From Land-Yog to Machinery-Yog to Information-Yog: How the Ages Reordered Work, Wealth, and Life 

Short version: Human economies have moved through dominant “yogs” (ages) — a land/agrarian yog, a machinery/industrial yog, and now an information yog. Each shift changed who creates value, how people make a living, where wealth concentrates, and what “good life” looks like. The information yog (digital/internet economy) is expanding faster than earlier transformations and is re-shaping finance, jobs, governance and everyday lifestyle — but it also creates new inequalities, new dependencies (platforms, data), and new policy challenges. Below is a long-form article with statistical anchors, a short CSV-style table of key indicators, and practical conclusions for citizens and policymakers.

 

1. Introduction — why call them “yog”?

“Yog” (a Sanskrit-rooted word often translated as “union” or “period”) here is a metaphor for epoch-making economic configurations.

• Land-Yog: economies organized around land — subsistence and commercial agriculture, local markets, land-owning elites.
• Machinery-Yog: the industrial era — mechanized manufacturing, steam/coal/petrol energy systems, urban factories and mass production.
• Information-Yog: the digital era — networks, data, software, platforms, services delivered globally in microseconds.

Each yog rearranged labour, capital, the state’s role, and daily life. The current information-yog is different in pace and scope: it compresses distance, creates abundant low-cost information goods, and builds new kinds of network power (social platforms, cloud providers, big tech). The rest of this article traces the transitions, shows hard numbers, analyses impacts, and sketches what to watch for next.

 

2. Snapshot: Where the world (and India) stand now — key statistics

Below are a few load-bearing facts that ground the analysis:

·         Internet reach (global): In 2024 there were ≈5.35 billion internet users — about 66.2% of the world’s population. This is the infrastructure of the Information-Yog: people, devices, and platforms connected. DataReportal – Global Digital Insights

·         India — sector composition (recent years): India’s economy has shifted strongly toward services. Official estimates and summaries show services contributing roughly 54–55% of gross value added/GDP in recent fiscal years (FY 2023–25 period). The services boom is a core sign of the Information-Yog in India. Press Information Bureau+1

·         Agriculture’s shrinking share in GDP (India): Agriculture’s share of GDP has declined over decades but still matters: about 16–17% of GDP (value-added share reported around 16.3–16.4% in 2023–24/2024 figures), while employing a far larger share of people. This mismatch (lower GDP share but high employment share) is a structural legacy of the Land-Yog. World Bank Data+1

·         Manufacturing activity (India): Manufacturing’s share has hovered in the mid-20s percent of GDP; manufacturing gross value added rose sharply in FY24 (GVA growth ~12% in FY24 vs previous year), indicating recovery/acceleration in the Machinery-Yog legacy industries — though these industries are increasingly automated and digitally controlled. The Economic Times+1

(These five citations are the most load-bearing factual anchors in this article; other supporting claims are either analytical or synthetic.)

 

3. Land-Yog: the first long-run human economy

For millennia most societies lived in a land-yog: livelihoods came from tilling soil, herding, fishing and local trade. The essentials of life — food, basic clothing, shelter — were produced locally or regionally. Important features:

• Employment concentration in agriculture: Even today, many low-income countries have a high share of employment in agriculture despite agriculture’s lower GDP share. In India, for example, agriculture still employs a very large fraction of the workforce while contributing roughly ~16–18% of GDP — a classical “dual economy” pattern. TheGlobalEconomy.com+1
• Property and status tied to land: Land ownership created social hierarchies and political power.
• Slow diffusion of technology: In the land-yog, productivity rose slowly — breakthroughs (irrigation, crop rotation, later the Green Revolution) spread over decades.

Implication: The land-yog offers stability and food security but limits urbanization, mass manufacturing, and the kind of rapid productivity growth that fuels a modern consumer lifestyle.

 

4. Machinery-Yog: mechanization, factories, mass production

From the 18th–19th centuries onward (and accelerating through the 20th century), societies transitioned to a machinery-yog. Private capital investment in steam, coal, electricity, factories and assembly lines produced dramatic productivity gains.

