Leaders of Analytics

Leaders of Analytics

Leaders of Analytics is a podcast about data-driven decision-making, modern business leadership and the use of data and artificial intelligence in business and society. Each episode investigates the strategies, tools, techniques and leadership required to succeed in a world increasingly driven by data and analytics. The show’s guests share their stories and experiences in a way that helps listeners understand the big concepts and small details that make all the difference in today’s world of business.

Episodes

June 16, 2022 47 min

In a recent conversation with data warehousing legend Bill Inmon, I learned about a new way to structure your data warehouse and self-service BI environment called the Unified Star Schema.

The Unified Star Schema is potentially a small revolution for data analysts and business users as it allows them to easily join tables in a data warehouse or BI platform through a bridge.

This gives users the ability to spend time and effort on d...

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Modern analytics teams are central business functions directly and indirectly responsible for increasing revenue, reducing costs, optimising processes and improving customer and employee satisfaction.

But there are many obstacles along the way. Data needs collecting, projects need careful design and execution and stakeholders need convincing. Analytics teams are required to cover a wide range of technical knowledge, business acumen...

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There is so much to learn!

If you’re anything like me, you’re overwhelmed by the number of books, articles, podcasts, online and offline courses, webinars and other training opportunities out there.

Today, we’re not short of learning materials, but often lack the time and capacity to learn new things.

But what if there’s a better way to learn?

Enter the concept of “Ultralearning”, coined by best-selling author Scott Young.

A few ye...

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An estimated 80 to 90 percent of the data in an enterprise is text. Sadly, this rich information is mostly neglected for analytical purposes.

Textual data is typically full of information, but also very complex to interpret computationally and statistically. Why?

Because textual data is both content and context. The same words and sentences can have very different meanings depending on the context.

Textual data is truly a goldmine,...

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This is the second episode of a two-part series of Leaders of Analytics featuring global data science thought leader and influencer Felipe Flores.

Felipe is a global thought leader and influencer in the field of data science and artificial intelligence.

He is the founder of Data Futurology – a podcast and events company with more than 10,000 weekly listeners, Head of Data & Technology at Honeysuckle Health and co-organiser of D...

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Automated decisions, personalised customer and employee experiences and data-driven decision-making are at the core of digital transformation in the 2020s.

In other words, data is eating the world and all modern leaders must know how to use data, analytics and advanced data science to power their organisations.

So, how do organisations set themselves up for success in a data-driven world, technically and culturally?

To answer this ...

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When we talk about data and AI ethics, we typically view this through a privacy lens.

That is, someone’s personal data has either been compromised and ended up in the wrong hands, or personal data is used to manipulate or create adverse outcomes for individuals or minority groups.

These factors are still fundamental to AI ethics, but there is now also a big focus on the broader social impact of AI, including human rights, data priv...

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Is the typical hiring and job search process broken?

It is definitely full of bias.

First, we get interested candidates to submit their resumes.

Then someone (typically not the hiring manager) will pick out the resumes that look most interesting to them.

Resumes that survive are typically carefully curated for someone to be able to form a positive opinion in just a few seconds.

Then the hiring manager will pick their favourites out...

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Blockchain technology, cryptocurrencies and decentralised finance are described by some as massively disruptive technologies that will turn our existing financial system on its head.

For the traditional financial services industry, these technologies have the potential to create huge efficiency gains and democratise more complex financial services for individual users.

On the other hand, DeFi also reduces – and potentially removes ...

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Data is everywhere, but do we know what it means?

A common problem for many enterprises wanting to adopt cutting edge, data-driven solutions is that they have a ton of legacy applications interlinking with more modern tech stacks.

If the organisation is large or complex enough, it typically becomes unrealistic for any one individual to understand how it all hangs together.

All of these applications generate data points with their o...

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Ever heard of ‘synthetic data’?

Synthetic data is data that is artificially created (from statistical models), rather than generated by actual events. It contains all the characteristics of production data, minus the sensitive stuff.

By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated, according to Gartner.

The reason organisations may use synthetic data over actual data...

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Is AI good or bad?

That would depend on how AI is applied.

AI is a revolutionary capability with the power to do a lot of good and plenty of bad, on purpose or by omission.

In order for AI to become a social good that improves our lives in broad terms, we must necessarily pick the right use cases and design solutions with a strong focus on ethics and privacy.

So, how is AI being used for social good today, and how do we ensure ...

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Data is eating the world and every industry is impacted.

In most modern businesses, customer and employee activities create a plethora of data points and information that can be analysed and interpreted to make better decisions for the business and its customers.

Unfortunately, this sounds a lot easier than it is. Despite the huge mountains of data being created, many organisations struggle to get their business intelligence to s...

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"We’re at a crossroads when it comes to data and its ability to make a difference. Data sprawl has become a real and costly problem inside organizations, and it is hurting innovation.

Throwing good money at bad ideals is no longer acceptable, ROIs must be attained. Let us embrace innovative technology, but let us also keep in mind that data itself is useless unless you do something with it!"

These are the words of Carla G...

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We are living in an artificial revolution where the balance of power and political influence is shifting towards those who control data and technology.

Automation is transforming our economies and making some jobs obsolete.

Companies harvest our most intimate secrets and use them to feed us tailored information and sell us products.

The metaverse is the development of a virtual world with the potential to separate us from the ph...

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When we talk about analytics and AI-driven organisations, we often think of the likes of Google, Amazon, Facebook, Netflix and Tencent, which have all risen to dominance during the internet era.

But what about companies that have been around for much longer, can they achieve the same results with their data?

To answer this question, I recently spoke to Tom Davenport who is one of the world’s foremost thought leaders and authors i...

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Why is the Data Scientist role called the sexiest job of the 21st century?

I believe it’s partly because the data science profession is constantly evolving to include new data types, new tech and tools, new modelling techniques along with an increasing ability to drive customer and business outcomes with data.

The main challenge for data scientists becomes one of bandwidth. Great data scientists are highly intelligent, technicall...

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In my opinion, any organisation with respect for its data should have a Chief Data & Analytics Officer (CDAO) as part of their C-suite.

Although the CDAO role is still nascent, business leaders across many industries are starting to appreciate the need for a data and analytics voice at board and executive level.

So, what does a CDAO do?

How should they spend their time to balance strategic influence with operational delivery...

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Data science and machine learning are integral parts of most large-scale product manufacturing processes and are used to understand customer needs, detect quality issues, automate repetitive tasks and optimise supply chains.

It’s an invisible glue that helps us produce more things for less, and in a timely fashion.

To learn more about this fascinating topic, I recently spoke to Ranga Ramesh who is Senior Director, Quality Innovat...

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Data science and machine learning are continuing to evolve as core capabilities across many industries.

But high-quality data science output is only half the story.

As the data science profession matures from “back office support” to leading from the front, there is an increasing need for more integrated systems that plug into business operations.

To get the most out of these capabilities, organisations must move beyond just bui...

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