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Part 3—Building robust data analytics capabilities in the engineering and construction industry

Power of trust in analytics

© 2019 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. The KPMG name and logo are registered trademarks or trademarks of KPMG International. NDPPS 874215

Contents

About the authors 2

Power of trust in analytics Can predictive analytics solve the engineering and construction industry’s most challenging problems? 4

A snapshot of the predictive analytics opportunity in E&C Striving for better prediction, prevention and efficiency 6

First steps: Developing predictive analytics capabilities Key activities and outcomes 8

Building a predictive model in greater detail Understanding data’s value, location, analysis and benefits 10

Prediction is possible A case study 14

Next steps: Putting predictive analytics to work Considerations for successful implementation 16

Summary Staying ahead of disruption by building trust in analytics 20

KPMG’s experience in the engineering and construction industry 21

Contact us 22


1Power of trust in analytics

About the authors

Clay Gilge, principal, leads KPMG’s Major Projects Advisory practice in the U.S. and has more than 20 years of industry experience and research. He has been at the forefront of KPMG’s efforts to advance industry-leading methods and tools to objectively assess and benchmark the maturity of construction programs, and he is on the cutting edge of applying advanced data and analytics to improve transparency and leverage the vast data sets associated with major capital projects and programs.

Colin Cagney, Major Projects Advisory director, has more than 15 years of engineering and construction experience. He has executed some of our leading construction data and analytics initiatives, including programs for Fortune 500 companies across North America, Asia, and the Middle East. Prior to joining KPMG, Colin worked in the E&C industry with responsibility for delivering large and complex projects.

Gregory A. Koenig, lead specialist data scientist, works in the KPMG Lighthouse Center of Excellence for data and analytics where he develops solutions that incorporate aspects of cloud computing architectures, big data engineering, and machine learning and optimization models. Greg has a PhD in computer science and more than 20 years of experience spanning academic and research environments as well as business environments, and he leverages best-of-breed technologies to create business value and impact.


Power of trust in analytics seriesOur three-part series explores how engineering and construction (E&C) executives including CEOs, COOs, and CIOs, in addition to Chief Data Officers, can effectively leverage data and analytics to build trust into their decision making and gain a competitive advantage in their industry.

The third installment focuses on how E&C organizations can establish industry-leading analytics capabilities, including advanced analytic dashboards and predictive models that empower project personnel with data-driven insights to make better decisions.

Did you miss Part 1—Understanding your data or Part 2—Leveraging existing data? You can access this series as well as thought leadership surveys and other helpful content on our website at “The Power of trust in analytics.”



https://read.kpmg.us/powertrust-analytics

Can predictive data analytics solve the engineering and construction industry’s most challenging problems?

Power of trust in analytics

With that knowledge, the superintendent quickly adjusts the toolbox talk agenda to focus on these imminent safety issues. Days go by, and no incident occurs.

A superintendent arrives at the construction site to find a blinking red light on the project’s daily dashboard. She knows that light indicates a higher likelihood of a safety incident within the next few days—but what, and why?

Predictive data analytics at work

She dives into the next level of detail and learns that a combination of poor weather conditions, the presence of a certain subcontractor on-site, and recent inspection results indicating workers aren’t wearing their gloves is resulting in a 40 percent increased risk for hand injuries and falls.

40%


This scenario may sound a bit like science fiction but is in fact a reality for many companies. (See case study “Prediction is Possible,” on pg. 12). When E&C organizations have the right data analytics capabilities in place, they can avoid incidents and dramatically increase the safety of the construction site.

Of course, predictive safety analysis is just one use case. E&C organizations are successfully applying predictive analytics to enhance business development, increase project performance, mitigate risk, monitor compliance, and improve their bottom line.

A significant majority of engineering and construction executives responding to a recent KPMG survey said their organizations will be “data-driven” within the next five years. This includes plans to routinely use data analytics and predictive modeling for project planning and monitoring.

Yet E&C companies still have urgent business priorities, including executing projects and turning around troubled ones, increasing efficiency, and generating new business—all while operating on slim margins. Establishing a

predictive data analytics program is no small endeavor, which in part explains the disconnect in the survey between the large percentage of E&C leaders who plan to be data-driven in the near term, and the smaller percentage who have actually launched robust programs to date.

