Of course, online marketers who understand the Big Data are obviously going to build better customer experiences based on evidence and hard data, rather than making guesswork and hunches. Instead of working with crude analytics or on intuition, you might consider using definitive data to design product pages to attract best and potential customers directly towards your shopping cart. Plus, you’d know exactly when to introduce your special offers and promotions and you would even know what promotion would work best according to your customers profile. In addition, you could even optimize your online interface, that way you can make sure that everything from registration to search to placing orders would be completely frictionless.

However, getting to that point does require you to first acquire the data. It’s not a small feat to gather huge amounts of data from various sources. It’s even trickier to understand and translate that information into fuller shopping carts and visits.

The good news is, there are tools and technologies that allow you to find that gold at the end of the customer data rainbow and will allow you to use it by refining your online marketing strategies with precision. However, there’s a way of thinking concerning the collected data that involves shifts in culture, according to where your company stands right now.

So, here are a few steps to help you tame your big data:

Don’t Think Instantaneous, Focus on Continuous Iteration and Evolution
Yes, Big Data has the ability to fundamentally cause a shift in the way you do business, but don’t change everything at once. It’s actually far more productive to adopt the “test and learn” approach rather than the “hit and run” philosophy. A dozen incremental changes in the sites personalization, design or word can get you further than simply trying to “innovate” all at once.

The most successful online marketers are continuously refining and optimizing. They steadily move forwards, by taking over a thousand baby steps, finding improvements along the way.

Incorporate Big Data Goals With Your Own Individual Goals
You need to set up separate projects and initiatives for your business goals like increasing lifetime customer value, improve customer loyalty, increasing conversion rates or acquiring new customers. This approach makes it easier to determine the kind of data you need to obtain, and how you need to use it. Remember to make your team focus on one project objective at a time.

Internally Sell the Concept
In some organizations, shifting towards a more evidence-based, data driven marketing might require some extra communication, especially if you want everyone on the same ‘page’.

• Let everyone know what you know, encourage continual learning and knowledge sharing.
• Simplify everything by using graphs and pictures.
• Present the data and the outcomes in a way that’s easy to understand allowing managers to make more accurate decisions.
• Don’t Hide the end results, make sure you communicate achievements and plans across the organization.

Form One Big Family
You need to include Web developers, analytical gurus and marketing strategists as well as the creatives, who might feel a bit threatened when working with hard evidence. Then integrate those responsible for site optimizations and e-commerce.

And finally, you need to dedicate, obsessed and committed executive who will drive and combine the process and will also act as the focal point for future customer experience innovation.

Once, you do all of this, you can know for sure that your customers will always be coming back to fill their shopping cart.

The agenda of the meeting is to discuss a reduction in bond buying later in the year, which has sparked a selloff not only in local, but global markets as well. This week it has bruised not only emerging market assets, bonds, stocks, but has also battered the dollar.

The U.S. currency continued to be sluggish against both the euro and the yen on Friday, but had gained a footing against the Japanese unit, which was around 95 yen, but was at an all time 2 month low at 93.75 the previous day.

With the unexpected U.S. retail sales data rising overnight, and with the impact of selloffs on riskier assets beginning to settle, the market noticed some degree of relief. In fact, the European stocks rose to 0.3% as they followed a rebound from the Asian shares.

The markets have been panicky because of the idea that the Federal Reserve could start pulling in its support, which has been known to increase asset prices over the past 4 years. However, Philippe Gijsels, who is head of research at BNP Paribas Fortis Global Markets, said that growth seemed a bit patchy; he wasn’t expecting the Fed to move till the end of the year.

“If you have easy monetary policy and improving economic conditions, which will also help companies to produce good earnings, … then you have a lot of the building blocks in place (to drive stock market gains),” he added.

Most of the major stock markets including the MSCI’s world index have substantially fallen for 4 straight weeks now; along with Europe right on course with a drop of 1.5% this week, Japan’s Nikkei is still trying to recover from its losses of a 15% fumble since mid-May, despite rising 2% on Friday.

The Japanese stock market has been on a constant roller coaster ride; being hurt by the stimulus has caused worries that have unveiled the government’s pro-growth package.

Peripheral Image

In Europe’s own debt market, the southern euro zone bonds in particular were back on top after a turbulent session, which includes the German bonds that rose up 43 ticks adding to this week’s gain. Standard and Poor’s rating agency helped the euro zone margin by keeping Spain’s debt over the ‘junk’ status, even though it does leave the country at a risk of being downgraded since it will be maintaining a negative position on the bonds.

