Making Sense of Big Data

Data, in whatever form it takes, is on the forefront of most business plans today. If you are the one responsible of making business decisions based on data, you are going to want to make sure you know how to analyze the information as quickly as possible. Take an e-commerce company for example. Your employees aren’t the only ones contributing to your data. Your customers submit data of their own every time they sign up for your service or purchase a product from you. Overall, the numbers from both sides add up quickly and it often takes a significant amount of effort to analyze. The progression of data creation is exponential, with colossal amounts of data generated every day.

Simply put, Big Data is comprised of extremely large amount of information. This definition is important for business owners to understand, but understanding the scope of Big Data isn’t the be-all or end-all. Business owners need to learn about how Big Data can be analyzed for insights that lead to more strategic business decisions and moves.

Below, you’ll find information that will help you make sense of Big Data. After you finish this article, you’ll understand how data is collected, the types of Big Data and what your business can learn from this this data.

The Collection of Big Data

Data collection methods often differ from organization to organization. Some industries and organizations’ Big Data encompasses information on transactions, while others is comprised of enterprise content. What is more standardized across industries, however, are the steps of data collection.

data collectionThe first step of Big Data collection is gathering information. Some companies use web scraping tools to gather their data, and others rely on their customer resource management tools to capture information. Next, companies need to store the data they collect. Many companies build internal automated processes that allow them to store their data in spreadsheets. Others might take advantage of a storing service that saves the information for them.

The third step is data organization. Even if an organization collects data efficiently, they’re likely to collect extraneous information they don’t need, too. So, every organization needs to sort and clean the information they collect and save. A company will likely also have to reorganize their data after it’s clean, so it’s optimized for further use. Last—but not least—companies need to verify their data. Until companies validate the authenticity of their data, they cannot trust any insights the information produces.

The Types of Big Data

Big Data is made up of a mix of unstructured, structured and multi-structured data. Unstructured data is information that’s not organized or easily interpreted by traditional techniques. A great example of unstructured data is a social media post. In general, standard databases and data models are unable to organize and understand this type of metadata.

structured and unstructured dataStructured data almost always has a defined length and format. Numbers, dates and strings of words are a few examples of structured data. Chances are your company already uses structured data that’s stored in a database to inform your business decisions.

Multi-structured data is derived from interactions between people and machines. One of the best ways to remember multi-structured data is to think of a web browser. As a user works on the browser, a combination of text and visual data is chronicled; the browser will also log structured data, like transactional information, about the user.

Understanding Data Improves Business

The amount of insight businesses can gain from Big Data is somewhat overwhelming. Due to this fact, experts suggest that companies focus on what they want to learn from Big Data, not what they can learn. To take advantage of all that Big Data has to offer, you need to establish a clear plan.

Several prominent companies use Big Data to decrease their expenses, and others use Big Data to improve their internal processes. One of the most popular processes right now is to use Big Data to reduce customer churn.

The Four V’s of Big Data

Industry leaders often use “The Four V’s of Big Data” to frame the Big Data discussion. If you need a quick way to remember what Big Data is and how its massive amounts of data are used, think of the following words—volume, velocity, variety and veracity.

The most obvious characteristic of Big Data is its volume. The amount information taken into consideration for business decisions also grows every year, making volume an essential component of Big Data. With an exponential growth model, Big Data’s velocity must also be addressed. Remember, everything from a text message to a credit card swipe can (and most often is) considered part of the Big Data collection process. As more technological advances become established, Big Data’s velocity will only continue to increase.

Variety is another important characteristic of Big Data. When you think of Big Data’s variety, remember that Big Data is comprised of unstructured, structured and multi-structured data. As discussed in “The Collection of Big Data” section, veracity is another part of understanding Big Data. Without prior data verification, you can’t draw valid insights.

ibm-big-dataThe Bottom Line

To use Big Data as effectively as possible, companies need to understand the ultimate value that Big Data offers their operations. More specifically, businesses leaders need to understand how seemingly countless attributes influence their data collection objects.

Screen-Shot-2015-09-28-at-2.05.01-PMThe high elevation view of Big Data can be overwhelming, but it’s pivotal to business success. Currently, companies that aren’t using Big Data to their advantage are stuck in the past. They’re scrolling through countless spreadsheets and data sources trying to make sense of everything. Then, they’re compiling analysis reports that take either months or years to create. By the time these groups are able to make business decisions, their data is often outdated and irrelevant.

On the other hand, companies that use Big Data are ditching the troubles and limitations of traditional business insight creation. These industry leaders use Big Data’s real-time cycle of analysis to make the most informed business decisions possible when they matter most.

If you want to run your company as efficiently as possible and improve your bottom line accordingly, you need to understand your data.