Standing up a data analytics program and, as importantly, embedding predictive analytics into everyday operations, requires an iterative process of development, deployment, measurement, and refinement. Each organization has a unique set of business objectives and practices, and there’s no one-size-fit-all approach to standing up a successful program.

Fortunately, E&C organizations can choose from a variety of techniques and tools for developing predictive analytics, and this paper provides a range of possibilities for beginning your organization’s journey.

Here we will explore current data analytics practices and their potential in the E&C industry, as well as steps toward developing a predictive model and how to operationalize the end result.

KPMG Global Construction Survey 2019

of engineering and construction executives say their organizations will be data-driven within the next five years.83%

do not perform data analytics across all projects.52%

use only basic data analytics.32%

are using advanced data analytics.14%

have implemented artificial intelligence (AI).1%

Is your construction firm future ready?



A snapshot of the predictive analytics opportunity in E&C

Historically, E&C project performance has heavily relied on the human element: exceptional project leadership and judgment across all levels and functions of the project team. While that will always be true to a degree, an opportunity exists to increase the consistency of strong employee-driven performance through data-driven decisions and actions.

Beyond safety, there are numerous opportunities for predicting and preventing the following:

Operational disruption

Quality and warranty issues

Financial performance issues early in the project’s life

Schedule activity delays

There are also significant opportunities to improve, automate, and streamline processes:

Developing early cost estimates and schedules based on past projects

Cost forecasts

Logistics decision optimization

Procurement timing optimization

Design automation and optimization

Instead of expecting project personnel to make decisions based on their limited historical experience and gut instinct, they can have all of their organization’s experience—and potentially that of the broader industry—at their disposal.

The superintendent in our example has near-real-time insight into the key factors that correlate with safety incidents, their status at that specific point in time, and a highly reliable indicator on safety risk. She doesn’t have to rely exclusively on past experience and judgment, but

rather, can combine personal understanding with the complete knowledge and history of the organization across all projects and over many years, improving the decisions made onsite.

With additional data from the industry or from a combination of multiple organizations, the superintendent is able to make decisions based on an even broader set of experience and knowledge.


All of this is possible today and quickly becoming a reality for more and more organizations. With new technologies, the opportunities to leverage advanced data analytics, automation, prediction, and optimization will increase significantly. On-site tracking sensors, drones, and robotics all can contribute more data for even better predictive capability.

Based on our Global Construction Survey, the most innovative E&C leaders are way ahead in adopting new technologies. As the kinks are worked out and benefits realized, these technologies will quickly become commonplace among the broader industry.

For now, the top 20 percent of E&C organizations identified by our survey have already implemented a number of innovations directly and indirectly related to data analytics.

Percentage of global construction companies deemed “innovative leaders” that have implemented each technology

N=155Source: KPMG Global Construction Survey 2019. To compile the Future-Ready Index, we looked at survey participants’ responses to 12 key questions relating to governance and controls, technology and innovation, and people. Based upon responses to these questions, the index yields a single score (between 1 and 100) to represent how effectively an organization embodies the core capabilities necessary to become efficient, diverse, and high-performing. Those scoring in the top 20 percent were identified as “innovative leaders.” For more details, visit https://home.kpmg/xx/en/home/insights/2019/04/global-construction-survey-future-ready.html

0%

20%

40%

60%

80%

100%

CognitiveM/C

learning

RoboticsAIM/C engineering

& design

3-D printing

RFIDARAdvancedD&A

Smartsensors

VRDronesMobileplatforms

PMISBasicD&A

BIM

86%

45%

28%

38% 38% 38%

3%10%

7% 7% 7%0% 0% 0% 0% 0%

63% 63%

53%

33%

13%

24%29%

5%

13%7%

3%

16%

3% 5% 5%

83%79%

69%72%

59% 59%

48%

38%

28% 28%24%

10% 10%

45%

Innovative leaders (to 20%)

Followers (middle 60%)

Behind the curve (bottom 20%)



https://home.kpmg/xx/en/home/insights/2019/04/global-construction-survey-future-ready.html



First steps: Developing predictive analytics capabilities

Establishing an effective model requires a thoughtful approach that builds on several fundamental steps. Here is how engineering and construction organizations can successfully plan and launch their predictive analytics programs.

