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Four high value use cases for Big Data, are you doing it right?

Four high value use cases for Big Data, are you doing it right?

With the ever increasing digitization, organizations are accumulating terabytes of data annually. Presently, most of this unstructured data goes unused, though it is being retained for regulatory purposes. However, the current trend of data analytic suggests that know-how of Big Data can work in favor of enterprises in the long run.

high value use cases for Big Data

Big Data will play a significant role in the enterprises; but one question that surrounds authority of big data analytics is how it can actually be used to add value.

Few managers have mastered the tact of making decisions based on data analytics, something they would do based on their gut feeling until just a couple of years back. But since the influx of data is such that tradition data management systems cannot cope up with it, managers have become dependent on Big Data analysts to turn this avalanche of data into meaningful decisions.

Having understood applicable use of Big Data to an extent, it becomes imperative to know where Big Data will work within the enterprise and what problems Big Data can address.

Here we have listed four instances, which according to experts make Big Data analytics worth the investment for organizations. These are high value use cases of Big Data as found by IBM.

1.    Exploration of Big Data

The idea of Big Data exploration is to make companies research the existing transactions and repositories using Big Data techniques. This allows companies to accumulate data from various sources stored over different places in order to create a clear picture of available data and gain insights on how to use it for value results. Data exploration thus implies finding, understanding and visualizing Big Data to improve the quality of decision making. Big Data exploration basically addresses the business problem of storage of data in different systems by accumulating it in one place for all to see and analyze.

2.    Enhancing customer knowledge

Companies use Big Data to have a 360 degree view of their customers to understand and engage more personally with them. For example, telecom companies using phone data records and social media usage to understand behavior of a customer. Enhancing customer view enables enterprises to gain full understanding of the customer and then place goods and services based on their analysis.

3.    Extension of security

Big Data analytics can be used to detect fraud by analyzing credit card transactions, or detect terrorism and cyber crimes by monitoring data processing, phone calls, social media, emails etc. constantly. With Big Data analytics fraud and cyber security can be monitored in real time.

4.    Using Big Data for operations analysis

Connected gadgetry and Internet of Things is creating new data with great speeds. Smart gadgetry is contributing immensely to the data stream. Analyzing this avalanche of data can allow companies to improve performance. The abundance of data coming from sensors, GPS devices, IT machines etc. can be analyzed using Big Data for operations analysis to allow companies to attain real time insight of what’s what.

 

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!

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.

9 Recent Surveys About Big Data

9 Recent Surveys About Big Data

big data surveyBig Data is the big word right now and many surveys have been conducted to find out just how big is Big Data. Take a look at the highlights of 9 such surveys to find out where big data is headed.

1. CompTIA

CompTIA, the IT association, surveyed 500 businesses and found out that:

  • 42% of businesses admitted that they have some type of big data initiative going on.
  • 93% said that data was critical for their business.
  • 18% thought that their business was ready for big data.

2. EMA and 9Sight Consulting

259 businesses and professionals were surveyed in this end user research. They found that:

  • 68% of the companies have at least 2 projects in their big data initiative.
  • 34% companies are using big data implementations in production.
  • 39% companies identified speeding operational time for analytics to be the number one driver for big data initiatives. Other drivers included competitive advantage with data use in business solutions (34%) and business requirements for higher levels of advanced analytics (31%).

3. Tech Pro Research

Tech Pro Research surveyed 144 businesses about the financial side of big data.

  • 54% have no interest in implementing big data initiatives. 8% have implemented some form of big data initiative, 12% are implementing and 26% are planning on implementation.
  • 82% of those who have implemented big data initiatives report seeing some form of payoff while only 4% believe they haven’t seen any benefits.

4. Gartner

They surveyed 720 members of the Gartner Research Circle.

  • 64% of companies are investing or planning to invest in big data.
  • But less than 8% have already deployed some form of initiative.

5. TEKsystems

They surveyed more than 2000 IT professionals and 1500 IT leaders. They found in their report:

  • 90% of IT leaders and 84% of IT professionals believe in big data as a good investment for time and money.
  • 14% of IT leaders said that big data is regularly applied in their businesses.
  • 66% of IT leaders and 53% of IT professionals said that their data is stored in disparate systems.
  • 60% of IT leaders said that their is no accountability for data quality.
  • At least 50% of IT leaders were not sure about the validity of their data.
  • 81% of IT leaders accepted that they do not have the adequate manpower with the right skill sets to implement big data initiatives.

6. Bain

Bain studied 400 large companies and found good results about big data.

  • They found that the companies that have big data analytics capabilities were outperforming their competition.
  • They were twice as likely to have better quarterly financial performance.
  • Five times more likely to make faster decisions.
  • Three times as likely to execute decisions.
  • And twice as likely to use data in their decision making process.

