Analytics

The American Dream. This is what most every American wants. To have wealth and prosperity. In the book Death of a Salesman by Arthur Miller, Willy Loman in pursuing this dream through all means. Willy Loman, a man in his 500s has worked hard all his life to bring what every parent wants for their family, everything. In Death of a Salesman Willy Loman has to go through hard times to try to bring this to his family. At the beginning of this drama Mr. Loman is just getting home from his job as a salesman. Where he drives a total of 500 miles a week to sell pointless items to pointless towns (Miller, 16). Mr. Lomanis kids love their father very much and only want the best for him.

Happy, which in known as Hap is a man in his early 30s who goes through business troubles himself and moves back in with his father and mother. Biff is younger than Happy and is an energetic man who wants to pursue his father’s footsteps. Willy is very happy when his two boys move back in and hurries to make the best out of every day.

Willy is not a type of individual who takes laziness, all his life he has got nothing or no where without working for it. This is the way he teaches his boys. Willy teaches his boys the best moral and standards he can. Although Willy helps everyone get through tough times, he himself starts to lose a little of his mind. Williells wife Linda knows that Willy in having hard times but does not say anything she just tries to cope with it and help (Miller, 154). Willy out as much as she can.

Seeing Willy loses his mind hurts Linda very much. Willy cannot distinguish between reality and illusion. This is the major conflict in the book. For everyone in affected by it. Willy believes that he and his sons have what it takes to become successful businessmen but he is mistaken. Willy believes that to be well liked you must be successful. This is Willie’s downfall through the book. Willy will sometimes be caught talking to his brother who is in the forest looking for diamonds (Miller, 78).

He will tell Willy to come with him so they can be rich together, but Willy always tells him that a salesman is the job to get rich at. Willy will also be caught having conversations that occurred 6-7 years ago. Mostly with Biff and Happy he talks to them about school or how they re going to be great men when they grow up and how everyone will like them. When Willy goes to get a job without driving his boss tells him hells fired and when another job comes around he call Willy. Willy does not take this well since he helped him get his job in the first place.

That night Willy has to meet with his sons at a bar to talk about Biff getting money to build a store called Loman & Sons when Biff tells Willy that he didnot get the money Willy has no hope for anything anymore. Willy was too wrapped up in getting ahead of the next guy that he never stopped to see what was happening around him, that his world was falling apart.

When Willy gets all this bad news hells loses his mind totally and start talking to his brother Ben and telling him that he’s tired of being a salesman. Willy then goes home and starts planting a garden (Miller, 97). He has always wanted a garden but the grass doesn’t grow in the backyard. At the end of the play this is one of his last futile attempt to plant seeds in the backyard. This is kind of symbolic of Willy as if hells doing this so people will remember him (Miller, Net). At the end on the novel Willy commits suicide for the reason of nowhere else to turn (Miller, 109).

The American Dream, to Willy this was the only purpose in life. To be better then the next guy and provide for your family. Even though Willy did not gain the dream as Biff would say he is still Da dime a dozen. Willy never achieved success in life, but for his family that knew him he was the best salesman and father around.

As technology and business advance, the world feels like a smaller place. Today’s recruiters need to be global agents in sourcing, assessing, and securing top talent from multiple countries and regions. Macro and micro shifts in recruiting require talent acquisition (TA) professionals to expand their expertise, thinking and working in new ways. The following are five emerging trends in global recruiting that require our attention and action.

Prioritizing the candidate experience

We talk a lot about candidate experience in TA, and it’s increasingly becoming a chief differentiator for companies. You have to get this right. Higher levels of communication, more responsiveness, efficiency in the recruiting process – the candidates you most want (need) to hire expect it. And when they don’t get it, well, bad news travels fast. In global recruiting, you have the challenge of managing the candidate experience across different cultures, local expectations, regulation and compliance requirements, not to mention languages. All of this has to be proactively and thoughtfully considered and coordinated. It’s a huge learning curve for most organizations and the pace of business doesn’t give them time to stumble through it.

