Nomura's Sonal Varma with BloombergQuint on India Growth Forecast

Bank of England Survey on Consequences of a No Deal Brexit

The Bank of England has published results from its Agents' Survey of Companies over the period December 2018-April 2019: The average numbers from the responses to its questions on what companies believe will be the macroeconomic consequences of a #NoDeal #Brexit were as follows: Overall Output (i.e. GDP) is expected to fall by 3 ½%, employment by 3% while investment, perhaps more optimistically was expected to fall only by 1 ¼%. Exports were expected to fall nearly as much as GDP itself. 

Note that, on the expectation for the immediate fall expected in output (GDP), the results of the companies' survey of the BoE roughly match the 3% contraction expected in the simulation results of the UK's National Institute for Economic and Social Research (NIESR), which I have blogged just below this ost.

Markets expect, however, that owing to the ensuing post-#NoDealBrexit slack in the economy,  that Interest Rates will fall during the year following the Brexit Date of October 31 2019, and rise gradually in the subsequent period, conditioned on a recovery. 

The figure below, represents a summary of the results of the survey, contrasted against responses elicited for a scenario where a Brexit Deal is reached between the EU and the UK, with a period to allow for transition, were presented in a July 23 2019 presentation by Andy Haldane, the Chief Economist of the Bank of England.

Macroeconomic Consequences of a No-Deal Brexit

Dr Jagjit Chadha (@jagjit_chadha), Director of the UK NIESR [National Institute of Economic and Social Research] @NIESRorg presents results of modeling the '#NoDeal #Brexit' scenario's impact on the British Economy. 

Immediately on impact, the model results project a 2-3% *contraction* in economic activity (GDP) and upon subsequent recovery, over the longer-term, the model projects a nearly 7% lower GDP below the #SoftBrexit scenario (which nearly approximates a Business as Usual / BAU) scenario that the model takes as the baseline). 

The contraction (of the 2-3% magnitude) is assumed to occur in the event that not only is no EU-UK deal reached, but the UK carries out no monetary/fiscal or other policy adjustments in anticipation. It thus represents a rather extreme scenario that, within the assumptions of the model is not likely to (fully) come to pass. Yet the discussion is important, so that even the worst consequences of a No Deal Brexit come to be more widely appreciated.

https://www.niesr.ac.uk/sites/default/files/publications/commentary%20August%202019.pdf

The Infosys Q1 FY20 Results Press Conference

Does India Need to Revisit its Macroeconomic Framework?

In his new IDFC Institute (@IDFCinstitute) Working Paper, Dr Niranjan Rajadhyaksha (@CafeEconomics)  asks if India simultaneously met the targets in a hypothesized '2-4-6-8'  macroeconomic framework that, without explicit enunciation, one can assume it has followed over the past decade or so: with a 2% target for the Current Account Deficit, an Inflation Target of 4%, a 6% target for its Consolidated Fiscal Deficit (i.e. fiscal deficits of states and the Centre combined, as a percent of national GDP) and an 8% (aspirational) target for RGDP growth. The short answer is No, but even the more nuanced answer cannot see the glass as half-full. the Table below shows the results over the last decade. The caveat being that, except for the inflation target and the target for the consolidated fiscal deficit (which have each been set out explicitly), the other two targets have not been explicitly stated, and the RGDP 'target' is more aspirational than normative in nature.

The data for the past decade with respect to these big macroeconomic variables is set out in the Table below.But, left-to-right, the targets for the variables in the columns are actually in the order 8-4-6-2]  

Dr Rajadhyaksha's IDFC Institute Working Paper on India's Macroeconomic Framework

The best years for Growth & Inflation: 2015-16 and 2016-17 (8% 4.9%; and 7.9% 4.5%) but these are also years with fiscal deficits higher than target & with the Current Account Deficit below target. The Working Paper nicely explains a #Trilemma that Indian macroeconomic policy faces (as do the macroeconomic policies of all countries similarly situated), suggests the right choice of path among the three, and further strongly recommends that future targets should not only be internally consistent but also with the Tinbergen Rule, named for Dutch economist Jan Tinbergen, that the number of variables to be controlled and the number of policy instruments available for the purpose must be equal.  

Added 11 June 2019: In this video clip, Dr Rajadhyaksha speaks with Ira Dugal of BloombergQuint on 'revisiting the 2-4-6-8 Macroeconomic Framework':

 

Mark Zandi of Moody's Analytics on the US Economy

Mark Zandi, the Chief Economist at Moody's Analytics (@economics_ma) said that the first quarter 2019 (Q1 2019) US RGDP growth of 3.1% resulted mostly from the precautionary #Inventory #Buildup that businesses carried out in anticipation of US tariffs on Chinese goods and services; and the second quarter of 2019, Q2 2019, the  increase in RGDP  would likely be just around 1%, reflecting the normal stage of the business cycle.  He further added, that, so far #China tariffs have mainly been borne by intermediate capital goods (used by businesses to make consumer goods). They were thus absorbed by businesses. However, the tariffs will now begin impacting consumer goods, and will thus directly impact total consumption in the economy. https://uk.finance.yahoo.com/video/trade-war-already-doing-damage-080815346.html via @HalfonJulien @YahooFinanceUK

How Age Affects Management Styles

.@JBirkinshaw @JNManktelow et al writing in the MIT Sloan School of Management Journal, @mitsmr of a survey of managers with a view to uncovering how Age impacts management styles via biological, generational, seniority-& experience-related effects. Such effects are often intertwined in their expression, even when a clear separation on a conceptual basis is possible. Their paper  report on the survey - of 10,000 managers, with ages in the range 21-70, and they discuss the nuances of their results. Overall, they contrast the external and internal (to the firm) focus as evident in their attitudes, actions and survey responses. They find that older managers focus more on Core Competencies of the firms and Client relationships as part of their external focus; but their focus within the firm is on building coalitions, developing empathy and  on effectively delegating functions.  While by contrast, younger managers focus more on learning about or developing entirely new Business Models & on their company's Competitive Positioning externally;  and on finding a good Mentor within the company or developing training programs as part of their internal focus.

