it to enter into a new geography or scale up?

There are some areas of focus that we have identified. From an industry standpoint, it is BFSI and retail. Any opportunities in these two sectors will be interesting for us. From a geographical perspective, it will be Europe. From this type of work, we are focusing on data engineering and supply chain analytics.

Source: The Hindu Businessline

This edition of Chennai Kalpandhu League is a training ground of football for boys and girls from government schools.

CHENNAI:  When Kaviya, a class 7 student of Jaigopal Garodia Higher Secondary School saw a women’s football match scene in the movie Bigil, she wondered why the sport is not commonly played by women and girls like her. Though she knew that there were women’s football teams, she never had the chance to see a live match or on television. Soon she developed an interest in the sport which was further encouraged by the school authorities and her dream of playing a tournament came true with the second edition of Chennai Kaalpandhu League (CKL). This is not just the story of Kaviya but of almost 180 kids who were selected for the league, a CSR initiative by LatentView Analytics.

Identifying talents
With twelve teams (six boys and six girls), the league witnesses the participation of children from 30 government schools — 15 each from Tiruvallur and Chennai. After a two-year hiatus due to the pandemic, this year’s edition focused on inclusivity in terms of students only from government schools. “Unlike the last edition, we have equal participation of girls and boys from classes 6 to 8. The last edition was a learning experience for us. Thus, we thought of expanding the participation this year.

We trained them for almost 40 days,” shares Mariam Alex, HR Executive, LatentView Analytics. Confirming that the efforts to train the kids don’t stop with the league, Mariam adds, “This year itself, we have a phase two, which will include identifying top talents and having them associated with clubs or training institutes where they can further develop their talent. Year after year, we will go searching for these talents, and we will try to have more engagement programmes with the respective teams.”

Lessons on empowerment
The training which started on September 16 continued for one hour every day at the respective schools. “In the training session, they taught us everything. They gave us the whole football kit including a jersey and boots. They are also taking care of our food and transportation. In the selection process, they checked the basic skills, who scored more goals, and the discipline of each player in a team. For our diet, the coaches instructed us to avoid oily food and consume more vegetables.

During the games, we drank juice and glucose,” Kaviya shares. With an illuminating spirit, the kids agree that the matches have instilled a sense of pride and happiness in them. A few of them even wish to pursue a career in the sport. Hallan Joseph and Vishal from MMD Higher Secondary School, Arumbakkam, concur that the coaches motivated them to take up an interest in football and continue playing games even after the league.

Speaking about the coaches and the league, Stephen Charles, referees committee convenor, Chennai Footballs Association, says, “The coaches are well-experienced and pay individual attention to the kids. This is a great experience for the kids. When academics is a necessity, we shouldn’t ignore sports. Initiatives like this motivate parents and children to focus on sports. Many NGOs and corporate institutions should collaborate with sports associations and take up initiatives like this and continue empowering children.”

Emphasising that children should persevere to cultivate their talents, LatentView Analytics aims to provide meticulous training by some of the best football coaches in the country. Achyuth, data analyst, LatentView Analytics and ex-football professional, shares, “Right now, the kids are getting an opportunity and they are having tournaments to progress and grow. That is a good start but, it is more about how they are continuing and holding their opportunities. Not many people get these types of opportunities. If you go on in the timeline, you will be getting other obstacles and reasons not to play anymore. Our company provides an opportunity, they have to continue their progress.”

With a focus on education, the global digital analytics consulting and solutions firm looks into providing a holistic approach. “We believe that sports are an important element for the holistic development of a child in addition to education. And CKL is a small step in that direction,” shares Mariam.


The business of marketing, and the marketing of business, is undergoing significant change – more so in the “New Normal.” Stakeholder expectations have been reset, new-age technologies are leading digital transformation, and businesses are trying to lead with “purpose” to create a more human brand.

Against this backdrop, will marketing undergo a paradigm shift in the future? How far will digital transformation impact the new age of marketing? Importantly, what will be the role of data analytics in creating insights and shaping marketing decisions? And what could be the probable pitfalls along this journey?

