Written by

Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

View Latest Book

5 Massive ‘Big Data’ Myths Most People Believe – But Shouldn’t

2 July 2021

There’s so much hype around the subject of Big Data that it’s inevitable it will sometimes be over-sold. Don’t get me wrong – revolutionary advances are being made every day by organ isation s and businesses learning to combine the vast amount of data at their fingertips with cutting-edge analytics and data science. But it isn’t a magic cure-all for your (or the world’s) problems and mistakes and miss-steps happen all the time, often at great cost.  





Here are some of the “facts” about Big Data which should be taken with a pinch of salt. Like all myths they may have been based on truths at some point but, though often believed, they don’t necessarily stand up to scrutiny.

1. Everybody is doing it

With anything new and exciting there is often an impulse not to miss out – and Big Data has proven to be no different. Despite all the words written about it, though, research still shows that the number of companies effectively putting true Big Data technology to work is small. For the majority, it remains an ambition – something which everyone knows they ought to be doing but haven’t quite got right yet.

The danger here is rushing in due to a fear of being left behind. While fear can sometimes be a great motivator, it can also cause us to do things in a rushed or sloppy manner. Spending time building a strategy and assessing the impact of moving to a data-driven business model may delay your entry and possibly let other, more hasty competitors steal a momentary lead – but it’s an essential part of the process and shouldn’t be rushed due to a (false) belief that you’re being left behind.

2. It’s all about size

Size – volume – is merely one of the defining characteristics of Big Data. Other things such as variety or velocity of the data are just as important. Data is coming in faster than ever – and the more quickly you can process it, the more up-to-date and relevant it is likely to be. Data is also available in increasingly diverse forms – a greater variety of data means you have more ways of looking at a challenge – and are more likely to find an innovative solution. I advise my clients to look beyond the size of their data and take into account the huge benefits faster and more diverse data can bring. In fact, too much data – particularly if it is unverified, old or from a limited number of sources, can be a very dangerous thing, making simple solutions appear complicated as well as incurring wasted expenditure on capture, storage and compliance.

3. It will tell you what will happen next

When it comes to predicting the future, data doesn’t actually tell us anything that is certain – and anyone who tells you it does, is trying to sell you something.

Big Data-driven prediction is about extrapolating what is most likely to happen in the future, based on what you know has happened in the past. If you are an alys ing real-time data, it can take into account what is happening right now, as well. But any predictions it gives you will be based on a probability, and there is always a margin for error. The more data you have, and the more relevant that data is, the more accurate your probability forecasts will be, but reality often has a way of throwing curve-balls – look at how inaccurate political forecasting has turned out to be during recent elections, in spite of the sophisticated statistical analytics which has been used.

4. Big Data needs a big budget

It’s true that large organ isation s such as governments and multi-national corporations are investing big bucks into football field-sized data   centre s and lightning fast, super-smart machinery. Hiring skilled and knowledgeable data scientists isn’t a cheap process, either. Believing that this puts you out of the game – and therefore data strategy isn’t something that should concern you – would be a huge mistake though. Due to its near universal usefulness, Big Data is becoming cheaper every day, as an increasing number of tools and services become available aimed at helping businesses crunch through the data they collect. If you aren’t thinking about how you can put it to work, it’s likely your competitors are, whatever level you’re playing at.

5. Big Data is only an issue if you are in IT

Decades ago, the only computers in an organ isation , when there were any, would be the sole domain of the IT department. As personal computers became cheaper and more accessible they began appearing on every desk and just about every employee of an organ isation   would be using one on a day-to-day basis. The same principle applies to data – although it may seem like its natural home, if you keep it locked up in the IT department then other parts of the business will miss out. Just about anyone can become better and more efficient at their job if they have access to the right data, and improving access to data and analytics throughout a business is likely to be the best path to success.

Data Strategy Book | Bernard Marr

Related Articles

Should I Choose Machine Learning or Big Data | Bernard Marr

Should I Choose Machine Learning or Big Data?

Big Data and Machine Learning are two exciting applications of technology that are often mentioned together in the space of the same breath [...]

What Are The Latest Trends in Data Science | Bernard Marr

What Are The Latest Trends in Data Science?

Here’s an overview of how this usage is evolving – signposts that point the direction of travel between where we are today and where data science will take us tomorrow [...]

3 Key Ways to Monetize Your Data | Bernard Marr

3 Key Ways to Monetize Your Data

I’ve written a book on data strategy, and one of my primary jobs is guiding businesses through the process of using their data effectively. [...]

The Future of Quantum Computing | Bernard Marr

The Future of Quantum Computing

A Chinese team of researchers has recently unveiled the world’s most powerful quantum computer [...]

How Facebook Is Using Artificial Intelligence

Every day, nearly 2.5 billion people log in to one of the [...]

Amazon: Using Big Data to understand customers

Amazon has thrived by adopting an “everything under one [...]

Stay up-to-date

  • Get updates straight to your inbox
  • Join my 1 million newsletter subscribers
  • Never miss any new content

Social Media

0
Followers
0
Likes
0
Followers
0
Subscribers
0
Followers
0
Subscribers
0
Followers
0
Readers

Podcasts

View Podcasts