Patterns For Big data Development

1. IT for IT Log Analytics:

Log analytics is a common use case for an inaugural Big Data project. We like to refer to all those logs and trace data that are generated by the operation of your IT solutions as data exhaust. IT  departments  need  logs  at  their  disposal,  and  today  they  just  can’t  store  enough  logs  and analyze them in a cost-efficient manner, so logs are typically kept for emergencies and discarded as soon as possible. Another reason why IT departments keep large amounts of data in logs is to look for rare problems. It is often the case that the most common problems are known and easy to  deal  with,  but  the  problem  that  happens "only once in a while is typically more difficult  to diagnose and prevent from occurring again".

The  nature  of  these  logs  is  semi  structured  and  raw,  so  they  aren’t  always  suited  for traditional database processing. In addition, log formats are constantly changing due to hardware and software upgrades.

Log analytics is actually a pattern that IBM established after working with a number of companies, including some large financial services sector (FSS) companies. We’ve seen this use case come up with quite a few customers since for that reason, we’ll call this pattern IT for IT.  

NOTE: 

step 1: create a log file using notepad and save it
.LOG
22:25 27/10/2014
Today's last entry 

step 2: now double click the created  log file 
.LOG
22:25 27/10/2014
Today's last entry 

22:26 27/10/2014

2. The Fraud Detection Pattern: 

Fraud  detection  comes  up  a  lot  in  the  financial  services  and you’ll find it in any sort of claims or transaction based on online auctions,insurance-claims,environment etc.  

Traditionally, in fraud cases, samples and models are used to identify customers that characterize a certain kind of profile. In  our  customer  experiences,  we  estimate  that  only  20  percent  (or  maybe  less)  of  the available information that could be useful for fraud modeling is actually being used. You can use BigInsights to provide an elastic and cost-effective repository to establish the remaining 80 percent of the information is useful for fraud modeling.

Typically, fraud detection works after a transaction gets stored only to get pulled out of storage and analyzed. As we can see that 80 percent of the data is costeffective and efficient in using the BigInsights platform. Now, of course, once you have your fraud models built, you’ll want to put them into action to try and prevent the fraud in the first place. Recovery rates for fraud are dismal in all industries, so it’s best to prevent it versus discover it and try to recover the funds post-fraud. 

Think  about  fraud  in  health  care  markets  (health  insurance  fraud,  drug  fraud,  medical fraud, and so on) and the ability to get in front of insurer and government fraud schemes. when  the  Federal  Bureau  of Investigation (FBI) estimates that health care fraud costs U.S. taxpayers over $60 billion a year. Think  about  fraudulent  online  product  or  ticket  sales,  money  transfers,  swiped  banking  cards, and more you can see that the applicability of this usage pattern is extreme.

Note: 

1.Traditional fraud detection methods with out big data

2.Traditional fraud detection methods with  big data

3. Big Data and the Energy Sector: 

The energy sector provides many Big Data use case challenges in how to deal with the massive  volumes  of  sensor  data  from  remote  installations.  Many  companies  are  using  only  a fraction of the data being collected, because they lack the infrastructure to store or analyze the available scale of data. 

Take for example a typical oil drilling platform that can have 20,000 to 40,000 sensors on board.  All  of  these  sensors  are  streaming  data  about  the  health  of  the  oil  rig,  quality  of operations,  and  so  on.  Not  every  sensor  is  actively  broadcasting  at  all  times,  but  some  are reporting back many times per second. Now take a guess at what percentage of those sensors are actively utilized. 

The location chosen to install and operate a wind turbine can obviously greatly impact the amount of power generated by  the  unit,  as  well  as  how  long  it’s  able  to  remain  in  operation.  To  determine  the  optimal placement for a wind turbine, a large number of location-dependent factors must be considered, such  as  temperature,  precipitation,  wind  velocity,  humidity,  atmospheric  pressure,  and  more.    

4.The Social Media Pattern: 

The data produced by social media is very huge and if we see the report of Visual Capitalist then it shows that every internet minute:

701,389 logins on Facebook

150 million emails sent

$203,596 in sales on Amazon.com

120+ new Linkedin accounts

347,222 tweets on Twitter

2.4 million search queries on Google

2.78 million video views on YouTube

20.8 million messages on WhatsApp  

5. The Call Center Mantra: <This Call May Be Recorded for Quality Assurance Purposes>: 

Call center analytics is the collection, measurement, and reporting of performance metrics within a contact center. It tracks call data and agent performance handling inbound or outbound calls. Common types of analytics include handle time, call volume, customer satisfaction, and hold time.

In most cases, call center supervisors can access this data using specialized analytics tools. However, accessing this call center data analytics is often limited to supervisors and team leads. More modern contact centers provide this real-time data to agents so they can mind increasing call volumes.

However, with the right tools and strategy, call data helps you provide exceptional customer experience, boost brand loyalty, and improve efficiency across the board.

6. Risk:

The risks of Big Data are manifold, and organizations need to carefully plan for their use of Big Data solutions. These risks include strategic and business risks, such as operational impacts and cost overruns, as well as technical risks, such as data quality and security. 

Here are the five biggest risks of Big Data projects 

• Security
• Privacy
• Costs
• Bad Analytics
• Bad Data