It is commonly known that if you want to get an IT budget approved, there are certain phrases you need to throw into the proposal that hit such a nerve the CFO will immediately approve the budget. One such phrase is "big data".
However, this phrase usage is wearing thin. It has been on top of the Gartner Hype Cycle since 2013 and is now falling into the trough of disillusionment, as big data was touted as the miracle that would solve all of a company's ills. To date, it has not.
In 2014, according to the IDC, companies spent $125 billion on hardware, services and software to deal with big data. However, at CeBIT 2015, SurveyMonkey's CEO Dave Goldberg highlighted that "[big data] has not delivered on massive promises" that were made by IT departments, and so the projects are starting to fall out of favour.
Why is big data failing?
It would easy to dismiss big data's weaknesses relating to too much data being collected, in too many silos - and that these silos are still not connected to each other. However, there is another side to big data that Goldberg said is missing from the discussion. Goldberg spoke about "implicit data" and "explicit data", and both are needed.
Implicit vs explicit
Implicit data is what we currently think of when we talk about big data. It is data that is collected by clicking on a mouse, listening to a song, entering and analysing search queries, etc. This type of data is collected on a massive scale. Everything is measured, and it is this type of data that concerns people when it comes to security and data privacy.
However, implicit data gets it wrong.
Goldberg offered an example of how implicit data can fail. Credit card companies analyse everyone's purchasing habits, and discover expenses on a customer's credit card, such as teeth whitening, gym membership and hotel rooms. Therefore, an assumption is made based on this implicit data that this person is getting divorced. The data doesn't take into account surrounding circumstances, such as upcoming business travel.
Shopping engines recommend products based on historical implicit data purchases. However, if shopping for a gift for a friend, this would throw off the personal recommendation engine.
"More data doesn't lead to better information, and this is how implicit data gets it wrong," noted Goldberg. "If you want to know how someone is feeling, what music they like - you need to ask them. Analysing their searches doesn't lead you to the correct answer as much as asking."
Asking is known as explicit data. This is the data collected by asking questions and receiving answers.
The Internet has allowed companies to retrieve this type of data en masse by asking the right questions to the right group of people.
How is explicit data used?
Goldberg presented another example:
Google realised it was losing female staff members, which was concerning. Instead of relying on implicit data of the female search history, Google surveyed the staff and realised it didn't have a female/male issue, but rather "new mother" issue. Females who were pregnant or thinking about becoming moms did not like Google's maternity policy. As soon as Google discovered this, it instantly fixed the issue and retained its female staff.
The key message behind Goldberg's talk is relying on causation without correlation doesn't paint a true picture. Have the conversation with your customers and listen to what they have to say. "Encourage customers and employees to give you feedback. People feel powerful and loyal when their voices are heard."
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