Archive for the ‘Big Data’ Category

 

Using Big Data to Improve Customer Metrics

Thursday, December 5th, 2013 by

Although Big Data projects can be helpful to many organizations, launching these initiatives using the wrong metrics can lead to poor practices. And even though most experts agree these endeavors aren’t a panacea, business decision-makers continue to hastily adopt strategies that increase challenges.

Using big data to improve customer metrics

Using Big Data to improve customer metrics

An Enterprise Apps Today report, which highlighted the mindset of customer analytics expert Larry Freed, noted that the gathered data by itself won’t necessarily provide firms with opportunities. Businesses must also embrace the right tools to take advantage of those digital resources.

Freed believes that the variety of information being collected will give organizations the biggest challenge when adopting Big Data endeavors because some of the information being gathered isn’t always accurate.

“If I am an anonymous visitor to a website on my tablet, my home PC, and my work PC, companies are not going to have the ability to recognize that I am the same person,” Freed said, according to Enterprise Apps Today. “Companies are gathering enormous amounts of digital data, but it can accelerate some bad practices.”

In other words, context is everything. Companies need to proactively establish parameters to ensure they understand prospective and existing customer behavior and have the tools needed to turn activity into useful information. Doing so, however, may require the development of new metrics.

More detail is needed
In the past, businesses would often use generic metrics that would put customers in one of three categories based on their response to whether or not they would recommend a company to other people, Freed asserted. The classifications — promoter, passive, or detractor — would then tell decision-makers how “loyal” clients are. During the past several years, however, executives have learned that these categories aren’t entirely accurate, which also means that the strategies built around this segregated system weren’t always effective.

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Big Data’s Theory of Relativity

Monday, December 2nd, 2013 by

During the past several years, Big Data has made headlines, entered boardrooms, and been prioritized by companies of all sizes in the hopes that their processes will present new opportunities for long-term success. In many cases, the programs will enable organizations to experience financial, operational, and managerial benefits much as squeezing juice from a handful of data-filled oranges will provide the much-needed drink for any thirsty business.

Big data's theory of relativity

Big Data’s theory of relativity

Still, executives can’t approach Big Data with the mindset that virtually any project will suffice. In fact, if firms take this route, they’ll often find themselves overburdened with complex information that doesn’t necessarily yield any advantages. IT and business decision-makers need to establish Big Data initiatives with their companies in mind because implementing a generic house-blend of products and strategies won’t respond to the unique challenges and demands faced by most organizations.

Although there are many important factors to prioritize when building a Big Data program, two in particular should be emphasized: Understanding the business and prioritizing quality over quantity.

Understand the business
If Big Data executives in large enterprises or small organizations don’t fully understand the needs of their specific companies, they’ll find it increasingly difficult to implement the tools, technologies, and processes necessary for employees to complete operations in a timely and efficient manner. Breaking down Big Data projects into smaller segments parsed out to multiple departments can help firms embrace more relevant information that augments key procedures.

Marketing, sales, and other decision-makers who intend to embrace Big Data must understand their core principles and needs if they want to ensure their endeavors are as effective as possible. If executives recognize the direction they want to travel, building an analytics program that caters to those specifications will be much easier, regardless of the volume of information those departments are responsible for managing.

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Practical Big Data Use in Marketing Delivers Big Returns

Monday, November 18th, 2013 by

Although organizations around the world continue to have mixed feelings about Big Data, the truth is that properly planned, launched, and managed programs will deliver significant benefits to those willing to follow through with comprehensive strategies. Doing so will require diligence on behalf of both decision-makers and employees, however, as well as a strong sense of collaboration between departments, teams, and company-wide goals.

Practical big data use in marketing delivers big returns

Practical Big Data use in marketing delivers big returns

The bottom line is that Big Data initiatives are effective for those who are adamant enough to experience the advantages. This conclusion was highlighted in a recent Rocket Fuel and Forbes Insights study of more than 200 senior executives, which found that roughly 60 percent of businesses that use Big Data at least 50 percent of the time have exceeded their initial goals for the strategies. Conversely, only about a third of companies that don’t use Big Data frequently were able to meet their expectations.

The moral of this story is that planning ahead and making effective use of available data will give companies an edge in the long run. This is especially true for marketing organizations that rely on information to build customized strategies to draw in, cultivate, and retain prospective customers.

