MITSloan case study on data and analytics at GE

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Introduction
This document is based on the MIT Sloan Management Review article on data and analytics at GE titled “Gone
Fishing - For Data”. The document should be viewed as a summary of some of the key points from the article.
The full case study is available at:
http://sloanreview.mit.edu/article/gone-fishing-for-data/
Before getting into the details of GE’s data and analytics efforts, a quick detour is in order to first establish
what is meant by the term “Big Data”.
Big Data a definition
In simple terms Big Data refers to a data environment that cannot be handled by traditional technologies.
Big Data is frequently described in terms of the three V’s, and if you are at IBM, it is likely to be the four V’s.
Figure 1 below illustrates the IBM four V representation of Big Data:
Figure 1: Big Data in dimensions
Figure 1. Four dimensions of big data. Copyright 2012 by IBM. Reprinted with permission.
Please see Appendix A for further elaboration on each of the four V’s.
GE’s objective
Turning to GE’s data and analytics efforts, the company uses sensors to collect data about the performance of
its industrial equipment, including turbines, jet engines and factory floors. Ultimately the company’s efforts
are aimed at being able to sell services to its customers based on detailed analysis of data streaming from its
equipment and the ability to predict failures and other key events.
To get things going
In November 2013, GE set out to connect with 25 airlines and to collect and manage engine data from 3.4
million flights. To do this GE had to build a Data Lake (see table for definition) and it did so with what GE’s
Vince Campisi calls “a two-pizza team,” meaning, a team no bigger than the number of people you could feed
off of two pizzas.

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Seventy days later GE had created a Data Lake which provided the company with the ability to ingest and
connect the full flight data from the engines, and also integrate the engine data with maintenance visits and
parts information. This data was then provided to GE’s data science community to look at things that were
reducing time on wing for customers.
What is a Data Lake?
A Data Lake is a central source in which data can be used in a variety of ways for many different internal
customers, some currently of interest, others to be discovered in the future. Importantly a Data Lake provides
the organisation with the centralization of data, a capability required in order to break down unwanted data
silos. The growing use of Data Lakes has been made possible by the relatively low cost of large-scale storage on
Hadoop.
A Data Lake brings a different paradigm
As articulated in the article, when using a Data Lake, the data is collected in its raw format and there is no
modelling (structuring) of the data up front like what would be done in a traditional data warehouse. Using
such an approach GE takes the position that they don’t understand the relationships that matter and don’t
understand fully what they are going to find when they bring all of these data sets together. In summary GE’s
Data Lake approach is all about collecting data in its raw format, pumping it into one place in order to break
down data silos, and then modelling the data based on the outcome they are trying to solve for.
More than just a technology solution
Moving beyond merely the technology solution GE also addressed organisational culture as well as the hiring
and development of analytics talent. According to Campisi GE’s talent resides in three communities which have
different data usage patterns.
1) The data science community.
This community is focused on a very specific item or outcome they are trying to solve, or a question
they are trying to answer. The objective of the data science community is to leverage the Data Lake to
look for the answer to the specific problem.
2) The software engineering community.
This community will operationalise the models created by the data science community into an
analytic application.
3) The traditional business intelligence community, which connects to the Data Lake in order to unlock
and answer questions that are more traditional in nature.
Getting all the plumbing right with Data Engineers
An important component to the functioning of data and analytics within an organisation are capabilities to
bridge the data management/IT group and the data science group. These capabilities are provided by Data
Engineers and as articulated in the article; “Data engineering is a discipline that sits in between the two, makes
data more accessible and provides the tools a data scientist would want to have. It allows the data scientist to
focus more on developing the model, developing the insight, not on how to stitch the information or stitch the
toolset to make it productive.”
Organisations lacking the combination of a Data Lake and Data Engineering capability all too often become
bogged down in data preparation efforts. The harsh reality is that Big Data is messy data and there is no quick
and easy way around it. People often think that because the data is there, it is ready to be used - but that is
seldom the case. Campisi provided a good example of this; “You go out and hunt for these coveted data
scientists and bring them in, only to frustrate them. They spend 80% of time trying to organize the