 

The overall set of results broadly resonate with conventional wisdom, but is invaluable in emphasizing the broad complementarity in what older and younger managers can bring to the table, and that age-diversity is as critical for a firm as diversity along any other axis. Even more, the authors go out of their way to assert that the qualities of older managers *(such as being reflective, intuitive, savvy, holistic or inclusive) cannot, quite obviously, be replaced by any 'artificially intelligent' machine!

CRISIL's Report on 'Whither Inflation?'

.@CRISILLimited presents a very deep dive on 'Whither #Inflation?' Many excellent insights emerge fro their analysis, especially on India #Food price inflation. They provide a great survey-based comparison of the general public's Inflation #Expectations: Before and After the introduction of Flexible Inflation Targeting in India (it was introduced 27 June 2016) and a particularly interesting comparison of the evolution of inflation expectations among the Public versus how they evolved for Forecasters.  

 

While public inflation expectations did indeed come down after Flexible Inflation Targeting was introduced, the expectations are still noticeably higher than actual, realized inflation. And while realized inflation did come down after Flexible Inflation Targeting was introduced, that was mostly because food inflation decreased (and that happened from idiosyncratic factors such as a good monsoon). The bottom line is that while both inflation and inflation expectations came down after Flexible Inflation Targeting, the decrease cannot be unambiguously attributed to monetary policy actions. Indeed, when FIT was proposed as a monetary framework for India, the biggest drawback foreseen was in monetary policy transmission, which even now, with the FIT framework being nearly 3 years old, is still an issue. via @EconomistDK

Will 5G Networks Give Chinese Firms a First-Mover Advantage?

Of 22 panelists that the MIT Sloan School Strategy Forum (Organized by the MIT Sloan Management Review, @mitsmr)  polled, on how strongly they agree with the statement 'Introducing 5G networks 3-5 years ahead of other countries will give Chinese firms a (#FirstMover) advantage' 19 panelists said they 'Agree or Strongly Agree'; only Timothy Simcoe of the Boston University Questrom School of Business  (@BUQuestrom) said that he #StronglyDisagreed. However. he did concede that wireless equipment makers (like transmitters, routers, towers, distributed antenna systems, interference mitigation devices, resource management software and hardware, ie all kinds of wireless equipment, but not the phones themselves) like @Huawei/@ZTEPress will have an advantage (but on cost, resulting from their overall cost structure, not as first-movers); while data carriers [because they will compete locally with other carriers in China, where 5G networks will already have high penetration] or device makers [who will all simply take the available 5G network as standard, so that it will not be a distinguishing feature between them] will have no advantage whatever). (Note: Huawei and ZTE are notable mainly for the equipment they make, but may also make phones. In that case, their equipment manufacturing divisions will gain on cost, not on first-mover, while the phone manufacturing divisions will not gain at all.)

Olav Sorenson (@OSorenson)  is the only other panelist who disagrees with the motion, but does not 'Strongly Disagree' - says that the question is moot because 5G is already being deployed, eg in USA and (South) Korea. One panelist, Richard Florida, hedged completely, neither agreeing nor disagreeing.

Nineteen of the twenty-two panelists (which includes, coincidentally, my old friend Ashish Arora of Duke University) thus agreed that introduction of 5G networks will give Chinese firms a first-mover advantage. To my mind, it was Tim Simcoe's disagreement that was best argued, being based on detailed knowledge of both technical and market issues, while all the rest, in agreeing, did not present any overwhelmingly compelling arguments, although some panelists did enlist 'network externalities', 'control of standards', 'learning-by-doing', 'scale economies', or the 'it depends on the merits of the Chinese technology' arguments. These are pretty old ideas and it is not absolutely clear that they do indeed apply in unmodified form, to the problem at hand. Erik Brynjolfsson's  'argument' if one could call it that, and even, or especially, if one agrees with it - was more a political statement than anything else: '5G is a Big Deal, and the US is fumbling its rollout'. Barry Nalebuff of Yale brings up a historical vignette - the case of France's Minitel (the introduction of which, in the early 1980s) was thought to provide French internet/phone companies a huge first-mover advantage. As things turned out, the advantage was very small or non-existent.

In the end, any decisions on 5G, and Huawei cannot be expected to be made solely on techno-economic arguments of the sort that Tim Simcoe provides, geoeconomics is likely to be much more important - an issue no panelist brought up even tangentially.