• Urbanization and factory employment: People migrated to cities for factory jobs. Wages and consumption patterns changed: mass housing, public transport, and systematized labor laws emerged.
• New scale of production: Standardized goods, brand manufacturing, and heavy industry grew. Countries that industrialized early (UK, USA, Germany, later Japan, South Korea, China) accumulated capital, developed infrastructure, and built geopolitical power.
• Industrial GVA share: Manufacturing became a large share of GDP in industrialized economies. In developing economies such as India, manufacturing hovered around one-quarter of GDP in recent decades while services grew faster. World Bank Data+1

Implication: The machinery-yog reorganized societies around factories, supply chains, and energy systems. It created middle classes, but also environmental externalities (pollution, resource extraction) and mass labor issues (working conditions, unionization).

 

5. Information-Yog: networks, software, data and platforms

What we call the information-yog began in earnest with computing, telecommunications, and the internet. Its defining features:

• Decoupling of value from physical inputs: Software, algorithms, and platforms can scale with near-zero marginal cost. A single app can serve millions without proportionally more physical inputs.
• Network effects and winners-take-most markets: Platforms (search engines, social networks, marketplaces, payment networks) gain disproportionate power. Google, for example, became central to information discovery and advertising — a classic information-yog superstar.
• Global real-time markets: Services can be exported digitally — call centers, IT services, online education, freelancing — making “location” less binding for some economic activities. India’s surge in services exports (business & IT services) is a concrete manifestation. Press Information Bureau

Scale and reach: By 2024 there were ≈5.35 billion internet users worldwide; this global network is the substrate of the information-yog and underpins e-commerce, digital finance, social media, online learning, telemedicine and more. DataReportal – Global Digital Insights

 

6. How the information-yog opened people’s financial and lifestyle possibilities

Below we examine concrete ways the information-yog changed finances and lifestyles.

6.1 Financial inclusion and digital payments

• Lower transaction costs: Mobile wallets, UPI-style instant payments (India is a standout example), and fintech APIs reduced frictions. Small vendors can accept digital payments with a smartphone.
• Credit and microfinance via data: Alternative credit scoring (transaction history, digital footprints) enabled lending to previously unbanked people. This has improved small-business finance but raises privacy/regulatory issues.

6.2 Work and income diversification

• New income avenues: Gig platforms, remote freelancing, microtasking and content creation let people monetize skills or time without local employers.
• Blurring of formal/informal: Informal-sector workers can access new markets (ride-hailing, e-commerce seller dashboards) but often lack social protections.

6.3 Consumption, information and lifestyle

• Access to information: Health guidance, educational content, market prices (for farmers), and government services are now widely accessible via the web and apps.
• Platformized consumption: Food delivery, streaming, ride-sharing, and online shopping change consumption patterns and time use. People can access global culture, but local businesses sometimes struggle to compete.

6.4 Data-driven public goods

• Better targeting: Welfare programs and subsidies can be targeted via digital IDs, mobile registries, and transaction histories — improving efficiency.
• Surveillance risks: The same data enables tracking and profiling — governance tradeoffs are acute.

 

7. Statistical mini-analysis: three comparative snapshots (CSV style)

8. What the numbers hide — inequality, employment and vulnerability

In 2010, agriculture contributed about 17.6% to India’s GDP, industry (manufacturing and construction) about 26.0%, and services the largest share at 56.4%. (World Bank / India sectoral series)

By 2020, agriculture’s share slightly declined to 16.4%, industry to 25.0%, while services rose further to 58.6%. (India Economic Surveys / World Bank)

In 2024, agriculture still stood at 16.4%, industry increased marginally to 25.9%, while services moderated to 55.3%. (World Bank; PIB Economic Survey FY25)

 

The information-yog brings gains but also sharp frictions:

• Employment mismatch (structural unemployment): In many economies the services sector (and high-productivity digital services) generates GDP faster than jobs. Manufacturing and modern services are capital-light or skill-intensive, leaving many low-skill workers vulnerable. In India, agriculture still employs a large share of people despite contributing a smaller share of GDP — a structural mismatch that requires policy action. Digital divides: Internet access is widespread globally but uneven (urban vs rural, rich vs poor). Even places with high internet user counts have gaps in quality (broadband speeds), digital skills, and affordable devices.
• Platform concentration and rent extraction: Large platforms capture advertising, search, and marketplace rents — reducing small sellers’ margins unless they adapt. This raises questions about competition policy and data governance.
• Precarity in gig work: Contracts, benefits, and labor protections lag behind the new work forms. Gig workers often lack health benefits, pensions, or stable incomes.