Data and analytics

The market is frustrated with antiquated ATS tools that do not parse data, facilitate reporting and comparison, or offer deep analytic capabilities. Data informs our decisions, and technology companies are being forced to innovate on the tools that deliver. An increasingly fact-based set of systems are emerging that provide triggers and alerts, directing the user in real-time. By making predictive decisions that improve efficiency, you can drive down costs and improve revenue. This is all being driven by a post-recession awareness of the need to be very thoughtful about how you spend and prove ROI on that spend.

Shifting workforce demographics and workstyles

Many companies are still set up primarily to address the values and needs of the Baby Boomer and Gen X workforce. They’re playing “catch up” on the Millennial generation even though it now comprises the largest share of the American workforce, and they’re just starting on the Post-Millennial generation. Companies are having to think very strategically about their employee value proposition (EVP) and how it plays against the needs of very different generations. These include the Millennial’s value of life first, work second; the rising Gig Economy, and the fact that people expect to change jobs roughly every three years. It’s a fact that being flexible in letting people choose how they get work done is becoming an expectation, not a nice-have. Running an efficient recruiting process while incorporating a finely-tuned EVP puts a lot of pressure on the organizations to think about what the process needs to look like. This is a level of sophistication and agility that recruiting has never had to rise to in the past.

Changing how people find jobs

Job boards used to be king. Now we’re using LinkedIn, social media, and job aggregators to market job postings with a focus on steering away from job postings and toward having conversations about jobs. Companies need to ramp up social networking and other strategies designed to personalize the candidate’s experience. It’s far more about relationship building early on, getting that pool of candidates and creating an engaged pipeline – and far less about posting a job and waiting for people to see it and respond. How you engage and attract talent is going to have to become far more creative and relational.

Leveraging technology-enabled recruiting

One of the biggest advantages of technology and machine learning, is that it lifts the burden of repetitious acts off of humans and frees us up to relate to each other. Leaders in recruiting have to start weaving a bigger picture for how tools can change and improve the way we approach recruiting. Candidates expect us to have all the same tools and technology that they do. If they can text us, why can’t we text them? Can we set up a video interview with ease? Can we automate scheduling? Can we have people do a realistic job preview online, fill out the application, upload a 30-second video interview, automate screening, and then deliver a batch of pre-qualified applications to a recruiter every morning? We have the tools for this now, and they will increasingly impact the way we do recruiting.

These trends require an agile, flexible, highly-efficient recruiting model. Organizations have to drive a global model with increasingly limited resources, yet still reflect their footprint, country-specific laws and customs, while meeting candidate expectations of ease, simplicity, and speed in the process. Some organizations are building recruiting functions to do this. Some do not have the time or operational bandwidth to redraw the function. Leveraging an experienced, skillful outsourced recruiting partner to help you meet these challenges may be a great option. No matter how you solve it, though, remember that at the end of the day, it’s still all about getting the best people for the jobs in the most efficient way possible. It’s just doing it in new ways.

The near future of artificial intelligence (AI) won’t be defined by ushering in a race of sentient machines. While we’re inching closer to the goal of, the next era of AI will be more about imbuing the software and applications we use every day with deep learning, machine learning, predictive analytics, and natural language processing (NLP). Those capabilities will run under the surface, along with serving as tools upon which to build. It’s about making AI a given rather than a novelty.

