Arjo Basu

#Leadership and #strategy are extremely misunderstood. This conversation is another example of that : youtube.com/watch?v=dHVMuj… . #SteveJobs was not successful because he was deciphering noise from signal on a '18 hour' cycle. Yes, he must have used that principle to make his #tactical to-do list or to shut-down a 'noisy' investor who had no idea what strategy is. #SteveJobs was successful because he developed a vision for a new #system of human interaction with technology and built effective #strategy for achieving that system change. He had enough time, way more than '18 hour' cycles, to achieve that vision once he created that #blueocean of new possibilities with his systemic vision and separated apple from the others. Don't be misled by wrong interpretation of people's success.

You can be successful by being a purely tactical person - success is relative and can be caused by many things. But you won't be a #leader with lasting impact without strategic and #systemthinking. Period.

#DataStrategy Forecast for American HealthCare Industry:

#datastrategy #strategymistakes #data #leadership #healthcare #dataprivacy #regualtion #healthinsurance #systemsthinking #systemshock #BigBeautifulBill #DataAnalytics

In my previous post (x.com/StrategyMoksho…) I talked about how the current mid-size manufacturing and financial companies would be extinct within the next decade if they don't make the three common strategic mistakes they are making today.

I did not include healthcare companies in that list - because they have a different strategic challenge and path forward. Let's discuss that here.

What's happening in healthcare, especially in health Insurance industry?
If you think of one industry that has been least adversely affected by the data bandwagon over the last decade, it's the healthcare service providers, especially the health insurance industry. Health care has been a heavily regulated industry and so protected from the disruptions brought in by the data innovations especially in the area of insight generation by AI/ML (in other words, insight generation which is not based on predefined rules).

But that's been changed by one 'big beautiful bill'.

The heavily government sponsored and so 'protected' system, that's now being shaken by the new bill passed in July 2025 ( and going into effect from Jan 2027), would go through a 4 phase transitions over the next decade.

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#DataStrategy Mistakes - #extinction looming large for Mid-size Companies

#datastrategy #midsizecompanies #ELT #startegicmistakes #data #leadership #manufacturing #finance #datamaturity #aiml #datanalaytics

Every organization wants to capitalize on data. Yet, most of them are failing miserably to leverage their data.

Many of them only waste money, especially the mid-size companies in manufacturing and financial sector, who have the right type and volume of data that can help them propel ahead of the competition or create new blue-ocean niches purely based on their data strength.

They are failing in three strategic fronts.

1. Embracing out of the box ELT (Extract Load transform) data warehouse solutions like Snowflake/Databricks etc. for all their data needs

While the ELT solution are great for what they offer, many mid-size companies are just buying into them without realizing what they are getting into and how these choices constraint their data ambitions.

all their data get locked into a vendor platform which are not the best when it comes to AI/ML
data governance go for a toss and data duplication become the norm - resulting in pure chaotic data
Without robust governance and AI/ML capacities, data just consumes money and does not return anything.

2. Choosing wrong leader for their data initiatives.

most companies are bringing in cloud and software engineering specialist as data leaders in director+ roles, or even worse just bringing in business leaders to lead their data organizations
most MVP's have no idea what data strategy means and are clueless about the organizations data maturity

3. FOMO forces the leaders at the top, who barely know what data means and what it needs but feel compelled to spend money in data. Result- companies are wasting money in data, which is by far the highest cost center within the entire IT portfolio, with almost zero ROI.

Time is running out for mid-size companies!

With AI everywhere, there is no middle ground to hide. My prediction is, these mid-size companies, especially in data heavy sectors like manufacturing and finance, would simply go out of business within the next decade, if they can't solve this problem within the next couple of years. They would be replaced/taken over with new generation companies with data first strategy.

Embrace right data strategy, get proper assessment of data maturity, hire right leaders and propel forward. Or face extinction. There is no middle ground.

#ChinaVsIndia #meritocracy #castesystem #futureofIndia #strategy #systemsthinking

#China and #India are two of the longest lasting civilizations on this planet. And they both are based on fundamental principles of #hierarchical societies - where everyone in the society has a well-designated place in the social hierarchy and follows their roles as per that designation. One must admit that hierarchical social structure has served both the civilizations reasonably well - they both have survived thousands of years.

