Editor DataBoost

The DataBoost Nexus #8

Big Data Implementation

Simply having access to big data repositories is meaningless in the absence of a worthwhile data management strategy. Your enterprise can import huge volumes of information at will, but without an achievable goal, and without the tools to analyze and organize that information, no benefit is gained.

Selecting a Data Strategy

The first step is laying out a tenable data strategy, and this generally revolves around shoring up a weakness or deficit in your organization. Has your enterprise historically had trouble making accurate sales forecasts? Does your customer support staff require more comprehensive information on client order history? Do you regularly encounter shipping or distribution problems that lead to lost sales and unhappy customers?

Whatever the weakness, a properly implemented data strategy can be extremely helpful in ironing the kinks out of your business process.

Big Data: Best Practices

This IBM-sponsored article offers a wealth of information on implementing big data solutions, and provides an excellent starting point for enterprises embarking on a big data strategy:

http://www.ibmbigdatahub.com/blog/10-big-data-implementation-best-practices

Perhaps the most important thing to remember from this list is the second point – implementing your big data solution should always be seen as a series of business decisions, and should not be hamstrung by your IT department.

IT departments can always be expanded and improved, and should never drive your core business decisions.

Big Data: Management

To drive the previous point home, the following article from TechTarget.com outlines a host of real-world big data projects that led to revolutionary improvements for several notable enterprises:

http://searchdatamanagement.techtarget.com/essentialguide/Big-data-applications-Real-world-strategies-for-managing-big-data

Had the businesses in this article let their IT departments determine which, if any, big data strategies were tenable, it is unlikely that the projects would have been so successful.

Big Data: Consulting Firms

To help implement your big data strategy, acquiring assistance from an outside agency that has already successfully managed a project similar to yours is one of the easiest and most cost-effective ways to ensure the success of your project.

Next week, we will discuss how to go about selecting an agency that can help you determine and implement a big data strategy.

The DataBoost Nexus #7

Big Data Resources

Previously, we discussed a proper definition for big data, and we considered how data sets can be used in myriad ways to accomplish and complement a wide variety of goals.

The next question to be answered is where to find large volumes of data that are applicable to particular enterprises or industries.

Internal Data

The first sources to consider, and some of the most applicable and readily available, are sources inside your enterprise. A complete listing of client data, including addresses, email information, and any available demographic metrics, could be considered a form of big data – especially for larger enterprises with client lists that number in the thousands.

As well, purchase and transaction histories can be considered big data, especially for order histories that go back years or decades.

External Data

External sources of big data can be broken down into public and private repositories. While private repositories are often confidential or require significant expenditures to acquire, there are a number of publicly available data sets that are both massive and highly useful to a broad range of industries.

Public Data Sets

This article from LinkedIn provides an excellent starting point for finding public data repositories, including data.gov – perhaps the largest public source of data on the planet:

https://www.linkedin.com/pulse/20141210080103-64875646-the-free-big-data-sources-everyone-should-know

A more recent article from BigData-MadeSimple.com provides an expanded list of public sources that includes many of the sets listed above as well as a number of internationally available big data repositories:

http://bigdata-madesimple.com/70-websites-to-get-large-data-repositories-for-free/

Finally, this most recent list from Forbes offers data hunters a list of the top 30+ sources of big data that can be acquired at no cost:

http://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#6a7da4167961

Big Data Strategies

Of course, acquiring a data repository is only the first step. To utilize the information contained in the data set, you’ll need an enterprise goal that can benefit from the use of large data volumes, and a technique for extracting, analyzing, and outputting the information contained in one or more of these repositories to fulfill that goal.

Next week, we discuss strategies for implementing big data.

The DataBoost Nexus #6

Harnessing Big Data

Big data:

“…A collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.”

http://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/#273362003487

Properly defined, the term “big data” isn’t quite as imposing as it initially seems. More importantly, now that we know what big data is and the potential it provides, the most important question becomes how do we put it to use?