(CRM) giant Salesforce unveiled its plan for more accessible, natively integrated AI for businesses today with the announcement of Salesforce Einstein, its “AI for CRM” technology. Einstein will be deployed across the Salesforce cloud to analyze the mountain of automated data the platform collects – activity data, sales, email, e-commerce and calendar, social data streams, and Internet of Things (IoT) data — and run machine learning and algorithms, NLP and what the company calls “smart data discovery” to offer data insights and recommendations across different business use cases within the platform. Many of the capabilities will also be offered as developer tools to build AI applications on Salesforce. “Think of Einstein as the intelligence layer between the data and the actual apps,” said John Ball, Senior Vice President and General Manager of Content for Salesforce, during a press briefing. “The best AI is when the user doesn’t necessarily notice it. We surface lead and opportunity insights, and we’ve baked AI throughout the platform and the user experience so that, over time, the user won’t even think of it as an AI-powered feature; it’s just part of the platform.”

In the main, Einstein shows itself in a couple of ways. A feature called Predictive Lead Scoring, where a machine learning model analyzes industry and engagement data is designed to help sales reps focus on the most promising lead. You can also use predictive scoring in other areas of the Salesforce Marketing Cloud, such as gauging prospective customer response to a campaign. In that scenario, Einstein could return results based on common customer behaviors and deliver advice like automated send-time optimization based on when subscribers have been historically most engaged. Another CRM AI feature called Opportunity Insights analyzes customer interactions such as inbound emails to alert sales reps to which way a deal is trending.

In Service Cloud, Einstein did a lot of automated workarounds case classification and suggested responses. In the Commerce Cloud, it’s doing product recommendations and personalized predictive search, meaning product search results modeled off their individual profile. On the question of customer privacy with all the data Einstein is analyzing, Ball avoided specifics but said Salesforce’s best practices around privacy and trust apply to Einstein. “Trust is our number one value. Everything we do is trusted. Einstein is no difernent,” said Ball. “The entire process is automated. We’re automatically building these models, so no data scientists are looking at the data. It’s a machine learning process, so no data is being shared between customers.”

Einstein’s impact continues in the Salesforce Community Cloud where it provides computer vision analysis of images in the Social Studio. You’ll also find it in the Analytics Cloud where it delivers Predictive Wave Apps and automated analytics for folks using business process management (BPM). It also provides predictive scoring and automation for the Salesforce Internet of Things (IoT) Cloud. Ball described it as having a data scientist in every part of the platform. “The whole reason we built Einstein is there aren’t enough data scientists in the world to go out and build predictive models for every company,” said Ball. “We’re democratizing AI so customers get the benefits without having to hire data scientists. The platform also enables developers at different skill levels to train their own classifiers with zero deep learning expertise.”

Where business apps meet deep learning

Another core aspect of Einstein is AI-powered app creation. Einstein will be available in the Salesforce App Cloud and as a set of tools that allow developers to train AI apps by using deep learning tools. Richard Socher, Chief Scientist at Salesforce, was on-hand during the briefing to discuss the new Salesforce Research Group, a team of data scientists and researchers focusing on deep learning, NLP, and computer vision innovation. As with Google’s open-source, Socher explained how Einstein’s developer tools are aimed at making deep learning more accessible using the Predictive Vision image analysis in Social Studio as an example. “You can train your image classifiers to do anything you want,” said Socher. “This is one of the first deep learning-based developer tools that will allow you to drag and drop inside an interface in Salesforce to make smart automated decision-making. It will allow you to do all sorts of new things, like go through millions of images and classify them to find your company logo or where your company is mentioned.”

Salesforce will be giving a keynote on Einstein at its Dreamforce conference in early October and will begin rolling out Einstein features across the Salesforce cloud in the company’s Winter ’17 release scheduled for October. Ball didn’t reveal much in the way of pricing for Einstein, only that “some capabilities will be bundled into existing editions and licenses, and others will require an extra charge.”

From a market perspective, Salesforce is essentially using AI techniques such as machine learning and predictive analytics the same way as Google, Facebook, and others do for consumers, only for its vast quantities of sales and business data. Brandon Purcell, Senior Analyst at Forrester Research said an AI layer such as Einstein between its data and apps gives Salesforce an opportunity to leverage its business data on that same scale. “In order for artificially intelligent systems to work, they need to be trained on massive amounts of data,” said Purcell. “In the B2C space, Google, Facebook, and Amazon have the most data on consumers. Einstein marks Salesforce’s ascendance to this top-tier, alone in the B2B space due to the incredible amount of data stored in their cloud.”

