Daily Archives: April 22, 2016

The DataBoost Nexus #8

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:


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:


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

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:


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:


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:


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.