Interestingly, the study found that organizations often have conflicting ideas of how they are using Big Data and the effects that are being introduced because of these perceptions. The survey revealed that the majority of marketing agencies said they are frequently or always taking full advantage of data within advertising processes, although only about 10 percent of companies manage more than half of their promotional endeavors with Big Data.

Bringing Big Data into marketing
Information is the basis for all effective decision-making, especially in marketing. If organizations blindly adopt promotional strategies without first assessing the landscape and how those endeavors will function, they risk not only failing in their attempt to acquire new customers, but possibly losing existing ones who find such attempts unappealing.

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Speed, Accuracy More Important than Volume for Big Data

Friday, November 15th, 2013 by

Although information has always had an important role in the business world, the Big Data movement has placed a larger emphasis on the aggregation and management of digital resources. Companies around the world are now seeking programs that help employees collect and store increasingly larger volumes of information because most decision-makers believe the chances of striking gold will increase if they have more data to analyze.

Speed, accuracy more important than volume for big data

Speed, accuracy more important than volume for Big Data

Ironically, organizations that believe in collecting massive amounts of information often find themselves inundated by the sheer size of the resources under their control and, as a result, encounter new performance obstacles that lead to substantial issues. This conundrum is forcing executives to ask, “Is bigger always better?”

The answer is no, especially in today’s Big Data world. Rather than focusing on the quantity of information they collect, organizations should focus on the quality. Because there’s no finite definition for “Big Data,” each company is forced to develop its own innovative strategy that aligns with the way employees work on a daily basis. In many cases, speed will outweigh a number of other metrics associated with the information being collected. If an organization has the power to quickly convert unstructured data into useful insight for its marketing and sales teams, for example, it will likely be able to reduce client churn and improve its ability to attract, engage, and retain customers.

Still, many experts believe that the data with the largest business impact is the most difficult to measure. The need to do so will pressure executives to build robust warehousing environments that can make sense of various information sets as quickly as possible, allowing firms of all sizes to use a broad range of information for numerous practices, improving their ability to compete and prosper in the long run.

Quality over quantity
Of course it will become increasingly difficult to find a needle if the haystack continues to grow in size. This prospect should encourage executives to build programs that regularly maintain the haystack, trimming the unnecessary heaps of hay to make sifting through the pile less complex. In terms of data management, this means that organizations need to understand the complications and opportunity costs associated with the inability to keep information storage environments in check.

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Big Data = Big Confusion? Hint: The Key is Open Data Services

Wednesday, November 6th, 2013 by

When folks refer to “Big Data” these days, what is everyone really talking about? For several years now, Big Data has been THE buzzword used in conjunction with just about every technology issue imaginable. The reality, however, is that Big Data isn’t an abstract concept. Whether you like it or not, you’re already inundated with Big Data. How you source it, what insights you derive from it, and how quickly you act on it will play a major role in determining the course—and success—of your company. Let’s talk specifics…

Big-Data-3Vs

Handling the increased volume, variety and velocity (the “3V/s”) of data requires a fundamental shift in the makeup of the platform required to capture, store, and analyze the data. A platform that’s capable of handling and capitalizing on Big Data successfully requires a mix of structured data-handling relational databases, unstructured data-handling NoSQL databases, caching solutions, and map reducing Hadoop-style tools.

As the need for new technologies to handle the “3V/s” of Big Data has grown, open source solutions have become the catalysts for innovation, generating a steady launch of new, relevant products to tackle Big Data challenges. Thanks to the skyrocketing pace of innovation in specialized databases and applications, businesses can now choose from a variety of proprietary and open source solutions, depending on the database type and their specific database requirements.

Given the wide variety of new and complex solutions, however, it’s no surprise that a recent survey of IT professionals showed that more than 55% of Big Data projects fail to achieve their goals. The most significant challenge cited was a lack of understanding of and the ability to pilot the range of technologies on the market. This challenge systematically pushes companies toward a limited set of proprietary platforms that often reduce the choice down to a single technology. Perpetuating the tendency to seek one cure-all technology solution is no longer a realistic strategy. No single technology such as a database can solve every problem, especially when it comes to Big Data. Even if such a unique solution could serve multiple needs, successful companies are always trialing new solutions in the quest to perpetually innovate and thereby achieve (or maintain) a competitive edge.

Open Data Services and Big Data go hand-in-hand

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