The amount and sort of knowledge being captured by firms these days is staggering. The abundance of information, combined with the poor availableness of powerful knowledge analysis tools, has given rise to a data rich but poor data condition. The widening gap between information and knowledge has generated a pressing want for brand pking new techniques which can flip these immense amounts of knowledge into “golden nuggets” of information.

This realization, spawned from four years of my undergrad studies, has motivated me to figure towards changing me into a veteran within the field of information Technology. To this end, I’m very excited to apply and in fact study at Northeastern University’s Master’s in skilled Studies in Analytics.

A successful Systems Analyst, has a perfect blend of analytical, managerial and communication skills, creativity and spirit of teamwork. My undergraduate education at D. Y. Patil College of Engineering and Technology – one of the foremost institutes in my region – affiliated to Shivaji University, has played a key role in developing these skills. It has given me a comprehensive exposure to courses like Database Management Systems, Data Warehousing and Mining. My proficiency in languages such as C, C++, and SQL has established a solid framework towards accomplishing my objective.

Through different testing ventures, I picked up knowledge to how the IT business functions. From building up a database administration system for my web application, I understood that while arranging toward the starting, collaboration is critical, cooperation is the way to the fruitful usage of undertaking. These undertakings associated with utilization of what was taken after inside the classroom and gave a dynamic practice with programming languages like PHP, HTML, XML, and MySQL. Courses like ‘Communication Skills’ and ‘Presentation and Communication Techniques’ upgraded my relational aptitudes which I had used to effectively execute my projects.

In the last year of my Bachelor’s degree, I worked on a project titled ‘Know Your Trade’ that went for building up a web application for the automation and maintenance of manufacturing progress of orders for the company ‘Nutrich Foods Pvt. Ltd.’, based in Kolhapur. In a joint effort with three of my partners, I achieved the objectives of making intuitive web application which enables user to create purchase and dispatch processing notes, view results of statistical analysis as well as output of the cashew cutting machine. Finishing these colossal assignments reinforced my investigative and critical thinking aptitudes. The most striking element of this project was the weight sensing by the load sensor. This web application can be utilized as a part of any commodity industry. The project was granted as the best project by the organization Nutrich Foods Pvt. Ltd.

This task has infused in me the organizational abilities adequate to organize work and oversee time viably keeping in mind the end goal to meet approaching due dates without compensating the quality of the output.

The mounting misuse of computers and expanding risk to individual protection has stimulated my enthusiasm for the field of Data Security. To protect critical information from getting harmed, a sound security framework is of most important for any association. In this way, to strengthen my foothold on Data Security, I completed a certification course on Microsoft Technology Associate-Security Fundamentals and scored 83% in the course offered by the Microsoft. This provided me the advanced skills needed to secure electronic assets, prevent attacks and build a secured network. It has also given me hands-on training on projects, and a profound knowledge about Network Security, Operating System Security, Security Layers, and Security Software.

During the term time at my college I have also participated in extracurricular activities like Presentations, seminars and organizing events that gave me a platform to interact with people from different background and with diverse culture which has helped me to learn more about social world. I have worked as volunteer for National Conference on Emerging Trends in Engineering & Architecture (NCETETA) held at Dr. D. Y. Patil College of Engineering and Technology.

Participated in Smart India Hackathon 2017 held in Chennai (the main motto of this event was to find out the solutions for the social problems which was identified by the ministries and government departments of India, where the participants have to sit locked in for 36 hours to develop the solution!) Even I stood 1st for poster presentation for Environmental Awareness Programme held at Dr. D. Y. Patil College of Engineering and Technology.