In China, #Confucius (551 -479 BCE) introduced the idea of hierarchy - bring harmony in the society by letting everyone know of their role in the hierarchy and how to play it well.
In India, the idea of #caste based hierarchy and work allocation took concrete shape around 3000 years ago (there is now well established genetic proof of that).

However, there are one major differences between these two hierarchies: #meritocracy

Although Chinese society was still hierarchical, more than 2000 years ago, Chinese society introduced the concept of a merit based hierarchical system. In 165BCE, the early Han dynasty introduced the concept of #ImperialExamination to find the right candidates for the officials roles. From that point, anyone could rise in the hierarchical ladder based on his/her hard work and merit. Each of the subsequent dynasties experimented with it and further refined it.

Introduction of #ImperialExaminations to fill up positions across government hierarchies made sure that the power centers within the society were continuously shifting. Although Chinese society was still hierarchical, anyone could rise in the hierarchical ladder based on his/her hard work and merit. This made putting in hard work in studies while young a phenomenon in the society with all its benefits - not only for a segment of the population, but for everyone in the society.

@RayDalio has described this very well in his book "Principles for Dealing with the Changing World Order - Why nations succeed and fail".

On the other hand, India's caste based hierarchical system was a system of hierarchy based on one's birth. A person's family lineage decided what kind of skills the person would be taught and what would be his/her profession. Kids from higher castes like #brahmins (and some other upper castes like #kshatriya - the warriors) were entitled to study the ancient texts and learn the wisdom and philosophies embedded in them. They would grow up to adorn the official positions in their respective states and advising the kings.

Anything that required deeper/purely mental abilities (including specialized warfare skills and tactics) were kept for the upper caste whereas the manual labor (including farming, pottery, blacksmith, cook, maid etc.) were kept for the lower castes.

But there were lower than the lowest castes - called #dalits (#untouchables), who were trusted with the most disrespectful but still essential duties of the society - including cleaning sewers and handling human waste - for generation after generation.

Surprisingly, the two upper castes of #Brahmins and #Kshatriyas were less than 10 percent of the entire population!!! So, all the sacred and significant skills, knowledge, wisdom... anything of value, anything that gives one power and influence, stayed locked within less than 10 percent of the population.

So, while their counter parts in China took a more progressive approach towars society building, empowering people from all walks of life to be part of power, for thousands of years, India's #castesystem not only assigned a role to every one in the hierarchy, it also was extremely vigilant of anyone trying to move up the ladder and served the cruelest possible punishments to set an example so that no one would dare to rise up.

Two important questions arise from this background:

1. Given the deeply compassionate and humane philosophical thoughts embedded inside the ancient Indian texts (#Veda s and #Upanishad s and the #puran s) , its surprising that the wise thinkers of India chose to establish a discriminatory social hierarchy (it's easy to understand that this is a detrimental system, even for the upper castes, in the long run) based on one's family lineage and went to great lengths to retain this discrimination for 3000 year. Why?? What prompted this?

2. How much repair does India need to capitalize on any opportunity to become a world leader in the modern world? Is its huge 1.4+B population a true opportunity or just a number?

part 1/

#post4 of my #datamaturity #masterclass #series as a foundational pillar of #analytical #datastrategy
Step 4: Primitive to #Platform – First Steps to Recovery

If your organization is in primitive mode, pivot from tactical projects to #strategic #platforms

Recovery Step 1: rationalize efforts into building platforms

Identify the most robust existing pipelines for each layer (ingestion, transformation, etc.) and halt new standalone builds. Channel future investment into those “best” apps to evolve them into enterprise platforms. This breaks down arbitrary silos quickly.

Recovery Step 2: Build trust with central metadata catalog

Launch a centralized metadata catalog to define all data assets and transformations. Populate it with names, definitions, owners, quality rules and lineage for each dataset. A vocabulary/ontology helps – e.g. map “cust_id” to “customer_id” so teams align.

#important: make it #searchable for all. Improved discoverability alone will reduce duplicate work.

These are tough changes - lines of business will resist vehemently as they fear of losing control and privileges of the data that they think 'only belongs' to them - but these two steps are essential.

Firms that recognize and pull this off can skip much of the painful Low maturity stage.

But those who can't do this in a timely manner, may never mature into anything significant - however hard they try after a few years.

#biggest #enemy of #datamaturity: proliferation of redundant data pipelines creating duplicate copies of data