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M A N AG E R ’S G U I D E

What Leaders Must Know About
Data for Machine Learning

ON BEHALF OF:


MANAGER’S GUIDE — WHAT LEADERS MUST KNOW ABOUT DATA FOR MACHINE LEARNING

1. Align machine learning initiatives with business priorities.

2. Create and maintain a comprehensive view of all data assets.
3. Lay the groundwork for data governance.

4. Identify the specific roles required to build a strong data foundation
for machine learning.

Data Management Strategy Checklist............................................................................. 5
Sponsor’s Viewpoint: Your Data Strategy Is Key to Machine Learning;
a Data Lake Can Help.................................................................................................... 6

C O N T E N TS

What Leaders Must Know About Data to Drive Success With Machine Learning ........... 2

MIT SMR Connections develops content in collaboration with our sponsors.
It operates independently of the MIT Sloan Management Review editorial group.
Copyright © Massachusetts Institute of Technology, 2020. All rights reserved.

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MANAGER’S GUIDE — WHAT LEADERS MUST KNOW ABOUT DATA FOR MACHINE LEARNING

What Leaders Must Know About Data to
Drive Success With Machine Learning

M

achine learning is taking predictive analytics to the

For example, Intuit’s machine learning initiatives aim to im-

next level to drive tangible business value for a wide

prove customer service by providing personalized recommen-

array of industries. Algorithms allow credit card

dations to subscribers of its accounting and tax software pro-

companies to detect fraud in real time and help retailers direct

grams. An online retailer may plan to use machine learning to

offers to the customers most likely to respond. In health care,

create more-effective targeted marketing campaigns, while an


tools powered by machine learning help doctors transcribe

automotive manufacturer may be building machine learning

notes more easily so they can focus on patient care. Manufac-

systems to predict equipment failures.

turers can take in data from sensors on plant equipment and
recommend maintenance before malfunctions cause produc-

Establishing which of a business’s strategic priorities have the

tion delays.

best potential to be advanced via machine learning provides
clarity around which data sets are most important to collect,

But machine learning models are only as good as the data

store, and prepare for analysis.

they ingest. “If data is not clean, if it’s not accessible, if it isn’t
stitched together to form a strong foundation, the machine

“Being focused on knowing what data is truly driving your

learning and artificial intelligence capabilities built on top of it

business and matters most is the first piece to a data strategy,”


will have problems,” warns Ashok Srivastava, senior vice pres-

says Juan Tello, chief data officer at Deloitte Consulting and

ident and chief data officer at financial software provider In-

principal in its Strategy & Analytics practice. “So, for example,

tuit. This can lead to difficulties such as inaccurate insights or

if business priorities are to win more customers and provide

inherent bias — factors that can hamper intelligent business

more-competitive pricing based on the products a company

decision-making.

sells, that requires three critical data domains: customer data,
pricing data, and product data. Prioritizing the data strategy

Fortunately, businesses can avoid these perils by designing a

on those areas as a starting point will maximize business out-

data management strategy that develops new capabilities, ini-

comes. Organizations should also reevaluate and adjust as their


tiatives, and roles around machine learning. This guide aims to

business priorities change.”

share lessons from business leaders and industry experts on
how, with the right policies and frameworks in place, data can

This focus is essential, given the vast volumes of data gener-

serve as a strategic corporate asset.

ated by enterprise applications, connected devices, and customer interactions via the web or social media platforms, to

1. Align machine learning initiatives with business priorities.

name just a few sources. However, by narrowing the scope for

The first step in creating an enterprise data management strat-

data management to three or four key sources, businesses can

egy is understanding the business’s goal for machine learning.

focus on those data sets that will deliver the most value.

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MANAGER’S GUIDE — WHAT LEADERS MUST KNOW ABOUT DATA FOR MACHINE LEARNING

At Intuit, data management experts meet with
the teams that own data to build a catalog of
that information, resulting in a robust list of data
assets within the company.

2. Create and maintain a comprehensive view of all data assets.

and transparency are key to building trust: Business units now

For data to be useful, a business must know it exists. Unfor-

collaborate so that the company knows the moment a new data

tunately, legacy systems, mergers and acquisitions, and poor

set becomes available.

data onboarding practices can create silos of unidentified and
untagged information.

3. Lay the groundwork for data governance.
At the core of every data management strategy is data gov-

At Intuit, data management experts “meet with the teams that

ernance — a set of rules and systems that ensures that data

own data systems or data pipelines, and we start to build a cat-


is secure, handled in compliance with applicable regulations,

alog of that information. That means understanding what data

accessible, and useable.

they have and how it is stored.” The result, says Srivastava, is
“a robust list of data assets that we have within the company.”

