Data is an unreliable friend, and hardly anything about it is actually scientific. So what, Data Science?
Over the past 5 years, I have interviewed more than 1,000 candidate data scientists for an apparently highly coveted set of jobs at Evo Pricing. In the process I have learned that the media are portraying a fundamental lie about this profession: throwing data at off-the-shelf algorithms is really not the point.
A fundamental rethink would be appropriate, and it is likely overdue.
At its heart, data science is a noble name for a broad set of number crunching activities that were mostly invented long ago, but recently received a new lease of life from being applied with greatly enhanced technical devices: more data, more processing power, more reasonable outcomes at a cheaper price. …
Historically, millions of Americans took advantage of Black Friday and Cyber Monday sales. This made Thanksgiving one of the most important times of the year for retailers. As the internet proliferated Black Friday and Cyber Monday deals abroad, maximising profit this time of year became vital for companies everywhere.
This year, Black Friday sales have become even more fraught. According to McKinsey, deep discounting has been a primary survival mechanism for companies in the wake of the Covid-19 pandemic. Retailers have already slashed prices to Black Friday and Cyber Monday levels with mixed results. On the oft-proclaimed biggest shopping weekend of the year, no one can afford to lose money. …
A friend of mine from business school recently joked that the aftermath of the U.S. election is more likely to give them an ulcer than the months of campaigning. Why? All the news of vote audits. Just the appearance of audit in headlines is causing her anxiety.
As a former management consultant, I know that no word causes executives as much annoyance and stress as audit. There’s a unique set of headaches that this kind of process brings to a business.
Yet audit shouldn’t be a dirty word. An audit is a chance to tackle critical business challenges head-on and reap the benefits of resulting improvements. This is especially true when it comes to data audits. …
Being data-driven is a must. Informed decision-making, especially when powered by automated business intelligence, delivers higher ROI and better business outcomes.
From AI engineering to hyperautomation, more efficient ways to use data across all business areas are at the heart of each of Gartner’s Top Strategic Technology Trends for 2021.
Data-centricity isn’t anymore optional to thrive Post-Covid. It’s a requirement.
Despite this, the Harvard Business Review reports that 77% of executives consider Big Data and AI initiatives their biggest challenge. Even worse, this percentage has grown exponentially over the past few years.
Is this trend likely to continue in the next decade? As companies attempt to convert to a data-driven mindset, they struggle to use their data effectively. A Catch-22 most businesses don’t know how to get out of. …
Optimization is a primary selling point of AI to businesses nowadays. Everyone wants to improve their efficiency, revenue and other critical KPIs. These are reasonable goals — and achievable ones. We can use optimization principles to improve performance significantly.
No matter how much you attempt to achieve optimal performance, however, full optimization is impossible. No model can account for all possibilities or achieve a perfect forecast. AI technology simply hasn’t advanced enough to make that possible for the foreseeable future.
Moreover, defining what is “optimal” provides its own challenge. Every business has unique needs and, as such, optimizes for diverse goals. A strategy that is optimal for one company is unlikely to perform as well in another situation. …
My definition of perfection: not a formula but the ongoing process of improvement through learning, innovation, and iteration.
Is there such a thing as an answer that’s always correct? Since the dawn of Western philosophy, humans have sought to organize the universe into unchanging mathematical certainties. A circle’s area is always πr(squared). Gravity decreases in proportion to distance. Energy equals mass times the speed of light squared.
Like most people in the Western world, I learned this same approach to problem solving in school. I learned that questions have one — and only one — correct answer. Own the correct answers, pass the test. …
There’s a lot of talk about a singularity event and artificial intelligence taking over the world. Will their own computers replace Jeff Bezos or Mark Zuckerberg any time soon?
Since I am a CEO building Sat-Nav (that’s short for satellite navigation) tools for managers, and with Sat-Navs being the first step towards self-driving cars, these days I often get asked the Skynet question — will scientists ever enable companies to be run by robots?
The answer to that is a definite no. Here’s why.
Sure, artificial intelligence has come on in leaps and bounds in the last 30 years. However, we are not even close to a singularity event where machines can replicate or surpass human reasoning. …
This is at least $200 billion too much, and potentially more. Here is why.
Product stock is generally perishable: some products lose value as trends shift, others are seasonal and seasonality plays a major role in demand, others just expire or get wasted. Some products may appear to last longer on shelf, maybe. Eventually, everything must go. And carrying inventory is anyways expensive, due to the financial cost required.
Today’s customers can choose from an increasingly wide range of options— and they’re taking full advantage of that variety, becoming more selective than ever before. Both in B2B and in B2C. …
Machine learning elevates the importance of people using the data. More data enables more automation, which requires more leadership.
Use more data, need more leadership.
In a pyramid-shaped hierarchy, each function is a pillar — each team is independent; its people collaborating to produce analyses that shape decisions at the top.
This pillar management structure only works if machine-assisted companies democratize their data. For people in a machine-assisted company to get the most out of each others’ insights, everybody must be able to access both the raw data and the actionable insights.
More importantly, after accessing the data, every team must be able to act independently on the insights it provides. Having to report up the chain of command means wasting time and allowing valuable insights to stagnate and become irrelevant. So having a team leader who understands how to read the data is vital. …
The best SatNav can even drive your car, but not tell you where to go. Asking the right question is the ultimate, supreme human capability.
Ever since science fiction movies have suggested that machines or robots will someday take over the world, people have entertained the notion that this may actually happen.
Technology has advanced in amazing ways, with artificial intelligence and machine learning pushed to the forefront of innovation. However, the idea that machines could transcend their need for humanity seems patently ridiculous.
This discussion often includes the question of relative value:
Are machines better than people, or are people better than machines? …