Jonathan Garner of Morgan Stanley on BloombergQuint

Jonathan Garner of @MorganStanley speaks with @BloombergQuint on is expectations for the Indian economy following the release of Exit Poll results on the 2019 General Elections (but pending the results themselves). He expects the Reserve Bank of India, @RBI to lower rates by about 50bp over the next 12 months; he expects that GDP growth will rise (more in an upward mean-reverting phenomenon than in reacting to any specific positive development). He expects this to happen as the Indian economy recovers from the series of demand and/or liquidity shocks it has experienced over the last 2 years - including demonetization, the introducton of the GST, the IL&FS and the non-banking financial companies scandals, and the monetary policy actions by the RBI [rate increases during 2018]). He expects as a consequence that the Indian Rupee will appreciate against the US dollar, to about $ @ mid60s₹  over the next 12-18 months; and the Sensex to rise to 42,000 some what sooner, over the next 12 months.

The AI Bubble

Gerbert & Shira of the Boston Consulting Group, @BCG write in the MIT Sloan Management Review @mitsmr that 'yes, today’s fascination with all things #AI has most of the trappings of a financial #bubble...in most cases there is no clear path for (AI startup) companies to become profitable.' Under the circumstances, small start-ups, having developed their technology, or having run out of venture capital money, will seek to be acquired by larger companies. These acquiring companies, they warn, should be wary. Two things may be worth noting when assessing AI startup companies - first, most algorithms (machine learning especially) used in AI applications are several decades, if not half a century old (a version of backpropagation was, for example, used in the Apollo-11 moon landings). The main new development has been in the development of faster computers on which machine learning algorithms can run, and in the availability of large new data sets on which neural nets/machine learning algorithms can be trained. 

But the first development is ironic in a way - in that, neural nets were intended to be archetypes of parallel computer architectures, so the fact that they are simulated in conventional (though very fast) computers of the conventional type should give one some pause. To be sure, there are some new special-purpose hardware architectures (including new chips) which are designed to optimize the computational power used by neural networks. But this has not yet become altogether commonplace. But even when this comes to become more common, rare will be the startup that owns the data which it will use to train its neural network application. The lack of clear ownership of data means that a startup may not be able to optimize and customize its application to the ultimate user, while the machine learning algorithms themselves are publicly available practically, or actually, for free. Thus, on the critical axis of value creation: the trained network, a crucial aspect - the data used - is not owned by the startup. How then can the typical AI startup aim for profitability? What will be its critical determinant of value and distinguishing characteristic? These questions are precisely the ones that acquiring companies will need to ask themselves and the startups they hope to acquire.

While of course one can see the bubble emerging in AI startups, and AI applications, this bubble may not ultimately prove as harmful as a purely financial bubble (eg the 2008 Global Financial Crisis) did. Some good may come even out of AI investments in companies that may ultimately have to be wound up before turning profitable. Some knowledge generation and diffusion will indeed occur, perhaps some patents will be filed or even approved, and ultimately some value may accrue to the investor, though perhaps not at the scale originally envisaged

The Evolution of Global Capability Centres (GCCs)

.@PhadnisShilpa & @sujitjohn write in the Economic Times that #GlobalCapabilityCentres set up by Multi-national companies in India initially in the 1990s, have transitioned from having business models based on cost arbitrage centers to #Engineering R&D value creators; developing, for example, generic malware signature generators (including by using Generative Adversarial Networks in #MachineLearning) to address issues in #Cybersecurity. The total employment in such GCCs located in India is now estimated at 1 million, with the overall value created of $28 billion.

Financial Volatility in an Age of Geopolitical Risks

.@NYU_Volatility conference on 'Financial Volatility in an Age of #Geopolitical Risks', Bennett Golub of @blackrock spoke on '#Geopolitical Risk Management: A Structured Approach'. The Blackrock Geopolitical Risk Dashboard uses subjective judgment to create hypothetical scenarios and then stress-tests portfolios against them. Blackrock South Asia Geopolitical Risk Index was at 1.33 on 18 April 2019 vs 1.37 on 18 Dec 2008 (~3 wks after 26/11). Highest (Impact, Likelihood) currently: Global TradeTensions v @MRVAssociates Cc: @pujamehra @IMateosylago

South Korea GDP Shrinks 0.3% 1Q2019 Q-to-Q

.@business #KOR GDP *shrinks* 0.3% 1Q2019 QtoQ; Exports, ~50% GDP, have been slowing (Fig ↓ ), esp those to #CHN; Also, falling #CapEx, perhaps related to US-CHN trade tensions; both fiscal & monetary (benchmark rate 1.75%) stimulus expected via @katiadmi
 

Global Warming Has Already Slowed Economic Growth in Poor Countries

.@PNASNews 'We find that global warming has very likely exacerbated global economic inequality, including ∼25% increase in population-weighted between-country inequality over the past half century.' e.g., #India vs #Norway... via @pdacosta @MRVAssociates

Figure abv shows the simulated impact on per-capita GDP from the effect of #GlobalWarming on #EconomicGrowth **that has already occurred** over 1961-2010, (using a model w quadratic dependence of GDP on Temperature and Precipitation). %ΔGDP/capita 1961-2010 #IND: -31%, #CHN -1.4%

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D Wallace Wells Speaks At Goldman Sachs on 'The Uninhabitable Earth'

@dwallacewells speaks @GoldmanSachs on his book #TheUninhabitableEarth: #ClimateChange not a #LongTerm issue or just about #ArcticIce Melt+ #SeaLevelRise: 'Let us not be afraid to be afraid' '#WW2 was fought w #Fear & #Alarm' #Speed #Scope #Severity

And a vexing #India-#China comparison - India will be hit hardest - whereas India will have 4X the #ClimateImpact relative to its #CarbonFootprint, China will have only 0.25X the Impact relative to _its_ Footprint (And China's C- Footprint is ~ quarter of Global C-Footprint!) 