 

9. Policy responses and institutional choices

To harness the information-yog while protecting citizens, several policy responses matter:

9.1 Invest in human capital (digital and cognitive skills)

Upskilling programs, affordable digital education, and vocational tracks that blend domain knowledge with digital skills will help workers transition from land/yarn/factory jobs into higher-value service and tech roles.

9.2 Strengthen digital infrastructure and inclusion

Beyond counting internet users, focus on broadband quality, rural last-mile connectivity, affordable devices, and local-language content so that the information-yog really reaches everyone. The 5.35 billion global users number is impressive, but the 2–2.7 billion not yet connected is where growth and inclusion are urgent.

9.3 Update social protection and labor rules

Portable benefits, contributory floors, and rules for platform accountability can reduce gig precarity. Recalibrating minimum wages, unemployment insurance and pension portability for digital gig work is essential.

9.4 Data governance and competition policy

Data portability, fair-access rules for platform ecosystems, and stronger antitrust action against market concentration will protect competition and consumer welfare. Governments must decide the boundary between innovation-friendly regulation and consumer/worker protection.

9.5 Support for agriculture in a connected age

Digital marketplaces, price transparency, crop insurance and supply-chain logistics can help farmers convert connectivity into real income gains. But digital markets must be paired with physical infrastructure (storage, transport) to avoid exploitation.

 

10. Business and personal strategies in the information-yog

For businesses:

• Digitize core processes (inventory, payments, customer data).
• Design for platforms, not just for stores — be present where customers are (marketplaces, search, social).
• Invest in data ethics and privacy to build trust.

For individuals:

• Acquire basic digital literacy (internet safety, online transactions, basic data skills).
• Build transferable skills (communication, problem-solving, domain knowledge blended with digital tools).
• Diversify income streams — part-time digital work, teaching, small e-commerce shops.

 

11. The paradox: abundance of information, scarcity of attention and trust

The information-yog creates abundant content and services but scarce attention. This creates incentives for sensationalism, misinformation, platform manipulation, and algorithmic filtering that can warp democratic discourse and private decision-making. Rebuilding public trust — through media literacy, platform accountability, and civic institutions — is a core non-economic task of this age.

 

12. Future contours — hybrid yogs, not single transitions

What comes after “information-yog”? Most likely hybrids — an era where information, automation (AI/robotics), and sustainable/material transitions (green energy, circular economy) interact. A few trends to watch:

1. AI + automation in manufacturing — “smart factories” that combine the machinery-yog’s output power with the information-yog’s intelligence.
2. Decentralized finance and web3 experiments — potential to reshape how financial inclusion and trust are architected (but with high risks).
3. Climate constraints — energy transitions will determine which industries are sustainable; the information-yog can help optimize systems to be low-carbon but also increases e-waste and energy demand.

1. Key background data — digital economy / e-governance in India (2022–2025)

Here are a few recent facts / projections:

Indicator	Value / Estimate	Year(s)	Notes / Source
Digital economy share of GDP (GVA basis)	11.74 %	2022-23	from PIB press note; expected to rise to 13.42 % in 2024-25
Projected digital economy share	~13.42 %	2024-25	MeitY / press note projection
Digital economy growth vs overall economy	~2× faster	Recent years	The digital economy is said to grow almost twice as fast as the rest of the economy
Internet / mobile connectivity	~96.96 crore connections (≈969.6 million)	2024	Jumped from 25.15 crore in 2014
Village connectivity with mobile/4G	~97.65 % villages with mobile coverage; 96.80 % villages with 4G	As of May 2025 / mid-2024	From PIB factsheet
UPI transactions (volume & value)	UPI recorded 1,867.7 crore transactions worth ₹24.77 lakh crore in April 2025	April 2025	Digital payments traction
Government e-Marketplace (GeM) GMV & users	GMV > ₹13.60 lakh crore; ~2.86 crore orders; ~1.64 lakh buyer orgs, ~23 lakh sellers/service providers	as of May 2025	Government procurement via digital marketplace
E-Government / e-participation index	EGDI = 0.6678; E-Participation = 0.6575	Recent UN data	India’s rank 97 of 193 in EGDI

