Over the past couple years, big data has become a subject gaining the attention of enterprises and the tech world alike. Big data typically refers to data sets so enormous and complex that standard data processes are no longer adequate. Enterprises hope to manage and utilize big data efficiently while the tech world develops tools to service enterprises.

1) WHAT IS BIG DATA?

Experts define big data differently. However, three defining components of big data are consistent:

1. Volume. Data falls into the big data category when it is massive. For example, a petabyte of data, which is 1,000,000 gigabyte of data, is big data.
2. Velocity. This implies that the data is continuous, increasing, and often available in real time. A good example is social media feeds such as tweets, which are text streams or data collected by motion sensors uploaded to a remote server.
3. Variety is also a common denominator when the data comes in different formats. It can be in a structured form (think relational databases) or unstructured form, such as audio and video signals.

In summary, big data refers to datasets, structured or unstructured in nature, that are large, and steadily increasing in size. Extracting meaningful value from big data requires a set of specific tools and technology that software companies are developing and improving daily.

2) WHY IS BIG DATA IMPORTANT?

Traditional relational database management systems and standard desktop statistical computing packages find it challenging to process massive datasets. Also, data management becomes complex when it is coming from multiple sources. Throw in the speed at which this data is sometimes generated and it becomes obvious that big data tools and technology, such as Hadoop (an open source technology platform), are mandatory to take care of these challenges.

Organizations in a competitive business landscape such as supply chain management (SCM) and logistics sector need real-time information that big data tools can deliver. Finance companies that want to detect fraudulent behavior before it affects the organization emphasize the relevance of big data. Big data tools can also lead to a reduction in cost for organizations that need business intelligence solutions that require horizontal scalability.

3) WHERE IS IT APPLICABLE?

• Governments and government agencies can use big data tools to drive significant logistics like traffic control and crime control.
• Healthcare is a mission critical sector and stakeholders use big data to uncover below-the-line information that improves patient care.
• In retail, customer spending and customer relationship are crucial to retailers. Effective use of big data will ensure that retailers can use information mined from data clusters to increase profitability and turnover.
• Big data analytical tools can give a finance company the ability to obtain a 360-degree view of customers and customer transactions. This implies a huge improvement in client/customer experience and reduction in fraudulent transactions.

4) FACTS AND FIGURES.

Here are a few important figures from big data and big data tools:

• We expect that by the year 2020, our amassed digital universe of data will grow from 4.4 zettabytes currently, to around 44 zettabytes: that is 44 trillion gigabytes.
• By 2020, at least a third of all generated data will pass through the cloud.
• Distributed computing, a process of sharing computing tasks among a network of computers to increase efficiency, is synonymous with big data. Google uses it daily to involve about 1,000 computers in answering a single search query, which may take no more than 0.2 seconds to complete.
• The Hadoop market will grow at a compound annual growth rate of 58 percent, surpassing $1 billion by 2020.
• By better integrating big data, the healthcare sector can save as much as $300 billion a year. This is equal to reducing costs by $1,000 a year for every man, woman, and child.
• A typical Fortune 1000 company that can create just a 10 percent increase in data accessibility has the potential to generate more than $65 million extra in net revenue.

5) FUTURE TRENDS.

As phenomena, such as the Internet of Things (IoT), grows there will be more collection of data and companies will combine the amassed data they collect with other disparate or similar datasets to generate useful insights from the data in real-time mode.

Major research work about big data is ongoing in fields such as Natural Language Processing (NLP). Experts will use NLP techniques on big data to find out trends useful in the political, business and health sectors just to mention a few.

There will also be convergence in the areas of EDI and big data. In the health sector, for example, experts have developed big data platforms where enterprises load all EDI files in big data paving the way for analytics and user-friendly reports.

Big data is not a passing trend. It is here to stay, and stakeholders who leverage on it can expect positive outcomes.

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