Among the entire education system, the American education is considered to be one of the best world-wide because of its excellent combination of theoretical and practical knowledge which I believe is a perfect solution for a student for his/her understanding. Getting enrolled in an American university would help me to learn and develop the required practical skills which I believe is needed for me. This is because of the programs taught in the university have well organised and structured curriculum which is perfectly suitable for the field that I am looking forward to stepping in.

Among all the programs related to computer networks the MPS in Analytics program at Northeastern University looked eminent as the master’s program covers the areas of critical importance to IT employers, such as – Probability and Statistics, Enterprise Analytics, Information Architecture, Information Security Governance, Data Management and Big Data, Predictive Analytics, Data-Driven Decision Making, and also GIS -and provides significant scope for career opportunities. All these units certainly ensure a good and strong career ahead.

The program at Northeastern University is suited for my goals as the faculties and the way the program is designed is quite impressive. I believe that to get linked with such faculties and program would give me a great opportunity to pursue my career in Analytics. I was also surprised by the facilities available at your university. As I discussed about this with my undergraduate professor, he also backed my decision to pursue my higher education at your esteemed university. I am confident, and I believe that with the required teaching I can be shaped into a well-balanced individual. I look forward in joining your esteemed university as a postgraduate student at computer science department.
Archana ViswwaranjanIndia

With the growing complexity and sophistication of the marketing function, marketers need to choose the right set of marketing technology based on a marketing maturity model. Peter Drucker once elevated the marketing function in his quote, “The business enterprise has two – and only two – basic functions: marketing and innovation. Marketing and innovation produce results; all the rest are costs. ” With time, marketing has been given lesser and lesser importance, so much so that today, almost every marketer faces hard questions about accountability.Let’s look at excerpts from an article by Mohanbir Sawhney, McCormick Tribune Professor of Technology, Kellogg School of Management, which elaborates the state of marketing in the current scenario: Philip Kotler, in a conversation with the vice-president of marketing of a major airline, asked what he did in his job. Did he control pricing? “Not really,” the marketing VP replied, “That’s the yield management department.

” Did he control where and how often the airline flew or the classes of service it offered? “Not really – that’s the flight scheduling department,” he answered.Did he control the services provided by the airline to its customers on the ground? “Not really, that’s the operations department,” he said. So what exactly did he control? “Well,” he told Kotler, “I run advertising and the frequent flyer programme. ” Most marketers’ jobs, at the end of the day, seem to boil down to creating collaterals, sending emails or designing beautiful websites. Day by day, marketing is becoming synonymous with advertising – while the core activities of marketing such as delivering qualified leads, linking marketing with critical business functions and generating new ideas are being put on the backburner.As a result of this, marketers are finding it difficult to put across a strong voice during the process of organisational strategic decision making, leading to executive level attrition. The results of a CMO tenure survey, carried out by executive recruiting firm Spencer Stuart involving top 100 branded companies, is an eye-opener: the average tenure of CMOs is just 23 months – less than half of a CEO’s tenure.

The major reasons cited for this situation are the failure of CMOs to speak the language of return on investment and to manage the increasingly complex environment of integrated marketing communication.A lot of marketing executives believe that measuring marketing performance is a key priority for their organisations to drive accountability and yet, only 20 per cent of the marketers have a formal marketing performance measurement system in place. To address this scenario and establish credibility in terms of creating customer value, CMOs need a combination of relevant marketing technology, with appropriate skilled resources to execute actionable marketing plans.With the growing complexity and sophistication of the marketing function, marketers need to choose the right set of marketing technology based on a marketing maturity model. At the early stage of maturity, marketers use marketing automation to manage content development, automate work flow and integrate back office work. Towards a later stage of maturity, dedicated marketing technology platforms come in to drive a customer centric approach – with the help of real time analytics and closed loop marketing processes linking every step in the marketing lifecycle.To sum it up, the hurdle faced by marketers to achieve the ultimate goal of accountability seems to be twofold.

