BIG DATA

 Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy.

The term often refers simply to the use of predictive analytic or certain other advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.

Companies use big data because it’s able to analyze a customer’s habits when using their services and they’ll be able to tailor an experience for a customer based on what they buy and their habits while using their services. For example Amazon are able to deliver a personalized service based what the customer previously bought or what they looked up on Amazon while on the site so they next time they are on the site suggestions will appear based on what they previously bought. If you buy an action movie DVD on Amazon a ‘Your recommendations’ tab will appear with similar items to what you bought. if you just view an item a tab will appear ‘related to items you viewed’ will appear.

Amazon collecting data on customers habits while on their site and delivering a tailored experience to the user.

Insights from big data can enable all employees and companies likewise to make better decisions—deepening customer engagement, optimizing operations, preventing threats and fraud, and capitalizing on new sources of revenue.

ADVANTAGES OF BIG DATA

 1. Offering enterprise-wide insights

Previously, if business users needed to analyze large amounts of varied data, they had to ask their IT colleagues for help as they themselves lacked the technical skills for doing so. Often, by the time they received the requested information, it was no longer useful or even correct. With Big Data tools, the technical teams can do the groundwork and then build repeatability into algorithms for faster searches. In other words, they can develop systems and install interactive and dynamic visualization tools that allow business users to analyse, view and benefit from the data.

2.  Reducing maintenance costs

Traditionally, factories estimate that a certain type of equipment is likely to wear out after so many years. Consequently, they replace every piece of that technology within that many years, even devices that have much more useful life left in them. Big Data tools do away with such unpractical and costly averages. The massive amounts of data that they access and use and their unequalled speed can spot failing grid devices and predict when they will give out. The result: a much more cost-effective replacement strategy for the utility and less downtime, as faulty devices are tracked a lot faster.

3. Create new revenue streams

The insights that you gain from analysing your market and its consumers with Big Data are not just valuable to you. You could sell them as non-personalized trend data to large industry players operating in the same segment as you and create a whole new revenue stream.

One of the more impressive examples comes from Shazam, the song identification application. It helps record labels find out where music sub-cultures are arising by monitoring the use of its service, including the location data that mobile devices so conveniently provide. The record labels can then find and sign up promising new artists or remarket their existing ones accordingly.

4. Re-develop your products

Big Data can also help you understand how others perceive your products so that you can adapt them, or your marketing, if need be. Analysis of unstructured social media text allows you to uncover the sentiments of your customers and even segment those in different geographical locations or among different demographic groups.

On top of that, Big Data lets you test thousands of different variations of computer-aided designs in the blink of an eye so that you can check how minor changes in, for instance, material affect costs, lead times and performance. You can then raise the efficiency of the production process accordingly.

THE 5 V’s OF DATA

5 V's

The general consensus of the day is that there are specific attributes that define big data. In most big data circles, these are called the four V’s:

  • Volume
  • Variety
  • Velocity
  • Veracity
  • Value

VOLUME

The main characteristic that makes data “big” is the sheer volume. It makes no sense to focus on minimum storage units because the total amount of information is growing exponentially every year. In 2010, Thomson Reuters estimated in its annual report that it believed the world was “awash with over 800 Exabyte’s of data and growing.

VERACITY

Veracity refers to the trustworthiness of the data. Can the manager rely on the fact that the data is representative? Every good manager knows that there are inherent discrepancies in all the data collected.

VELOCITY

Velocity is the frequency of incoming data that needs to be processed. Think about how many SMS messages, Facebook status updates, or credit card swipes are being sent on a particular telecom carrier every minute of every day, and you’ll have a good appreciation of velocity. A streaming application like Amazon Web Services Kinesis is an example of an application that handles the velocity of data.

VALUE

It may seem painfully obvious to some, but a real objective is critical to this mashup of the four V’s. Will the insights you gather from analysis create a new product line, a cross-sell opportunity, or a cost-cutting measure? Or will your data analysis lead to the discovery of a critical causal effect that results in a cure to a disease?

VARIETY

Variety is one the most interesting developments in technology as more and more information is digitized. Traditional data types (structured data) include things on a bank statement like date, amount, and time. These are things that fit neatly in a relational database.

CONCLUSION

The ultimate objective of any big data project should be to generate some sort of value for the company doing all the analysis. Otherwise, you’re just performing some technological task for technology’s sake.

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