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Unabated Experimentation is Way Forward in Big Data

Unabated Experimentation is Way Forward in Big Data

Big Data Experimentation

While it is true that analytical modeling is calling for nonstop testing of big data, the equation isn’t that straightforward and holds certain potential challenges.

The need of the hour is active experimentation in the big-data zone to help in-progress analytical model to make precise correlations. But since statistical models have their own risks, their astute application is going to be a must, especially as long as we want the results to be positive.

While a few groups are still hesitant, most full-size organizations have been able to hone their insight to realize that big data calls for incessant experimentation, and are all in support for the alteration. They also know, at the same time, that practical scenario of the booming field of big data involves certain risks associated with statistical models, especially when their implementation is not flawless.

Statistical Modeling –Practicality and Risks

Statistical models are simplified tools employed by data science to recognize and validate all major correlative aspects at work in a particular field. They can, however, make data scientists have a fake sense of validation at times.

And despite fitting the observational data quite rightly, various such models have been found to miss the real major causative factors in action. This is why predictive validity is often missing in the delusion of insight offered by such a model!

What May go Wrong?

Even though the application of a statistical model is practical in business, there is always a need to scrutinize the true, fundamental causative factors.

The lack of confidence may prove to be the biggest risk, particularly when you doubt the relevancy of the standard (past) correlations constituting your statistical model in near future. And obviously, predictive model of product demand and customer response in a particular zone which you have low confidence in will never be able to pull in huge investments during a product launch!

What is the Scope?

Even though there are certain risks involved, statistical modeling can never be completely dead. To be able to detect causative factors more quickly and effectively, statistical modeling will need to be based on real-world experimentation. This innovative approach that employs a boundless series of real-world experiments will be highly helpful in making big data business model and economy more authentic and reliable.

So How’s Real-world Experimentation Going to Be Possible? 

Exactly the way data scientists have developed advanced operational functions for ceaseless experimentation, big organizations look forward to encouraging their expert business executives to lead the charge in terms of running nonstop experiments and for better output. And to add to their convenience, the big data revolution has already offered in-database platforms for proper execution of a model and economical yet high-output computing power to make real-world experimentation feasible everywhere including scientific and business domains.

The basic idea is to prefer spending time, capital and other resources to conduct more low-risk experiments to putting extra efforts building the same models back and back again!

Are Businesses Already Expecting Healthy Big Data ROI?

Are Businesses Already Expecting Healthy Big Data ROI?

Businesses in the UK use big data to mostly support their sales and marketing campaigns, reveals Big Data Survey 2013 carried out by MBN Recruitment Solutions.

The survey maintains that more than 80% of the total survey respondents look forward to harness and leverage their data to be able to generate new revenue.

At the same time, over 95% people agree that more revenue generation is going to be the only purpose of businesses using big data in near future!

Use of Big Data Till Now and In Future

The first annual survey by MBN also exposes that over 71% of the total respondents have been using data analytics to foresee all major functions and aspects of future businesses doings around the world.

Are Businesses Already Expecting Healthy Big Data ROI

MBN non-executive chairperson, Paul Forrest concludes that companies use big data as they grow larger and need to stay competitive against potential competitors.

Most survey respondents believe that right now there is too low ROI in leveraging big data. They, however, are expecting greater ROI prospects in future. Also, over 40% respondents think that the current initiatives will eventually fetch desired results for businesses. Forrest told that one of the biggest issues has been the importance of tools, but 72% respondents believe that tools are important only in the beginning and it is people who unlock the set value on a later stage.

Monetizing Big Data: What 2014 Might Have in Store

Monetizing Big Data: What 2014 Might Have in Store

Once we are able to invest in the big data technology after successfully analyzing it, the next move will be to monetize it to obtain its monetary equivalent. To know what is the scope of big data monetization on 2014 and beyond, read on!

‘Big Data’ is already a familiar term for most of us, especially those who are into some serious business. It has been a hot topic in the media almost throughout the year 2013.

Big Data - Return on Investment - What 2014 Has in Store

All small and big businesses, however, are still trying to augment their knowledge about what actually big data is and what they should be doing about it and how. And what seems to be adding to the complications are the challenges involved in the process of big data investment.

