How Big Data Can Help Your Organization Outperform Your Peers

Big data has a lot of potential to benefit organizations in any industry, everywhere across the globe. Big data is much more than just a lot of data and especially combining different data sets will provide organizations with real insights that can be used in the decision-making and to improve the financial position of an organization. Before we can understand how big data can help your organization, let's see what big data actually is:

It is generally accepted that big data can be explained according to three V's: Velocity, Variety and Volume. However, I would like to add a few more V's to better explain the impact and implications of a well thought through big data strategy big data world.

Velocity

The Velocity is the speed at which data is created, stored, analyzed and visualized. In the past, when batch processing was common practice, it was normal to receive an update to the database every night or even every week. Computers and servers required substantial time to process the data and update the databases. In the big data era, data is created in real-time or near real-time. With the availability of Internet connected devices, wireless or wired, machines and devices can pass-on their data the moment it is created.

The speed at which data is created currently is almost unimaginable: Every minute we upload 100 hours of video on YouTube. In addition, over 200 million emails are sent every minute, around 20 million photos are viewed and 30.000 uploaded on Flickr, almost 300.000 tweets are sent and almost 2,5 million queries on Google are performed.

The challenge organizations have is to cope with the enormous speed the data is created and use it in real-time.

Variety

In the past, all data that was created was structured data, it neatly fitted in columns and rows but those days are over. Nowadays, 90% of the data that is generated by organization is unstructured data. Data today comes in many different formats: structured data, semi-structured data, unstructured data and even complex structured data. The wide variety of data requires a different approach as well as different techniques to store all raw data.

There are many different types of data and each of those types of data require different types of analyses or different tools to use. Social media like Facebook posts or Tweets can give different insights, such as sentiment analysis on your brand, while sensory data will give you information about how a product is used and what the mistakes are.

Volume

90% of all data ever created, was created in the past 2 years. From now on, the amount of data in the world will double every two years. By 2020, we will have 50 times the amount of data as that we had in 2011. The sheer volume of the data is enormous and a very large contributor to the ever expanding digital universe is the Internet of Things with sensors all over the world in all devices creating data every second.

If we look at airplanes they generate approximately 2,5 billion Terabyte of data each year from the sensors installed in the engines. Also the agricultural industry generates massive amounts of data with sensors installed in tractors. John Deere for example uses sensor data to monitor machine optimization, control the growing fleet of farming machines and help farmers make better decisions. Shell uses super-sensitive sensors to find additional oil in wells and if they install these sensors at all 10.000 wells they will collect approximately 10 Exabyte of data annually. That again is absolutely nothing if we compare it to the Square Kilometer Array Telescope that will generate 1 Exabyte of data per day.

In the past, the creation of so much data would have caused serious problems. Nowadays, with decreasing storage costs, better storage options like Hadoop and the algorithms to create meaning from all that data this is not a problem at all.

Veracity

Having a lot of data in different volumes coming in at high speed is worthless if that data is incorrect. Incorrect data can cause a lot of problems for organizations as well as for consumers. Therefore, organizations need to ensure that the data is correct as well as the analyses performed on the data are correct. Especially in automated decision-making, where no human is involved anymore, you need to be sure that both the data and the analyses are correct.

If you want your organization to become information-centric, you should be able to trust that data as well as the analyses. Shockingly, 1 in 3 business leaders do not trust the information they use in the decision-making. Therefore, if you want to develop a big data strategy you should strongly focus on the correctness of the data as well as the correctness of the analyses.