4 reasons why big data isn’t coming for your job

One of the key misconceptions about big data and machine learning is that algorithms will replace human data scientists. There is something of a debate around whether a team of analysts is better or worse than a well produced algorithm, but the truth is that the most effective way to prise actionable insights from your data is to make the most of both. While a machine eliminates human error, a human is vital for contextualising and creating a narrative from the insights, as well as working around the limitations of the data.

It only makes sense that CTOs and other decision makers should utilise the best aspects of machine and human performance. While they both have extremely strong points, there are drawbacks to both. Humans are a limited resource, as data scientists are in hot demand right now. Further, we can only process so much information at a time. On the other hand, big data without human input is still essentially just raw data. Let's explore why humans are still critical to leveraging useful insights from big data.

1) Humans understand the difference between correlation and causation

Just because two variables appear to be linked does not mean that they are.

Here's an interesting statistic. Between 2001 and 2009, per capita consumption of mozzarella cheese rose at almost the exact same rate that civil engineering PhDs were awarded. Is it because qualified civil engineers are known to eat stretchy cheeses exclusively? Is it because the cheese industry is handing out scholarships? Or is it simply a random coincidence?

Just because two variables appear to be linked does not mean that they are. You've heard the phrase "correlation does not imply causation." It might sound obvious because there is a clear lack of causation between mozzarella sales and civil engineering doctoral graduates (or vice versa), but when every metric you are reporting through your big data analysis tools relates to your business, it's often difficult to distinguish correlation from causation. A human consultant can help you differentiate the substantial relationships from the meaningless.

The difference between correlation and causation in big data.Is mozzarella the key to completing a PhD in civil engineering? No, no it isn't.

2) It takes a human to ask the right questions

When we say "ask the right questions" we mean identifying the right parameters and constraints for your analysis. A useful simile here would be asking a toddler what they want to have for dinner. They will likely reply "ice-cream", "candy", or "cake" and of course they did, because you didn't set realistic parameters for the question. All of the toddler's responses are legitimate answers to your query, but they don't get you any closer to a real, workable solution.

Such is the case with leveraging insights from big data. Asking the wrong questions usually means getting the wrong answer, but in some cases these incorrect responses can appear to be useful, actionable insights. Making decisions based on this information can be disastrous, The key lies in understanding how to use your analytics platform as well as how to engage with it to produce the information you desire. This is something that can be taught, but it must be taught by a human data scientist, without whom you might be left with the enterprise equivalent of a toddler in a sugar-rage.

Why is it essential to ask your analytics tool the "right" questions?Asking the wrong question almost guarantees you'll get the wrong answer.

3) Imprecise data collection leads to imprecise results

There are a few ways in which the data you've collected can be biased. This is particularly true in the case of user-data. In your marketing department, you might have metrics to collect data from those who have engaged with your website, ads, or other digital properties. The issue here is that this information doesn't represent your target demographic as a whole, but only those who have already engaged with you. Subsequently, analysis of this data will relate to your users only, not necessarily your target market as a whole.

A human analyst understands the limitations of data collection.

In the healthcare sector, you may be using patient records to help you provide a greater level of care. Since there is no centralised medical record database, anything you collect from the archives you can access will be limited. Whether this data covers a geographic area or a particular kind of ailment, the data set could be either too small for meaningful insight or too specified to be of much use on a larger scale.

A human analyst understands the limitations of your data as well as collection methods, helping you make more informed decisions based on the information that is available. Note that biased data can still be useful, as long as the bias is acknowledged and, as previously discussed, you're asking the right questions.

4) Humans provide all-important context

There's often talk of "creating a narrative" with data insights, and this is very much a skill that, at this point, belongs to human analysts alone. While visual analytics platforms can deliver a huge amount of value and simplify difficult relationships in order to inform business practice, a meaningful interpretation and simplistic explanation are what turns this information into good, workable decisions.

AtoBI can not only help you implement the right solutions, we can make sure your human staff have everything they need to get the most out of your data analysis tools. For more information, get in contact with us today.