The first hurdle is the lack of defined marketing processes and metrics. For example, today, organisations have access to a lot of data but when it comes to marketing needs, due to lack of defined metrics and coordination with analytics teams, leveraging data to deliver key insights remain a challenge. The second hurdle is the lack of willingness to adopt a performance orientated mindset.One of the strong reasons behind this is the fact that marketing has been managed by creative thinkers and strategists who are quite averse to process oriented approaches. Therefore, an organisation-wide change in mindset is essential to determine the success of any marketing automation. An enterprise-wide marketing automation approach, combining the prowess of technology with a change in mindset, is essential to make the marketing function more accountable. This approach, called Enterprise Marketing Management (EMM), has been propagated to address the mentioned twofold problem.

Fundamental building blocks of EMM consist of marketing planning, campaign management, lead management and performance measurement. EMM will help marketers to plan, coordinate and measure the business impact of their branding and marketing efforts, addressing a broad array of marketing requirements/pain points such as:

How was the marketing budget spent? Where were marketing investments made against key priorities?

1. Which digital marketing vehicles provide the greatest return on investment? Swiftly launch campaign in an agile marketing environment and respond to marketing competition.
2. Predict customer behaviour to help understand the marketing mix and how investments can achieve the maximum marketing impact.
3. Reduce effort on manual consolidation and approval process. At the end of the day, the aim is to answer the famous riddle in marketing by John Wanamaker, founder of the first department store, “One half of my advertising budget is wasted. The trouble is I don’t know which half. “

Two most notable acquisitions in 2016 till date has been Bayer’s $ 66 billion buy out of Monsanto and Verizon’s $4.83 billion buy out of Yahoo. While these mergers come from seemingly two very different industries, there are some common underlying currents in these mega-corporations. 

Let’s look at Monsanto first. Monsanto has taken a lead in developing the integrated digital platform for farmers to access and analyze agronomic data. The platform aims to track farm operations including soil moisture, weather, crop data to make better predictions. Since Monsanto’s acquisition of Climate Corp in 2013, Monsanto has partnered with companies like Vermis Technologies (for soil mapping) and invested (through Monsanto Growth Ventures) in ag data companies like Resson (for image analytics). Clearly, data is integral part of Monsanto’s growth strategy.

Likewise, Verizon is working on an ag-software platform supported by Internet-of-Things (IoT) on the farm field including weather stations, sensors, and devices for real-time capturing and reporting of data. The Company is already working with vineyards in the California’s Monterey Country and Napa Valley (Source: Company Website). It has tied with a crop modeling company called ITK to provide intelligence on irrigation management and sustainable farming practices to the farm owners of the vineyards.

There is no doubt that giants like Monsanto and Verizon are targeting ag-data as one of key growth drivers. “Data” is now becoming the most precious commodity in the global agricultural market. What does it mean for Indian Agriculture?

A: Is India ready for the data-centric agricultural economy?

The answer is unequivocal “Yes”. The panacea for the majority of problems in Indian agriculture lies in “data”. The problems of inflation, wastage, low productivity and lack of institutional farmer financing can be addressed through data.  Here’s how?

Food inflation can be solved by timely availability of data. Food inflation is a persistent problem in Indian economy. While demand patterns in food category are more predictable; estimating supply is bit of a challenge. Usually, the cause of sudden, unpredictable and sharp rise in food inflation is lack of requisite and timely supply.

We often see more price volatility in perishable crops like potato, tomato, coriander and onion. In recent times, even staples like oilseeds and pulses have also shown sharp pricing twists both in upside and downside. The solution to this problem is timely availability of data for a) sowing, b) harvest and c) production.

Farmer advisory can be issued if sowing is much more than what market can absorb. Likewise, stocks can be released and import orders can be placed in time if harvest / production data shows lower throughput than market demand.

