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The three V’s for successful Big Data adaptability programs

The three V’s for successful Big Data adaptability programs

Three Vs Big Data-1Research and development is part of every successful organization’s mantra and to fuel this research Big Data plays a very important role. And how Big Data analytics is used is determinant in the success or failure of an organization. In today’s time when there is so much information to process it is imperative to experiment with dynamic and variety of Big Data strategies so that you assimilate the best mix and match for sure shot success. A good mix of Big Data strategies is ruled by three major principles, the three V’s – Volume, variety and velocity which all function like a force to shoot your organization into the realms of success.

# Volume

A successful big data strategy should have perfect conscience of high volumes of data and experimentation with the products/services. If you want to go one step further, a Big Data strategy should always be coinciding with the big picture strategy of the organization itself so that after giving a shot at experimentation, the ideal plan is adopted. According to experts the best way is to run multiple strategies at one time, providing the team with whatever resources they want and hope that something really innovative comes up.

# Velocity

Time is the key to building successful products/services and velocity at which an idea is generated using Big Data is determinant factor. All that needs to be done is channelize all the ideas and suggestions into an innovation tunnel that finally filters out the winning product. This is where fast execution of plans comes in. After having a clear vision of what needs to be achieved the next step is to work on the Big Data along with the experimentation process. For this good tools in the operational systems are required so that time lag is minimal and this finally leads to an improvement in innovation cycle which leads to big ideas propping-up.

# Variety

It is a known truth that variety is the flavor of success and Big Data analytics in no different. If you have a variety of innovative ideas that could be something big, the chances are you are in fact going onto something big. The best strategy is to get an array of products/services and the potential customers to combine it with the collection of ideas in the innovation tunnel. Just to keep in mind, the best way is to get a clear picture of your USP and then get everything to revolve around it.

All these V’s help you to take advantage of Big Data in an optimal way to get competitive advantage over competitors and there is no denying that Big Data and rapid experimentation go hand in hand for successful innovation programs in organizations.

Obama demands review of Big Data industry in light of privacy

Obama demands review of Big Data industry in light of privacy

President Obama in his recent speech at the Justice Department has urged the National Security Agency (NSA) to improvise on security of networks to protect important user information.Obama Big Data

In his address Obama showed concern over security, especially because of a year full of privacy upheavals, thanks to the prominent Edward Snowden leaks, which exposed facts about NSA tapping foreign fiber optic cables and cracking encryption protocols of famous tech companies. Obama opined that such leaks have made the task of national security difficult.

Big companies likes Google and Facebook, based on recent events of information theft, have already strengthened their security systems. Understanding the position, Obama has advised the NSA to review the current role of its security apparatus and to understand how widespread future Internet surveillance programs need to be.

US government has understood the complexities of Big Data analytics and has realized the benefits enterprises have reaped with data interpretation at such large magnitude. President has ordered a comprehensive review of Big Data analytics and privacy, for this, a group of government officials will be constituted who will work in tandem with President’s Council of Advisors on Science and Technology and reach out to business leaders and privacy experts to understand how public and private sectors are facing the challenges imposed by Big Data.

The presidential working group will examine how private and public companies are collecting Big Data and how the collection of avalanche of data and its analysis for purposes besides intelligence and law enforcement is affecting privacy.

In his speech, Obama has guaranteed reforms in safeguarding the privacy of Americans by providing transparency and protecting personal information offline and online. Thus, prima-facie motive of reviewing the Big Data analytics and privacy is to comprehend how NSA can promote free flow of information consistently with both security and privacy, and to identify areas where reform in policies might be required to restrain Big Data technologies.

Identifying important information in Big Data to answer real world challenges

Identifying important information in Big Data to answer real world challenges

Over the past one year, knowingly or unknowingly, Big Data has become the biggest buzzword enterprises are finding hard to pass. Believe it or not, according to the current dependence on Big Data and its allied technologies, we can assume that Big Data is here to stay, and we all will have to use it to address our real world problems.

big-data

The way in which Big Data technologies have evolved in the real word enterprise goes on to show that even technologists and scientists who might have disparaged the word (Big Data) previously, will now be acknowledging it.

Like everything has loopholes, Big Data isn’t any different. Big Data problems are basically issues caused not because of the unavailability of data, but by the abundance of available data. There is so much influx of data that is rather impossible to know which piece of information is actually important and how different important information pieces can be put together for meaningful information.

Researching on a general way to understand complex systems and to answer the biggest question that Big Data can’t answer – ‘how to know what’s important in complex world?’ president and scientist of the New England Complex Systems Institute Yaneer Bar-Yam has devised a trick to identify patterns in largest scale of behavior. Bar-Yam has revealed his findings in the article titled “Beyond big data: Identifying important information for real world challenges”

According to Yaneer, to understand and address most social and biological challenges, it is important to frame a scientific inquiry with an idea to objectively conclude what is important or unimportant instead of amassing larger and larger sets of data.

Yaneer explains that the identified patterns of behavior determined from handful of information are the key to understanding a system and to inform how the behavior can be influenced in the future.

Yaneer Bar-Yam and his team have used the successful tested this approach by predicting various complex systems and real world challenges like market crashes, ethnic violence, food prices and many more biological and complex social systems.

Via: NECSI

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.

 

3 Big Problems Big Data Will Probably Create in Near Future

3 Big Problems Big Data Will Probably Create in Near Future

Big Data has undoubtedly been the biggest buzzword in the past one year. One can look back at the just concluded 2013 and consider it as the breakthrough year for the term Big Data.