7. BCG

BCG surveyed 10,000 consumers in 20 countries worldwide. They found that:

  • 75% of consumers are concerned about privacy of their data.
  • The young generation is just as concerned about privacy as older generations.
  • Consumers will allow use of data as long as they trust the business with their data.

8. IBM

IBM studied 900 businesses from around the world. They found that the companies that were outperforming their peers were:

  • 166% more likely to make decisions based on data.
  • 2.2 times more likely to have a clear path for big data analytics in their organization.
  • driven by growth as the main source of value from data analytics.
  • measuring the impact of investment in analytics.

9. Forbes Market Insights

Rocket Fuel sponsored this study by Forbes Market Insights of 211 senior marketers.

  • The marketers that used big data at least half of the time in their campaigns said they exceeded their goals 3 out of 5 times.
  • Those who used data less than half of the time achieved similar results only 1 out of 3 times.
  • 92% of companies who used big data, exceeded their goals and only 5% fell short.

From such surveys it is clear that it is still early days for big data. Those who have taken the initiative have found dividends in big data while some still remain skeptical. Information taken from the article on Forbes.

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.

Big Data Analysts Have a Great Career ahead

Big Data Analysts Have a Great Career ahead

Looking for a career, one might not always consider becoming a data scientist. But given the demand for Big Data analytic specialists or data scientists, a career in Big Data can seem a good choice or even the best one for most new age junkies.

Since an increasingly large number of medium-scale and multi-billion dollar companies are now using Big Data, the demand for big data specialists or Data scientists (as they are better known) has risen tremendously.

Today, all businesses, small and big, require data scientists who know how to manage the influx of huge information and draw a conclusion and insight from the tsunami of data. Thus, when looking for an option at college course or a career change, you can give Big Data a chance.

Big Data Analysts Have a Great Career ahead

Data scientist is by far one of the most sought after career options, not just because of its demand, but also because data analysts are commanding impressive salaries, which are at par with some big career positions.

Career in Big Data

Big Data is basically extremely large amounts of structured and/or unstructured information/data, which is too much for the traditional databases and tools to handle. This large amount of data comes from all possible sources such as social media, posts, multimedia and files (to name a few). Businesses need to set up state-of-the-art technologies to manage and comprehend all this data. They need someone who can help them manage and draw insights from the data; insights that help increase profits in one way or the other.

This is why the position of a data scientist becomes all encompassing in an organization. The position is spread across three specialist fields – technologists, statisticians and quantification experts.

Technologists – these are data scientists who are experts at writing algorithms and codes to transverse such large amounts of data. Statisticians and quantification experts on the other hand are creative fellows expected to navigate content and find things others can miss.

Career in Big DataSkill set required to be a data scientist

Having already understood that Big Data analysts can have a great career, since it is driving job growth, we need to understand how one can get into the position of a data analyst. It is important to know the skills need to pursue a big data career?

If seen from a larger prospective, Big Data jobs need a wide range of skills. But in a very realistic sense, many of the Big Data jobs do not require major programming skill, instead strong analytical skills and knowledge of analytical tools is probably something that is more required.

Education to acquire requisite skills

You may be able to acquire many skills on-the-job, but if there is still a need to enhance your data analyzing skills, interested candidates can enroll with a good big data training school or institution for a well planned course program and acquire  necessary skills. Vendors like EMC and IBM also offer courses on Big Data. In addition to these, colleges and universities also offer degree programs in analytics and other related fields to prepare the prospective aspirants for a Big Data career.

10/18/20131 commentRead More
IBM Launches Accelerated Discovery Lab for Big Data Apps

IBM Launches Accelerated Discovery Lab for Big Data Apps

IBM Almaden Research Center in San Jose, CaliforniaColossal data volumes and confidential business information are stored in massive data centers all across the globe. The Technology Giant, IBM Research has developed a new and one-of-its-kind lab. It has been named the ADL (Accelerated Discovery Lab). It aims to find ways to make use of
Big Data for various purposes.

Technological world today relies heavily on Big Data and therefore ADL has been developed to find relevant connections amongst these huge sets of data, to be used for analytical purposes, etc.  According to IBM Fellow and Director of Technology and Operations, Laura Haas, “IBM is focusing on the four V’s of data, namely volume, velocity, variety and veracity.”

According to her, tackling big data challenges isn’t easy and businesses around the globe would require the expertise to achieve the desired means. Apparently, getting better and finer insights from big data is what IBM researchers are currently focused at.

The ADL Lab will be set at the IBM Almaden Research Center in San Jose, California. The main target of the company will be the clients who understand the value of Big Data but need help dealing with it. Michelle Zhou is amongst the team of researchers at IBM’s lab and will have about 15 projects running at a time.