Commodities, including metals have been avoided because of the striking swings seen by currencies and equities this week. However, they too were affected by the stimulus concern by the Feds.

Copper had edged itself up to $7,094 a ton, after recovering from a six week low drop on Thursday, while precious metals such as platinum and gold lingered on their recent lows.

Brent Crude rose up to $105 a barrel for the first time in recent months, even though analysts had suggested that the volatile dollar would have its impact.

According to Ric Spooner, Chief market analyst at CMC Markets, “The key driver of oil has been the weakness in the dollar rather than any fundamental factors,” he further went on to say that, “Traders are wary about pushing things higher because they are confronted with a situation of plenty of supplies when seasonal demand is supposed to pick up”.

An appraisal of the brand and loyalty marketing emails received by many reveals that only a few of them mention any form of relationship that had existed between the user and the company, whether it’s an address or a past buying behavior. This is a serious oversight made by many companies, since research has shown that if companies provide customers with relevant personal information, they’re much more likely to buy something from them.

In fact, the unfulfilled promise of using big data was even presented during the DM News Roundtable. Although, there were a small number of people on the panel who had paid to combine, “research, analytics, and transaction data… in the hope of creating a more tailored form of communication”. The conversation between the panel members was simply all talk and no work.

However, there was one statement that took place on the roundtable that was true, and it simply sums up the current situation of the big data. The statement came from the straight shooter IIana Rabinowitz, the CMO at Lion Brand Yarns, who said, “I think that for most small and midsize companies Big Data is irrelevant. It’s like the Wild, Wild West of information. You can’t use it, you don’t have people who can analyze it, and if it’s there, you don’t know how to get to it.”

It may seem like a derogatory statement, but it’s true nonetheless. Plus there were concerns with big data extending into the loyalty marketing business also. In a recent report by Retail Wire a question had been raised that: “Why Aren’t More Companies Connecting Big Data Dots for Loyalty Programs?” the online publicist referred to the lack of effort by marketers, and concluded that, ““While most respondents feel the need to engage customers based on their needs and expectations, they’re not leveraging the data in a way that maximizes the potential loyalty offered by customers.”

We can try to explain it a bit simpler using a football analogy. Simply big data feels like a potential superstar player who came from the first draft pick, but still hasn’t proven himself while he’s on the field. Plus, the coach isn’t really sure on how or when to use him. So, for the time being, he’s confined to the bench; he may get a chance to play in the future, but it’s anyone’s call when he gets to play.

Since, it appears that most marketers haven’t quite understood how to use the big data, it’s perhaps time to go old-school. Again quoting Ernan Roman, “Customers, both B2B and B2C, are sophisticated enough to recognize that to receive increasingly relevant offers, they must share detailed preference information.”

So, if you’re not capable of leveraging big data, the next best way to collect ‘customer preferences data’ is by sending out surveys, both online and offline. With this approach, you create a form of communication between yourself and the customer, offering them personalized communication, featuring services, products as well as benefits that could interest your customers.

It was written by Viktor Mayer-Schoenberger who’s a professor of Internet Governance and Regulations at Oxford Internet Institute and Data Editor of the Economist, Kenneth Cukier.

What’s so ingenious about the piece is that it puts Big Data into a much larger context, as if you would be dipping back in time to the 3rd Century BC, where the sum of all human knowledge existed only in the Library of Alexandria, which would be about an estimate of 1,200 exabytes. However, by doing so Mayer-Schoenberger and Cukier have changed how we perceive Big Data. According to them, Big Data changes the playing field not just for technology, but for all data types and our understanding of them.

In their piece, both Cukier and Mayer-Scheonberger have identified three ways Big Data will change our understanding of data:

Sampling from Some To All

Before the inception of Big Data, the only way to truly understand human behavior was based on sampling of specific segmented group. Now, with Big Data, the size of the sample is much larger. In fact, we can even go as far as saying “all”.

“Big data is a matter not just of creating somewhat larger samples but of harnessing as much of the existing data as possible about what is being studied,” the authors write. “We still need statistics; we just no longer need to rely on small samples.”

Moving From Organized To Clutter

Data Management experts will spend most of their time discussing the importance of data governance and data quality. There have been discussions on how these disciplines could be incorporated in the use of Big Data; that way you can make informed decisions.

