LATEST POSTS

Data Isn't Just Data

Nicholas Hartman

Read more

Data and analytics are powerful business tools. But if you don’t truly understand where your data comes from, and don’t view that data in a broader context, then you can get into trouble really quickly.


Beware of ‘Magic Machine Learning Algorithms’ and Embrace Your Data

Nicholas Hartman

Read more

The secret behind successful applications of machine learning in business has more to do with the proprietary nature of the underlying data - and what’s done with that data - than with the machine learning algorithm.


Let’s Talk About ‘Automation’ In Service Operations

Nicholas Hartman

Read more

Automation is too often seen as a panacea for all of a business's operational issues. Before automation is implemented, taking a truly data-driven approach to solving an operational business problem first requires additional steps: Eliminating unnecessary work and Optimizing remaining processes. Then, insights from the data can drive the selection of an appropriate automation solution.


Pro Bono Recap: DonorsChoose.org

Carolina Gonzalez

Read more

During the first four months of 2018, a team of five Data Scientists from CKM’s Pro Bono Team – Pranav Badami, Lorena De La Parra Landa, Elya Pardes, Lex Spirtes, and Michael Zhang – worked with the data team at DonorsChoose.org to help them discover new trends in what teachers across the country need.


Asking Questions

Inayat Khosla, Ivan Vlahinic, Jaime Gacitua

Read more

Often overlooked humility in the face of new problems, listening and asking questions, are core attributes of a strong Data Scientist.


Pro Bono Recap: Unite Ideas

Gerardo Veltri

Read more

CKM Advisors was announced the winner of the United Nation's challenge to build a prototype for the Technology Facilitation Mechanism online platform.


CKM Advisors Wins Prestigious International Data Science Competition

CKM Team

Read more

EINDHOVEN, THE NETHERLANDS September 8, 2014 – For the last few years the concept of ‘Big Data’ has dominated headlines and corporate catch phrases. However, below these headlines are many loud grumbles of frustration as companies realize that mountains of data will not, on its own, translate into valuable output. For many, the expected promises of ‘Big Data’ may remain unfulfilled. Thus, when a data science firm is consistently and independently recognized for delivering the best results, it is worth taking note...


CKM’s “100+/100” Rule Replaces Consultants’ “80/20” Rule

Pierre Buhler

Read more

Consultants live by the famous 80/20 Pareto principle: generate 80% of the expected impact with just 20% of effort or information. However, in a world where new data is generated at an accelerated, exponential rate, and where the cost and time to process data are continually reduced, does it still make sense to apply the 80/20 rule? In fact, no...


Too many tools... not enough carpenters!

Nicholas Hartman

Read more

Most of the world’s largest corporations are flush with data, but frequently still struggle to achieve the vast performance increases promised by the hype around so called “big data.” Too often large enterprises fail to understand that the core issue limiting their ability to derive more value from data is not a lack of tools but a lack of carpenters.


Data Scientist and Business Expert: the Sloth and the Moth

Pierre Buhler and Nicholas Hartman

Read more

Nature is full of symbiotic relationships between different species: the three-toed sloth and its moth , the leaf-cutter ant and its fungus, the agouti rodent and its Brazil nut tree, the hermit crab and its algae, are just a few examples. Similar to the way these pairs of species complement each other, data scientists and business experts complement each other. One, alone, can not identify and realize the extraordinary benefits that can be harvested from the vast quantities of data generated, but not typically analyzed, by complex organizations...


Data Preparation for Process Mining — Part II: Timestamp Headaches and Cures

Nicholas Hartman

Read more

Timestamps are core to any process mining effort. However, complex real-world datasets frequently present a range of challenges in analyzing and interpreting timestamp data. Sloppy system implementations often create a real mess for a data scientist looking to analyze timestamps within event logs. Fortunately, a few simple techniques can tackle most of the common challenges one will face when handling such datasets.


Data Preparation for Process Mining - Part I: Human vs. Machine

Nicholas Hartman

Read more

This is the first in a four-part series on best practices in data preparation for process mining analytics. While it may be tempting to launch right into extensive analytics as soon as the raw data arrives, doing so without appropriate preparation will likely cause many headaches later. In the worst case, results could be false or have little relevance to real-world activities. This series won’t cover every possible angle or issue, but it will focus on a broad range of practical advice derived from successful process mining projects...


Are You Effectively Managing Hidden Real Estate Vacancies?

Nicholas Hartman and Curtis Morikawa

Read more

Walk around the floors of just about any office building and you will typically see a fair number of empty desks, offices and cubicles. Some desks belong to those on vacation or working in another location, there’s the cube that those summer interns used up until two weeks ago, there’s the cube that nobody’s sure what it’s for but it seems to have accumulated an impressive collection of office plants, there’s… well you get the idea...


The Highest Return is in the Enterprise Blood - Deciphering Enterprise Data

Pierre Buhler and Curtis Morikawa

Read more

Heads of businesses are continuously searching to improve their business performance. They generally ask their internal teams to do more: save more, produce more, sell more. In short, do always more with less. The basis is often to rely on external studies performed by top advisers to compare the company’s performance to benchmarks and best practices provided by independent companies, to show how much the internal teams should improve. But it is often easier to ask for an external palm reader for advice than to understand what is really happening internally, the good and the bad...