A real-time assessment of likely throughput for any crop can reduce existing data asymmetry which results in volatility in prices. Thus, the problems of inflationary pressure (in case lower than expected output) and panic among farmers (in case of more than expected output) can be addressed with timely and accurate data.

The current forecasting methods include time-series analysis for predicting crop output. Past data will become less relevant for future predictions in the context of climate change and fast changing weather patterns across the globe. So need of the hour is to have tools to get the data on a real-time basis.

So how does one capture real time data for millions of hectares of arable land?

The answer lies in satellite imagery. It has the potential to capture images of farmer fields to 1 m x 1m resolution (20 – 25 pixels), which is improving further with invent of technology. These images can capture various data points such as Leaf Area Index, plant height, canopy etc which is indicative of crop vigour and hence can be used to accurately estimate farm yield.

Since the launch of  NASA’s satellite Landsat-8 in 2013, many companies including Geosys, Planetlabs, Skybox have launched satellites who are providing satellite images to millions of farmers. The data can be bought at approx. cost $ 0.01 to $ 0.02 per hectare.

I am not sure about the of the capability to existing satellites to cover 161 mn hectares of arable land in India at desirable resolution levels. Given the importance of agriculture to Indian economy and India’s capability in launching satellites (as demonstrated time and again by ISRO’s scientists), there is a merit in government exploring the option of launching dedicated satellite with focus on agricultural applications.

In short, food inflation, though cannot be controlled fully but can be predicted with reasonable accuracy with data and technology intervention as discussed above.

Post-harvest wastage of farm produce can be reduced with intervention of data. The criminal wastage of food in the supply chain runs into billions of dollars. Multiple handling points along with lack of quality control points from farm to consumer is the root cause of wastage. For example, apples traveling in trucks from Himachal and Kashmir bear more pressure (because of overloading / poor crate designs) than they can absorb and likewise Alphonso mangoes transported in extreme summer from Konkan to Mumbai go through heat stress even in temperature controlled trucks.

Is there a way, one can monitor quality of produce in transportation and storage? It is possible to capture real-time data through web-enabled devices which can solve the problem. There are devices / remote sensors which can monitor factors causing wastage during storage (pest, rodents, moisture), transportation (high temperature, excessive pressure, humidity). Alarms and data patterns available from the devices can help develop solutions to control factors resulting in wastage. For example, if we have rodent-specific sensors in warehouses to detect / trace their movement, the traps can be design and configured to prevent loss of grain. This innovation is worth investing as rodents alone eat about 2-4% (approx. 5 -10 million tonnes per annum) of grain produced in India.

Soil fertility and productivity can be improved with data application. Poor productivity in India in most crops can be largely attributed to lack of soil fertility. Soil fertility in India is further going down due to erroneous application of fertilisers (more Nitrogen (N) and less of Phosphorus (P) and Potash (K) than the recommended mix). NPK mapping at each field is necessary for the right prescription including type of seed, seed rate, irrigation, plant growth regulators, fertilisers etc.

Again data obtained from a combination of on-field devices and satellite imagery can estimate nutrient value of soil at a given point of time. This can be complemented with data obtained from soil health cards (which can be digitized).

The data on crop imagery can also be captured through farmer’s smartphones and shared with agronomist to find solution of pest attacks or poor growth. Temperature and humidity data from weather stations can be overlapped to see impact of temperature / humidity on crop growth.

Not only agriculture but even animal husbandry industry can benefit a lot by use and application of data. Data from RFID tags can track cow movement and data from collar tags can be used to monitor body / neck movement which can help in heat detection and understand cow health. These data points can be used to improvise feed intake, lactation cycles and ultimately increase milk productivity.

Financing to farmers is another challenge which data can solve. The current priority sector lending to farmers stands at approx. USD 135 bn. However, majority of bankers still face the challenge of determining credit-worthiness of farmers due to lack of KYC records. Lending to farmers can become very efficient, logical and data-driven, if the bankers have access to data on likely crop output from farmer’s field (which can be determined as mentioned earlier). Likewise, insurance companies can ascertain risk premium if they have access to weather, soil, pest and output data.