Investigate the size, scope, and

goals the project

Design and configure systems

for data storage and processing

Develop and deploy the predictive

analytics model

Conduct model trials and plan for

operational handoff

Phase 1

Phase 2

Key activities — Determine your primary objectives for the predictive analytics capabilities effort and align your work plans accordingly.

— Conduct a scoping workshop to identify critical success factors and KPIs.

— Agree on desired predictive outputs, and determine the overall scope of the project.

— Start collecting data so you can assess its quality.Key outcomes

— Alignment on critical success factors — Collection of key data to enable machine learning models

— Data diagnostics to explore data quality and relationships

— Value map prioritizing areas of highest return based on data availability

Key activities — Identify the feature variables or key characteristics to include in the data set for the initial analytics model, and develop a schema and database tables for organizing and classifying the data.

— Set up a structured data lake for storage, and then create the necessary scripts and extract, transform, and load raw data into the data lake.

Key outcomes — A structured data lake housing data streams

— A user and system configuration plan


Investigate the size, scope, and

goals the project

Design and configure systems

for data storage and processing

Develop and deploy the predictive

analytics model

Conduct model trials and plan for

operational handoff

The effective implementation of an innovation strategy requires executive leadership and support. Absent this, the organization will default to the low-risk strategy of maintaining the status quo. It is important that the company's leadership foster an environment where the investigation and adoption of new technologies is done within a framework that manages the downside risk in the event of failure.

—Berry MurphyFormer Senior Vice President Technical Services

Yamana Gold KPMG Global Construction Survey 2019

Phase 3

Phase 4

Key activities — Create the model, train it, and evaluate its effectiveness against KPIs.

— Augment the model by introducing additional feature variables, reevaluating again for performance and making adjustments based on functional trial results and stakeholder feedback.

— Identify the functional needs of the end user and mock up a draft of the user interface.

Key outcomes — Initial and final working models — User validation plan and results — User interface model

Key activities — Host workshops and demos with stakeholders to promote buy in, identify criteria, and determine how to operationalize capabilities across the organization.

— Develop and test user dashboards or model interfaces.

— Finally, develop a handover and training plan to ensure a successful rollout to user groups.

Key outcomes — Daily operations processes — Training artifacts — Hand-over and operations checklists



Building a predictive model in greater detail

While data scientists are typically responsible for building specific models, it’s helpful for E&C personnel throughout an organization to understand what kind of data is valuable, where to find it, how the data is processed, and what type of analyses and associated benefit they can expect.

E&C personnel are quite familiar with the examples of useful data for building models. Clearly, there’s a wealth of information already collected for safety reports, such as glove and hard hat use on a project site. But where the data comes from and how it “behaves” have different impacts on the model.

Both static and dynamic data are required for predictive models. Static data includes asset management system details that remain unchanged, such as the manufacturer, manufacture date, and operating parameters of a machine or part. This information can help predict the risk of failure based on age, for example.

These static fundamental inputs are then supplemented with details from different source systems that are more dynamic in nature, such as inspection or real-time sensor data.

Predictive models also use both structured and unstructured data from sources that vary across an organization, including procurement systems, asset management systems, and SMS/SCADA collection systems, as well as crowdsourced data from outside the organization.

Collecting data: Model inputs


Structured data is the most logical place to start when developing any predictive model, as it is the most accessible and requires the least amount of effort to structure and load into the model.

Until recently, unstructured data was often only useful in very specific situations due to the extensive effort involved with accessing and restructuring it into a usable format.

With the advent of new technology, unstructured data—such as asset inspection reports that describe conditions using free-form text—is a viable option in predictive models.

In these cases, natural language processing models can produce additional predictive

outputs regarding the likelihood of an event occurrence,

as well as the specific type of event based on the narrative.

However, despite the new tools and techniques, using unstructured data can still be very costly and time consuming if not

managed properly.

Crowdsourced data is the most overlooked. Industry organizations and state, local, and federal departments are two examples of sources that can be accessed for free or for a small fee.

Crowdsourced data is particularly good for models that either have a very low incident rate (liquid leaks, gas explosions, wildfires) and require larger data sets, or in situations where outcomes are highly impacted by external factors such as weather, commodity prices, etc.

