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Introduction to Data Lineage

November 11, 2015 by Zygimantas Jacikevicius

Sophisticated modern businesses like banks and insurers are data rich. Data is fundamental to their business effectiveness and efficiency. However, data is not just relevant to the business processes that create it. Many classes of data are essential outside of their main business purpose. This may be for internal reporting and analysis, for use by other applications or for exchange with third parties.[read more]

Creating Value for Business: 2 Data Science Questions You Must Ask

October 14, 2015 by Damian Mingle

Most individuals will recognize balance as a principle of art, but the notion of creating a sense of equilibrium between the business and the Data Scientist is just as foundational in today’s insight economy. To not cultivate this balance is to invite ruin into the organization.[read more]

5 Unbelievable Ways You Can Be a Better Data Scientist in Business

October 8, 2015 by Damian Mingle

Most Data Scientists like to get their hands dirty with data just as quickly as possible, but it is important to practice some delayed gratification and first dig into the details of the Data Science project before you start modeling. A Data Scientist who has the business in mind will attempt to determine what factors might get in the way of the business experiencing success with the project.[read more]

From Master Data to Master Graph

October 6, 2015 by Peter Perera

Today’s CRM and Master Data Management (MDM) technologies don’t enable complete customer knowledge. In fact, they unwittingly turn customer focus into customer tunnel vision. We need an epistemic graph database - a context-aware master graph - to make possible richer, fuller customer stories and expanded 360-degree views for total awareness.[read more]

How Is Knowing the Business Important to Data Science?

September 30, 2015 by Damian Mingle

Businesses around the world are involved in a multitude of projects at any given time. As Data Scientists come into the business fold, it becomes more important with each passing day to have both parties – “the business” and “the Data Scientist” – begin to define successful strategies of working together.[read more]

7 Questions Every Data Scientist Should Be Answering for Business

September 25, 2015 by Damian Mingle

Business professional of all levels have asked me over the years what is it that I should know that my Data Science department may not be telling me. To be candid, many Data Scientist operate in fear wondering what they should be doing as it relates to the business. In my judgment the below questions address both parties with the common goal of a win-win for the organization – helping Data Scientist support their organization as they should and business professionals becoming more informed with each analysis.[read more]

How to Balance the Five Analytic Dimensions

September 11, 2015 by Damian Mingle

So many data scientists select an analytic technique in hopes of achieving a magical solution, but in the end, the solution simply may not even be possible due to other limiting factors. It is important for organizations working with analytic capabilities to understand the various constraints of implementation most real-world applications will encounter.[read more]

Big Data: Where Did All The Water Go?

July 15, 2015 by Shawn Gordon

Nighttime Flow Analysis works by using an optimal time to analyze for leaks. This is typically at night, when household water consumption is significantly low. At the lowest point, the observed GPM from the area is entered into the solution. By comparing this observation to the expected flow, the utility can iterate through different Sub-DMA configurations and target the problems so they are fixed much sooner.[read more]


A Better Way to Model Data

April 21, 2015 by Mark Hargraves

Data modeling.

Over 8 years ago, the Spider Schema Data Model was created to provide an easier way to model OLTP data into a supported OLAP data model with the advantages of the OLTP data model. Over the last 8 years this data model has been proven out and is: faster at data processing, uses less storage space, is more flexible, and provides full support for not only OLAP, but OLTP, and Big Data.[read more]


New Generation of Technology Will Rely on Analytics, Study Shows

December 16, 2014 by Jeff Brown

“We are in a time of profound change for business, technology and IT leadership,” said Leon Kappelman, Ph.D., Professor at the University of North Texas and SIM’s lead researcher and Professor of Information Systems at the University of North Texas. “Results show the focus of IT organizations and their leaders is changing, with a new emphasis on business value, strategy, innovation and speed.”[read more]

Ignore Your Business, Rake in the Profits

December 12, 2014 by Bill Franks


Bring together a few people for several hours. Bring an inventory of the various data sources your organization has access to. Then, brainstorm ways that the analysis of that data can be of value to anyone except your organization. Don’t talk about understanding your business with the data, but rather talk about what opportunities the data can support for others outside the organization.[read more]


How Data Visualization Can Benefit SMBs

December 1, 2014 by Cameron Graham

Data visualization.

Data visualization is a term mostly associated with big brands and corporations. Companies like Target, Deloitte, GitHub and Time Warner Cable all use data visualization tools to analyze and interpret information about their customers, allowing them to better target their marketing and sales efforts, improve internal processes. For many small businesses, data visualization is still a foreign concept.[read more]


5 Common Use Cases for Hadoop in Retail

November 20, 2014 by Sameer Nori

Hadoop in retail

Retailers can focus on the shopper as an individual, rather than aiming at the masses and hoping to snag a few. Where there once were customer panels, in-store surveys, focus groups, and guesswork, there is now social media, online search behavior, and easy-access customer input.[read more]


Big Data Meets Fantasy Football

November 10, 2014 by Gil Allouche

Fantasy sports (image: Pixabay)

Because fantasy sports have become such a lucrative business in the United States, there are numerous platforms that companies are using to grab a share of the market. The 2014-2015 season will signal the entrance of another business, perhaps unexpectedly — big data.[read more]

How to Get Started with Value Add Forecasting

November 6, 2014 by Ray Major

Value add forecasting.

So the promise of using statistical algorithms, forecasting and predictive analytics is now added to the list of a company’s number one priorities. There is a sense of urgency surrounding this new high profile initiative. One may ask, “What’s next?” Well, here are a few steps that you will need to take to deploy your forecasts successfully.[read more]