#Geopolitics around #ClimateChange will transform the world. #ParisAccord2015 is failing unraveling... hope is US-China will pool resources for joint R&D on climate change mitigation, adaptation, and particularly on #NegativeCarbonEmissions, (since CC M&A won't be enough).

 

Characterizing Successful Digital Executives

Fabian Reck, a PhD candidate at the University of Bamberg, and Alexander Fliaster, a Professor there (and also a Visiting Profressor at IIM Bangalore) have a very interesting paper in the April 10, 2019 issue of the MIT Sloan Management Review. The paper reports on a Survey of 211 #ChiefDigitalOfficers of manufacturing companies in Germany, Austria and Switzerland. Based on their observed characteristics, four profiles of Chief Digital Officers (which include a number of executives in the CXO category from CEO to people directly reporting to them) are arrived at.

Chief Digital Officers which fit the #Networker/#Catalyzer profile were found to have been very effective across many situations (varying in the degree of competitive pressure faced by the company, and the amount of control and authority that the CDOs possessed). When both the Competitive Pressure and the degree of Authority of CDOs are high, then the situation best utilizes the strengths of CDOs possessing the  Networker/Catalyzer and the Lone Icebreaker  profiles, who therefore do better in that set of circumstances than either the Insider Expert or the Innovation Evangelist.

 

Of the four profiles, all except the Lone Icebreaker possess Strong Interpersonal Skills, while only the Insider Expert and the Lone Icebreaker possess deep strategic and business knowledge & IT Expertise. The Innovation Evangelist, possesses limited IT knowledge, but has a deep knowledge of business and economics. Given their strong external and internal networks, they receive a variety of ideas, and evangelize cross-fertilized versions of these ideas in their companies. Thus, Networker/Catalyzer types flourish in a wide combination of external and internal circumstances, while the Lone Icebreaker needs strong executive authority to function well in a highly competitive environment.

What the paper is invaluable in uncovering about the Digital Transformation journey is that as between companies and CDOs, there will not be a one-size fits all formulation, but instead there will be a number of competitive situations and possible CDO profiles, and different situations will call for a play of different strengths for the CDOs to be most effective. All real CDOs will possess some combination of the strengths identified - they will not strictly fall in one or other of these 'profiles'. So the contribution of the paper is in helping identify the strengths that CDOs will need to call upon, based on the specific internal and external environment that they face - so that they can be successful in taking their companies on a successful Digital Transformation journey!

Shradha Sharma of YourStory Interviews KK Natarajan of Mindtree

I thought this interview worth blogging for several reasons. First, the impending merger between Mindtree Ltd and Larsen & Toubro Global Infotech (LTI) raises a number of issues which Shradha Sharma (SS) and KK Natarajan (KK) discuss in great depth. However, this is not the exclusive focus of the interview, in fact, the interview is set up in the same format as the ones that SS normally does with founder-entrepreneurs for YourStory; and Mindtree, while no longer a startup as such, is still a fairly young company. So the interview provides KK's perspective on many of the issues that arise in the context of the 'startup ecosystem'. Then of course there is the utter unflappability of KK himself, leaving one absolutely astonished to see the CEO of a company that is about to be 'taken over' without its approval so calm and composed in the middle of all that might be happening. I thought this was definitely worth writing about.

KK begins by mentioning the original Mindtree motto 'Welcome to Possible' to set the tone for the entire interview. He mentions 'Optimism, Optimism, Optimism!' as the basic ingredient of his overall mental makeup several times (but especially when SS asks him what the secret of his unflappability is.)

In the first few minutes, KK briefly reviews the current situation, in which a large Mindtree shareholder agreed to divest his 21.5% stake, which LTI agreed to buy. LTI further placed an order to buy 15% of Mindtree shares from the open market, which gave them the ability to make an 'open offer' to buy another 31% stake; this would result in LTI owning a 67% (21+15+31) stake in Mindtree.

KK explains that Mindtree has always seen itself as fostering a 'High-Performance, High Caring' culture; with impeccable integrity & governance; that has generated high value for all its stakeholders. Thus, any intervention that could alter or 'take away' part of that culture is seen as ultimately lowering value to investors  - both the ones in LTI and the ones in Mindtree. One source of competitive advantage for Mindtree was that it was able to see the idea of Digital Transformation as a business need as far back as 2012, and has made proactive investments in it, which are beginning to pay off now. The implication seems to be that this is one reason why it is the object of a 'hostile takeover' (i.e. that the takeover is not solely about scale).

KK further expands on the theme of Mindtree strongly believing that 'startups are the lifeline of the business ecosystem' - responsible both for Innovation and for Job Creation. The entrepreneurial mindset is that which forsakes the 'Job Seeker' mentality for the 'Job Creator' mindset. Further, he believes that both central and state level governments are doing a lot to facilitate entrepreneurship today, so that the path for would-be entrepreneurs now is much easier than it was when Mindtree was a startup two decades or more ago. Today, however, the entrepreneur needs to choose his initial investors very much more carefully. Such initial investors cannot just have a financial interest but must also be willing to leverage their own networks to help grow the business they are investing in.