Data security and compliance with privacy laws are table stakes
and as such have been the primary drivers of data governance

But data troves are constantly evolving as businesses deploy

for most enterprises. In addition to guarding against intrud-

new systems. GE Healthcare offers a perfect example of how

ers via cybersecurity measures that protect the IT perimeter,

to stay ahead of the curve. The manufacturer of diagnostic im-

businesses must also establish controls that limit how data is

aging equipment, which uses machine learning algorithms to

accessed, used, and managed by employees. This typically

improve traditional imaging technologies like CT scanning and


means granting different access levels depending on vari-

X-ray, continuously works with collaborators and partners to

ables such as role, tenure, and function. Compliance with

inventory and onboard de-identified data. A dedicated team

regulations such as the European Union’s GDPR (General Data

of data specialists receives, processes, and properly catalogs

Protection Regulation) and similar requirements in other

contractually de-identified data sets and then uploads them

jurisdictions means that companies must also be prepared

for use in AI development. This process leads to greater data

to explain to consumers how their data is being used to make

transparency and availability.

decisions that affect them.

Business leaders must also be held accountable for maintain-

Another key component of data governance is quality: A


ing a comprehensive view of data assets. At GE Healthcare,

machine learning model’s output depends on the quality of its

chief data officer Derek Danois says, broad communication

training data.

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MANAGER’S GUIDE — WHAT LEADERS MUST KNOW ABOUT DATA FOR MACHINE LEARNING

At GE Healthcare, for instance, a team of data architects and

According to Peter Nichol, director of IT portfolio management

data scientists evaluates data quality based on a variety of

for research and development at Regeneron Pharmaceuticals,

metrics. A medical imaging study might be vetted for standard-

some of the key roles required to execute a data management

of-care parameters (such as slice thickness or scan geometry),


strategy include the following:

field of view (the area of a scanned object), and metadata
content requirements. If quality standards are met, GE Health-

• Chief digital/data officer: Oversees all digital functions,

care de-identifies or anonymizes the data and establishes a

provides support and leadership, and articulates a strategy

chain of custody that chronicles the data’s control, transfer,

for data governance that’s consistent across the company.

and analysis, before it’s uploaded for use in AI development.

• Data scientist: Creates tools or processes based on
machine learning and applies them to well-defined

Maintaining consistently high levels of data quality calls for

business problems.

continuous monitoring of metrics and key performance indi-

• Decision scientist: Uses expertise in technology, math,

cators such as accuracy, timeliness, consistency, and integrity


and statistics, along with business domain knowledge,

— a process that can become overwhelming, according to

to enable informed decision-making.

Tello. Using AI-powered data quality tools can accelerate the

• Compliance/legal team member: Handles privacy,

ability to manage and govern data, he says. Enterprise master

compliance, data rights, and regulatory aspects impacting

data management software can also ease the burden by creating

a business.

a single master reference source for all critical business data,
thereby reducing redundancies and the likelihood of errors.

Ancillary positions include data management specialist, business intelligence specialist, and data architect.

4. Identify the specific roles required to build a strong
data foundation for machine learning.

But there’s also a place for sales executives, HR managers, and

An explosion of new data science job titles has raised questions


chief marketing officers in machine learning initiatives. “The

regarding who is responsible for which tasks within a machine

business owners who are making decisions on a daily basis are

learning practice. A well-thought-out organizational structure

some of the most important contributors to our overall data

can make sense of this landscape by clarifying roles and delin-

strategy,” says Intuit’s Srivastava.

eating responsibilities.
That’s because business leaders possess domain knowledge

“The business owners
who are making decisions
on a daily basis are some
of the most important
contributors to our overall
data strategy”

ASHOK SRIVASTAVA, INTUIT

— an in-depth understanding of the relevant data within the
enterprise, the processes that generate useful data, what data
might be useful for a model, and how different variables might
impact a model’s output. Without this guidance, businesses

risk creating machine learning applications that don’t deliver
useful results.
Looking Forward
Machine learning has the potential to improve results in nearly
every aspect of business. But to harness it, businesses need a
data management strategy that will continuously improve the
quality, integrity, access, and security of data. l

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MANAGER’S GUIDE — WHAT LEADERS MUST KNOW ABOUT DATA FOR MACHINE LEARNING

DATA MANAGEMENT
STRATEGY CHECKLIST
Keep the following practices in mind to successfully design and execute
a data management strategy in support of machine learning:

[3] Establish rules and processes around how data is sourced, managed, accessed,


and used across the business.

[3] Ascertain which data sets are driving the business and how they can be used to help solve problems,


generate revenue, and deliver customer benefits.


[3] Inventory known data assets, classify them, and organize them in a data catalog.
[3] Meet with the teams that own and operate data systems to better understand what data they


have and how it is stored.

[3] Understand where your data comes from, who has access, and how it can be used.
[3] Establish internal security precautions (such as provisioning user access), as well as external safeguards


(such as anonymizing data), to protect sensitive data.

[3] Create access controls that set limitations around how data is accessed and how it might be used.
[3] Design processes and systems to ensure that data created is accurate and useful.
[3] Identify specific roles required to build a strong data foundation, including chief digital officer,


data scientist, decision scientist, and compliance team member.

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MANAGER’S GUIDE — WHAT LEADERS MUST KNOWCABOUT
U S T O DATA
M R FOR
E S EMACHINE
A R C H RLEARNING
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