Data-driven marketing – the challenges and the remedy

Data-driven marketers are outperforming competitors on key metrics such as brand awareness, customer satisfaction and retention, and conversion rates. Taking a data-driven approach to marketing allows marketers to make more informed decisions, with two out of three marketers agreeing it is preferable to make decisions based on data than simply on instinct.

Yet, marketing teams are struggling to use data effectively to drive marketing decisions and actions. For example, less than 25% of companies have developed data-driven cultures within their organizations. At a sectoral level like the automotive and utilities industries, the number drops further. Siloed data sources, investment challenges, and traditional mindsets that deter commitment at the organization level are some other concerns in building a data-centric approach in business.

For marketing to derive the full benefits of data-driven insights, it’s critical to take a robust approach to the discipline. Data mastery – meaning extracting insights from deep data analysis to shape marketing decisions and customer activation – will be key to marketing in the future. Other steps include investments in AI technologies, as well as in talent and skills to create actionable and real-time insights.

A starting point could be building a framework for collecting customer data to enable a single view of the customer. For example, to better serve its 80 million guests, Airbnb restructured much of its processes by gathering key insights from data to assess its best and worst performances geographically; analyse the back data for recommendations; and upscale the level of service. The result? It generated $5.9 billion in revenue in 2021, a 73% year-on-year increase.

Real-time data is starting to be another key enabler for marketers to deliver personalized content and responses. This helps understand how customers interact with brands and when and where to engage with them. Zappos, the US online shoe and clothing retailer, sends an e-mail response to customers immediately after product delivery, with an image of the items delivered and a short quiz inviting feedback. It also has a dedicated space on its website for agents to share their customer stories.

With the subscription economy set to grow to $1.5 trillion by 2025, business strategy, organization culture, processes, product, and people are all being driven by a strong foundation of data management and insights. Take the example of Netflix, a pioneer of subscription-based content; if a customer logs in late night, the platform recommends the shows they have already watched or those of shorter duration instead of longer ones. This data-based subscription model saves Netflix over $1 billion per year.

Unfortunately, few marketers can act this quickly. Per a CMO Council study, only 7% of respondents said they were able to deliver real-time, data-driven experiences across physical and digital touchpoints regularly. Many marketers struggle with the volume of real-time insights they access. Only a minority react to online customer interactions immediately – 43% in the pre-purchase stage, 38% during purchase, and just 35% post purchase.

A new trend in data-driven marketing has been the emergence of ‘zero-party’ data as a tool to understand the customer better. Zero-party data is collected when customers agree to share data with a brand to allow a deeper and more meaningful experience. As third-party cookies are gradually phased out, zero-party data has assumed increased significance as a marketing tool.

To connect with a new customer group of younger customers interested in “meme stocks” (i.e., investing based on what they hear on social media), Fidelity Investments started an “Ask me Anything” forum on Reddit for the purpose of collecting insightful data directly from this younger customer base for their campaigns. Needless to say, it’s taken off really well.

With the rise of e-commerce and social media, social-driven growth will be increasingly important to reach customers. The onus is clearly on marketers to use data gathered across customer touchpoints to analyse how customers interact with brands. And marketers can anticipate high revenue from social commerce, an e-commerce subset that involves selling products directly on social platforms.

The future of business … led by data

We are looking at an age where marketing executives will need to have a clear vision in their marketing strategy and build a framework-driven data-collection process. New modes of interaction have emphasized real-time data as the marketer’s biggest asset. Indeed, the profile of the marketing function has evolved over time, and will evolve further, to drive business.

The marketers of today are more purpose-led, more data-driven, and more human-centred and collaborative. The past two years have brought significant change – and opportunities – in how customers approach brands and what they expect. It’s left to marketers to harness this data and shape deeper engagement with customers to deliver a superior experience and in ensuring loyalty and brand building.

Welcome to the new age of marketing … led by data!