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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]

Do Predictive Workforce Analytics Actually Work?

October 5, 2015 by Dave Weisbeck

While many HR software vendors talk the talk of predicting “at risk employees,” how many can prove they walk the walk, and that their predictions actually work? How can you ensure a vendor’s claim to predict employee retention risks is valid? What should you look for?[read more]


Saving Money and Lives With Predictive Maintenance

October 2, 2015 by Saar Yoskovitz

It’s more important than ever to think about how we can employ predictive maintenance to keep everything, from our air conditioners to our cars, in working order. Most people only know of two ways to maintain machines — reactive maintenance or preventative maintenance. But these aren’t always the best or most cost-efficient processes.[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]

How Big Data Is Changing the Way You Fly

September 28, 2015 by Mark Ross-Smith

As airlines and frequent flyer programs gather more intelligence on your day to day lifestyle, flying and financial position - they begin to build a data profile on your interests, goals, psychometric assessment, your motivations to engage with a brand at any given every point throughout the day, what has driven you to purchase in the past – and most importantly – where your thresholds are.[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]


Leveraging Metadata for (Really) Big Data

September 23, 2015 by Don DeLoach

The word “metadata” has different meanings for different people. Most people think of this as the embodiment of big brother grabbing information about everything we do and say. More fundamentally, metadata is really data that describes other data. In essence, it allows for quicker insight or easier interpretation of the data than one might get from analyzing all of the data at an atomic level.[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]

Ants, Padlocks, and Cyber Security

September 10, 2015 by Bill Franks

If you’ve followed the news recently, I don’t need to tell you that cyber security is a topic of major importance today. It seems that every week there is another revelation of a security breach at an organization thought by many to be a leader in data and network security.[read more]

Forecast Product Demand with Confidence

September 9, 2015 by Keith Peterson

Is your company using demand forecasting in your planning process? And are you happy with those results? Based on our interviews with hundreds of companies worldwide, we have found that among midsize manufacturers and distributors, many still use error prone spreadsheets or forecast based on historical sales data.[read more]

Why Analytics Professionals Are Destined for Leadership

September 2, 2015 by Linda Burtch

From the phenomenal increase in the number of calls that analytics professionals are getting from recruiters, to the fact that nearly every quantitative team is planning to hire this year, it is overwhelmingly clear that there has never been a better time to be a Quant.[read more]

What Is Your Big Data Analytics Stack?

August 21, 2015 by Radhika Subramanian

We often get asked this question - Where do I begin? How are problems being solved using big-data analytics? To answer this question we need to take a step back and think in the context of the problem and a complete solution to the problem.[read more]


How Airlines Are Using Big Data: The Benefits and the Controversy

August 17, 2015 by Jonathan Buckley

With big data now being used in so many different industries, it should probably come as no surprise to hear commercial airlines have been adopting big data analytics to improve their own businesses. Big data can be a highly versatile tool, something that helps companies be more responsive to change and become more prepared for the future. Airlines have long seen the inherent value in this and have slowly evolved to take advantage of big data’s strengths.[read more]

Automate the Boring But Essential Parts of Your Data Warehouse

August 12, 2015 by Keith Peterson

To deliver on your company’s future demands for data and insights, you will need to maintain your existing data warehouse – and add the great new capabilities available with big data management and in-memory analytics. The real opportunity is in making those technologies work together smoothly with minimum effort and risk.[read more]

Six IT Essentials for Life Science Systems Integration

August 11, 2015 by Glenn Johnson

Companies of all sizes, from early-phase start-ups to Big Pharma must continuously collaborate to ensure quality, comply with regulations, and mitigate risks in each stage of the product development life cycle. Here are six key requirements for a future-proof integration platform that can help meet the demands of life science organizations.[read more]