Majorly, businesses don’t know how to obtain value from data and have to go a long way to be able to define the much-awaited big data policy. Even more importantly, they’ll have to attain the required skills and then execute them in a nifty manner to make the most of the strategies they’re working on!

Big Data – Future and Monetary Equivalent

While we are already in the first phase of the grand big data revolution where we’ve seen big investments in the technology, the next important step would be to generate revenue through big data.

Having a lot in reserve, the year 2014 is ready to play an important role in this regard:

Revenue Generation

Though businesses are all for huge investments in big data, they still need to predict how quickly it can generate revenue. The need of an effective way to measure ROI over a specific period of time may prove to be one of the potential challenges!

But despite all these assessments, most business leaders are expecting big data to be highly helpful in making the right business decisions. However, they believe that it won’t be possible to predict time and money associated with a ROI target without a guiding hand. This may cause giant businesses to opt for big data-based solutions rather than directly using big data as the only solution in 2014. The ultimate goal would be to boost up overall revenue by saving on costly technologies and data consultants.

Big Data as a Marketing Investment 

While it is true that big data has been more of a technology investment till now, we’ll see it as a marketing investment in 2014 and further, and retail brands will lead the charge in that case.

The key will be to persuade people to ‘buy’ by making all the offers directly customer-oriented. Big companies have already begun to prepare for the shift by motivating their CMOs, technology officers and information executives to work in unison to derive the best results.

Utilization of Big Data-based Solutions

With big data-based solutions surfacing quickly, all businesses will have to go for data analytics sooner or later. Though Google analytics have already been used for the same purpose for years, the latest big data-based solutions will allow all small and big companies to access solutions and methods that can ‘practically improve revenue.’ Hopefully, the year 2014 will be big for both those starting-up and well-established businesses in terms of using big data to get the best results!

Oracle Launches 5th Gen Database Machine

Oracle Launches 5th Gen Database Machine

Oracle Exadata Database Machine X4, the 5th gen database machine form Oracle is a revolutionary step in the field of database management. Keep on reading to know what it has to offer!

Oracle Launches 5th Gen Database MachineOracle recently launched Exadata Database Machine X4. It has hi-tech hardware and software that can increase capacity, boost performance and maintain quality and efficiency of service for database operation.

The update focuses mainly on the optimization of Online Transaction Processing (OLTP). The machine is mainly aimed at providing businesses with a permanent solution for all major database challenges and has advantages like Data Warehousing and Database as a Service (DBaaS).

Oracle Exadata Database Machine X4 – Features

  • The machine is the 5th gen of Oracle Exadata that was launched in the year 2008.
  • It is a fifth gen machine featuring improvements that focus on improved performance as well as quality of service for OLTP, Database as a Service and Data Warehousing.
  • It uses high speed flash compression and larger physical flash in perfect combination to increase the capacity of flash memory that eventually accelerates the performance of OLTP-based work.
  • The latest Flash Caching algorithms help accelerate the performance of all workloads in Data Warehousing.
  • Many databases can be merged with help of the Database as a Service design because of extreme capacity and performance. This will help businesses improve on quickness and more importantly, reduce costs.
Be Smart With Big Data

Be Smart With Big Data

smart dataSome companies get scared of big data. They think that since data is inherently dumb, a lot of it would be dumber still. But by being smart about big data, analysts can make sure that they get the most out of it. Handling big data can be a security risk and needs to be handled smartly.

The Present Way of Doing Things

Usually companies have one of three ways to handle data. They either go with the Heroic Model in which individuals take charge of requests and make decisions on their own without consulting with others. This model can work well for small businesses where individuals are usually aware of most situations across all areas of the business. But in bigger businesses, it can lead to confusion and chaos.

The Culture of Discipline on the other hand is one where individuals don’t make any decisions and follow a set of rules set by the management. Employees in this model can’t use data for their own decision making and just have to follow the processes set up for them.

The best way to handle data is to have a Data Smart Model in which data is managed on an evidence based management system. It is a combination of the first two methods and it works on a disciplined processing method but decision making is allowed at the individual level. This is the method that should be used to handle big data and it can result in smooth operation without much hassles.

