Big Data-online and offline

Data is everywhere, people generating data all the time. In my opinion, since we were born, we are been affected by different kinds of information and experiences could also be “data”. For instance, the potatoes sold with a lower price in a shop or the rating of a restaurant in our minds. We could deduce human behaviours from these data, and predict the result more precisely. When it comes to a huge amount of data, we call it “Big data”.

blog-customer
The graph we made based on customer feedbacks on trade fair

It is used in various areas, such as healthcare, preventing crimes, transportation system, and of course in business. Nowadays, big data is considered an important element in business strategy. Approximately 59% of the companies agreed that data is an essential part of their business and it is used by all parts of the business. (Economist Intelligence Unit, 2015) If businesses look into the right resources and analyse it properly, it helps companies make decisions and keep better customer relationship.

When I participate in start-up, we did a customer survey and visualised the data. It helps us make further marketing strategies and make the improvement. Although the amount of data is small, the spirit is the same.

There is a video which explains big data in an easy way by Tim Smith.

Gartner gave a clear definition of Big Data: “Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization” Laney (2001) gave big data three characteristics with 3Vs: Volume, Variety, Velocity. In 2012, veracity and value were also added.

Volume, the first characteristic that comes to everyone’s mind when big data is mentioned. It refers huge amount of data, and its size is constantly increasing. There is no doubt that people now are living in an information explosion era. Every minute, 2.46m Facebook post are shared, 4m Google search requests and 200m emails are sent. 90% of all data on the internet were generated less than past 2 years ago. Also, the total amount of data is estimated to grow 40% years on year for the next data. (Great Britain, House of Commons Science and Technology Committee, 2016) As the result, the quantity of data is increasing while its quality is becoming hard to control.

Variety, which refers to various types of data and sources.

Velocity. It refers to the time that data could be processed. Data is been streamed continuously and fast. Marketers should look for these real-time data from various resources depends on their business needs.

 Veracity is the most difficult thing to achieve. It not only refers to data accuracy, but also data understandability. (Saporito, 2013) Due to the volume and variety of big data, identifying accurate and useful data from “dirty data” is a big problem.

The potential of data is huge, but it is useless unless people turn it into value. It is the most important aspect of the big data. (Ishwarappa and Anuradha J, 2015) These achievement based on big data are, in other word, creating the “value

Data itself is meaningless, unless they can be analysed, interpreted, and create value

Using big data to collect information is opposite way from design thinking module. When we ran our start-up, we made a list of assumptions and tested them on the street. However, surprisingly, I still found they are somehow similar.

Take our experience as an example:

First, we have to ask a certain volume of passengers, otherwise, a small

number of samples would generate a biased result. Then, we should choose the people carefully. We should make sure that it involves different groups of people. (Variety)

Although we had an assumption, we still need to test it on different groups. For instance, we assumed that elders had a problem to take their cards out of the wallet, but after testing the assumption, we found that everyone has the problem (even ourselves!) In terms of Velocity, although we are not able to gather data that fast. It may be the biggest difference. However, we should also pay attention to customer’s behaviour or attitude change immediately in order to improve our business. Veracity, the original content is bad data. In design thinking thought, I would define it as ‘non-target user’. Our product is a card holder, so people who only use cash would not find our product useful and their feedbacks might not help us to develop this product either. The final one-Value. There is no doubt that value is what people with design thinking thoughts are seeking for. After collecting, analysing the data, eventually it creates value-not only for business, but the customers. Business mind and design thinking mind seem quite different, but we could still find out there are something similar.

references

Economist Intelligence Unit (2015) The Business of Data. [Online] Available at: https://www.eiuperspectives.economist.com/sites/default/files/images/Business%20of%20Data%20briefing%20paper%20WEB.pdf

Laney, D. (2001) ‘3-D Data Management: Controlling Data Volume, Velocity and Variety.’ META Group Research Note, February 6, 2001.

Great Britain, House of Commons Science and Technology Committee (2016) The big data dilemma : Fourth Report of Session 2015–16. [Online] Available at: https://www.parliament.uk/business/committees/committees-a-z/commons-select/science-and-technology-committee/inquiries/parliament-2015/big-data/

Ishwarappa ., Anuradha J (2015) ‘A brief introduction on big data 5vs characteristics and hadoop technology’, Procedia Computer Science, 48(2015) pp.319-324

Saporito, P. (2013). The 5 V’s of Big Data. Best’s Review, (7), p38.

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Big Data-online and offline

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