Three Big Puzzles in Big Data

Big Data analysis applications provide a highly scalable framework to deliver advisories that drive smarter day-to-day decisions.

Big Data is big phenomenon, also a big puzzle, how big is it? how does it change the work and life, there’re many articles on daily basis to keep us update on progress made via exploring Big Data’s big potential. Still, each one of us may have our own inquiries.

1. How Big is Digital Universe made of Big Data? 

According to an updated Digital Universe study released recently, during the next eight years, the amount of digital data produced will exceed 40 zettabytes, which is the equivalent of 5,200 GB of data for every man, woman and child on Earth,.

To put it in perspective, 40 zettabytes is 40 trillion gigabytes -- estimated to be 57 times the amount of all the grains of sand on all the beaches on earth. The majority of data between now and 2020 will not be produced by humans but by machines as they talk to each other over data networks

1) Here are further statistics:

• 6 billion mobile phones in use today, representing 87% of the world’s population
• 1.2 billion1 mobile Web users in the world today, representing 17% of the world’s population
• More than 60 billion intelligent devices exist in the world today and is expected to rise to more than 200 billion by 2015
• 40% projected growth in data volume every year
• All data is expected to double every two years through 2020

2) Add some new coined Data Volume Measures:

• Megabyte (~1,000,000 bytes)
• Gigabyte (~1,000,000,000 bytes).
• Terabytes (~1,000,000,000,000 bytes)
• Petabyte (~1,000,000,000,000,000 bytes)
• Exabyte (~1,000,000,000,000,000,000 bytes)
• Zettabyte ( ~1,000,000,000,000,000,000,000 bytes)
• Yottabyte ( ~1,000,000,000,000,000,000,000,000 bytes)
• Brontobyte ( ~1,000,000,000,000,000,000,000,000,000 bytes)

Calculators would need new buttons with multiple 000's!

Puzzle one: How do data scientists define such Big Data measures?  Would it be easier to memorize if they had been defined following alphabetic order A to Z? Is data scientist sometimes also out of logicl?

2. Is Big Data a Money ball or Value Pie? 

1) Four Main Dimensions of Big Data:

Big Data deals with four main dimensions:

• Velocity: Refers to the frequency of data generation, including batch, near-time, real-time, or streams
• Variety: Refers to sources of data and can be classified as structured, unstructured, or semi-structured
• Volume: Refers to the amount of data - that is measured in terabytes today, but will likely be measured in petabytes, exabytes, and even zettabytes in the future
• Complexity: Refers to the different sources of data and the multiple formats within those sources

2) Big Data Wove Money ball:

Moneyball, the 2011 film based on a bestselling book by Michael Lewis, reveals how the Oakland Athletics won almost two-thirds of its games in 2001. The well-documented key to their success was making strategic decisions (assembling the team) based on deep data analysis. They ignored conventional wisdom.

3) Slice Big Data Value Pie:

Though Big Data is still in its current immature state, high performance firms are establishing a new level of collaboration between business and IT; finding correlations across multiple, disparate data sources (click streams, geospatial, transactions, etc.), developing new processes to deliver solutions; The slices of Big Data value pie include:

• Predicting customer behavior
• Predicting product or service sales
• Predicting fraud or financial risk
• Identifying computer security risks

Puzzle 2: Is Data Gravity another kind of gravity?

data gravity: means that the infrastructure where you perform actions upon a dataset will attract more and more data over time and get more and more difficult to drastically changes.

Big Data analysis applications provide a highly scalable framework to deliver advisories that drive smarter day-to-day decisions. Big Data applications’ true power comes from the use of analytics to deliver actual analysis – actionable recommendations – directly to the front line of business.

3. Can Big Data Declare Big Victory yet? 

Digitization has made data an indispensable resource for any enterprise, second only to people. Big data is about applying enriched data in the most optimal way, changing the way we live and work, and giving rise to an engaging work environment and digital economy.

 Mining enterprise data effectively to extract actionable business insights can provide executives insight to make better strategic decisions for long run, but also improve the critical, day-to-day decisions made at the front lines of business. Together, data-based strategic and tactical decision making for companies creates a clear competitive advantage, which can dramatically protect and accelerate the revenue and ultimately, their overall business success.

And while big data may not change everything, it does impact multi-facet of business as well as life, now organizations focus on Big V-Value. Organizations that begin making the process, technology, staffing, cultural and risk changes required to turn big data into strategic insight will likely be ahead of their competitors.

Puzzle 3: Does Big Data make “Small Data” irrelevant?

 Big Data's 3V+1C (volume, velocity, variety and complexity) attributes make it more interesting, also more challenging, with potential to predict the future of business. 

Still, the hindsight businesses gain from “small data”( transactional data) like the rear mirror, also provides historical pattern and certain business value.

The advantages of big data and business intelligence are based on “discovery and agility”—the ability to mine existing and new data sources continuously for patterns, events and opportunities.

Big Data may not declare the big victory yet, as majority of Big Data projects do not deliver the expected result, Big Data can also turn to be big distraction, with big puzzles surrounding it, still, it’s winning competitive advantage for high-performing businesses cross sectors, it also enriches our life with abundance of information.  

Posted in: Big Data