Big Data – A Weapon, Not A Superhero
Though big data is the current obsession of marketing analytics teams, it is important that businesses understand the potential of the concept before investing in it. This post tells you what big data can or can’t do.
Over the last few months, not a single day has gone by without the mention of the term ‘big data’ in the marketing analytics world. Everyone is talking about it and everyone is trying to learn and reap the benefits of big data at their organizations. But doing that is functioning like a horse with blinders on.
It is essential for organizations to realize that every new development in the world of marketing technology doesn’t need to be adopted by everyone. What matters is whether it makes sense for the organization in question to adopt it and if adopting it will align with the organizational goals. For instance, for an organization that focuses on marketing consultancy, it doesn’t make logical sense to invest in, say, the Google Glass (unless it is to provide support for a client who specifically requests a marketing strategy for a similar product).
Organizations need to do the following:
a) Understand what the ‘new shiny thing’ can or can’t do
b) Check whether the benefits offered by the product/service are aligned to the business’ overarching goals
Most organizations fumble in the very first step. With a very basic idea of what the product/service can do, they insist on jumping on the bandwagon. This is precisely the problem when it comes to big data. More specifically, organizations believe in what Michael Wu, Chief Scientist at Lithium, claims are the fallacies of big data.
- People believe that big data = more insights when the converse is more likely.
- People obsessively remove statistical redundancy from big data in the belief that it is counterproductive. Michael Wu believes that the redundancy “reveals the reality” of what is being measured.
Considering that so many of us have such massive misconceptions about big data as well as marketing analytics in its entirety, it may be much more useful to know what it can’t do in order to understand its limitations.
More data ≠ more information
Hoarding data is not going to be helpful by any stretch of imagination. If anything, it reduces the relevance of the data being collected, thereby making extraction of relevant information more difficult. Also information ≠ insights. According to Michael Wu, information needs to be interpretable, relevant and novel to provide any insight. This means that more information doesn’t proportionately mean more insights.
Means not an end
Collecting volumes of data is quite frankly the easy bit. What follows is interpretation which is often fraught with biases.
The irreplaceable human element
Data and the machines that crank it out may seem fabulous on their own but they are inadequate without the human element. Analytics tools cannot weave multiple causes and contexts together independently. Also these tools cannot address the ethical and moral dimensions in data collection. Think of it as the Iron Man suit without Tony Stark!
Not a guarantee of objectivity
Interpretation of data is anything but objective. Considering that it relies heavily of human judgment, the entire hoopla over data providing objectivity is rather silly.
Data and its interpretations are not invincible. Why? Since data modeling is devised by humans, there is a significant chance of errors.
Data on its own is meaningless. It requires to be set in a relevant context and provide some value as well.
Data cannot decide
When decision-making is on the cards, data cannot lead from the front. It can provide clues or act as supporting evidence but the buck stops there. Also if your decision requires social cognition, data is of little use there.
Why do you think it’s called ‘big’?
Given that volumes of data are large, physical challenges such as mining and storage become more pressing. Collecting large volumes of data invariably leads to more data.
Learn how Xerago’s service can help your organization achieve its objective. Click here