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Marketing - BLOG

The Eight Most Common Big Data Myths

Joerg Niessing, INSEAD Affiliate Professor of Marketing, and James Walker, Partner Demand Analytics, Strategy& |

The hype surrounding “Big Data” does businesses a disservice by making it all look much too easy.

Data analytics and “Big Data” promise to revolutionise marketing. Most companies are sitting on tonnes of data from various sources: financial data, mobile data, transactional data, customer research data, behavioural data, social media data, etc. The combination of new analytical techniques, amped-up computer power and instantaneous online resources has resulted in incredibly powerful tools that have changed the game forever. So powerful, in fact, that analytics can go beyond merely lending support to unlocking new opportunities and strategies, as well as opening up possibilities never before imagined.

But the ease of analysing “Big Data” also has been overstated. In reality, harnessing Big Data is still a messy and very labour-intensive business. Take it from two people who do this work for real: Some of the hype is doing us a disservice, because it creates a false expectation of how easy this is going to be.

So that we can start getting real about Big Data, it’s time to put to rest these commonly heard myths.

Big Data Myth #1: It’s Big
Big Data isn’t “big”. It is diverse. “Big” is misleading. What we’re talking about is a large volume of data points, updated at high-frequency in real-time, from various sources. It’s very granular. It’s individual transaction data; it’s a certain credit card, paying for a certain amount of gas, at a certain gas station. Big Data is actually lots and lots of very small data. It’s not a landslide of data; it’s a sandstorm. And sandstorms can blind and disorient you. So, to help see in the storm, what other myths do we need to debunk?

Big Data Myth #2: You need to apply it right away
Most things in life that are important and worthwhile are difficult, and the analysis of Big Data is no different. The solution is to take small steps and start with very specific objectives. Think carefully about what you want to do with the information before you start stockpiling data.

Big Data Myth #3: The more granular the data, the better
Is real-time and granular data always better? No, it’s not. The first quarter of a football game doesn’t predict how a whole game plays out. Real-time can be too close to the action. Sometimes, you need to pull back for the long shot to reveal what’s really going on.

Big Data is encumbered by a huge amount of white noise. The noise as a proportion of the total signal increases with higher resolution, for example, data by minute rather than by week, or data at a town level rather than state. Do not confuse precision with accuracy. Big Data, in its raw disaggregate form, can be misleading. There needs to be an appropriate level of aggregation to cancel out all the white noise.

Big Data Myth #4: Big Data is good data 
There is a distinction between a lot of data and a lot of good data. Poor quality data has lots of errors, lots of missing data that can be misleading. Photographs and videos can be tagged incorrectly, and is unstructured text written by teenagers reflecting a positive or negative sentiment? It takes a smart model to figure that out sometimes. To make sense of data, you need to throw some of it away. To analyse Big Data, one of the first things you have to figure out is what data to include in your analysis, and what you need to throw away.

Big Data Myth #5: Big Data means that analysts become all-important
It is often said that Big Data will see the rise of the analysts, “the new gods of the Information Age”. But the rise of the analytics team is exaggerated. The dramatic increase in data velocity means there’s no time to “brief the analytics team” now. We need fast tools that can cope with the velocity, volume and granularity of the data. Ideally, a small group of master-analysts would leverage technology to empower marketers to do more of their own analytics and scenario-modelling and decision-support. We predict the death of the Analytics Department, and the rise of self-service. The era of the pre-eminence of the Data Scientist will not last forever – there is just too much data!

Big Data Myth #6: Big Data gives you concrete answers
Ambiguity is the dominant characteristic of Big Data. Multiple sources of data (for example, transaction, customer acquisitions and media) can lead you away from what the evidence is telling you. Different data, analysed incorrectly, can yield conflicting evidence. Which data do you believe? Big Data requires human judgment to intervene and resolve seemingly conflicting evidence, and that’s where the skilled analyst comes in.

The more data you have, the more likely you are to have contradictions and ambiguities that require resolution. Big Data is not all-powerful. Quite the opposite, in fact. More data gives you more witnesses, but doesn’t get you closer to the truth until you leverage experienced human judgment to reconcile conflicting evidence. The future of analytics is all about combining, weighing and judging multiple sources of information and different analyses.