How to Cultivate the Data Smart Culture

Certain steps need to be taken to create the data smart culture.

  • There should be a single source of truth. Decision making can be moved to the employee level but the guiding principles should be set from a single source.
  • Use ways to keep track of progress. Using a scorecard system, even on a daily basis, can help managers across different branches know how they are performing in relation to the other departments and they can then send in better data to record their progress.
  • Rules are important but there should be enough flexibility. Rules and guiding principles are needed but there should be flexibility to know when to bend the rules and when to break them. Sometimes what works in most parts of the country might not be best for a certain area. Businesses need to be able to adapt to such situations and change their rules accordingly.
  • Work on cultivating human resources. The people are the biggest asset of a company and it is important to educate them and provide them with the proper know-how to handle data. Managers need to be trained to educate the people working under them and give them a one to one engagement.

These steps can help businesses handle big data smartly and without much confusion. Every level needs to be trained to handle big data as the future is going to be all about big data.

Hadoop Can Come Handy Even When You are Not Dealing with Big Data

Hadoop Can Come Handy Even When You are Not Dealing with Big Data

Hadoop was developed to cater to the needs of web and media companies for managing big data. But even if you don’t have to deal with big data, you can still use Hadoop in many ways to enhance your data and resource management. Today Hadoop is being used by almost every business, whether they have big data or small, to manage their data.

The Main Features of Hadoop

The main feature of Hadoop is the HDFS storage system. HDFS stands for Hadoop Distributed File System that operates on low cost hardware.

MapReduce was developed for resource management and data processing but with Hadoop 2.0 it has been left just to focus on data processing while YARN is used for resource management.

These features of Hadoop can be utilized in many innovative ways by big and small businesses.

Data Archive

One straightforward use of Hadoop is to archive data files. Since HDFS runs on commodity hardware it is simple and cheap to scale so businesses can start small and expand as their business grows. They can store all their data at a very low cost.

Instead of destroying data after the regulatory period is over, companies can store decades of data and analyze it in real time to help their decision making process.

Data Staging Area

Traditionally ETL tools are used for extracting and transforming data. When Hadoop came to the scene, it could have killed ETL forever if ETL providers hadn’t been smart enough to provide HDFS connectors so that Hadoop could be used along with their ETL software.

By using Hadoop you can store the application data and the transformed data in the same place. This makes it easier to process the data at a later time and reduces the time to process the data. Hadoop can help ETL in improving data processing.

Data Processing

Instead of sending data to the warehouse and then use costly resources to update it in the warehouse, you can use Hadoop and its MapReduce function to process and update it before it goes to the warehouse. Hadoop’s low cost processing power can be used not just for your warehouse data but for other operational and analytical systems as well.

HadoopHadoop is a very powerful tool that can help all businesses to handle their data in a better way. You don’t have to be sitting on top of big data to use Hadoop. You can start even when you have small data and Hadoop will let you collect decades of data till it becomes big data and then you can start making use of all this data by using big data analytics.

Is Big Data a Threat to Your Privacy?

Is Big Data a Threat to Your Privacy?

Big Data is growing bigger every day and along with it the concern over invasion of privacy is also growing. Tracking all the data generated by your mobile and other devices and your interactions on social media, is beneficial for advertisers to tailor their ads to suit you. But there’s more to the story than that. Companies have now begun to come up with very creative ways to use real time data.

Let’s look at some interesting examples.

Smart Rubbish Bins in London

An advertising firm in London came up with the idea to use strategically placed dustbins to track the wifi signal of phones of the people passing by. They could use the serial number of the phones to track the movement of every individual. They could then use this data to show advertisements on the screen of these bins, that are targeted at the person passing by.

smartbins

Now even dustbins are becoming smart!

The officials have asked Renew, the responsible ad firm, to take down the smart dustbins as there has been a lot of concern about the invasion of privacy of the people.

