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How to win : Data. 

The COVID pandemic has unfathomably changed our worlds in nearly every aspect of our lives. 


From socializing, to work, to physical and mental health - it is well documented how our quarantines have only magnified these changes in our lives.


The core technology that has powered these changes - every click, swipe, share, zoom and stream - has created pools of rich data to make informed decisions at any time, on any device in a radically different world.


Data is now the frontline power of the future. 


Data is an integral factor of production that will crush business models, demolish industry silos and create instant market adjacencies. 

Think Amazon x Twitch. Uber x Food and Delivery. Google x Waymo. Facebook x Lybra (or it's incantation). WeChat ... everything. TikTok's closed-loop ecosystem of entertainment. 

Data has powered all of these. 

Data has also powered those individuals who seek better outcomes, from those who've resigned to spend more time with their families (cost benefit analysis), to those who prefer working from home avoiding the snarl of traffic and logistics to navigate on the way to do the very same work you could have done from the comfort of your home. 


Companies with advanced digital data capabilities across assets, operations and workforce will accelerate market share brutally faster than their peers.

7x more Profit


McKinsey has stated that those who embrace the race to data will improve profit margins up to seven times more rapidly than the average company.


Seven times. Read that again. 


They will be the fastest innovators in not only their native sectors but those they encroach. Data omnivores don't care which sector they eat.  

The seeding, gathering, harnessing and refining of data has become an invaluable tool. Without a proprietary data backbone that is secure real time, it is practically impossible to provide relevant insights at the point of decision across the organization. The speed of data and it's use will determine tomorrow's winners more than their historical scale. 

Data is Agnostic

Today's technology giants are more incredibly powerful than most people understand - and getting more so due in a large part to proprietary data. Their clean, efficient fuel powers them to make  quick decisions in multidisciplinary fields.


Supercharging their data analysis has enabled the emergence of totally new business vehicles and massive disruption not only in the technology game - we're at an exciting point in time where these same superchargers are being applied across disciplines. Think Medicine, Education, Language and Finance. All of these mature segments are about to be massively upended in previously unimaginable ways - for the better. 

Why ?

Data is Agnostic. 

Data is Apolitical. 

Data is Anti-Geographic.

Data is Binary.


It is just a frozen moment in time (more on this later). 

Artificial intelligence, machine learning, deep learning and infact much of what you consume today - is powered by data.  Companies are gaining a competitive edge with their use of data and analytics, which enables speeds far faster than human touch, and larger-scale evidence-based decision making, insight generation, and process optimization.


But the opportunity-capture is grossly uneven. While there is both room to catch up and excel, harnessing data potential is similarly uneven. For analytics and advanced-AI models to scale correctly, organizations need clean, strong data gathering processes to load, test, train and evaluate models in an industrialized process. Only then can predictions be reliable. 

The Knowledge Gap

 The flood of data now available to most organizations is astounding compared to just a few brief years ago. And the deluge is only starting. API feeds are now starting to generate data sources from multiple pipelines - making sense of it is now a challenge in itself. With literally billions of data points being created every second, translating has become the new gold miners. 

Even when data becomes accessible - customers, suppliers, cost structures - using this data and translating it into valuable nuggets, often presents a challenge in itself. Continued investments need to be made to support high-quality data categorization, labeling and cleaning so that it is digestible. Multiple stakeholders need to commit to storing data that can be accessed in a coordinated way and to use the same data standards where possible to ensure seamless interoperability. 

While large corporates often have data-silos, these need to be completely broken down due to the impending force of software and the ubiquity of data. Often these silos are what are holding organizations back from growth, acquisition and divestment of key assets and liabilities. Without accurate measurement, understanding what & why it is actually being accounted for often leads to failure. 

The War for Talent

A major obstacle to growth is the incredible shortage of talent & experience. Both skilled talent to hunt for data and talent that has expertise in translating key value drivers once the mammoth is captured. The talent challenge is two-fold: a shortage of workers with high-level data science expertise - who are able to develop and train more complex models and a lack of data scientists, translators, and other AI / ML practitioners who can become involved in the translation, prediction and iteration phase. 

Issues of data quality as well as potential bias and fairness also need to be addressed if the data is to be deployed usefully. Transparency is critical. A deep understanding of the quality of the data, their source and categories needs to be secured so that users are aware of potential strengths, weaknesses, biases and supervision. Meaning can then be extracted more systematically, processed and retrained. 

Through understanding the supply of data, production managers can then harness these streams in the logic that is required for each individual business. Creating new value from existing streams of previously untapped resources. 

An exciting time for the strongest new factor of production : data. 

Peter Toumbourou

Charleston Advisory Group



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