How Analytics Has Changed in the Last 10 Years (and How It’s Stayed the Same) · Thomas H. Davenport June 22, 2017 · Summary · Save · Share · Comment · Print · 8.95Buy Copies Recommended · Blockchain: Tools for Preparing Your Team for the Future Book 49.95 View Details · Clean Edge Razor: Splitting Hairs in Product Positioning HBS Brief Case 8.95 View Details · Deutsche Allgemeinversicherung Photo by Ferdinand Stöhr Ten years ago, Jeanne Harris and I published the book Competing on Analytics, and we’ve just finished updating it for publication in September. One major reason for the update is that analytical technology has changed dramatically over the last decade; the sections we wrote on those topics have become woefully out of date. So revising our book offered us a chance to take stock of 10 years of change in analytics. Of course, not everything is different. Some technologies from a decade ago are still in broad use, and I’ll describe them here too. There has been even more stability in analytical leadership, change management, and culture, and in many cases those remain the toughest problems to address. But we’re here to talk about technology. Here’s a brief summary of what’s changed in the past decade. The last decade, of course, was the era of big data. New data sources such as online clickstreams required a variety of new hardware offerings on premise and in the cloud, primarily involving distributed computing — spreading analytical calculations across multiple commodity servers — or specialized data appliances. Such machines often analyze data “in memory,” which can dramatically accelerate times-to-answer. Cloud-based analytics made it possible for organizations to acquire massive amounts of computing power for short periods at low cost. Even small businesses could get in on the act, and big companies began using these tools not just for big data but also for traditional small, structured data. Insight Center · Putting Data to Work Analytics are critical to companies’ performance. Along with the hardware advances, the need to store and process big data in new ways led to a whole constellation of open source software, such as Hadoop and scripting languages. Hadoop is used to store and do basic processing on big data, and it’s typically more than an order of magnitude cheaper than a data warehouse for similar volumes of data. Today many organizations are employing Hadoop-based data lakes to store different types of data in their original formats until they need to be structured and analyzed. Since much of big data is relatively unstructured, data scientists created ways to make it structured and ready for statistical analysis, with new (and old) scripting languages like Pig, Hive, and Python. More-specialized open source tools, such as Spark for streaming data and R for statistics, have also gained substantial popularity. The process of acquiring and using open source software is a major change in itself for established busines ...

1. How Analytics Has Changed in the Last 10 Years (and How It’s
Stayed the Same)
· Thomas H. Davenport
June 22, 2017
· Summary
· Save
· Share
· Comment
· Print
· 8.95Buy Copies
Recommended
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Blockchain: Tools for Preparing Your Team for the Future
Book
49.95 View Details
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Clean Edge Razor: Splitting Hairs in Product Positioning
HBS Brief Case
8.95 View Details
·
Deutsche Allgemeinversicherung
Photo by Ferdinand Stöhr
Ten years ago, Jeanne Harris and I published the book
Competing on Analytics, and we’ve just finished updating it for
publication in September. One major reason for the update is
that analytical technology has changed dramatically over the
last decade; the sections we wrote on those topics have become
woefully out of date. So revising our book offered us a chance
to take stock of 10 years of change in analytics.
Of course, not everything is different. Some technologies from a
decade ago are still in broad use, and I’ll describe them here
too. There has been even more stability in analytical leadership,
change management, and culture, and in many cases those
remain the toughest problems to address. But we’re here to talk

2. about technology. Here’s a brief summary of what’s changed in
the past decade.
The last decade, of course, was the era of big data. New data
sources such as online clickstreams required a variety of new
hardware offerings on premise and in the cloud, primarily
involving distributed computing — spreading analytical
calculations across multiple commodity servers — or
specialized data appliances. Such machines often analyze data
“in memory,” which can dramatically accelerate times-to-
answer. Cloud-based analytics made it possible for
organizations to acquire massive amounts of computing power
for short periods at low cost. Even small businesses could get in
on the act, and big companies began using these tools not just
for big data but also for traditional small, structured data.
Insight Center
· Putting Data to Work
Analytics are critical to companies’ performance.
Along with the hardware advances, the need to store and
process big data in new ways led to a whole constellation of
open source software, such as Hadoop and scripting languages.
Hadoop is used to store and do basic processing on big data, and
it’s typically more than an order of magnitude cheaper than a
data warehouse for similar volumes of data. Today many
organizations are employing Hadoop-based data lakes to store
different types of data in their original formats until they need
to be structured and analyzed.
Since much of big data is relatively unstructured, data scientists
created ways to make it structured and ready for statistical
analysis, with new (and old) scripting languages like Pig, Hive,
and Python. More-specialized open source tools, such as Spark
for streaming data and R for statistics, have also gained
substantial popularity. The process of acquiring and using open
source software is a major change in itself for established
businesses.
The technologies I’ve mentioned for analytics thus far are
primarily separate from other types of systems, but many