Police Cars in Australia get Number Plate Recognition Cameras

The Aussies have come up with another great use of Big Data by using number plate recognition cameras that can read multiple number plates simultaneously and also search their database to find out all the information about that driver. They can tell if a car is stolen or if you have unpaid parking tickets just by looking at your car’s number plate.

police car

The hand of the law gets longer.

Are Such Examples a Threat to Your Privacy?

When CCTV cameras first came on the scene, the public responded to them with an outrage similar to what we see now in terms of Big Data. But once people got used to the new technology and saw the benefits in solving crimes and catching miscreants swiftly, the fears of Big Brother always watching them subsided.

The truth is that people will allow collection of any data as long as it is collected with their permission and it is used to create value for them. Instead of shoving ads in people’s faces, companies should try to find other ways to use Big Data, not only to reduce costs for the company but also to provide quality to the customer.

One great example to highlight the creative use of Big Data is the potential for insurance companies. Today all natural or man made calamities generate a lot of data in the social media.

data

Data about Hurricane Sandy

Insurance companies can use this data along with before and after images on Google Maps Street View, Flickr, Instagram etc. to find out how much destruction of property their clients have suffered.

torn houseThey can estimate the number and amount of claims that they will have to deal with. They can provide quick claim settlements to their customers which will be appreciated by all and people will readily agree to data collection if they are told of such rewards.

Great Opportunities

A Westpac survey showed that it only took 30 months for mobile usage to reach 1 Million as compared to 80 months it took for online usage to reach the 1 Million mark.

graphThis means that there are great opportunities available to use this rapidly growing Big Data but it will have to be done with care and while keeping the interests of the consumer in mind.

Gartner Big Data 2013: Highlights

Gartner Big Data 2013: Highlights

Gartner’s annual big data survey report for the year 2013 was released recently. As expected, the highlights of the survey were pretty startling. The survey revealed some beliefs in big data backed by evidence.

Gartner Big Data 2013 - HighlightsThe biggest revelation of the year’s Gartner survey was that 64 percent of companies globally have already implemented or are planning to implement big data systems. The percentage reveals that nearly 30 percent companies have already invested in the big data systems and 19 percent are on the verge of investing in the technology over the next one year. Additionally, the survey shows that another 15 percent companies are willing shell out some money over the next couple of years.

The percentage exposed by the survey is a significant number, which goes on to prove that there is a genuine interest amid the companies to imbibe the new big data system. A large chunk of enterprises are looking at ways they are managing their data and wish to hunt for new ways to get the best out of the ever growing data industry.

The surveyed  

Gartner LogoAccording to Gartner, the survey was basically focused on companies (720 Gartner Research Circle members) and was carried out in June 2013. Designed primarily to understand the investment plans of various organizations for big data technologies, what stage of implementation the companies have reached and how the big data is helping these enterprises solve problems.

Despite being a very confined survey, due to the variety of companies surveyed, this survey is a broad and effective representation of how the world of big data is shaping up and how the enterprises (big and small) are adapting it.

The Prominent Findings

The survey reveals that the industries that lead the big data investments for 2013 include media, communication and banking.

According to Gartner, about 39 percent of media and communication organizations vouched to have already invested heavily in big data technologies. 34 percent of banking organizations also said they have made investments in big data. According to the survey, investments for the next couple of years are majorly lined up in the transportation, healthcare and insurance sectors.

What Is Instigating Companies To Invest In Big Data?

Following a strong precedent set by the billion dollar companies like Google and Facebook, almost all enterprises worldwide have understood that big data usage can have a significant impact on revenue. Therefore, it is not a surprise that more and more organizations are looking to invest in big data.

 Big data in most cases, if analyzed and used properly, can help companies learn about customer experience and customer expectations. Big data analysis helps produce highly useful insights that helps companies make really smart business decisions.

Data Management & Analysis at LinkedIn

Data Management & Analysis at LinkedIn

data management enables LinkedIn to provide hiring solutions, marketing solutions and networking opportunities to its members

Data management enables LinkedIn to provide hiring solutions, marketing solutions and networking opportunities to its members

LinkedIn is the world’s largest professional network. It has over 187 million members from over 200 countries. Its members include everyone from freelancers to CEOs of Fortune 500 companies. The company started out from California Mountain View in 2003 with the mission to connect the world’s professionals and it surely has achieved that in the last 10 years.