Different firms are going to use big data in different ways, but the same data repositories can also be put to use in completely different ways. For example, suppose I have access to a catalog of every gasoline transaction made throughout the country over the last year. What could I do with this information?

Data Has Multiple Uses

First, I could determine the average price of gasoline in America, and the average amount purchased per transaction. Easy enough. Or I could analyze repeat transactions to reveal how many Americans spend more than $100 on gasoline per month. I could even compare all these purchases against known state populations to determine the average amount spent on gasoline per person per state.

The point is that too often, when considering functions for big data, project managers tend to think that the type of information a company has access to determines what kind of data output is possible. This is not always true. A single big data source can provide a long list of possible areas of study which, while related, can be quite different.

This is why choosing the appropriate big data source for your project is crucial – not because the project is defined by the data, but because the data can be implemented and analyzed in so many different ways.

Where Can I Find Big Data?

Naturally, this begs the question – what are the best sources for big data? Next week, we work on answering that question.

The DataBoost Nexus #5

What is Big Data?

Now that we’ve covered the basics of business intelligence and data visualization, there’s another component that needs to be understood. You won’t get far in any conversation of business intelligence without running across the term Big Data.

Defining Big Data isn’t as easy as some of the other terms we’ve already discussed. Depending on who you’re talking to, Big Data is used in different ways, which means managers understand the term in different ways.

But before we get ahead of ourselves, let’s nail down a working definition.

Wikipedia defines Big Data as:

“…A term for data sets that are so large or complex that traditional data processing applications are inadequate.”

https://en.wikipedia.org/wiki/Big_data

This is an accurate definition, but it’s a bit general for our purposes. Large data sets could include star charts, or global climate tracking, or a list of everyone who’s ever subscribed to the New York Times. For our purposes, discussions of Big Data should be constrained to information that affects, comes from, or relates to your company or industry.

Techtarget.com refers to Big Data as:

“…An evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.”

http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data

This is much better, but still leaves a few questions open. First of all, what’s the difference between structured and semi-structured data? Also, what kind of information are we hoping to mine?

Forbes has defined Big Data as:

“…A collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.”

http://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/#273362003487

Perfect! Here we have an ideal working definition for Big Data. Big Data is information gathered by your company or by sources related to your company that could provide new innovations or discoveries if it is properly analyzed.

Examples of Big Data might include a data dump of all the orders ever made by every customer that has patronized your business, a spreadsheet of every credit card transaction your company has ever made, or metrics of purchasing habits of customers that use your product or service at the national level.

Each of these huge volumes of data would require more than Microsoft Excel to properly analyze. To extract meaningful information, a specific type of software must be found, modified, or produced to properly catalog all this information.

Now that we understand what Big Data is, the next step is figuring out what to do with it.

The DataBoost Nexus #4

Data Visualization – Why is it important?

Last week we covered the definition of data visualization. According to SAS.com:

“Data visualization is the presentation of data in a pictorial or graphical format.”

http://www.sas.com/en_us/insights/big-data/data-visualization.html

So why is transforming information into pictures or graphs a big deal?

The short answer is because it works.

For a longer answer, consider the pages and pages of information you look at every day to run your business. As your company grows, more and more data becomes available to feed into reports, and therefore more pages are placed on your desk.

A good data visualization solution will gather all of the pages on your desk and summarize them, and then summarize the summaries. Then it will allow you to take your newly summarized summaries and compare them to summaries from past days, or weeks, or years, on whatever subject is being summarized.

Sound confusing? Time for a familiar example.

A Familiar Example

A superb data visualization solution can be found in the dashboard of your car. Here, you’ll find a number of instruments and electronics all competing to provide you with information about what’s happening inside your automobile. And all this information about what’s happening inside your car is coming from data sources that you wouldn’t care about otherwise.

When you’re driving, you’re not interested in the number of times your wheels turn per second, and you don’t want a constant update of transmission gear ratios. But you do want to know how fast you’re going, preferable with some degree of accuracy. That’s why you have a speedometer, and a speedometer is a form of data visualization.