3. organizations today want and need to integrate analytics with
their production applications. They might draw from CRM
systems to evaluate the lifetime value of a customer, for
example, or optimize pricing based on supply chain systems
about available inventory. In order to integrate with these
systems, a component-based or “microservices” approach to
analytical technology can be very helpful. This involves small
bits of code or an API call being embedded into a system to
deliver a small, contained analytical result; open source
software has abetted this trend.
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This embedded approach is now used to facilitate “analytics at
the edge” or “streaming analytics.” Small analytical programs
running on a local microprocessor, for example, might be able
to analyze data coming from drill bit sensors in an oil well drill
and tell the bit whether to speed up or slow down. With internet
of things data becoming popular in many industries, analyzing
data near the source will become increasingly important,
particularly in remote geographies where telecommunications
constraints might limit centralization of data.
Another key change in the analytics technology landscape
involves autonomous analytics — a form of artificial
intelligence or cognitive technology. Analytics in the past were
created for human decision makers, who considered the output
and made the final decision. But machine learning technologies
can take the next step and actually make the decision or adopt
the recommended action. Most cognitive technologies are
statistics-based at their core, and they can dramatically improve
the productivity and effectiveness of data analysis.
Of course, as is often the case with information technology, the

4. previous analytical technologies haven’t gone away — after all,
mainframes are still humming away in many companies. Firms
still use statistics packages, spreadsheets, data warehouses and
marts, visual analytics, and business intelligence tools. Most
large organizations are beginning to explore open source
software, but they still use substantial numbers of proprietary
analytics tools as well.
It’s often the case, for example, that it’s easier to acquire
specialized analytics solutions — say, for anti-money
laundering analysis in a bank — than to build your own with
open source. In data storage there are similar open/proprietary
combinations. Structured data in rows and columns requiring
security and access controls can remain in data warehouses,
while unstructured/prestructured data resides in a data lake. Of
course, the open source software is free, but the people who can
work with open source tools may be more expensive than those
who are capable with proprietary technologies.
The change in analytics technologies has been rapid and broad.
There’s no doubt that the current array of analytical
technologies is more powerful and less expensive than the
previous generation. It enables companies to store and analyze
both far more data and many different types of it. Analyses and
recommendations come much faster, approaching real time in
many cases. In short, all analytical boats have risen.
However, these new tools are also more complex and in many
cases require higher levels of expertise to work with. As
analytics has grown in importance over the last decade, the
commitments that organizations must make to excel with it
have also grown. Because so many companies have realized that
analytics are critical to their business success, new technologies
haven’t necessarily made it easier to become — and remain —
an analytical competitor. Using state-of-the-art analytical
technologies is a prerequisite for success, but their widespread
availability puts an increasing premium on nontechnical factors
like analytical leadership, culture, and strategy.

5. What’s Your Data Strategy?
· Leandro DalleMule
· Thomas H. Davenport
From the May–June 2017 Issue
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May–June 2017 Issue
Explore the Archive
More than ever, the ability to manage torrents of data is critical
to a company’s success. But even with the emergence of data-
management functions and chief data officers (CDOs), most
companies remain badly behind the curve. Cross-industry
studies show that on average, less than half of an organization’s
structured data is actively used in making decisions—and less
than 1% of its unstructured data is analyzed or used at all. More
than 70% of employees have access to data they should not, and
80% of analysts’ time is spent simply discovering and preparing
data. Data breaches are common, rogue data sets propagate in
silos, and companies’ data technology often isn’t up to the
demands put on it.
Having a CDO and a data-management function is a start, but
neither can be fully effective in the absence of a coherent
strategy for organizing, governing, analyzing, and deploying an
organization’s information assets. Indeed, without such
strategic management many companies struggle to protect and
leverage their data—and CDOs’ tenures are often difficult and
short (just 2.4 years on average, according to Gartner). In this
article we describe a new framework for building a robust data
strategy that can be applied across industries and levels of data
maturity. The framework draws on our implementation