But more than just that, KK also believes that if  and when the initial investors want to 'exit', then the business founders must have the 'right of first refusal', i.e., the exiting investors must first offer to sell their stake to the business founders or their nominees. Mindtree had such clauses in its agreements with its initial investors for as many as three rounds of investment, but once it went public, such clauses became invalid. He laments the lack of 'poison pill' type of regulatory exceptions in India, which elsewhere allow founders greater control over the total shares of their companies. 

In response to a question from SS about how founders could 'build a moat' to prevent hostile takeovers, KK mentions the 'Dual Voting Rights' (DVR) idea, on which he said the Securities & Exchange Board of India (SEBI), the regulator, has put out a white paper for consultation very recently. Such DVRs would provide founders much greater voting rights than happens currently. Depending on the details of the DVR, for example, founders could get a 10X voting share. Thus with slightly more than 5% of the shares, founders could, have a controlling vote share. 

SS asks about 'unicorns' and 'soonicorns' (soon-to-be unicorns) - to which KK provides some statistics about how the venture capital funding pyramid gets built - only 4% of startups that get Angel funding get 'Series A', and at most 8-9% of those which get 'Series A' funding, also get 'Series B'. He adds that, for a startup, 'scaling up' is an essential part of the game, but it should only be undetaken when the founders have understood the path to unit profitability. Without that understanding, it makes no sense to try to scale rapidly. As for unicorns, he mentions a statistic he read in a book - that only 1 in 20,000 startups becomes a billion dollar company!

When SS asks him about a people management tip - his response is that 'one must be a patient and active listener', because all engaged employees want to be heard. But it must be clear also who makes the final decision. The result of active listening must be a fully-aligned team, which is essential for efficient execution. Aligned teams deliver 40-50% better results than teams which are even slightly mis-aligned. 

The interview ends with the listener/viewer (including me!) marveling at KK the man and the CEO, and with SS expressing the hope that all would turn out well.  KK reiterates that he would like the present management team to continue to remain in charge of Mindtree.

Solely-for-Scale 'Mergers of Equals': The Python That Died Eating The Deer?

Siddhartha Pai, of Siana Capital has a very interesting article in today's Mint newspaper, also carried in the online version livemint regarding the impending merger between Larsen & Toubro Infotech and -Mindtree Ltd where he uses the colourful metaphor of the python eating a deer its own size, failing to digest it, regurgitating the contents of incomplete digestion, and possibly dying a premature death itself - to focus attention on the supposed 'Merger of Equals', that the 'suitor' is pursuing 'solely for scale' in this case.

In such a case, he argues, clients of the pursued company are quite likely to adopt a 'Wait and See' attitude to understand how the merger will play out, especially whether the 'pursued company' in the merged entity will have the same strengths that it did previously. Talented employees more in demand elsewhere will not wait and see - they will leave, if only to extinguish the uncertainty in their own professional lives. This is always the case even when the merger is 'friendly' because mergers make sense only when the merged entity, by virtue of larger scale, 'de-duplicates' some functions in the new company and brings about at least some cost savings. Why, one may indeed wonder, do companies look for scale anyway? Because, especially in an intelligently organized company, larger scale can enable the realization of economies - both of scale and also of scope.

However, it is the 'human capital intensity' of the software industry and the very high mobility-quotient of critical human resources in it that makes the possibility of losing such talent during a prolonged merger-transition particularly deleterious. Traditional HR management techniques in 'traditional' industries or companies are built around the idea that no single person can be seen as  indispensable. In the software industry, for many critical domains, this is just not true.

But there are other issues too - if a merger is 'confrontational' then it takes much longer to close than a friendly one would. This then exacerbates the risk of the 'Wait and See' approach of clients and potential clients. Worst of all, rather than focus on new possibilities that the merger makes available, dealing with such internal issues ('friendly fire') tends to distract management in both companies impending merger, and then beyond, in the merged entity. Often this is serious enough to question the logic of the merger itself and often drastically reduces any 'value added by the merger' calculations.

Dr KK Natarajan, CEO of Mindtree Ltd, speaking with Shradha Sharma of Yourstory.com:

Digital Transformation is not about Technology, but about Mindset!

.@HarvardBiz According to this article in the Harvard Business Review, about 70% of the $1.3T (trillion) that was spent on #DigitalTransformation by businesses worldwide in 2018 was wasted. Because, businesses did not bring the right Mindset to the act. This manifested itself in various ways - for example, businesses did not adopt an #agile decisionmaking or a rapid prototyping culture, with a flat organizational structure (i.e. the 'Fail Early, Fast and Often (till you succeed)' mantra that is said to describe current-day Silicon Valley Culture.

Or, because businesses ignored (and/or failed to recognize subtly expressed) employee fears about technologies, such as 'Artificial Intelligence'. Whether the fear were 'legitimate' or 'ill-founded' they must be responded to, never ignored. In the same vein, management is known to ignore existing in-house talent, experience or  skill sets (preferring instead the quick 'external consulting fix'). For something like digital transformation to work, it needs to be fully owned by current employees, ideally co-created by them with external consultants or advisers (but only when necessary).