These data show the scale of digital penetration, the growing share of digital economy, and the volume of digital transactions. They set the stage to examine how customer data fuels value to governments and business.

 

2. How customer / citizen data helps governments — thematic table + mechanisms

Here’s a table summarizing use cases, benefits, and challenges in government using citizen data:

Use / Mechanism	How Customer Data Is Used	Benefits to Government / Public	Metrics / Indicators	Risks / Challenges
Targeted welfare / subsidies	Using transaction history, mobile IDs, geo-location, social registry data to allocate subsidies, direct benefit transfers, food/energy credits	Lower leakage, better targeting (less wrong inclusion/exclusion)	Reduction in subsidy leakage, share of subsidy reaching intended recipients	Privacy, exclusion errors, data errors
Tax compliance and revenue	Data from digital payments, e-commerce sales, transaction trails help detect evasion or assess taxes more accurately	Greater tax base, more efficient audits, real-time tax enforcement	Increase in tax revenue, compliance ratio, reduction in audit cycles	Data security, false positives, overreach
Urban planning / mobility / infrastructure	Mobility data (from smartphones, transport cards), utility usage data, citizen feedback portals	Better infrastructure placement, demand forecasting, congestion control, resource allocation	Reduction in congestion, optimized public transport, improved infrastructure utilization	Data privacy, surveillance, algorithmic bias
Crisis / disaster response / public health	Health data, mobility/contact tracing (in pandemics), usage of services, complaints data	Faster response, resource allocation, predictive warnings	Response time reduction, lives saved, service reach	Consent, data misuse, data security
Service delivery optimization	Usage logs of government websites/apps, feedback, service requests (e.g. complaints)	Identify bottlenecks, streamline processes, personalize services	Reduction in turnaround time, user satisfaction scores	Digital exclusion, algorithmic discrimination
Policy design & evaluation	Aggregated consumption data, survey data, digital behavior data to forecast effects of policies	More evidence-based policymaking, dynamic adjustment, evaluation metrics	Success rates of programs, forecast vs actual deviations	Data representativeness, bias, overfitting policies to data quirks

Extent (scale) indicators:

• The fact that UPI handles ~1,867.7 crore (≈18.677 billion) transactions in a single month (April 2025) is a huge data flow that governments can tap (for analytics, taxation, monitoring)
• The GeM portal’s GMV of ₹13.60 lakh crore and millions of orders suggest a massive volume of procurement and supplier data that government holds and can use for analytics, supplier performance, price benchmarking, fraud detection etc.
• E-Transaction aggregation (via platforms like eTaal, NIC’s dashboard) enables real-time monitoring of e-governance usage across ministries and states
• High levels of mobile/4G coverage in rural India (96–97 % villages) means that data coverage is widespread, offering governments almost universal sensor/participation points
• Thus government has large-scale, continuous, high-resolution citizen/transaction data to improve governance — but it has to manage privacy, equity, and digital divides.