The problem, however, lies in the fact that you’re talking about data that’s not under your control nor is it under an easy governance program, but when you’re dealing with larger data sets that’s generally not the problem. The only reason you believe in it is that statisticians will tell you that, but you need to remember that when you’re talking about the quality of the data set, you might encounter such problems with Big Data.

The article cleared all that up by describing the history of computer aided language translation. For instance, in the 1990’s IBM made an attempt to create a translation machine that used masks, perfect translation of parliamentary transcripts and probability. It wasn’t really appreciated, nor was it that great.

Moving forward to today, what do you look for when you want on the spot translation? You look for Google. The company has accomplished something quite astounding; they’ve used “messy” data from the internet which includes billions of translations from all over the web, resulting in a translation of over 65 languages.

“Large amounts of messy data trumped small amounts of cleaner data,” the authors write.

Its one thing to know, it’s another to actually grasp the idea of understanding data sets and moving forward with Big Data and according to the writers it’s the reason for the third shift of how we comprehend data sets, which is our focus on correlations, instead of causations.

Although the age of Big Data security analytics is here, yet most organizations face a growing issue. On one hand they need Big Data to make more informed decisions, and on the other, they don’t have the skills, staff or process to handle big data analytics, let alone reap the benefits from it.

This is actually a really big deal with enterprise security being a story of haves and have not’s. According to the ESG research, about 17% to 22% of large enterprises fall into the “advanced” category, suggesting that these organizations are capable of embarking on big data security journey. That leaves about 80% of the organizations that requires some form of assistance if they decided to ‘tag’ along on the voyage.

In order to bridge this gap, the big data security analytics have responded with a few solutions such as:

Canned Algorithms

Although, large organizations will have the teams of data scientists, programmers, and security analysts working together, they will still be dependent on their security analytics vendors who can deliver a constant flow of canned algorithms that will detect infected hosts, Command and Control (C&C) communications, credential harvesting and network reconnaissance.

Intelligence

Generally, large organizations need to know everything that there is about their network like, what assets are connected, what other assets communicate with, how are they configured and they need to fully grasp the concept of network trafficking patterns in order to detect suspicious and anomalous behavior. The Big Data Security acts as an intelligence hub by correlating situational awareness and continuous monitoring.

Automation

In today’s world there are too many network packets, events, vulnerabilities and threats to people to keep track of. Although the security community is shy about the idea of installing security devices in blocking mode, this can actually be a part of the big data security analytics solution of moving forward where an analytics engines spots a threat and takes action.

Grouping

It was in 1955 that the idea of grouping came about, especially when a group of IBM 701 users in Los Angeles had gotten together to exchange experiences, practices and ideas. This resulted in the formation of ‘SHARE”, which is a mainframe user group. This form of collective collaboration is actually important for those inexperienced users who are looking to benefit from Big Data security analytics. Vendors who focus on promoting and organizing such efforts among the industry line can actually gain an advantage.

Big Data Security Analytics might be an enterprise inevitability, however, it may turn out to be too geeky for most organizations.

If you think about it, it must be. In practice, large data sets are known to be “messy” and most of them are from external sources such as open data and social media, which simply makes it harder to keep practicing traditional forms of data governance.

Maria C. Villar, who’s a managing partner at Business Data Leadership, a business data management firm, recently focused on addressing this issue in her excellent Information Management article.

When concerning Big Data, you have to discard any need you may have of total control and perfection, especially when it comes to quality and governance.

Similarly, Villar warns that you can’t govern every aspect of Big Data. Instead, the idea of Big Data governance is based on collaboration and specifying what matter and what is “good enough”.

“First, it is important to go back to the basic definition of data quality as ‘fit for purpose,’ Villar writes. “In this case, fit for purpose means evaluating how this data will be used in the intended business use cases and just how ‘good’ the data needs to be in order to provide meaningful value to the business goal.”

Villar states, that Data governance should be a process based on collaboration that identifies, “the most critical, strategic, shared big data sources and specific data fields that are the most important to govern”.

To be brief, Big Data will actually force a conversation between data governance and quality. However, the tone of the conversation will be more towards the risk factor involved and what’s considered practical.

Villar suggested that there are a few areas that need a new game plan, these include:

• Data Quality Criteria

• The Lifecycle Management (Villar adds that, “you really can’t keep Big Data sets indefinitely, nor should you since it can drive up the costs of ownership and degrade analytics results”)

• Metadata requirements

• Compliance

Sure consultants and analysts always tell people that it’s never early to start a conversation about data quality and governance. Since people are people it’s actually a very common mistake that we simply ignore the conversation and move on without focusing on data governance at the start of the program.