Or,  what really did them in - failure in implementing Digital Transformation occurred because businesses had a technology-centric strategy, instead of having a business-centric one. The peer-pressure businesses feel to 'adopt AI' is well-known. But unless a business has thought through how additional technological capabilities fit into their business strategy, and whether, how and where in the organization these can credibly fit in, any investment into 'digital transformation' will not achieve its potential - rather the opposite would happen.

The bottom line in this discussion then comes down to realizing that Digital Transformation is not about a specific technology, it is instead about the Mindset. Not having the right mindset, which needs the right culture and cues from management on down - is thus a recipe for failure!

Simon Johnson and Jonathan Gruber New Book: Jumpstarting America

Challenges and Opportunities in the IoT-IIoT Cluster

.@debjani_ghosh_ Debjani Ghosh, President of NASSCOM has a very insightful article  in Business Line on what the #IoT-#IIoT cluster needs for it to thrive. Most importantly, it needs #VisionaryLeadership & a very strong and deep pool of talent, especially in Artificial Intelligence #AI . But in order to really thrive, there are also important challenges in developing widely-agreed standards so that Interoperability between different IoT devices and products is facilitated. Similarly,  because the Cybersecurity dimension is now central,  on the one hand, while connectivity is also critical for it to function, widely agreed standards are equally essential. On the hardware end, since bandwidth is not yet available to the same extent everywhere, it may be necessary also to specially enable computing at the Edge with #LowLatency and #HighBandwidth hardware.

Morgan Stanley India's Ridham Desai on BloombergQuint

Nasscom NISC 2019: Joydeep Dam on Integrating AI into the Digital Enterprise

Joydeep Dam, Director of Algorithm Development & AI at BridgeI2I Corporation, talks about the distinguishing features of an AI-enabled Digital Enterprise.  He defines a digital enterprise as one with a well-characterized way of capturing data, plus a smart way of utilizing it, so that the result is a good way of basing decisions on it. The typical case is that a particular firm might have a lot of data, which then results in a lot of fancy dashboards or reports being created (Joydeep mentions a bank which had a database that created about 2000 [two thousand] different reports every single week.) But the question is, do the insights derived from that data actually flow into decision making?

He then goes on to describe the basics of the 'AI wrapper' one could put around the data generator/report generator stage:

1. A Watchtower - a component that (figuratively) looks over data, identifies anomalies in it, and detects errant or deviant behavior from it or in it. A Watchtower could be conceived as a Digital Dashboard with an Intelligence Module overlaid, which one could perhaps call 'DDI'. Or one could have an 'SPI' (Signal Pool Intelligence) Watchtower, which might perform roughly the same function (but with signals of a more elemental form, for example, the outputs of an IoT array). Or one could have a BI Watchtower, i.e.,  a Watchtower with Business Intelligence, on which a structure is added that extracts insights or adds graphics and visuals, or even creates entire presentations based on the input, 
2. A Recommender - which, based on data input of various kinds (e.g., browsing, personal, credit or purchase history of an individual or firm) creates a set of customized recommendations for either the sales or the purchase side of any business transaction. This could be based on a ranking such as 'most often first' or 'most recent first' or, if likelihood can somehow be estimated, 'most likely first'.
3. An Optimizer - a module which runs either a mathematical or heuristics-based optimization routine under a set of given constraints, and objective function(s), and provides a unique answer if available, but could also yield a set of sensitivity analyses.
4. A Converser - (The word in this form is mainly used in French, as an intransitive verb, similar to the English 'to converse'). However Joydeep uses it in the sense of  'that which converses or that which is conversed with'. This module has the function of turning the analysis into a form that the user can 'consume' - that is, it converses with the user. This could take many forms, starting with just more intelligent graphics or video, to conversational 'chat-bots' (which may analyze text and/or speech to generate probability distributions of word occurrence, and then, based on semantic and/or grammatical rules, output text and/or speech).

These four elements can be taken as the basic elements which, if present in a given software product or service, can be talked about as representing an 'AI-enabled Digital System'.

A question asked by a member of the audience after the talk deals with the issue of Data Paucity. He asks, what do you do if the AI algorithm needs a lot of data but you only have a little of it? This is particularly the case for smaller companies and early-stage startups, but even otherwise, is a very good quesition. The answer is, the solution depends on the situation. One strategy is to wait until there is more data, or try to acquire it from elsewhere if it already exists, or to acquire (or occasionally, even generate) what might be called 'proxy data' that can 'stand in' (as an alternative which may have much the same statistical characteristics). Many AI algorithms can 'learn continuously', i.e., you can train them with the data you have, and then continue training when more data becomes available.

Nasscom International SME Conclave (NISC) 2019: Prof V Srinivasan IIMB Workshop

There are some things that you experience in the world that really make you go wow, Wow, WOW! This workshop, conducted by Prof Vasanthi Srinivasan of IIM-B at the NASSCOM International SME Conclave 2019 in Mumbai was for me, one such.