Today LinkedIn earns $252 million in revenues every year and employs over 3200 people worldwide. It has become the go to resource for HR executives whenever they have to look for someone to fill up a position. The profiles of members are their online resume that every employer can see and access. It also provides opportunities for people to connect with the right persons to take their career ahead.

All this is possible because of data collection and management. All information provided by a member in their profile is collected, analyzed and sorted so that whenever anyone wants to access it, they can do so quickly and effortlessly. This data management enables LinkedIn to provide hiring solutions, marketing solutions and networking opportunities to its members.

Not only is the data invaluable to employers but individuals too can use it to search for talent matches, similar jobs, interesting events and networking opportunities. This huge amount of data also allows LinkedIn to customize products through out the world.

LinkedIn uses data scientists to analyze the data collected by it so that they can rapidly make some sense out of it and use it to recognize opportunities and take advantage of these opportunities. These data scientists are usually qualified to analyze data and statistics and also need business skills and knowledge to make sense of this data.

LinkedIn’s success can be attributed to its decision to develop its own data management application. The company used market solutions and customized them for their own particular use to collect, sort and analyze data. It stores data online using Oracle and Expresso. It uses services such as Voldemort, Zoie, Bobo, Sensei, D-Graph, Kafka and Databus. The offline data store uses Hadoop for machine learning, ranking & relevance, Teradata etc. It also uses MapReduce Analytics, Clickstream for A/B site testing etc.

Corporations and business use LinkedIn to search for people to fill up key positions and people with social influence to test new products. Analyzing viral marketing results and recommendation engine optimization are two other services that LinkedIn offers to businesses. It helps create specialized marketing services for different businesses.

LinkedIn’s value creation is based on this data management and making their analysis of the data to key players in a short amount of time. As long as they have the ability to analyze and manage all this data, they’ll continue to grow and market new and customized products. In order to maintain their edge LinkedIn needs to find ways to handle this ever growing stream of data and also improve the quality of their data analysis.

The Demand for Hadoop & NoSQL Skills Goes Up

The Demand for Hadoop & NoSQL Skills Goes Up

Every since organizations have begun using Big Data to their advantage, a demand for data analytic specialists or Data scientists has grown manifold. Increase in demand for big data experts means an automatic increase in demand for experts with Hadoop and NoSQL skills.

A rise in big data has compelled companies and organizations both big and small to desperately start looking out of IT professionals who can help them in maintaining and monitoring their database for them.

Big data market is still in very early phase, it has a long way to go, but businesses have realized that there is no future if they do not manage this large data adequately. Therefore, a demand for database management skills has increased to many industries beyond web and software, where it started. Today industries like retail, healthcare and even government are seeking professionals with skills to manage and analyze the large data sets for them.

The Demand Hadoop & NoSQL Skills Goes Up

When we talk about big data experts, one of the most desired skills is NoSQL and Hadoop knowledge. An individual cannot be a data expert without thorough knowledge of Hadoop and NoSQL. Data experts have become a professional really in demand, and knowledge of Hadoop and NoSQL adds to the prowess of an individual who can earn highly competitive salaries with data expertise.

Thanks to the companies like Amazon, Apple etc that are looking for big data experts, there has been a significant jump in salaries of data experts and the profession has suddenly become a dream job for many.

Some careers that need NoSQL and Hadoop skills

Some of the careers where NoSQL and Hadoop skills are being put to good use include:

Data Scientist: Data scientist or big data analytic specialist is a profession that requires a person to have a variety of data driven skills. Data scientists gather, analyze, present, and predict the data. Currently given the size of data ever increasing, data scientists are highly in demand.

Data Architect: Data architects are professionals who create data models, analyze data and assist in data warehousing and migration. To be a Data architect an individual required DBA and Hadoop skills.

DBA: DBA or Database Administrator is a career that is massively in demand lately. Companies that hire DBAs look for professionals with skill sets to handle platforms like Oracle, MongoDB etc. The more familiar an individual is with the NoSQL and Hadoop skills the better package he/she can seek.