Finally, models must be trained on historical data that looks back and encompasses both past data inputs and the actual outcomes associated with it. Typically, such historical data is available from one or more data warehouses or data lakes within the organization.

Structured — Cost reports — Safety metrics — Schedule KPIs — Estimates to complete

Unstructured — Daily logs — Inspection reports — RFIs — Submittals

Crowdsourced — Weather data — Commodity pricing — Regulatory issues — Traffic/logistics data — Claims/legal filings

The challenge is often knowing how to link various structured data sets together, as well as which data to leverage and how much.



Analyzing data: Model outputsOnce the data is collected, artificial intelligence and automation allows E&C organizations to process the massive amounts of information with speed and accuracy that can’t be matched by humans. And through machine learning, computer-driven models can identify patterns, predict outcomes, and continue to improve output as more data is collected, all with minimal human intervention.

Model output is often the prediction of some type of event, such as a safety incident or asset failure. Because these types of events are more rare than common, models often predict nothing will happen—“nothing” is the predictive outcome.

At the same time, data inputs to machine learning models are typically skewed toward nonevent data points; we have far fewer data points corresponding to event occurrences to feed into the model.

Models that have a higher false positive rate—predicting an event will occur when it does not—are of course generally favored above models that have a higher false negative rate.

The consequence of false positive predictions are usually low, in the form of additional scrutiny (e.g., additional safety briefings, additional inspections, etc.), compared to the potential for significant impact on business and safety of failing to anticipate an event.

In some cases, this additional scrutiny might be in the form of a second-level model, such as one that uses deep learning to review the false positives in more detail for a more comprehensive predictive analysis.

Below is a simple depiction of the various data inputs, refinement and outputs of a typical predictive data analytics model.

Making predictions: A sample model

Historical data (Partial)

— Static (structured)

— Dynamic — Crowdsourced

Historical data (Expanded)

— Static (structured/unstructured)

— Dynamic — Crowdsourced

Real-time data — Static — Dynamic — Crowdsourced

Model developmentCreated based on a subset of historical data

Refinement & TestingTested and refined based on reminder of the data

PredictionsBased on real-time data


Prediction is possible

Case study—One company’s path to forecasting safety incidentsChallenge: A top-40 U.S. energy and natural resources sector contractor sought to grow its business and become a leader in construction data analytics. Like most engineering and construction organizations, the contractor was challenged with a voluminous and unintegrated data set along with significant data quality issues.

For example, the safety data set included: Safety data challenges included:

Solution: Over the course of three months, the company began the phased approach of developing predictive analytics capabilities.

Investigate — Performed a 43-element data diagnostic to determine the company’s current state relative to industry-leading practices.

— Created a value map to assess data analytics opportunities both qualitatively and quantitatively. The qualitative assessment was based on stakeholder workshops and KPMG experience, while the quantitative assessment was based on more than 50 key portfolio metrics.

Develop and deploy — Executed on quick win opportunities, including analytics for safety, financials, schedule, work acquisition, and the contractor’s self-perform subsidiary.

Conduct operational handoff — Developed a data roadmap which outlined a two-year strategy to improve data maturity and enable data insights to drive business performance.

Design and configure — Implemented a cloud-based data analytics environment.

— 600 projects

— 40 million labor hours

— 126 subcontractors

— 116,000 inspections

— 4,000 incidents

— 1,000 claims

— Inconsistent use of project names and numbers causing challenges linking datasets

— Inability to link incidents and claims

— Incomplete labor data

— Inconsistent use of fields

— Many recent changes between systems and tools

Phase 1

Phase 2

Phase 3

Phase 4


Results: — Developed a model capable of predicting safety incidents three days in advance

with 89% accuracy.

— Identified up to $40 million of near-term margin opportunities.

— Identified up to $500 million in overall project value opportunities.

— Helped the company stand up their own internal data analytics team.

This contractor, like many others, was unable to pursue data analytics opportunities because of poor data. The organizations feel caught in a vicious downward cycle, because they also can’t improve their data without a vision or tangible results to build momentum and drive change within their organization.

Fortunately, with the recent advances in data analytics discussed throughout this paper, there is more value in current data than ever before if your organization has the right framework and takes the right steps. Focusing on quick wins helps create momentum within the organization to reverse the downward spiral, facilitate immediate value generation, and increase long-term capability due to improved data governance.