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I was amazed first of all that Nasscom was organizing a workshop on such a theme, then to find that a Bangalore-based academic was running it, but most of all by the experience of how she ran it (with really amazing enthusiasm, enormous energy and an incredibly magnetic connection with her audience). She has the audience's rapt attention from the very beginning, and holds it till the very end; begins by saying she will be provocative, and makes sure that she is; is both entertaining and educative, and has a simply great sense of humour.  It would have been great to have attended the workshop in person, but I watched it on the video that Nasscom made available on youtube, and got much out of it nevertheless. This is often taken for granted, but I want to make sure that I also thank the cameraperson(s)/videographer(s) who have done an excellent job; one certainly gets the visual perspective from different angles as would befit an event, such as this one, in which the speaker is constantly in motion! My regret however is that the videographer was unable to capture the content of the slides, which, had they been available in the video, would have been very useful indeed. But the Professor makes this up by clearly enunciating, and repeating often, the sub-themes she covers over the course of the workshop.

The first thing Prof Srinivasan emphasizes is that there is no real consensus around the definition of the buzzword 'Digitalization' (or Digital Transformation) that has gripped the IT industry (everything from Robotic Process Automation [RPA] to Machine Learning to Sentiment Analysis, and a lot else besides, comes up when people are asked to define Digitalization. But the basic point is that no matter the definition one adopts or has become comfortable with, Digitalization is Different - it is not the same thing that IT companies were doing in the past. Much practical wisdom can emerge from surveys and studies of how SMEs are faring in developing and/or adopting products and services related to (what they think of as) Digitalization, even if the technical definition is set aside for a moment.

To frame the workshop she uses three questions (which also happen to be the questions around which she has run other focus groups for Nasscom in the recent past):

1. What is changing in your environment? (i.e. in client expectations, in the competitive milieu, etc)

2. How is the change impacting you and your business (i.e., who you are, and what you do)?

3. Are you ready for the change?

What emerges from focus groups she has organized in the past around these questions is that many of the issues brought up in answers are actually independent of the scale, size, geography and client base of the enterprise. And for many of the issues, there are no differences even among product and service oriented software companies.

To the question, who's driving the Digital Transformation, the majority of firms picked the choice 'The Customer!'. Also, the majority of firms felt that the Digital Transformation was happening NOW! Among those who felt that the Digital Transformation was four or more years away, SMEs were in the majority!

On queries related to the Barriers for Digital Transformation, the most common response was 'Lack of Overall Vision for the exercise', followed by the related 'Action Items are not clear!'. For the largest companies, the fact that business units were organized as verticals or existed in silos was mentioned prominently.

And then comes one of the Half Truths that the Professor has promised to bring out during the workshop - being, 'I need more customers!' This is only half-true, she emphasizes, because in this new environment, customers may place demands on the company that it is unable to fulfill! In the previous world, any customer was fine, but not now!

On the labour market, Prof Srinivasan points out that it is shifting in favor of SMEs. Overall, job growth in the formal sector is slowing, which means there are (or will be) more people from among whom SMEs can choose their future employees. In this context, 'Lack of (skilled) Employees' does not become a specifically 'Digital' challenge, but is just a (regular business) challenge.

In addressing challenges related to scaling up small businesses, Prof Srinivasan emphasized that often the 'Entrepreneur him/herself is the biggest stumbling block'. Entrepreneurs should remember that once they start to scale up, it is no longer about them. (And if they want it to be, they should go and start up a kirana shop, she half-joked!). A scaling business will need to have systems and processes in place (such as swiping ID cards at the entrance) and these should apply to everyone without exception. Similarly, when a reskilling or training opportunity is scheduled, then everyone including the entrepreneur-boss should go and 'get trained'. Often, even 'innovative entrepreneurs' lose their 'innovative mindset' when the company starts scaling up. Their emphasis shifts to 'management' and 'transactional efficiency' instead of remaining focused on creating value for the client, the basis of entrepreneurship!

Then the discussion moves on to the criticality of delegating functions to specialists or professionals in the organization - here the interesting issue comes up of the difference between professionals who turn entrepreneur versus the 'non-professional' entrepreneur in how they carry out the delegation task. Professional entrepreneurs oddly find it hard to delegate tasks they see as very important, even if it is in an area different from the one they specialize in. Many companies in the survey answered 'the entrepreneur/owner' when asked 'who is responsible for sales in your company?'. Sales are critical, and only companies that can successfully close on deals, repeatedly, are able to make the transition to scale. But entrepreneur-bosses must nevertheless learn to delegate this task. Because 'if you are running around trying to close a deal, then who runs the company?'. To which one respondent memorably said, 'But Professor, if I don't go, the client won't close!'. Which in turn is answered by: a) Why not? and b) This is a great way to transition from doing things 'best in class' to 'worst in class'. Entrepreneurs have to be good at selling, but at some point others can do this as well or even better than them. Another issue that comes up in delegating tasks is the time-sensitivity (or insensitivity). Beware of thought patterns where one assumes the delegate/subordinate can 'surely' do the task in 24 hours if one can do it in 6 onself. Because, the subordinate is an employee, and does not devote themselves to the business 24 hours a day, but can be expected to do so for at most 8! So,  the message is: delegate, but just not at the last minute.

Towards the end of the workshop, the Professor carefully spells out the distinction between 'Enterprise Competencies' and 'Entrepreneurial Competencies'. While the former seems generally well-understood, considerable confusion exists around the latter. One of the most important required competencies in an Entrepreneur is Sensing, which is the ability to pick up significant non-verbal cues from the environment. She noted that IT entrepreneurs often lack this ability. Another entrepreneurial competency is Agility, which one might try to define, based on the literal meaning, as quickness, but which must also include, especially in this context, the ability to try something quickly, and if it doesn't work, to also realize quickly that one has failed, and to move on to the next potential solution.