Big Data Myth #7: Big Data is a magic 8-ball 
Well, yes, but you need to ask the question in exactly the right way. It’s a bit like when a genie gives you your three wishes. You have to phrase your wishes very carefully. Applying analytics with a lack of precision or detailed hypothesis creation in advance, when dealing with complex data sets such as cell phone or calling network data, can actually lead you astray and give an incorrect answer. You need to ask your questions very carefully of the “Big Data” crystal ball.

Big Data Myth #8: Big Data can create self-learning algorithms
False positives from rogue data (for example, call centre call volume prediction from direct response TV ads) indicate the limits of automated models from a marketing perspective. Rogue data from a Super Bowl weekend could distort an auto-update algorithm.

When set up in the right way, algorithms can be very powerful, but they always require human intervention. Cell phone operators, for example, have demonstrated good use of non-marketing data for marketing. They know who your friends are, they can guess your age, they know the parts of town where you hang out, they know what websites you visit, what apps you use, and when. Insurance companies can use telemetrics for obtaining data for marketing, not just underwriting.

At bottom, debunking these myths is about discarding blind faith that the formulae for business success are set down in the data. Truth is, Big Data is a tool in itself, like a computer or smartphone - an awesome, game-changing tool, but only when wielded by people who know the right commands and coordinates.

Joerg Niessing is an Affiliate Professor of Marketing at INSEAD. You can follow Joerg on Twitter @JoergNiessing

James Walker is a partner with Strategy& based in London.

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Many companies still struggle with antiquated systems and processes (eg. homegrown Excel spreadsheets for critical data). I would argue that many if not most organizations need to first focus on improving existing systems before they should even think about Big Data.

Didier Baudois,

When confronted with Big Data, just ask yourself "What is the added value ?" or "Which added value do I want to obtain from that bunch of data ?"

Big data without added value is just another waste of time and money...

Sandra Pickering,

Timely warnings.Thank you.

May I add:
Myth #9 - Big Data measures important things. In fact, big data often measures what is easy to measure rather than what is important. Don't behave like the drunk man looking for his keys not where he dropped them but under a lamppost because "that's where the light is".

Myth #10 - Big Data contains the answers. In fact, it mainly raises questions of 'why?' To make sense of 'why' requires genuine understanding of human psychology.

JV @ l'Attitude! in Cairns,

Seems to me that many data analysts attempt to use "big data" like they would a street lamp - for support rather than enlightenment!

My tip is to first qualify the answers you seek, then seek data - big or small - to assist finding those answers

Joerg Niessing,

Thanks for your comments. Most of them are related to 'asking the right questions' (linked to Myth #6 and #7). I cannot agree more. Most companies struggle to leverage 'big data' because they don't know what answers they are looking for and they don't know where to start. It is very important to have a strategic approach that is focusing on the objectives first. Then you might realize that the answer to your question could be just a ‘small step’ away.


Maybe I don't understand what's meant by 'analysts' in 5, but it's certainly been my experience that the main issue is to do with understanding what the data point to in business terms. I think that's what's been identified in 6. A corollary of trying to understand the data semantics is trying to understand what the data analysis means - particularly 'results' that come from over-fitted models.

Together, at least in my mind, these two issues (what the source data mean and what can be derived from their analysis) make self service difficult for the average business user.

Christopher Aw,

Great article, but I would amend your third "myth." I would say that gathering real-time and granular is always better because you can always group data together later in your analytic phase or through coded workflow, but will not be able to parse the data into a more granular method if you do not first collect it in that manner.

Often the challenge when you begin collecting or combining data sources is knowing the nuggets of information that will be helpful in the future, so over collecting is a more forgiving mistake than the alternative. The real potential tradeoff is the cost of collection, integration of data, and data storage, to balance out how closely the level of collection is scrutinized.

Justin James,

There are two sides of a coin, like every service big data also has its own set of highs and lows but it has brought a major transformation in how the data is fetched from the database. Cassandra and Solr have enabled the websites to fetch and display the data within no time compared to the earlier time lags.


I like this article, but in a few moments, I want more specificity.
For example, how used Big Data in different areas ( or big companies (
I looking forward to reading your next article about Big Data, Joerg.

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