 

3. How customer / consumer data helps businesses / entrepreneurs / firms

Here’s a parallel table:

Use / Mechanism	How Consumer Data Is Used	Benefits to Businesses	Metrics / Indicators	Risks / Challenges
Personalization / recommendation	Use browsing history, purchase history, demographic data to recommend products or services	Higher conversion, better customer retention, cross-selling	Click-through rate (CTR), average order value (AOV), repeat purchase rate	Privacy, filter bubbles, over-personalization
Dynamic pricing / demand forecasting	Use real-time demand data, competitor pricing, consumer behavior to optimize pricing and inventory	Maximized revenue, reduced wastage, efficient inventory	Revenue per unit, margin uplift, deadstock reduction	Consumer pushback on price discrimination, fairness concerns
Customer segmentation & targeting	Segment consumers (by location, behavior, preferences) to run targeted marketing campaigns	Improved ROI on marketing spend, better reach	ROI on ad spend, conversion per segment, customer acquisition cost (CAC)	Data silos, privacy regulations (consent), bias in segmentation
Product development / innovation	Analyze feedback, usage data, complaints, and behavioral data to iterate or create new products	Better market fit, faster cycles, reduced risk of failure	Time to market, success rate of new features, churn reduction	Misinterpretation, overfitting to current data, ignoring latent demands
Supply chain optimization	Demand data, order patterns, returns data used to optimize logistics, inventory, procurement	Lower costs, faster delivery, reduced stockouts	Lead time, inventory turnover, logistics cost as % of revenue	Data integration across partners, data quality issues
Credit, lending & fintech services	Use digital payment history, transaction logs, alternate data (mobile usage, utility payments) to assess creditworthiness	Serve more customers, reduce defaults, better risk pricing	Default rate, NPA (nonperforming assets) ratio, number of new accounts	Data privacy, fairness, algorithmic discrimination
Churn prediction and retention	Use patterns (reduced usage, support tickets) to predict likely churn and intervene	Lower customer attrition, improved lifetime value (LTV)	Churn rate, retention uplift, cost per retention campaign	False positives, mis-targeting, privacy backlash

Extent & scale indicators:

• With the digital economy expected to contribute ~13.42 % of GDP in 2024–25 (rising from 11.74 %) , firms in digital platforms, fintech, SaaS etc. are working at significant scale.
• Because internet/mobile penetration is so widespread, businesses can collect data from vast user bases. For example, ~96.96 crore internet connections (in 2024) gives a huge potential user pool
• The expansion of data centres (IT load, cloud infrastructure) is supporting data-intensive business models; India’s data centre capacity is growing to support this scale.
• The AI market in India is projected to reach USD 8 billion by 2025, so businesses are embedding AI/ML models in consumer data processing to derive value.
• Thus, the scale is large: businesses can use granular consumer data, in real time, for nearly every stage of their value chain.

 

4. Statistical / numeric tables combining government & business uses and scale

Below are two tables summarizing “extent / scale” in 2025 of data-driven interaction, and then an example synthetic analysis.

Table 1: Scale of data-driven digital economy & interaction (Indicative 2025)

Metric	Value / Estimate	Relevance to data-driven systems (govt or business)
UPI transactions (monthly)	~1,867.7 crore (≈18.68 billion) & ₹24.77 lakh crore (monetary value)	Massive flow of financial data for analysis, risk models, public finance insights
Internet connections	~96.96 crore (≈969.6 million)	Large user base generating usage logs, clickstreams, location, etc.
GeM procurement GMV	₹13.60 lakh crore	Data from huge government procurement provides benchmarking, tracking, supplier performance data
Digital economy contribution to GDP	~13.42 % (projected for 2024–25)	Indicates that data-driven sectors are materially significant in economy
AI market size	USD ~8 billion (India)	Data-intensive AI models consuming consumer data to deliver insights, predictive services

Table 2: Synthetic example — hypothetical performance gains from using consumer data

Note: These are illustrative / hypothetical numbers to demonstrate possible gains.

Use Case	Baseline Metric	After Data-driven Intervention	Improvement
Government subsidy targeting	Leakage 20% (i.e. 20 % of subsidies go to ineligible)	Using transaction + ID data, leakage falls to 12%	8 percentage point gain
Business e-commerce conversion	Conversion rate 2.5%	After personalized recommendations, conversion rises to 3.2%	+0.7 pp (28% improvement)
Inventory carry cost (retail)	12% of revenue tied in inventory	With demand forecasting, reduced to 9%	3 pp lower cost (25% reduction)
Customer churn (subscription business)	Annual churn 15%	Using churn prediction and retention campaign, churn reduces to 11%	4 pp gain in retention
Tax collection (small business sector)	Yield shortfall 25% due to under-reporting	With digital payments data audits, shortfall reduces to 15%	Additional revenue capture

These illustrative examples show how tracking of consumer / citizen data can yield measurable improvements in government efficiency and business performance.