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information. One of our first use cases, before using our current approach with the data lake plus data
engineering we went through 10 months of organizing data and figuring out where it existed and breaking
down silos, in order for someone to actually go after the outcome. It’s not effective.”
To paraphrase the Ancient Mariner, without a Data Lake and Data Engineering capability organisations can
easily find themselves in the situation of; Data, data, every where, Nor any drop to drink.
Finding people is a challenge
One of GE’s major challenges has been acquiring capable people in the data and analytics domain. This is made
worse by the scale at which GE is doing things. As stated in the article; “Anybody who can spell “Hadoop” is
heavily recruited. It’s hard to find people who’ve really done it at the scale we’re talking about and looking to
do it, so even in the data management space, it’s hard to find talent at the levels we’re constantly searching
for.” Organisations considering undertaking efforts in the data and analytics space clearly should not refrain
from doing so, but are well advised to spend as much consideration on the human talent component as on the
technology component.
Data governance not to be underestimated
Aside from the challenges of finding the right people, being awash with data brings its own set of challenges.
According to the article these data governance challenges are dictating GE’s speed at which it is able to scale
its data and analytics initiative. Also worth noting is that many of these challenges are being brought on by
technology that is so new that there is no precedent on how they should be addressed. Addressing these data
governance challenges for the first time and doing so consistently is a critical consideration for organisations
looking to exploit opportunities in data and analytics – where the difference between those that succeed and
those that fail could well rest on the strength or weakness of the organisations data governance foundation.
Summary
The article clearly demonstrates the opportunities opening to organisation pursuing data and analytics
initiatives. While Big Data has been enabled by technologies like Hadoop, challenges are arising on two fronts.
Firstly organisations face challenges finding people skilled in this environment. Secondly data governance
challenges are increasing in number and evolving in complexity. While these challenges are not trivial, those
organisations that successfully navigate these challenges will be rewarded with opportunities yet to be
discovered.

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Appendix:
Volume refers to the quantity (gigabytes, terabytes, petabytes etc.) of data that organizations are trying to
harness. Importantly there is no specific measure of volume that defines Big Data, as what constitutes truly
“high” volume varies by industry and even geography. What is clear is that data volumes continue to rise.
Variety refers to different types (forms) of data and data sources. When referring to data types this includes;
numeric, text, image, audio, web, log files etc., whether structured or unstructured. The growth of data
sources such as social media, smart devices, sensors and the Internet of Things has not only resulted in
increases in the volume of data but increases in the types of data as well.
Velocity refers to speed at which data is created, processed and analysed. Velocity impacts latency, which is
the lag time between when data is created or captured, and when it is processed into an output form for
decision making purposes. Importantly, certain types of data must be analysed in real-time to be of value to
the business, a task that places impossible demands on traditional systems where the ability to capture, store
and analyse data in real-time is severely limited.
Veracity refers to the level of reliability associated with certain types of data. According to IBM some data is
inherently uncertain, for example: sentiment and truthfulness in humans; GPS sensors bouncing among the
skyscrapers of Manhattan; weather conditions; economic factors; and the future. When dealing with these
types of data, no amount of data cleansing can correct for it. Yet despite uncertainty, the data still contains
valuable information. The need to acknowledge and embrace this uncertainty is a hallmark of Big Data.
(IBM, 2012, pg. 5)

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Reference:
IBM. (2012). Four dimensions of big data. [Diagram] Retrieved from IBM, (2012). Analytics: the real-world use of big
data. [pdf]. Retrieved from
http://public.dhe.ibm.com/common/ssi/ecm/en/gbe03519usen/GBE03519USEN.PDF
IBM. (2012). Analytics: the real-world use of big data. [pdf].
Retrieved from http://public.dhe.ibm.com/common/ssi/ecm/en/gbe03519usen/GBE03519USEN.PDF