Category: Big Data

So what exactly is big data analytics? Bigger numbers than regular data?

Well, IBM defines big data analytics as the “use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.”

So how big are we talking?

To be deemed big data, the data set must have a size or type that surpasses the ability of a traditional database to record, understand, and process the data without lengthy delays.

 The data must also possess one of the 4 “V’s” of big data:

  • Volume: the scale of the data

You and I have gigabytes of data on our mobile phones, now a zettabyte is 43 trillion gigabytes.

  • Velocity: how fast data is streaming

For instance, Facebook uploads 900 million photos per day. THAT is high velocity

  • Variety: different forms of data

Email is filled with variety, everyone’s email has a sender and a recipient. However, the contents of the emails vary greatly. They can have images, document attachments, audio files, or even emojis.

  • Veracity: the uncertainty of data

The inability to trust the accuracy or quality of data.  

Big data is most often derived from one of three main sources: social data, machine data, or transactional data.

Social data is bigger than ever.  This comes in the form of Likes, Tweets & Retweets, Video content uploaded via digital media and shared with others.  This data is great for understanding consumer preferences, trends, and demographics.

On the opposite end, machine data is generated from industrial equipment through sensors, tracking user behavior. As the Internet of Things grows even larger, sensors will be integrated into more areas of daily life making big data, well, bigger.

Transactional data is comprised of on and offline interactions.  This can be financial like payments and invoices or logistical, like deliveries and travel logs. Thus far this has been the least useful type of big data because it gives little insight into consumers.

So what industries are using big data?

  1. Finance
  2. Media and Entertainment
  3. Healthcare
  4. Education
  5. Manufacturing
  6. Retail
  7. Insurance
  8. Government
  9. Transportation
  10. Energy

Companies aren’t the only ones that can use big data. Mobile app users, like you or I, are constantly generating data. While yes, companies can use this data, so can we to boost our productivity on our devices.

Mobile app developers are beginning to share data with us about how we are spending our time.  We are then able to categorize different apps as productive or unproductive and receive an unbiased report of our productive and unproductive screen time.

The data is also being used to boost our productivity by sending push notifications when we are most active on our devices.  This is a large reason you don’t get receive notifications at 7 AM if you typically don’t check your phone until 9 AM.

Performance improvements are carried out through big data. For instance, if consumers typically respond better to one email rather than another, marketers are likely to respond and provide you with the content you’re more interested in.

If you’d like to read more about big data and how it influences your productivity, check out some of these popular posts:

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