. Savings in the Information-Yog

In the Land-Yog, wealth was stored in land, cattle, and grains.
In the Machinery-Yog, savings shifted to banks, gold, and lockers.
In the Information-Yog, savings are increasingly digital and data-driven.

Key Trends

• Rise of Digital Payments & Wallets:
• UPI monthly transactions in April 2025 crossed ₹24.77 lakh crore ([PIB, 2025]).
• Over 500 million Indians use mobile wallets and digital payments.
• Decline in Physical Cash Savings: More customers keep liquidity in wallets, instant-pay accounts, and digital gold.
• Financial Inclusion via Tech: Jan Dhan accounts (~51 crore as of 2024) linked with UPI/DBT have shifted rural savings into formal channels.
• Shift from Lockers to Digital Assets: Mutual funds, SIPs, and online FD platforms attract more funds than traditional lockers.

 

2. Lockers in the Information-Yog

Traditionally, bank lockers were used for gold, jewelry, and physical documents.
Now, two shifts are visible:

Digital Lockers

• DigiLocker platform by Government: Over 250 million registered users and 6.7 billion issued documents (as of 2024).
• Customers now store Aadhar, PAN, degrees, insurance papers, property docs digitally.
• Reduces demand for physical lockers for documents.

Gold Lockers → Digital Gold

• Platforms like Paytm, PhonePe, and MMTC-PAMP allow fractional digital gold purchases.
• Customers prefer digital gold + ETFs rather than storing ornaments in lockers.

Physical Lockers still relevant

• Gold ornaments and real estate papers remain in lockers.
• But demand is growing slowly, as per RBI’s locker safety reforms (2022 guidelines tightened liability).

. Statistical Indicators (Savings & Lockers, India)

Year	Household Physical Savings (% of GDP)	Financial Savings (% of GDP)	Bank Locker Demand Trend	Digital Locker / DigiLocker Growth
2010	~12%	~7%	High (gold & documents)	Negligible
2020	~10%	~10%	Moderate (jewelry still strong)	100M users
2024	~9%	~11.5%	Flat growth	250M+ users, 6.7B docs
2025 (est.)	~8.5%	~12.5%	Slight increase only in metros	Rising – projected 300M+ users

 

4. Impact Analysis

Positive Impacts on Customers

• Convenience: Digital lockers reduce dependency on physical visits.
• Liquidity: Wallets, UPI, SIPs, and instant-pay instruments give faster access than lockers.
• Safety: Less risk of theft compared to physical lockers.
• Returns: Savings are shifting from idle gold in lockers to earning instruments like SIPs.

Impacts on Government

• Data Visibility: More financial savings → more tax transparency.
• Reduced Black Wealth: Decline in locker-based storage of unaccounted cash/gold.
• Policy Effectiveness: Easier to channel savings into bonds, infrastructure, etc.

Impacts on Businessmen & Banks

• Locker Rent Income: Stagnant or declining in physical format.
• New Opportunities: Banks offering digital vaults, document storage, and gold ETFs.
• Fintech Boom: Wallets, neobanks, and wealthtech apps monetizing digital savings.

 

5. Interpretation – Information-Yog Effect

• Customers are shifting savings from “hidden wealth in lockers” → “visible, digital wealth”.
• The locker is transforming from a steel box in a bank → a cloud-based encrypted vault.
• Data becomes the new locker – holding customer trust, financial records, and digital assets.
• Transparency increases, reducing tax evasion but raising privacy debates.

 

✅ In short:
The Information-Yog reduces dependence on physical lockers and increases digital savings, helping customers with liquidity, the government with transparency, and businessmen with new digital services.

 

5. Extent, limitations, and caveats

While the potential is large, the extent of data-driven capacity has boundaries and caveats.