Big_Data challengesBig Data may not be an outright term in innovation but it certainly is in awareness. In spite of the Big Data receiving more attention in the mainstream, there are business and individuals who still confuse the term and use it inappropriately.

All things said, business enterprises are investing big time in Big Data with the motive to have the best from advanced data analytics. As mobile data, internet data and cloud data trends multiply, a need for more sound Big Data adaptation platforms such as Hadoop have been felt. Though, real potential of Big Data is still very abstract to nail down, the ramifications and business challenges it will create have already begun to show from.

Let us read on for three most important problems Big Data analytics will probably create in the near future.

  1. 1.    Legal and privacy are risk issues

Big Data can be used for good, and obviously it can be harnessed for the betterment of the society. But it can also be abused! So, not everything is sunny about Big Data. Since the accumulation of data means more threat to privacy, privacy challenges around Big Data are nothing new. It may be the dark side of Big Data but an average consumer has begun to understand the implication.

This becomes challenge since enterprises use Big Data to benefit from advanced analytics. It is believed (and explained by Sand Hill survey) that almost 62 percent enterprises use Hadoop for advanced analytics it can provide.

In 2014, Big Data with the rise of Internet of Things, leading to more mobile data, drone data, sensory data and even image data is bound to create more legal concerns over Big Data privacy. This, as explained, because consumers are becoming more aware of the real impacts of Big Data on their lives. It is therefore important for enterprises to remain ahead with compliance law and keep themselves to date with changing data protection laws.

2.    Human decision making Vs. data-driven decision making

As more businesses pursue Big Data to drive their decision making, there is soon going to be a clash in ways of doing things. As MIT Sloan School of Management research scientist Andrew McAfee points out, most management education programs train employees to trust their gut. Trusting the gut feeling is the old way of decision making, so changing it with data-driven decision making can lead to conflict. Becoming data-driven will require businesses to undergo a paradigm shift, since whether the company is data driven or not will become the competitive differentiator between successful and not so successful businesses.

3.    Big Data used for discrimination

Many research projects based on the use of Big Data have raised concerns of data being used for discrimination in addition to looming privacy concerns.

Researchers including Kate Crawford of Microsoft suggest that Big Data is being used speedily for precise forms of discrimination. We are not new to discrimination, but Big Data creates a new form of automated discrimination. Researchers suggest that social media and health care are the most vulnerable.

To safeguard against the issue of discrimination, organizations can create transparent Big Data usage policies in order to protect consumer data.

 

Big Data & LAPD: Predicting Crime before It Happens

Big Data & LAPD: Predicting Crime before It Happens

We are all aware that explosion in data (Big Data) gives us the ability to do a lot of different things. The potential things to do with Big Data are fantastic. Given the fact, the dynamic nature of data is fast changing the way we live our lives. When I was watching BBC’s video (placed below) titled The Age of Big Data, I was moved by the way police in Los Angeles is trying to predict crime even before it actually happens.

Big Data and LAPD

The initial segment of the video details a pilot project undertaken by the Los Angeles Police Department in collaboration with the University of California. The Foothill Division of the LAPD has been using a trial algorithm to predict crime that is about to occur in the division.

According to experts, in the last few years the world has seen more data than in all of human history, and with the same progress this avalanche of data is surely heading in the direction of becoming the greatest sources of power of this century. Thriving on this advantage of big data, officers in the LAPD are using predictive policing software dubbed the PredPol, which relies on the collection and analysis of Big Data to allow them to anticipate where crimes are likely to happen, well before they happen.

The PredPol basically works by collecting and analyzing crime data and then running it through an algorithm to generate maps. The maps inform (with bright red squares drawn on them) the police officers about where crime might take place. Police spends more time patrolling identified areas with crime risk and as presumed, prevent the crime from happening.

According to LAPD, predictive policing has assisted the department in reducing the crime rate in the Foothill Division station area.

The predictive policing software, which employs mathematics to study crime was started in University of California, Los Angeles some seven years back. UCLA mathematician and one of the postdocs on the predictive policing software project, George Mohler, has introduced an equation, which has transformed the work process. According to Mohler, mathematically speaking earthquakes and crime work in a similar way. So, if earthquake aftershocks can be predicted with mathematical model, after-crimes can also be predicted too. This Mohler concept works on the model that one crime sets tone for another crime in the area, so understanding the first the after crimes can be prevented.

Predictive policing software and Mohler’s equation can predict crimes such as robbery, car stealing etc., it can predict the areas but not who will commit the crime.

Hit the jump to see BBC’s video ‘The Age of Big Data’ to know about people mining the avalanche of big data.

01/14/20141 commentRead More
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!

Big Data Startups Luring Huge Investments

Big Data Startups Luring Huge Investments

Big data startups baiting enormous investments in 2013 experts bet are going to redefine investors’ attitude toward them. Keep on reading for the most recent stats from 2013!

Big Data Investments May Pay Rich Dividends in Near FutureIn  2013 alone the startups that have been focusing on big data were able to pull in investments worth over $3.6 billion. The stats have simply surpassed all the presumptions and experts find it truly intriguing, for it is nearly three quarters of the total capital that the huge gap of almost five years from 2008 to 2012 had witnessed going into such companies!

As reveals the latest infographic from Big Data Startups, following big data startups have captured the most investments:

  • Cloudera is leading Hadoop distributing vendors and was reported to raise $65 million till December the last year
  • Palantir was reported to raise $100 million with a valuation of over $9 billion
  • Mu Sigma, a company dealing in analytics tools, had raised $108 million two years back and has now has Microsoft as one of its current customers!
  • MongoDB had declared a $150 million round till October
  • Opera Solutions, a predictive analytics provider, had grabbed $84 million in the year 2011
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!