6. experience at the global insurer AIG (where DalleMule is the
CDO) and our study of half a dozen other large companies
where its elements have been applied. The strategy enables
superior data management and analytics—essential capabilities
that support managerial decision making and ultimately enhance
financial performance.
The “plumbing” aspects of data management may not be as sexy
as the predictive models and colorful dashboards they produce,
but they’re vital to high performance. As such, they’re not just
the concern of the CIO and the CDO; ensuring smart data
management is the responsibility of all C-suite executives,
starting with the CEO.
Defense Versus Offense
Our framework addresses two key issues: It helps companies
clarify the primary purpose of their data, and it guides them in
strategic data management. Unlike other approaches we’ve seen,
ours requires companies to make considered trade-offs between
“defensive” and “offensive” uses of data and between control
and flexibility in its use, as we describe below. Although
information on enterprise data management is abundant, much
of it is technical and focused on governance, best practices,
tools, and the like. Few if any data-management frameworks are
as business-focused as ours: It not only promotes the efficient
use of data and allocation of resources but also helps companies
design their data-management activities to support their overall
strategy.
Data defense and offense are differentiated by distinct business
objectives and the activities designed to address them. Data
defense is about minimizing downside risk. Activities include
ensuring compliance with regulations (such as rules governing
data privacy and the integrity of financial reports), using
analytics to detect and limit fraud, and building systems to
prevent theft. Defensive efforts also ensure the integrity of data
flowing through a company’s internal systems by identifying,
standardizing, and governing authoritative data sources, such as
fundamental customer and supplier information or sales data, in

7. a “single source of truth.” Data offense focuses on supporting
business objectives such as increasing revenue, profitability,
and customer satisfaction. It typically includes activities that
generate customer insights (data analysis and modeling, for
example) or integrate disparate customer and market data to
support managerial decision making through, for instance,
interactive dashboards.
Offensive activities tend to be most relevant for customer-
focused business functions such as sales and marketing and are
often more real-time than is defensive work, with its
concentration on legal, financial, compliance, and IT concerns.
(An exception would be data fraud protection, in which seconds
count and real-time analytics smarts are critical.) Every
company needs both offense and defense to succeed, but getting
the balance right is tricky. In every organization we’ve talked
with, the two compete fiercely for finite resources, funding, and
people. As we shall see, putting equal emphasis on the two is
optimal for some companies. But for many others it’s wiser to
favor one or the other.
Some company or environmental factors may influence the
direction of data strategy: Strong regulation in an industry
(financial services or health care, for example) would move the
organization toward defense; strong competition for customers
would shift it toward offense. The challenge for CDOs and the
rest of the C-suite is to establish the appropriate trade-offs
between defense and offense and to ensure the best balance in
support of the company’s overall strategy.
Decisions about these trade-offs are rooted in the fundamental
dichotomy between standardizing data and keeping it more
flexible. The more uniform data is, the easier it becomes to
execute defensive processes, such as complying with regulatory
requirements and implementing data-access controls. The more
flexible data is—that is, the more readily it can be transformed
or interpreted to meet specific business needs—the more useful
it is in offense. Balancing offense and defense, then, requires
balancing data control and flexibility, as we will describe.

