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Modeling

How to Personalize the Retail Experience with Data

March 23, 2016 by Larisa Bedgood

How to Personalize the Retail Experience with DataLeading retailers have made great strides in becoming omni-channel, multi-channel marketing experts. Reaching consumers across multiple channels, having a strong digital presence, engaging on social platforms, and embracing mobile continue to be key themes in targeting today’s shopper....[read more]

Is this data alive through deep learning and intelligence?

March 21, 2016 by Bruce Robbins

I read a description of deep learning as, 'a powerful set of techniques for learning in neural networks', it also described neural networks as 'a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data'. Both are tools that can be applied to data to do all kinds of useful things such...[read more]

Long Term Financial Planning with Financial Data Analytics

January 19, 2016 by Sarah Smith

Big data is a statement that covers all information processing and gathering on a macro scale. With so much data flowing, a common thread is needed for actionable insights that are based on inputs. For online businesses, user behavior analytics and marketing are two sources of information, which trigger the need for taking action. Without efficient data optimization, there is poor use of money. It is due to the poor retention and lost conversations. Big data without action and insights becomes numbers without any real purpose. Most international online companies use big data to make improvements on customer, conversion marketing and website designs.[read more]

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5 Innovative and Diverse Uses of Big Data

December 22, 2015 by Daniel Matthews
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As we move into 2016, it’s worth looking at how people are using big data to do what they do. It’s exciting. From these real-life cases you can learn a lot about what big data is up to—you’re getting a peek into the workings of companies who are using this stuff in real-time, in different ways. From this peek, you can draw some conclusions about trends for 2016, about how industries are going to be using big data.[read more]

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How to Overcome BYOD Security Challenges

December 9, 2015 by Ryan Kh

Bring your own device is one of the fantastic ways businesses can cut costs at no real impact to their employees. However, there are challenges, specifically ones related to security. We’ve compiled a list of them and also how to overcome the most common and harmful ones.[read more]

3 Effortless Tactics to Be a Data Science Success in Business

December 4, 2015 by Damian Mingle

Do not view the Data Science project plan as training wheels for a junior Data Scientist who is new to working with business, but rather what a skilled Data Scientist will review each time his or her team begins a new task within the Data Science project.[read more]

Top 6 Data Modeling Tools

November 27, 2015 by Zygimantas Jacikevicius

Businesses these days rely heavily on data to make important decisions on a day-to-day basis. The flow of correct and consistent data is of great importance for business users to make quick and well informed decisions. The flow and relationships of data need to be defined and structured to ensure best results.[read more]

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
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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
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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]