Next steps: Putting predictive analytics to work

Developing a great predictive analytics program is one thing, operationalizing it and leveraging it broadly across the organization is another challenge. Key items to consider include:

Establish processes for ongoing data management. The challenge of setting up a live environment that leverages data from multiple systems across a broad user base and is updated in real time is a much bigger challenge than a pilot or sandbox environment using offline data.

Protect the data. As data volume, systems complexity, and user base diversity continue to evolve, security will remain a major concern. Be sure to include experienced data access management and cybersecurity expertise on your data analytics team.

Create a flexible, easy-to-use data analytics interface and demonstrate positive results. From project managers to site superintendents and journeymen, construction personnel are starting to see their industry disrupted by innovation, with both positive and negative impacts on their jobs. If these key employees don’t benefit from the dashboard and analytics your organization is creating, they simply won’t use them. Beginning day one, find creative ways to engage personnel and demonstrate the positive impact of the analytics program.

Set up resources for ongoing maintenance, enhancements, and troubleshooting. Predictive analytics tools and dashboards typically have some issues that require support. Organizations should plan accordingly to make sure tools stay in use and program efforts are not sabotaged by user frustration.

Explore potential external value. Some of the leading E&C organizations are positioning themselves to leverage their internal investments in data analytics capabilities across the industry through start-ups, license agreements, and other teaming arrangements.

Incorporate the model and output into existing processes. In the safety example cited at the beginning of this paper, we saw how the blinking red light prompted the superintendent to adjust the approach to upcoming safety activities. To achieve such an outcome, organizations must ingrain the predictive tool into existing workflows.


Like the subject matter experts, functional and business process owners bring their specific knowledge to the team. For example, a predictive model for safety should include input not only from safety subject matter experts, but also from individuals responsible for safety processes including systems for reporting safety incidents and inspections, as well as those responsible for tracking activities such as safety orientation, safety training, safety job walks, etc. These team members can help expedite the data gathering and structuring and help plan the operational roll out or pilots under development.

No matter what type of predictive model an organization is trying to develop, it is critical to have individuals on or supporting the team that have deep knowledge of the focus area. Not only can they quickly identify key source data, they also can help develop hypotheses and problem solve challenges. Finally, these individuals can help ensure that the model makes sense, can be effectively operationalized, and will add value to the organization.

While it may be tempting to leverage engineers with strong analytical and statistic backgrounds to develop a robust predictive model, it is important to have data scientists on the team. More than just bringing data science backgrounds to the table, they have hands-on experience developing predictive models that are not only theoretical but have been launched effectively.

Because this process requires a stable, secure, and functional data environment, team members with IT systems and cloud expertise are a must. These individuals can help to not only set up the data environment, but operationalize the predictive model and work through the key technology considerations as well.

Subject matter experts Data scientists IT systems and cloud engineers

Functional and business process owners

Keys to successEstablish integrated teamsUntil recently, a majority of transformational or internal technology innovation initiatives at E&C organizations were led either by high-performing

engineers or project managers, or by individuals from the IT department. The concept of data scientists or chief innovation officers participating in or even leading a multi-disciplinary team to leverage technology for improved safety, quality, scheduling, labor productivity, etc. was completely foreign.

This is rapidly changing as E&C organizations have realized the value of collaboration and innovation in not only how they execute projects for their clients, but also how they execute their own internal projects. Tightly knit cross-functional teams have demonstrated they can drive faster iteration that results in an end product more likely to create meaningful business value.

Many structures and models can be successful as long as a few key participants are present:

Project Lead

1


Gain buy-in from the fieldEfforts to improve project performance and data quality require the buy-in of all project personnel, including those directly on project sites.

In fact, data analytics initiatives should be driven by or organized around the ultimate impact on field operations, which project employees understand and will buy into. These employees often both contribute data to and use the results of the model.

While there are various ways to accomplish this objective, roadshows to gather input from both business and geographic leaders and field representatives help make sure the data and analytics will be impactful.

Following up with communication and messaging focused on meeting the field’s objectives—in contrast to simply meeting management’s directives—will help drive commitment.

Start with large, easily accessible data setsDeveloping a good predictive model is as much about the quantity and quality of the data as it is about the approach to developing and operationalizing the predictive model. Those

models that are based on small, low-quality data sets produce results with limited value or practical use.