8. Single Source, Multiple Versions
Before we explore the framework, it’s important to distinguish
between information and data and to differentiate information
architecture from data architecture. According to Peter Drucker,
information is “data endowed with relevance and purpose.” Raw
data, such as customer retention rates, sales figures, and supply
costs, is of limited value until it has been integrated with other
data and transformed into information that can guide decision
making. Sales figures put into a historical or a market context
suddenly have meaning—they may be climbing or falling
relative to benchmarks or in response to a specific strategy.
A company’s data architecture describes how data is collected,
stored, transformed, distributed, and consumed. It includes the
rules governing structured formats, such as databases and file
systems, and the systems for connecting data with the business
processes that consume it. Information architecture governs the
processes and rules that convert data into useful information.
For example, data architecture might feed raw daily advertising
and sales data into information architecture systems, such as
marketing dashboards, where it is integrated and analyzed to
reveal relationships between ad spend and sales by channel and
region.
Many organizations have attempted to create highly centralized,
control-oriented approaches to data and information
architectures. Previously known as information engineering and
now as master data management, these top-down approaches are
often not well suited to supporting a broad data strategy.
Although they are effective for standardizing enterprise data,
they can inhibit flexibility, making it harder to customize data
or transform it into information that can be applied
strategically. In our experience, a more flexible and realistic
approach to data and information architectures involves both a
single source of truth (SSOT) and multiple versions of the truth
(MVOTs). The SSOT works at the data level; MVOTs support
the management of information.
In the organizations we’ve studied, the concept of a single

9. version of truth—for example, one inviolable primary source of
revenue data—is fully grasped and accepted by IT and across
the business. However, the idea that a single source can feed
multiple versions of the truth (such as revenue figures that
differ according to users’ needs) is not well understood,
commonly articulated, or, in general, properly executed.
The key innovation of our framework is this: It requires flexible
data and information architectures that permit both single and
multiple versions of the truth to support a defensive-offensive
approach to data strategy.
The Elements of Data Strategy
DEFENSE
OFFENSE
KEY OBJECTIVES
Ensure data security, privacy, integrity, quality, regulatory
compliance, and governance
Improve competitive position and profitability
CORE ACTIVITIES
Optimize data extraction, standardization, storage, and access
Optimize data analytics, modeling, visualization,
transformation, and enrichment
DATA-MANAGEMENT ORIENTATION
Control
Flexibility
ENABLING ARCHITECTURE
SSOT
(Single source of truth)
MVOTs
(Multiple versions of the truth)
From “WHAT’S YOUR DATA STRATEGY?” BY LEANDRO
DALLEMULE AND THOMAS H. DAVENPORT, MAY–JUNE
2017
© HBR.ORG
Find this and other HBR graphics in our Visual Library

10. OK. Let’s parse that.
The SSOT is a logical, often virtual and cloud-based repository
that contains one authoritative copy of all crucial data, such as
customer, supplier, and product details. It must have robust data
provenance and governance controls to ensure that the data can
be relied on in defensive and offensive activities, and it must
use a common language—not one that is specific to a particular
business unit or function. Thus, for example, revenue is
reported, customers are defined, and products are classified in a
single, unchanging, agreed-upon way within the SSOT.
Not having an SSOT can lead to chaos. One large industrial
company we studied had more than a dozen data sources
containing similar supplier information, such as name and
address. But the content was slightly different in each source.
For example, one source identified a supplier as Acme; another
called it Acme, Inc.; and a third labeled it ACME Corp.
Meanwhile, various functions within the company were relying
on differing data sources; often the functions weren’t even
aware that alternative sources existed. Human beings might be
able to untangle such problems (though it would be labor-
intensive), but traditional IT systems can’t, so the company
couldn’t truly understand its relationship with the supplier.
Fortunately, artificial intelligence tools that can sift through
such data chaos to assemble an SSOT are becoming available.
The industrial company ultimately tapped one and saved
substantial IT costs by shutting down redundant systems. The
SSOT allowed managers to identify suppliers that were selling
to multiple business units within the company and to negotiate
discounts. In the first year, having an SSOT yielded $75 million
in benefits.
A New Data Architecture Can Pay for Itself
When companies lack a robust SSOT-MVOTs data architecture,
teams across the organization may create and store the data they
need in siloed repositories that vary in depth, breadth, and
formatting. Their data management is often done in isolation
with inconsistent requirements. The process is inefficient and