Limitations and challenges

• Digital divide & data gaps: Not all citizens are fully connected or digitally active; some rural or marginalized groups may generate little digital footprint, creating blind spots in data systems.
• Data quality / noise / bias: Raw data often has missing, noisy, or biased records; analytics must correct for these, or risk misleading policies.
• Privacy, consent, regulation: Citizens’ consent, rights to privacy, and data protection laws limit how aggressively governments or businesses can use data.
• Algorithmic bias / unfairness: Automated decisions, if trained on historical biased data, can entrench discrimination.
• Trust & legitimacy: Citizens may resist intrusive data usage; lack of transparency can erode trust in government or in brands.
• Overfitting to data regimes: Relying too much on historical patterns can stifle innovation; new trends may not follow old models.

2025 (real-world constraints)

• Even though UPI usage is massive, not all financial transactions are digital — cash and informal economies still persist.
• Government schemes may not yet integrate all data silos (e.g. health, education, tax, utilities) seamlessly.
• Many small businesses or vendors are just beginning digitization; their data may be fragmentary.

 

6. Summary & policy/business implications

• The extent of customer/citizen data available in 2025 in India is large and growing: digital payments, procurement platforms, mobile usage, internet connectivity give continuous streams of data.
• Governments can harness this data for better targeting, transparency, revenue, service delivery, and infrastructure planning.
• Businesses can exploit data for personalization, forecasting, credit, retention, and supply chain optimization.
• But scaling these benefits requires addressing privacy, inclusion, data governance, bias, and trust

Here’s a structured analysis

 

 Negative Impacts on Customers

Issue	Explanation	Data / Evidence
Privacy Loss	Every digital transaction, search, or click leaves a trail. Customers feel constantly monitored.	India ranks 97/193 in UN’s E-Government Index (2022) → showing digital use but also vulnerability.
Cybercrime & Fraud	Online scams, phishing, and UPI frauds have risen sharply.	RBI reported 13.23 lakh digital payment fraud cases in FY23–24, mostly small-ticket but massive in number.
Over-Spending Culture	Instant credit (BNPL, app loans) → customers spend more, save less.	India’s household financial liabilities rose to 5.8% of GDP in 2023, highest in a decade.
Digital Divide	Rural/elderly struggle with apps; urban youth adapt quickly → inequality widens.	~40% of Indians still lack digital literacy despite connectivity (NSSO 2023).
Loss of Traditional Safety	Earlier, lockers & gold ensured psychological security. Now, customers worry about data hacks and account freezes.	Rising cases of DigiLocker phishing attempts reported by CERT-In (2024).

 

. Negative Impacts on Government

Issue	Explanation	Example
Data Overload	Too much raw data, not enough capacity to analyze → “information pollution.”	Government collects trillions of UPI & Aadhaar-linked records, but only fraction is analyzed.
Surveillance Concerns	Citizens fear misuse of data by state (e.g., tracking dissent).	Civil society debates around Aadhaar, social media monitoring.
Security Risks	Government data leaks = national security issue.	In 2022–24, multiple Aadhaar-related leaks exposed millions of records.
Policy Bias	Over-reliance on data dashboards may ignore ground realities (e.g., poverty not captured digitally).	Example: ration card cancellations in 2023 due to data mismatch, leaving genuine poor excluded.

 

. Negative Impacts on Businesses

Issue	Explanation	Example
Data Breaches	Firms handling sensitive customer data are frequent hacking targets.	In 2023, AIIMS Delhi hack compromised millions of patient records.
Platform Dominance	Few giants (Google, Amazon, Reliance Jio, Paytm) control most customer data → small firms sidelined.	India’s top 5 e-commerce platforms control >70% market share.
Customer Distrust	Repeated misuse of data (spam calls, hidden charges) reduces loyalty.	TRAI 2024 survey: 70% mobile users complain of unsolicited commercial messages despite DND rules.
Regulatory Burden	New data protection laws increase compliance costs for startups.	Digital Personal Data Protection Act, 2023 → fines up to ₹250 crore per violation.
Short-termism	Businesses chase click-based sales instead of building long-term trust.	BNPL apps aggressively push credit → leading to defaults.