But your speedometer isn’t alone. Your temperature gauge, oil pressure indicator, check engine light, even your climate control readout all pull in information from multiple sources to provide you with a constant update in real time on something that matters while you’re driving.

A Business Dashboard

So, imagine a dashboard for your business. You’d have constant updates on important metrics from throughout your company, all created from multiple sources of data, and all summarized in a way that allows you to read them instantly. Like a speedometer.

As your business grows, the amount of information that needs to be processed grows along with it, and the need for a business dashboard that makes sense of all this data becomes increasingly integral to maintaining a competitive advantage.

So, the real question is: If your competitors are using data visualization solutions, can you afford not to?

The DataBoost Nexus #3

Data Visualization – What is it?

Over the last ten years, few terms have been bandied about the tech sector as often or with as much force as “data visualization.” Anyone presently researching business intelligence will no doubt come face to face with the term on a regular basis.

Understanding data visualization is an important step in comprehending the larger landscape of business intelligence, so we think it’s important to discuss not only what data visualization actually is, but also how it plugs into the greater framework of BI.

Definitions

In 2009, Michael Friendly, a well-known contributor on the history of data visualization, defined it thus:

“Information that has been abstracted in some schematic form, including attributes or variables for the units of information.”

http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf

Granted, this is an exceedingly academic definition, but the elements that explain what data visualization are all here. This is Friendly’s definition in plain English:

“Information that has been transformed into graphs, pictures, or other easily readable forms.”

This definition is echoed on Techtarget.com:

“Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context.”

http://searchbusinessanalytics.techtarget.com/definition/data-visualization

And SAS.com shares the most succinct definition so far:

“Data visualization is the presentation of data in a pictorial or graphical format.”

http://www.sas.com/en_us/insights/big-data/data-visualization.html

An Example of Data Visualization

In short, data visualization is nothing more than the use of graphical representations of information that are more easily processed by the eye and the mind, allowing an individual or a group to more readily absorb and react to that information.

A simple example of data visualization would be the bar graph in Microsoft Excel. Anyone familiar with excel knows how easy it is to transform a string of raw sales numbers into a series of colored bars that allow a reader to instantly see an increase or decrease in sales over time. While today’s data visualization solutions are far more sophisticated than the Excel bar graph, the function is identical.

Now that you know what data visualization is, placing it squarely into the broader context of business intelligence is the next step. Join us next week to see what we mean.

The DataBoost Nexus #2

Defining Business Intelligence

Understanding what business intelligence can do for your organization requires first understanding how it is defined.

To put it bluntly, there are a great many people inside and outside the tech industry with wrong-headed ideas about what business intelligence means. Before you can appreciate the importance of BI or recognize how BI application s are changing the face of commerce, you need to be sure you have the correct definition in mind.

TechTarget.com defines business intelligence as:

“A technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions.”

http://searchdatamanagement.techtarget.com/definition/business-intelligence

It’s sort of accurate, but it’s nebulous. What qualifies as “actionable information”? Who are these “other end users” that might be utilizing the application? I think we can find a better definition.

CIO.com defines business intelligence as:

“An umbrella term that refers to a variety of software applications used to analyze an organization’s raw data.”
http://www.cio.com/article/2439504/business-intelligence/business-intelligence-definition-and-solutions.html

This is a correct definition, if a bit uninspired. BI is certainly an umbrella term, but that’s the problem. A single, unifying definition of BI would cure much confusion. Also, there’s more to BI than just software, so this definition is lacking.

Forrester Research defines business intelligence as:

“A set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information.”

https://www.forrester.com/report/Topic+Overview+Business+Intelligence/-/E-RES39218

Bingo! As is so often the case, Forrester Research nails it.

Business Intelligence is more than just software; it is a combination of disciplines that come together to improve efficiency and accuracy of data output organization wide. BI is not limited to keyboards and command lines; it is a complete business philosophy on how to best move and manage information.

Now that we understand what BI means – what it REALLY means – we can talk more about how implementing it properly can help organizations like yours.