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Transformative Analytics: Building and Embedding Analytics into Business Functions
In the modern business landscape, the utilisation of data and analytics has transcended its conventional role of providing retrospective insights and has emerged as a catalytic force for driving meaningful impact across organisations. This article explores the concept of transformative analytics, delving into its significance, challenges, and strategies while shedding light on real-world instances of organisations that have successfully harnessed analytics to bring about prof
Feb 267 min read


Demand for Data Engineers exceed Data Scientists – An Analysis
Multiple recent recruitment surveys revealed that the demand for data engineers has recently exceeded the previous demand for data scientists. The Dice 2020 Tech Job Report said data engineer was the fastest growing job in technology with a 50% year-over-year growth in the number of open positions. Many mentees asked me how do I perceive this shifts in demand, and what area should they pursue. Actually, I see a course correction happening in the industry. Most organisations t
Feb 265 min read


Prompt Engineers are not here to stay – This is why…
In the ever-expanding realm of generative artificial intelligence (AI), the term "prompt engineering" is ascending to the forefront, capturing the attention of professionals and scholars alike. As this emerging discipline gains traction in various job listings and educational programs, an intriguing question arises: What transformative value do prompt engineers bring to this dynamic field? Originally published at LinkedIn Pulse on 22 July 2023. Photo by cottonbro studio from
Feb 222 min read


Bridging the AI Divide: Unleashing Opportunities in an Evolving Digital Landscape
Imagine a world where information and opportunities are not just a click away but are rather confined to certain demographics and regions. Sadly, such a world existed well before the late 20th century when the term 'digital divide' was coined. It served as a stark reminder of the gaping chasm that separated those who had the privilege of accessing modern information and communications technology from those who were left behind, either with no access or restricted access. Orig
Feb 225 min read


Machine Learning Models are Whistleblowers of Unethical Practices of Society
With the advent of the data science practises, especially the likes of Artificial Intelligence (AI) or machine learning and their adaptation in sensitive areas, has drawn massive attention to this technology in the recent years. Whether it is cancer diagnosing system, Amazon’s AI recruiting tool or the US court’s profiling system Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), we can always disagree to the decision made by the AI systems. In fac
Feb 223 min read


Build vs Buy: If you are Buying Machine Learning, albeit you are Doing it Wrong
Artificial Intelligence (AI), or more precisely Machine Learning (ML), has become an industry trend in the past 10 years. From a buzzword to a new way of automation and decision-making, ML has become mainstream. I have had conversations with multiple organisations keen to use ML, even without a real-world use case. Graduate schools are offering lightweight courses to the masses, consulting companies are providing services on adopting ML, and technology companies are building
Feb 225 min read


Large Language Models for Business: How to Make the Right Decision
Large Language Models (LLMs) represent a transformative technology in the realm of generative artificial intelligence (AI). These machine learning models are designed to understand and generate text that mirrors human language, learning from vast amounts of text through rigorous self- and semi-supervised training. LLMs have a broad range of applications, from generating text and automating workflows to sparking creative ideas and even writing software code. Some of the most p
Feb 2212 min read


How is it like Leading Data Scientists and Engineers together in the Technology Space?
Most corporate structure consists of various departments that contribute to the company’s overall mission and goals. Common departments include Marketing, Finance, Operations, often collectively referred to as Business, Human Resources, and Information Technology (IT). These five, or three, divisions represent the major departments within a publicly traded company, though there are often smaller departments within autonomous firms. With recent interest in Artificial Intellige
Feb 217 min read


Data Scientist Mindset
There are many definitions of data science and is enough to confuse anyone, the question remains what is data science? So, before discussing this topic I often lay out the definition I go by. Data science is an interdisciplinary area about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, like Data Mining or Knowledge Discovery in Databases (KDD) . My definition of data science is rather s
Feb 1710 min read


What does success look like in Data Science?
Defining success is a crucial part of managing a data science experiment. Of course, success is context-specific. However, some aspects of success are general enough to merit discussion. A list of hallmarks of success includes: New knowledge is created. Decisions or policies are made based on the outcome of the experiment. A report, presentation, or app with impact is created. It is learned that the data cannot answer the question being asked of it. Some more negative outcome
Feb 175 min read


The Data Strategy Playbook: Transforming Actions into Achievements
A data strategy is a comprehensive blueprint that outlines how an organisation will harness data to drive business objectives and deliver customer value. It is a strategic imperative that aligns with the organisation’s vision, mission, and values, while also adhering to industry regulations and ethical standards. The strategy takes into account the expectations of customers, partners, and stakeholders, ensuring that data management is not only effective but also a catalyst fo
Feb 179 min read


Why do AI projects fail?
Every business wants to leverage big data and artificial intelligence (AI) initiatives. Lately, many businesses have tried to dive into big data and AI, but only a few have truly reaped its benefits. Though they had the right intentions, the failure occurred far too often. Here, we categorise common failures and provide guidelines for solving or avoiding them. For context, we present each failure type as a scenario and place these scenarios under three themes: Organisational
Feb 174 min read


From Data to Strategic Action: Why Most Companies are Stuck at the Bottom of the Value Chain
We’ve all heard the phrase “data is the new oil”, and companies are capturing immense amounts of it. Despite this, a surprising number still struggle to realise its full potential, not because they lack data, but because they fail to move up the data value chain strategically. This journey from raw information to tangible business value is not a single leap, but a progressive and methodical ascent. It can be conceptualised as a climb up a value chain, with each step offering
Feb 177 min read
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