See What We Can Do For Your Data

9 steps to a successful data analysis

In 2o11, Peter Huber wrote a book about what we (the collective we, or the we of data analysis enthusiasts) have learned about the science and art of data analysis over the past 50 years.

To anyone who is interested in data visualization we highly recommend reading this book. Over the course of about 200 pages Huber carefully outlines exactly what data analysis is, what the current challenges are and how they can be overcome.

One of the aspects of this work that we find particularly interesting is the fact that he gives us, in great detail, a road map or checklist of activities that should be completed, in a particular order, in order for any data analysis project to be successful and meaningful.

Allow us to examine Huber’s checklist and provide our own insights into how we can help.

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Visual Data Exploration

Visual Data Exploration

In the most recent issue of the Harvard  Business Review we saw an article that we just had to read: Visualizations That Really Work.

Being pretty obsessed with all things data visualization everyone in the office read the article and we were immediately impressed with everything that we read, but we also had a few questions and insights of our own that we could share.

We have been at this data visualization game for pretty much one reason: we realized a long time ago that using data to make smarter decisions and improve your business was going to be a key competitive advantage in the future. It’s just nice to have some third-party confirmation that we were right all along.

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Can you eat big data?

It all started with an article we found about robot shepherds on large Australian cattle farms.

As with any new implementation of technology, the cattle farmers had a problem. According to the article, on a particularly large cattle farm the cows were too spread out for the ranchers to adequately monitor them and they were losing many animals to injury and illness as a result.

The robot shepherds used advance thermal imaging and other methods “to detect changes in body temperature and walking gait” in an effort “to improve the quality of animal health and make it easier for farmers to maintain large landscapes where animals roam free” according to the robot’s designer.

That got us thinking: what kind of data does a cow generate?

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Asking Smart Questions

It’s one of those clichés everyone knows: you ask someone wise for answers and they respond with “you should first learn to ask the right questions.”

This might cryptic and confusing but, believe it or not, there is wisdom in learning to ask the right questions: asking the right questions can lead to the right answers. But if you don’t have the right questions then you could be spinning your wheels endlessly searching for the answers to questions that might not even lead to the most benefit.

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The End of the Lone Wolf

It’s a pretty common trope in media today, probably because it’s such a romantic figure: the lone wolf. Just thinking about it conjures up sentiments of independence, ferocity, determination and power. The lone wolf is master of all he or she surveys, successfully carving out a place of dominance in an unforgiving wilderness.

What a lot of baloney.

Anyone who’s read Kipling’s The Jungle Book will know that the strength of the wolf is in the pack, and that without their pack a lone wolf would starve before too long.

But the idea of the lone wolf is even more pernicious when you take that mentality and place it into a business scenario. Yet, unfortunately, that is precisely what we are creating with sales representatives when we evaluate, judge and compensate them based on only one or two KPIs. An entire sales force of lone wolves, each of them slowly starving to death.

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VisualCue for Salesforce

What are sales control systems?

In the October 1987 Journal of Marketing an article appeared that has since come to inform, in very large degree, how modern organizations manage and motivate their sales forces. The article, written by Erin Anderson and Richard Oliver, introduced the world to the concept of “control systems.”

What they discovered should change the way you think about how you manage your sales team, and it should. They discovered two main approaches to organizing, motivating and compensating a sales force and, as usual, both systems are not created equal.

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Data visualization is history

We know that might sound like an odd title for a post on a website that usually dedicates itself to celebrating all things data visualization all the time, but hear us out.

We’re talking about looking at history through data visualizations. We think it can be a fantastic new way to look at historical events in a completely new way. We’ve all taken history courses and know that, for the most part, historiography is dominated by long-winded essays and even denser textbooks.

But if a good data visualization makes things clear and easy to understand for everyone, then surely we can shed light on some of history’s most famous events through the power of visualization.

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The Two Types of Decisions

Business is made of decisions. While some have a more dramatic impact on the business all decisions, large and small, are important. That’s why discovering how to make smarter decisions is so crucial to the continued growth, or even existence, of any enterprise. Luckily, the scientific community has recognized the importance of understanding the decision-making process and has made numerous, recent discoveries that shed light on the process.

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What is the impact of IOT on Big Data?

“Big data” is a catchphrase in both the tech and business worlds. Referring to the vast amounts of data generated by connected technology, big data is a tool that many businesses can use to make their advertising and other marketing efforts more effective. Data and using data for analytic purposes is not new, but what is new is the vast amounts of data now available to us, and that data has come available largely due to the Internet of Things (IoT).

The more devices and machines get connected to each other and the Internet, the more data is going to flow through those devices into the pool of “big data.” But what is the relationship between the two? Are they two sides of the same coin, as Tamara Dull of SAS implies, or are they connected but different? While they may not be the same thing, the Internet of Things and big data are definitely connected.

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