In summary, use of data has the potential to solve most ag-supply-chain problems. Inclusion and incision of “Data” can be a game changer for Indian agriculture. The question is: are Indian farmers ready for it? Who will pay for data? Do we have talent to deliver on ag-data models?

B.  Readiness of Indian farmers and entrepreneurs for data-centric models

My interaction with farmers in states like Rajasthan, Madhya Pradesh, Himachal gives me confidence that farmers are ready to embrace any technology which can improve farm economics. Of course, there is a need to make them aware about the potential upsides and risk mitigation possible with data. For example, in a pilot project conducted by an ag-data company; many farmers found that their estimation of own farm area was different from farm area estimated through geo-tagging. Once they knew the differential to the last digit, they were able to improvise the input application.

In general, farmers are open to adapting technology. Farmers are downloading apps for real-time access to data such as market yard prices. Though transactions and data sharing through such apps are limited, they are bound to go up with increasing use of vernacular language and improving UI and UX.

It’s unlikely that farmer is going to pay for the data (at least in the near future) till they see clearly economic benefits. If a farmer is not going to pay, then the question is who will pay? Here are the potential buyers of data:

Agri input and output companies: The data analytics will be very useful for farm input companies (like agrochemicals, seeds, fertilizers, implements) as well as aggregators, processing companies, retailers who are buying farm produce from the farmers.

The input companies can be more prescriptive with data available on soil and crop health. Output companies can use data to ascertain and pre-book their supplies with estimates of crop output available in advance of harvest.

Banks and insurance companies: Both can save lot of cost by analyzing farm data to enable farmer KYC, credit worthiness and risk profile of farmer and the field.

Government: Various ministries and departments in central and state governments can also buy data to improve accuracy, frequency, and timeliness of the data collected and reported by them (such as advance estimates of sowing area and production).

Thus, there are enough buyers who will be willing to pay for data. The premium can be built with more analytics, syndication and advisory.

Last but not the least, do we have enough ag-data-preneurs who see the potential and have the capability to seize this opportunity?

My mentoring relationship with many ag-data start-ups give me confidence that there is enough talent and risk-taking appetite in India. Likes of CropIn, Skymet, AgRisk have made inroads in this space and there are many who are soiling their hands in fields to build applications to capture data. Also many farm to consumer and direct to farm startups are converging to data as logical extension of their businesses. Globally, the success of the model is demonstrated by likes of Farmer’s edge, Farmlink, PrecisionHawk; who have scaled up and attracted significant funding in short period of time.

The challenge remains in upscaling data-transaction values (usually calculated in Rs. Per hectare) which can be addressed by building analytics and developing syndicated products. These models require patient capital as gestation periods are long. Also, small farm holdings pose some challenges in making some of the applications viable, which can be mitigated with land consolidation going forward.

C.  Time for “Data-revolution” in Indian Agriculture

To conclude, Indian farm economy can benefit enormously by availability of timely, accurate and actionable data. Investment in ag-data will benefit the farmer the most by making agriculture more predictable and remunerative for him. Data intervention in agriculture can go a long way in realizing Prime Minister’s dream of doubling farmer’s income in five years.

India is also blessed with a vibrant IT and analytics industry who over last three decades have brought huge amount efficiencies to corporations across the world. The same talent teamed up with agronomists and scientists can bring much-required-efficiency to Indian agriculture.

Over a period of time, we should strive to develop an open-source-digital-platform in agriculture (capturing farm, farmer, soil, crop data) which can open doors for more applications and rural entrepreneurship.

“Green revolution” in sixties and seventies in the last century made us self-sufficient in food needed for the country. Indian population has more than doubled since then. Time is ripe for “Data revolution” which can make Indian agriculture more efficient to cater to growing food demand for the country.