Understanding the causes and risks of data waste

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In recent years, there has been a significant focus on individuals and organisations to reduce waste in support of global climate change and environmentally conscious efforts. In my blog post this month, I turn the spotlight on a different sort of waste. This is something which every business around the world struggles with – data waste.

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Conference report back: The practical ways data is transforming healthcare

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I was fortunate to have been invited to attend the recent ‘Data & Analytics in Healthcare’ conference in Australia which took place at the end of March. The event was positioned as a ‘deep-dive into the data transformation taking place in healthcare across Australia – and aimed to explore efforts to transform the use of data, analytics, and AI in order to deliver great efficiencies and better, more accurate, patient care’. In my blog this month, I will explore whether this was achieved and my key takeaways from the event.

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Managing data-centricity during down-sizing

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In my blog post this month, I will be discussing how an organisation can stay focused on improving data-centricity even if it is embarking on down-sizing initiatives. It stands to reason that during tougher economic times it is much more difficult to motivate for an upgrade to data-related infrastructure, an increase in staff, or even for advanced training.

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How Chief Data Officers can generate and show value

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Long-time readers of my blog will know that I’m a great advocate for the Chief Data Officer (CDO) role – and in the right context. This is even more so the case in companies where the CIO has more of a technology focus than a data-oriented one. I was therefore understandably excited by this Harvard Business Review (HBR) article which illustrates how the CDO can demonstrate value inside the organisation.

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A deep dive into responsible data and analytics

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Having done some work in permaculture and sustainability, I was very interested in this paper that I recently came across entitled ‘Responsible Data and Analytics’. We know that data and analytics can do a lot to enrich our lives, increase productivity, even assist in improving health. But if done incorrectly, or deployed irresponsibly, it can also ruin businesses, lead to great failures, expose private details, and cost lives.

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Unpacking the top data trends for 2023

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As the year rushes to an end, it is interesting to see what the analysts are indicating as the top expected trends for next year. With that in mind, I recently took a deeper look into two independent trends focused papers I came across – ‘The Top 5 Data Science And Analytics Trends In 2023’ by Bernard Marr published on Forbes and ‘Top 10 Future Data Analytics Trends in 2023’ by Sonia Mathias in Data Science Central published on TechTarget’s site – to see which were the common trends they both expect to rise in 2023.

Cloud and Data-as-a-Service (DaaS)

Marr grouped these two topics together, while Mathias kept them apart. However, she also had five more topics in her list.

Regardless, it is becoming increasingly apparent for small to medium sized businesses that having their data in the cloud is the way to go. And I couldn’t agree more – it just makes so much sense when you do not have to manage on-premises systems or even have system and DBA experts on the payroll to manage these systems. The cloud is also an operational expense, as opposed to a capital expense, which is more tax efficient.

Add to that the rich set of functionalities you can now get from cloud platforms, such as self-service data analytics, subscription-based access to third party data, advanced analytics services, and so forth, which to quote Marr: “allows businesses to work with data without needing to set up and maintain expensive and specialised data science operations.”

Advanced forms of analytics

I have grouped a wide, but related set of technologies together here. These featured strongly in both papers and included artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and data analytics automation.

As is currently the trend, the use of these technologies will steadily increase as organisations realise the kind of insights they can achieve, thereby gaining good business value. I have purposely used conservative phrasing here as I do not think we will see massive ‘hype-driven jumps’ in the adoption curves of these technologies, but rather steady growth.

The one area that excites me is the ‘productionisation’ of analytics, including analytics automation. Here we will see an increase in the adoption of analytics to operationally manage business processes. This is a great move to making analytics more useful on a day-to-day operational business and is a good step away from the ‘slightly academic,’ ad hoc, and independent advanced analytics project that was often done in isolation where nobody knew how to integrate the outcomes into the business.

Another area that I get excited about is Natural Language Processing (NLP). It is almost 2023 and we are still hammering away at our keyboards. There must be a better, faster, and more natural way. Granted, it does become a bit eerie and intrusive when you talk about something and the next minute, without even having searched for it, the adverts start popping up on your phone!

Data governance

I must say, this is one that surprised me. Do not get me wrong, I am a big advocate of data governance. It needs to be stronger and more intrenched in business and data-related processes, especially if businesses are going to make more informed and more far-reaching decisions based on insights derived from their data. I just did not expect it in the top trends for 2023.

I anticipate that with the ongoing increase in data privacy breaches there will be a stronger emphasis on data privacy with most countries governed by strict laws and regulations. This is hardly surprising given that others are trying to monetise data and trying to make a large amount of money by selling data that was never put up for sale to begin with. It will be interesting to see how these trends track through the new year. One thing is sure – we will not be bored!

Soft skills key differentiator of good data analytics professionals

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The technical skills required to be a data analytics professional form the cornerstone of training approaches. If these individuals are not proficient in things like data wrangling, analytical approaches, modelling techniques, data exploration, or data visualisation, then they will likely not qualify. But that is just one part of what the job requires. I believe the difference between a good and an average data analytics professional can be found in the soft skills they possess.

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Understanding the essential KPIs for data and analytics

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To be successful, any organisation must be able to measure what it manages. This is especially true when it comes to data – easily one of the most significant assets any digitally driven business has access to. I recently came across this Gartner report that examines the five data and analytics (D&A) KPIs that every executive should track. I found the information greatly insightful and so wanted to take the opportunity to add some of my views.

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Can an organisation be data-centric without a CDO?

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Regular readers of my blog will no doubt know that due to my immersion in the data-information-insights spectrum, I’ve been very interested in data centricity and the role of the Chief Data Officer (CDO) for some time. On reading a recent industry piece about the role of the CDO, a question that came to my mind is this: can an organisation be truly data-centric without having an official CDO role in place?

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