It's Time to Move on from Re-Engineering

Pierre Buhler and Curtis Morikawa

Read more

Why use the same old re-engineering approach? 1. Because that’s how we’ve always done it! 2. No need to explain to senior management what we’re doing, as long as it brings even minimal improvements. 3. Vendors invent cool, new names like “lean” to fool us into believing we’re doing something other than re-engineering, but it’s still re-engineering...


Efficiency and Effectiveness Improvement in the Age of “Big Data”

Pierre Buhler and Curtis Morikawa

Read more

Digital analytics, or the systematic analysis of the data generated within organizations, is creating opportunities for significant change in how we manage processes and people. The benefits are evident in financial services, insurance, health care, government and education but also in many service segments of large industrial enterprises...


Coffee Break Insights: A Closer Look at #WhosGonnaWin

CKM Natural Language Analytics Team

Read more

This weekend sees Super Bowl XLVIII come to New York (yes, we're well aware that the stadium is technically in New Jersey). Earlier this week one of our data scientists noticed the Empire State Building lights putting on quite a show. A quick search revealed that the iconic building's lights are being used as a giant 'sentiment meter' to show the results of a Twitter war between Broncos and Seahawks fans. Fans were instructed to tweet responses including the hashtag #WhosGonnaWin to a series of questions about the upcoming game, with results announced on Verizon's website...


Data Analytics: Keeping it Clean with a Nod to History

Mark Ginsburg

Read more

In the Internet’s infancy, Unix shell commands were very terse such as ‘rm’, ‘mv’, ‘cp’ and so on. There was a good reason for this. The poor programmers had to work on so-called ‘typewriters’ (also known as teletypewriters) and it took physical exertion to press the keys down! To exacerbate matters, the devices operated very slowly...


Replaying the NFL Championships with Twitter Language Analytics

CKM Natural Language Analytics Team

Read more

Last year we posted a popular piece offering our view on defining the characteristics of a data scientist. Perhaps we should have added to that the quality: "during the NFL playoffs, they have one hand in the chips-n-dip and the other typing away in an Emacs terminal." OK well it may not describe all data scientists, but at least some of ours couldn't resist the opportunity to analyze the gridiron action with code...


Should You Invest in Big Data? Why That's Likely the Wrong Question…

Nicholas Hartman

Read more

During an earlier post I wrote about how the use of ‘small data’ can still have a big impact on an organization’s ability to drive performance improvement. While so called ‘big data’ techniques can be incredibly powerful, not every data analytics challenge is a ‘big data’ analytics challenge. That said, we’re still asked nearly every day “should we invest in big data [or insert name of generic big data technology]’?”...


Twitter Weather Radar - Test Data for Language Analytics

Nicholas Hartman

Read more

Today we'd like to share with you some fun charts that have come out of our internal linguistics research efforts. Specifically, studying weather events by analyzing social media traffic from Twitter...


Smart Use of Small Data Can Still Have a Big Impact

Nicholas Hartman

Read more

Whenever I introduce the data analytics we do here at CKM, an ever increasing percentage of people will respond along the lines of “So like Hadoop / NoSQL / [insert generic ‘big data’ term]?” Many are surprised when I respond saying not always...


Columbia University Data Science Society Talk

CKM Team

Read more

Last night we had a packed room for our invited talk to the Columbia University Data Science Society. One question that we commonly get asked is "what's the most important language or tool that I should learn to make myself marketable as a data scientist?"...


BPIC 2013

CKM Team

Read more

A team from CKM Advisors recently participated in the 2013 Business Process Intelligence Challenge (BPIC). CKM won the competition last year and we're pleased to announce that we had a strong showing again this year after placing in the top 3 amongst a strong field of 12 teams from around the world...


Coffee Break Insights: It’s a Fire Hydrant Waterpark Kind of Day

Nicholas Hartman

Read more

It’s a hot one out there. As the mercury rises WNYC, the New York Times and others frequently cite the cracking open of fire hydrants as a sign that summer has arrived in NYC. While cooling off via fire hydrant can be done legally, rogue streetside waterparks are generally frowned upon by the city. Over a quick coffee-break a few of our data scientists wondered how hot does it need to get before New Yorkers open up fire hydrants?...


Big Data on the Big Data Conversation: Tracking the NSA Story

Nicholas Hartman

Read more

Recent revelations regarding the National Security Agency's (NSA) extensive data interception and monitoring practices (aka PRISM) have brought a branch of "Big Data's" research into the broader public light. The basic premise of such work is that computer algorithms can study vast quantities of digitized communication interactions to identify potential activities and persons of interest for national security purposes. A few days ago we wondered what could be found by applying such Big Data monitoring of communications to track the conversational impact of the NSA story on broader discussions about Big Data. This brief technical note highlights some of our most basic findings...


The Data Scientist: Elusive or Illusive?

Nicholas Hartman

Read more

Quality data scientists may be elusive, but they do exist. From our experience in the trenches, we've found seven core characteristics of a successful data scientist. This is a developing view, but offers some insight into the position and the types of skills we are seeking for our team...


The 2012 BPIC Challenge - Process-Mining Driven Optimization of a Consumer Loan Approvals Process

CKM Team

Read more

The 2012 Business Processing Intelligence Challenge (BPIC 2012) was an exercise in analyzing a set of real-world data from a financial institution in the Netherlands. This data set, comprised of 262,200 events within 13,087 total cases, contained information for a loan and overdraft approvals process from submission to eventual resolution (Approval, Cancellation or Rejection)...