According to Capgemini’s Scott Schlesinger, who specializes in Business Information Management, it will be much better to think that data governance will just agree with the processes and standards you will use for using, storing, identifying and collecting data and if you really think about it that’s exactly what you’ll be doing anyways.

However, beware of governance laggards that focus on conversations about governance becoming more crucial, especially when your data governance is weak or nonexistent.

“Your data governance may not be that strong to start with. Big Data will make it worse,” Schlesinger warns.

Data is a very crucial element for businesses. However, data is, and will become incredibly powerful, especially for those who are looking to leverage their marketing, video, local, mobile, social, search and various emerging technologies, in order to compete and gain more market share.

Big Data is a term that is associated with large corporations. These enterprises have great resources to invest in collecting and analyzing data. However, data can equally be important to small medium enterprises who are seeking to grow.

Big Data Means Big Business

The effective use of big data will actually be the determining factor of who will win, and who will lose in every sector of the economy. For instance, many analysts believe that Big Data might actually increase retail margins up to 60%.

It appears that the retail sector is the obvious benefactors of the Big Data, given the abundance of information that is being conveyed through transactions. However, every sector in the economy stands to benefit from the large amounts of data being generated. All it simply requires it so act on the findings, to have quality data and quality queries.

The Value of the Data Set

Big Data analysis, in its earlier life was referred to as data mining, which was considered highly useful when it came to making decisions about directing business strategy, profitability, effectiveness, increasing productivity, tax liability, marketing, sales, human resource, relationships and business operations.

Since each business is unique, the value of the data will actually vary according to the purpose of the data. Let’s focus on the use of Big Data in the context of digital marketing, search, social media and e-businesses.

Never Dip Away from the Wave

The growth of the big data can be compared to a tsunami, just like the ocean, never turn your back on the growing sea of data sets.

Big Data Predictions:

• The Number of “files” of information within the digital universe is anticipated to grow about 75% between 2011 and 2021. Source: EMC

• The volume of data and information is anticipated to be 44 time greater in 2020 as compared to 2009. Source: Computer Services Corp

Unstructured Search and Data

Unstructured data, referred to as “messy data” is expected to implode to epic proportions. Incidentally, Google, Yahoo and Bing are putting greater emphasis on unstructured data that they create, when it comes to especially identifying social media and content on the web.

Conclusion

Big Data has always been considered as a gold mine for marketers whether they’re strategy based, social, PPC, PR, SEO or Search. Nonetheless, it is safe to suggest that the expected growth of data and the emerging of unstructured data will actually be the ‘game changers’.

Those who will recognize and embrace the power of the data mountain will be able to reap the benefits of it, while competitors will try to figure out what they did to become successful.

Rather than running for cover, many big money managers have seen this selloff as an opportunity to invest in a wide array of assets at lower prices, allowing them to diversify their portfolio. They believe that the gloomy days may be overdone and that the concern with the Federal Reserve not throwing money at the economy forever is simply an overreaction.

The US economy had posted some sluggish growth figures. However, talks about an improving economy as well as a growing job market proved that investors had a golden opportunity here. In fact, the Euro zone debt crisis has decided to decline, along with the monetary union which has also established that it was no longer going to drag itself on world growth.

Traders with Big Investors such as Loomis Sayles & Company and Pacific Investment Management Company have been benefiting from this opportunity, with the potential of having ‘hot money’ being flushed in-and-out of the system.

According to Curtis Mewbourne, who’s a managing director and head of portfolio management for PIMO’s New York Office, (known to manage over $2 trillion globally), “We’re finding a lot of opportunities coming out of the volatility,”

On May 22nd, Bernanke said that the central bank, “could in the next few meetings … take a step down in our pace of purchases.” This comment however sparked uproar in volatility, which still hasn’t reduced. In fact, investors are actually recalibrating their expectations on lower bond yields that have increased borrowing and have encouraged investors to start taking investment risks on other asset groups.

Japan’s Stock market has actually plummeted 19% since that day. And last week, the 10 year Treasury yield hit an all time 14 month high. The BofA Merrill Lynch US high yielding index dropped to a 3 month low, the MSCI EM stock index benchmarked a slump of more than 17% this year, and the dollar itself is at an all time 4 month low as compared to other currencies.