Are Generalists or Specialists Better in a Data Science Value Chain?

.@HarvardBiz: A #DataScience #ValueChain such as Research Scientist, Data Engineer, Causal Inference Specialist etc…coordinated by a Product Manager, is a useful conceptualization. But since there are no blueprints to follow & ‘you learn as you go’...https://t.co/rs8qPwX6z3

...and multiple iterations are involved, excessive specialization inordinately increases coordination costs & wait times. So, start w generalists - '#FullStack #DataScientist', but modify if cost of error is high, or eg yottabytes of data! via @digitalarati @ericcolson @netflix

How Data Bias in AI-based Healthcare can Harm Vulnerable Populations

.@BrookingsEcon-@SchaefferCenter: When @UPMC evaluated the risk of death from pneumonia of patients in their ER, the #AI model predicted that mortality dropped when patients were over 100 years of age or had a diagnosis of asthma!  UPMC did indeed have low mortality for these two groups, but the assessed risk of pneumonia for them was so high that staff had given them lifesaving antibiotics before they were even registered into the #ElectronicMedicalRecord: thus antibiotic administration time stamp would have been wrong! #DataBias. AI-based medical protocols like this could harm  the most vulnerable and high-risk of patients. Therefore, one must identify #Bias in data sets, & also, evaluate AI-based medicine on whether it mitigates or perpetuates healthcare disparities, i.e., with a social impact metric.

— Satyen Baindur (@Satyen_Baindur) March 9, 2019

The Limits of Ethical AI

.@Joi on 'The Limits of Ethical AI' (which he concedes more properly is 'Limits of Algorithmic Fairness in Actuarial Practice'). On the pervasiveness of AI #Hype: AI has gone 4m being #WhatWeCannotDo (Yet) to being #WhatWeAreMostDefinitelyDoing (Right Now). Martha Minow begins by saying that she cannot think of anyone better than the moderator (Prof Sheila Sen Jassanoff) to bring politics, journalism, philosophy, psychology & empiricism together, adding  ‘that’s probably the most important thing I’m going to say’. But then goes on to make a series of very powerful contributions to the discussion. I don't want to excerpt for fear of quoting w/o context... except an irresistible line 'We are in the year 1900 when it comes to law...' (And @yes_VY, she mentions @JuliaAngwin's work around 1:10:45)

— Satyen Baindur (@Satyen_Baindur) March 8, 2019

https://vimeo.com/311460280 

How Reliable are Surveys of AI Adoption by Companies?

.@bershidsky Leonid Bershidsky, a Bloomberg columnist, argues that the problem with surveys of Artificial Intelligence (AI) adoption in companies is twofold: 1) The lack of agreement on a definition of AI itself; 2) Executives feel unbearable peer-pressure to say they are using it!  But even among companies who actually adopted AI, https://t.co/RpBiN7MHwC few applications are being developed to be unambiguously labour-saving. The much-vaunted cost savings from AI deployment may then not arise. Given the extraordinary peer-pressure executives now feel to deploy AI (or at least not to admit that they are not deploying it!) the likelihood now is that the very attempt to deploy AI might itself becomes a recurring cost, thus weakening the business rationale for AI! So the somewhat cynical wisdom would be 'Wait till the dust settles to get on board'.

— Satyen Baindur (@Satyen_Baindur) March 10, 2019

The AI Hype and VC Funding for Startups

.@ft According to this article from the Financial Times, as many as 40% of 2830 'AI startups' in Europe (@MMC_Ventures Survey) don't use any AI programs at all, in their products (as of the time the survey was taken).  The same survey said that the median size of a funding round for an 'AI startup' in 2018 was 15% larger than a similar funding round was for 'just software' startups. One can see how big an incentive this creates for a 'just software' startup to declare itself an 'AI Startup'. Given this, one is actually a bit surprized that only ~8% of all 2018 startups were AI startups! (though, to be sure, the universe here includes non-software startups too.) But there sure is a lot of #AI #Hype via @IanHathaway https://t.co/2qscQS8PeF

— Satyen Baindur (@Satyen_Baindur) March 5, 2019

The Fusion Between Quantum Physics and Machine Learning

.@physicstoday 'It sounds like science fiction, but the fusion (between quantum physics and machine learning) is happening right now.' @carrasqu * @rgmelko used Monte Carlo-sampled equilibrium spin configurations of an Ising Model to train a feedforward neural network https://t.co/qctnlEvF7Z with the Adaptive Moments (Adam) algorithm (using a cross-entropy loss function w L2 regularization) to identify paramagnetic/ ferromagnetic states; after training, network correctly classified samples not seen before. +, it located Tc & found the critical scaling exponents! pic.twitter.com/dWOPgo0NTh

— Satyen Baindur (@Satyen_Baindur) March 2, 2019

Figure above shows critical point Tc for the triangular lattice Ising model (4m C&M @arxiv 1605.01735v1). PT article also discusses work of @gppcarleo& @matthiastroyer on Restricted #BoltzmannMachines & quantum states in detail; + has nice intro to #TensorNetwork Representations.

— Satyen Baindur (@Satyen_Baindur) March 2, 2019