However, don’t get bogged down by concerns about gathering perfect or well-organized data.

Rather, focus on the outcome—a usable prediction—and start with the large volumes of typically high-quality data such as safety, schedule, and estimating data that you already have. Build and test the model, and then move on to collect additional information to hone the results.

Keep in mind that some sources and types of data that could be useful may not come to light until after the more typical or fundamental inputs are run through a predictive analytics model.

For example, the addition of crowdsourced weather data to a predictive model for the construction industry built on internal data can enhance safety predictions, as workers tend to get hurt more during extreme weather conditions.

Adding such additional information can make the model even more effective, and this iterative process can continue to produce more effective results with each turn, what we call “gaining insights from insights.”

2

3



Launch targeted pilotsWhile it may seem obvious, it is surprising how many organizations roll out new capabilities without going through a sufficient pilot to ensure they work properly and achieve objectives. Implementing a targeted pilot is not only a great quality control, it also is a learning

opportunity that can generate internal buzz and excitement as well as have a dramatic impact on the final outcome.

When structuring targeted pilots, think about:

— Timing—The timing of the pilot is often as important as the pilots themselves. Pilots need to be early enough so it is easy to quickly adapt and make changes, but not so early that the concepts aren’t fully formed and the benefits haven’t been well defined.

— Engagement—The target group, process, or area must have people who are engaged and determined to help the pilot succeed.

— Leadership and organization—The structure and leadership of any pilot requires a resilient and adaptable team that can quickly overcome roadblocks and make informed decisions in an efficient manner. Furthermore, leading a pilot requires not only experience but credibility, which can be a challenge to find in one person.

— Focus—Often pilots fail simply because they were too ambitious or took too long, resulting in the cancellation of broader roll outs due to costs or obsolescence. To avoid this end, pilots must be very focused and structured, with clear timelines, objectives, and roles.

4

Summary: Staying ahead of disruption by building trust in analyticsE&C leaders understand the power of predictive data analytics and, for many, the need to improve their capabilities. Where they differ is in their approach. No organization is the same, and there is no one right way to standing up predictive analytics capabilities.

However, by following a few key steps for establishing a program and paying particular attention to integrating capabilities into their operations, E&C firms can transform from organizations that perform basic data analysis into industry innovators that use predictive analytics to make better decisions.


KPMG’s experience in the engineering and construction industry

KPMG’s multifunctional teams combine E&C industry veterans and subject matter professionals with data scientists to help organizations accelerate their data analytics objectives. Our knowledge and experience allows us to tailor programs to meet the unique needs of E&C organizations.

We help companies leverage data to analyze safety, project costs, schedules, business opportunities, and many other core E&C operational processes.

When E&C leaders turn to KPMG for advice, they do so because our professionals understand the industry on a local, national, and global level.

KPMG serves nearly half of the top 50 U.S. engineering and construction firms named by Engineering News-Record.

Our goal is to help E&C firms generate real business value from their vast amounts of data.

Data analytics is just one of the many services we tailor for the E&C industry.

Our diverse practice includes certified public accountants, professional engineers, architects, project managers, owner representatives, contract and procurement specialists, finance and tax professionals, business valuation specialists, cost estimators and specialists, certified fraud examiners, and forensic technology specialists.

These KPMG professionals—2,000 professionals in more than 40 countries worldwide—provide strategic insights and guidance wherever our clients operate.

We work with professionals across E&C organizations to help stand-up data analytics functions, identify opportunities to leverage data, assess current states, implement end-to-end data analytics environments, deliver quick wins, and develop roadmaps to introduce industry-leading practices.



Colin CagneyDirector, Major ProjectsT: 206-913-4984 E: [email protected]

kpmg.com/socialmedia

Clay GilgePrincipal, and KPMG’s Major Projects Advisory practice leadT: 206-913-4670 E: [email protected]

Gregory A. KoenigLead Specialist, and Data ScientistT: 865-293-3607 E: [email protected]

Contact us

Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates or related entities.

© 2019 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. Printed in the U.S.A. The KPMG name and logo are registered trademarks or trademarks of KPMG International. NDPPS 874215


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power of trust in analytics - kpmg...be data-driven in the near term, and the smaller percentage who...

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