The one place that Mewbourne is completely focusing on is government debt, although it’s considered to be highly ‘sensitive’ according to Federal Reserve expectations. He further went onto suggest that it’s the best time to buy 5-10 year treasuries, since the yields are expected to fall because the Federal Reserve has hinted that it won’t be stopping its stimulus program. He even sees more potential gains in mortgages, which are not guaranteed by the government.

The Federal Open Market Committee will issue its next decision on Wednesday, and quite recently the Federal Reserve officials have stated that inflation is significantly low, and could reduce the $85 billion-per-bond program, which is referred to as ‘quantitative easing’.

Mewbourne further went onto say that, “we think that the Fed will signal to investors that the markets have overly priced in expectations for a reduction in quantitative easing”.

Other Assets with Higher Yields

Among many other assets that are being viewed by large money managers, they’re now eyeing high-yield debt, for both material and industrial stocks, along with some emerging market bonds and stocks. In fact, junk bonds have been hurting, especially because of the rise in Treasury yields, with Bank of America/Merrill Lynch High Yield Master Index dropping to 2.91% from its peak early on in May. And, according to Lipper, which is a Thomas Reuters company, the past 2 weeks have seen a substantial outflow of nearly $9 billion from high yield funds.

Surely, there’s a lot of data that is linked with security, and most of that Big Data is stored on huge repositories of logs. What’s even more enlightening is that Big Data is captured on the network and is then analyzed according to records and events of the networks activity. However, does this mean that Walmart is using Big Data? Or the way Netflix or Amazon could be using Big Data to figure out what you like? Or the way Facebook magically sends you ad feeds based on what your friends have been posting? Or is this Big Data the means in which you would want your credit card company to take note that you’ve purchased an overseas flight, so they shouldn’t call you with fraud alerts, when they find transactions in different countries?

No! Not at all, Big Data Security is all about matching security intelligence with the right amount of collected data. Security intelligence consists of Knowledge of threat actors, tools they use, the IP address they use, and who they’ll be targeting. Security intelligence is known to reduce the amount of data that has to be managed, stored and captured.

Nevertheless, if you’re attacked by a group of ten on any given day, so do you really get any value out of collecting alerts from hundreds of applications, thousands of users and millions of devices when only a few of them are part of the attack? The data itself is considered to be of zero value. The value that the Big Data is given comes from the security intelligence that can remove the attacks from the data. In fact, most people should view Anton Chuvakin’s of Gartner post on Big Data Security where he discusses responding to alerts versus security exploration.

And as the Director of Marketing RSA, Security Division of EMC, Rob Sadowski points out that the network activity is changing the way security teams will operate. Data collection and instrumentation are still very crucial, but applying filters which are derived from intelligence is the right path to truly achieving better security.

You have several adoption pockets

When you consider data and the ways in which it can be accessed, the adoption pockets probably might not be of any help. If the situation is this or something similar, remember one thing that a data handling issue can be created with only a single spot. If you want to adopt data governance, you will have to consider the entire cycle along with the scope of your business. If even a single system is not efficient, and handles sensitive data in an appropriate manner, an issue can easily occur.

There is no dictionary or glossary associated with data governance

The concept of data dictionary is often utilized when implementing database systems and other enterprise applications. As you keep involving more and more systems in your IT infrastructure, it becomes vital that all the data dictionaries and glossaries which you use are absolutely the same throughout. Thus, your organization should have just one data dictionary and you should model all your data profiles around it.

You do not have a data steward

If there is no individual or team in your organization whose sole responsibility is to administer the data and ensure that the quality is correct, then you have a huge data govern issue. This duty cannot be assigned to an individual who is already responsible for purchasing the hardware or perform backup. It has to be assigned to someone who has no other task within your organization other than ensuring that the correct data is provided in the correct places, that all information is accurate and that the entire data life cycle is managed effectively.

You have multiple data stewards

While you should definitely have a data steward, you just cannot have two groups who are claiming to perform the same tasks. This is more of an organizational issue rather than a data governance problem, but it can still increase the risks associated with data being governed incorrectly or things being handled the wrong way.

Your governed data is accessed by multiple systems

If your company has issues with file sharing, storage systems, encryption methods and other related technologies, you may also have problems with your data governance system. As an example, there may be stored sessions or logs that contain private and confidential systems, which will then have to be protected and included in your data governance system as well.

Your company has issues that are hard to solve

If you cannot fix any of your data issues and instead prefer working around it, you may have to change your mindset completely. Situations like these can be really bad for your business, particularly if you are interested in expanding it.