The Chief Data Officer (CDO) role is the newly minted role in the CXO pantheon. More than 68% of Fortune 500 companies have reported having the CDO role. This role is expected to live up to the expectation of a slogan that is used a zillion times: “Data is the new oil.”
There are great expectations with the CDO role. However, CDO’s success is peppered with impediments. In a lot of organizations, they are set for failure even before the CDO joins the organization. Let us look at five strategies that can set the CDO for failure.
CDO deals with…
In the article, Making Data Lakehouse real…yet effective, the concept of Data Lakehouse was introduced. Its Architectural paradigm was discussed and the components that weave the Architecture together were explained.
In this follow-up article, the Data Lakehouse Architecture will be fruition using Microsoft Azure services.
This article will explain various Azure services that will fit into different components of the Data Lakehouse Architecture.
As a recap in the precursor article, the conceptual Data Lakehouse Architecture was introduced. The seven components that form the conceptual Data Lakehouse Architecture are as follows:
Once upon a time, decisions were supported by decision support systems called as the Data Warehouses.
It neatly designed structured data into different schemas: stars, snowflakes, and normal forms.
Then came the 2010s. Three things culminated in a perfect storm:
Artificial Intelligence (AI) is set to change the way the world works. It is the engine that fuels the digital transformations. Organizations and societies are optimistic about AI, its potential, and its ability to transform the world we live in.
Like the breaking of atoms can be harnessed to light cities or to destroy one so is AI’s promise marred by its perils.
It is time to harness AI technologies for their goodness and creates a framework for them to be responsible. …
Today is the age of data. Every organization is becoming a “data company.” Today, if an organization is not thinking about its data as a strategic asset, then it has already missed the bus.
Data has evolved over the past decade. The rate of evolution has been exponential. But have the Data Architecture practices managed to keep up with the same pace?
McKinsey recently published an article that formulates the building of modern data architecture to drive innovation. The article explains the foundational shifts for modern data architecture.
Being a practitioner in this field, I found Mckinsey’s view interesting. …
There should no doubts in anyone’s mind about how Big Data and AI are fueling the next revolution. Data is the new oil, and AI refines the oil. The questions to ask is the following:
Data Engineering has become vital for any organization that is serious about…
Is Artificial Intelligence (AI) the new elixir of all modern problems? Or is it a double-edged sword, sometimes destructive and at other times life-saving?
The fact is this: With the right framework, AI has the potential to be harnessed.
According to Gartner, around 37% of organizations are implementing some form of AI. Yet, according to a survey conducted by EY, only about 20% of firms are seeing have strategic AI capabilities. Very few organizations have successfully managed to harness the real power of AI to create meaningful impact.
Machine learning algorithms have a method of learning patterns from data. The method is intuitive. The model determines the underlying pattern from a given data set. This process is called training the model. The trained model is tested on another data set that it has not seen before. The goal is always to find the optimal model. The endeavor is to hit the sweet spot where the model performs satisfactorily on both training set and test set.
The test error is the mean error that occurred when the model on the new observation makes the prediction. This new observation is…
Like any industry, retail has gone through its share of transformations. Now the exciting thing about retail is that its development has been symbiotic to consumer behavior. It had to adapt rapidly based on how consumer behavior changed from time to time. For example, between 1900–1940s, cars were not mainstream; the retail shop formats focused on catering to the neighborhood mom and pop shops.
However, as cars became mainstream, large format stores started opening as the consumers preferred living away from cities and drive. Fast forward 1990s-2000s, as the internet became mainstream, e-commerce rose into prominence. Companies like Alibaba and…
Circa 1997, the reigning world chess champion Garry Kasparov was against an unknown opponent. The opponent was formidable. Garry was not playing a human. He was playing the game with IBM’s behemoth supercomputer, Deep Blue.
Garry had beaten the opponent in the last few games. However, the game played on 11th May 1997 game was different. Garry lost the game. Deep Blue made history:
The First computer program to defeat a world champion in a match under tournament regulations.
This game was significant for many reasons. It caught the world imagination. It laid the foundation for many possibilities that will…
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