11. expensive and can result in the proliferation of multiple
uncontrolled versions of the truth that aren’t effectively reused.
Because SSOTs and MVOTs concentrate, standardize, and
streamline data-sourcing activities, they can dramatically cut
operational costs.
One large financial services company doing business in more
than 200 countries consolidated nearly 130 authoritative data
sources, with trillions of records, into an SSOT. This allowed
the company to rationalize its key data systems; eliminate much
supporting IT infrastructure, such as databases and servers; and
cut operating expenses by automating previously manual data
consolidation. The automation alone yielded a 190% return on
investment with a two-year payback time. Many companies will
find that they can fund their entire data management programs,
including staff salaries and technology costs, from the savings
realized by consolidating data sources and decommissioning
legacy systems.
The CDO and the data-management function should be fully
responsible for building and operating the SSOT structure and
using the savings it generates to fund the company’s data
program. Most important is to ensure at the outset that the
SSOT addresses broad, high-priority business needs, such as
applications that benefit customers or generate revenue, so that
the project quickly yields results and savings—which
encourages organization-wide buy-in.
Read more
An SSOT is the source from which multiple versions of the
truth are developed. MVOTs result from the business-specific
transformation of data into information—data imbued with
“relevance and purpose.” Thus, as various groups within units
or functions transform, label, and report data, they create
distinct, controlled versions of the truth that, when queried,
yield consistent, customized responses according to the groups’
predetermined requirements.
Consider how a supplier might classify its clients Bayer and
Apple according to industry. At the SSOT level these companies

12. belong, respectively, to chemicals/pharmaceuticals and
consumer electronics, and all data about the supplier’s
relationship with them, such as commercial transactions and
market information, would be mapped accordingly. In the
absence of MVOTs, the same would be true for all
organizational purposes. But such broad industry classifications
may be of little use to sales, for example, where a more
practical version of the truth would classify Apple as a mobile
phone or a laptop company, depending on which division sales
was interacting with. Similarly, Bayer might be more usefully
classified as a drug or a pesticide company for the purposes of
competitive analysis. In short, multiple versions of the truth,
derived from a common SSOT, support superior decision
making.
A company’s position on the offense-defense spectrum is rarely
static.
At a global asset management company we studied, the
marketing and finance departments both produced monthly
reports on television ad spending—MVOTs derived from a
common SSOT. Marketing, interested in analyzing advertising
effectiveness, reported on spending after ads had aired. Finance,
focusing on cash flow, captured spending when invoices were
paid. The reports therefore contained different numbers, but
each represented an accurate version of the truth.
Procter & Gamble has adopted a similar approach to data
management. The company long had a centralized SSOT for all
product and customer data, and other versions of data weren’t
allowed. But CDO Guy Peri and his team realized that the
various business units had valid needs for customized
interpretations of the data. The units are now permitted to create
controlled data transformations for reporting that can be
reliably mapped back to the SSOT. Thus the MVOTs diverge
from the SSOT in consistent ways, and their provenance is
clear.

13. 5 Ways Your Data Strategy Can Fail
· Thomas C. Redman
October 11, 2018
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Mint Images/Getty Images
There are plenty of great ideas and techniques in the data space:
from analytics to machine learning to data-driven decision
making to improving data quality. Some of these ideas that have
been around for a long time and are fully vetted, proving
themselves again and again. Others have enjoyed wide
socialization in the business, popular, and technical press.
Indeed, The Economist proclaimed that data are now “the
world’s most valuable asset.”
With all these success stories and such a heady reputation, one
might expect to see companies trumpeting sustained revenue
growth, permanent reductions in cost structures, dramatic
improvements in customer satisfaction, and other benefits.
Except for very few, this hasn’t happened. Paradoxically, “data”
appear everywhere but on the balance sheet and income

14. statement. Indeed, the cold reality is that for most, progress is
agonizingly slow.
It takes a lot to succeed with data. As the figure below depicts,
a company must perform solid work on five components, each
reasonably aligned with the other four. Missing any of these
elements compromises the total effort.
Find this and other HBR graphics in our
Visual Library
Let’s explore each component in turn.
Quite obviously, to succeed in the data space, companies need
data, properly defined, relevant to the tasks at hand, structured
such that it is easy to find and understand, and of high-enough
quality that it can be trusted. It helps if some of the data are
“proprietary,” meaning that you have sole ownership of or
access to them.
For most companies, data is a real problem. The data is
scattered in silos — stuck in departmental systems that don’t
talk well with one another, the quality is poor, and the
associated costs are high. Bad data makes it nearly impossible
to become data-driven and adds enormous uncertainty to
technological progress, including machine learning and
digitization.
Then, companies need a means to monetize that data, essentially
a business model for putting the data to work, at profit. This is
where selling the data directly, building it into products and
services, using it as input for analytics, and making better
decisions come to the fore. There are so many ways to put data
to work that it is hard to select the best ones. A high-level
direction such as “using analytics wherever possible” is not
enough. You have to define how you plan to use analytics to
create business advantage and then execute. Without a clear,
top-down business direction, people, teams, and entire
departments go off on their own. There is lots of activity but
little sustained benefit.
Organizational capabilities include talent, structure, and