 

. Cultural & Psychological Negatives

• Information Overload → Too much news, ads, fake content = stress + decision fatigue.
• Attention Deficit → Customers struggle to focus; businesses fight for micro-seconds of attention.
• Reduced Human Touch → From face-to-face banking to app-only service → customers feel alienated.
• Trust Shift → From family & local banks → to faceless algorithms & platforms.

 

. Statistical Snapshot: Negative Effects

Aspect	Pre-Information-Yog (2010)	Information-Yog (2025)	Negative Outcome
Household Physical Savings	~12% of GDP	~8.5%	More dependence on volatile digital assets
Cyber Fraud Cases	~2 lakh annually	>13 lakh annually	Rising fraud exposure
Digital Divide (urban vs rural internet use)	Urban 60% vs Rural 20%	Urban 80% vs Rural 50%	Gap narrowing but still significant
Data Breaches (India)	<50 reported	>1200 reported (2023)	Customer distrust
Household Debt (% of GDP)	3%	5.8%	Over-borrowing due to instant credit

. Interpretation

• Customers gain speed and access, but lose privacy, security, and control.
• Governments gain data visibility, but face data overload, cyber risk, and trust deficit.
• Businesses gain market insights, but risk over-regulation, monopolies, and customer backlash.

 

✅ In short:
The negative side of the Information-Yog is that while it creates efficiency and transparency, it also produces vulnerability, inequality, and psychological insecurity.

Table: Positive vs Negative Impacts of Informative Yog

Stakeholder	Positive Impacts	Negative Impacts
Customers	• Easy access to savings via UPI, wallets, SIPs • Digital lockers (DigiLocker) for safe document storage • Lower dependence on cash and physical lockers • More transparency in financial tracking	• Cyber fraud and phishing risks (13+ lakh frauds FY23-24) • Privacy loss due to data trails • Over-spending via BNPL & app loans (household debt 5.8% of GDP, 2023) • Digital divide leaves rural/elderly behind
Government	• Financial inclusion (51 crore Jan Dhan accounts linked with UPI) • Easier tax compliance & subsidy targeting • Black wealth in lockers reduced • Paperless governance through DigiLocker	• Data overload → “information pollution” • Security threats (Aadhaar & govt leaks) • Risk of surveillance mistrust • Policy errors due to data mismatch (ration exclusions, 2023)
Businesses & Banks	• Fintech growth (wallets, gold ETFs, wealth apps) • New revenue streams from digital locker services • Better customer analytics for products & loans • Lower cost vs physical locker infra	• Data breaches reduce trust (AIIMS Delhi hack, 2023) • Compliance costs under Data Protection Act, 2023 • Dominance of a few tech giants squeezes small firms • Short-term push sales (spam calls, app loans)

 

✅ Closing Remarks

The Informative Yog is a double-edged sword.

• On one side, it drives efficiency, transparency, and inclusion, moving savings out of hidden lockers and into visible, productive channels.
• On the other, it creates new insecurities, from cyber fraud to privacy erosion and unequal access.

The challenge for policymakers, banks, and businesses is to transform raw information into trusted knowledge, ensuring that customers feel not only connected but also protected.

🔑 Final Thought:
Just as land anchored wealth in the Land-Yog and machines powered growth in the Machinery-Yog, information is the currency of the Informative-Yog. Its value, however, lies not in abundance, but in how securely and wisely it is used.

 

References (selected)

·         DataReportal / WeAreSocial — Digital 2024 (Global internet users ≈5.35 billion). DataReportal – Global Digital Insights+1

·         World Bank — Value added by agriculture/manufacturing (% of GDP), India (time series 1960–2024). World Bank Data+1

·         Press Information Bureau / Economic Survey summaries — Services contribution to GVA/GDP (FY24–FY25). Press Information Bureau

·         Economic Times — Manufacturing GVA rises 12% in FY24 (context on manufacturing recovery). The Economic Times

·         Statisticstimes / India sector-wise summaries for recent fiscal numbers. Statistics Times