15. culture. Some years ago, I noted that most organizations were
singularly “unfit for data.” They lack the talent they need, they
assign the wrong people to deal with quality, organizational
silos make data sharing difficult, and while they may claim that
“data is our most important asset,” they don’t treat it that way.
If anything, this problem has grown more acute.
Start with talent. It is obvious enough that if you want to push
the frontiers of machine learning, you need a few world-class
data scientists. Less obvious is the need for people who can
rationalize business processes, build predictive models into
them, and integrate the new technologies into the old. More
generally, it is easy to bewail the shortage of top-flight
technical talent, but just as important are skills up and down the
organization chart, the management ability to pull it all
together, and the leadership to drive execution at scale.
Consider this example: Many companies see enormous potential
in data-driven decision making. But to pursue such an objective,
you have to teach people how to use data effectively (HBR’s
current series on data skills will focus on this topic). Leadership
must realize that earning even a fraction of the value data offer
takes more than simply bolting an AI program into one
department or asking IT to digitize operations.
Insight Center
· Scaling Your Team’s Data Skills
Help your employees be more data-savvy.
Structure and culture are also a concern. As noted,
organizational silos make it difficult to share data, effectively
limiting the scope of the effort. All organizations claim that
they value data, but their leaders are hard-pressed to answer
basic questions such as, “Which data is most important?” “How
do you plan to make money from your data?” or “Do you have
anything that is proprietary?” Some even refer to data as
“exhaust” — the antithesis of a valued asset! Without an
abundance of talent and an organizational structure and culture
that value data, it is difficult for companies to grow successful
efforts beyond the team and department levels.

16. Fourth, companies need technologies to deliver at scale and low
cost. Here, I include both basic storage, processing, and
communications technologies, as well as the more sophisticated
architectures, analysis tools, and cognitive technologies that are
the engines of monetization.
Quite obviously companies need technology — you simply can’t
scale and deliver without it. Facebook, Amazon, Netflix, and
Google, who have succeeded with data, have built powerful
platforms. Perhaps for these reasons, most companies begin
their forays into the data space with technology. But from my
vantage point, too many companies expect too much of
technology, falling into the trap of viewing it as the primary
driver of success. Technology is only one component.
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The last element is defense, essentially minimizing risk.
Defense includes actions such as following the law and
regulations, keeping valued data safe from loss or theft, meeting
privacy requirements, maintaining relationships with customers,
matching the moves of a nimble competitor, staying in front of
a better-funded behemoth, and steering clear of legal and
regulatory actions that stem from monopoly power. You’re
unlikely to make much money from defense, but poor defense
can cost you a lot of time, money, and trouble.
Thus, data require a range of concerted effort. At a minimum,
HR must find new talent and train everyone in the organization,
tech departments must bring in new technologies and integrate
them into existing infrastructures, privacy and security
professionals must develop new policies and reach deep into the
organization to enforce them, line organizations must deal with
incredible disruption, everyone must contribute to data quality
efforts, and leaders must set off in new, unfamiliar directions.

17. Adding to complications, data, technology, and people are very
different sorts of assets, requiring different management styles.
It’s a challenging transition. Many companies have tried to
resolve their data quality issues with the latest technology as a
shortcut (e.g., enterprise systems, data warehouses, cloud,
blockchain), but these new systems have missed the mark.
It is important to remember that the goal is not simply to get all
you can out of your data. Rather, you want to leverage your data
in ways that create new growth, cut waste, increase customer
satisfaction, or otherwise improve company performance. And
“data” may present your best chance of achieving such goals.
Successful data programs require concerted, sustained,
properly-informed, and coordinated effort.
Thomas C. Redman, “the Data Doc,” is President of Data
Quality
Solution
s. He helps companies and people, including start-ups,
multinationals, executives, and leaders at all levels, chart their
courses to data-driven futures. He places special emphasis on
quality, analytics, and organizational capabilities.