Winning the Hardware Software Game book Winning the Hardware-Software Game

Using Game Theory to Optimize the Pace of New Technology Adoption

Innovators of new technology systems requiring users to combine both hardware and software components often face delays in adoption of their new systems.  Users will not buy the hardware until enough software or content is available, while at the same time software providers will not provide content until enough users have adopted the new system.  This book examines the dynamics of this adoption process and provides methods for optimizing the pace of adoption of new technology systems.     Read more...

The IoT Ecosystem Contains a Vast Array Of Components

The Potential Value of Iot Will Increase Exponentially Over Time

Barriers Are Currently Impeding Adoption of Iot

How the Evolution of Iot Will Proceed

Why Be an Early Adopter?

What Will Be Important for Success in Iot?

 

Introduction 

Vasyl Mylko of SoftServe notes that the Internet of Things is emerging at the intersection of Semiconductors, Telecommunications, and Big Data, through the evolution of their respective laws (see Figure 1)

  • Moore’s Law observes that semiconductors have been achieving a 60% increase in computer power every year.
  • Nielsen’s Law observes that Internet bandwidth has been achieving a 50% increase in speed every year.
  • Metcalfe’s Law observes that telecommunications networks increase in value with the square of the number of nodes
  • Law of Large Numbers observes that the average obtained from a set of data approaches the true value as the size of the dataset increases.

Charles McLellan, in “The internet of things and big data: Unlocking the power,” describes more directly how the confluence of trends inspired by these laws is enabling the rise of IoT:

A huge number of 'things' could join the IoT, whose recent rise to prominence is the result of several trends conspiring to cause a tipping point: low-cost, low-power sensor technology; widespread wireless connectivity; huge amounts of available and affordable (largely cloud- based) storage and compute power; and plenty of internet addresses to go round, courtesy of the IPv6 protocol…

 

Figure 1

1 iot intersection

When you bring these three sets of trends together, you get the emergence of

  • A proliferation of devices that are
  • Able to collect vast amounts of information, which can be
  • Aggregated and analyzed to
  • Create new insights that
  • Enable providers to enhance the value and efficiency of services they are able to provide consumers.

More formally, Wikipedia defines the Internet of Things (IoT) as

the network of physical objects—devices, vehicles, buildings and other items—embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data. The IoT allows objects to be sensed and controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit.

 

The IoT Ecosystem Contains a Vast Array of Components.

The IoT industry consists of a large ecosystem of component products and services, provided by an assortment of suppliers. Bruce Sinclair at Iot-Inc. provides a clear, simple view of the IoT ecosystem, as shown in Figure 2. He divides the ecosystem into four general sets of components:

  • HW-Defined Products: Beyond the physical product, the hardware-defined product consists of sensors/actuators and an embedded system.
  • SW-Defined Products: The software-defined product consists of a product model and the application that runs it. The model virtually represents what the product is and can do. The app provides product intelligence and orchestrates communication between the model and sensors, and interfaces with external systems and other IoT products.
  • Network Fabric: The product’s network fabric consists of one or more IT and OT networks generally bridged by wireless connections and network protocol translation.
  • External Systems: The IoT product is networked to external systems and other IoT products to augment its functionality.

ο  Analytics: Analytics examine the product’s sensor data and model to provide insights on how to improve the customer’s product and business. Analytics is controlled by the app in real-time or run as a post process on its data at rest.

ο  External Data: e.g. weather conditions

ο  Business Systems: Business systems such as CRM and PLM, as well as ERP and SCM are interfaced to exchange enterprise operational information.

ο  IoT Products: Other connected IoT products.

Figure 2

2 iot ecosystem

Source: Bruce Sinclair, “IoT Tech for the Manager” 

The Potential Value of IoT Will Increase Exponentially over Time

As we see, generating the full, potential value of IoT will entail large, complex ecosystems, containing many different components, that all work well together. Of course, the actualization of IoT will be realized over time, in stages or steps, where potential for new value generation will increase exponentially with each step.

Various conceptualizations of IoT stages are presented in Figures 3 – 5. The conceptualizations generally encompass the following stages:

1.  Understanding what happened in the past;

2.  Decreasing the costs of what is happening now;

3.  Expanding the value of what is happening now; and

4.  Creating new value by anticipating what will happen in the future

Figure 3A

3A value chain

Figure 3B

3B value chain 2

Source: Vitria, “The Analytics Value Chain”

 

Figure 4

4 iot adoption path

Source: WEF, “Industrial Internet of Things: Unleashing the Potential of Connected Products and Services”

 

Figure 5

5 Operational Experience Journey

Source: Schneider Electric Operations Management Systems Evolution

 

Barriers Are Currently Impeding Adoption of IoT

There are various barriers to companies’ adoption of IoT. The main barriers include

  • Data privacy, security, and ownership: Companies are struggling with insuring customers that data will be secure and their privacy will be maintained. There is also uncertainty regarding who owns each piece of data collected from the vast array of interlinked sources and sensors.
  • Lack of interoperability of system components: There are numerous standards that need to be established so that system components can seamlessly work together to collect data. Industry participants are working on any number of industry standards, propriety systems, and open source systems. The battle will be lengthy.
  • Uncertain return on supplier investment: Companies are struggling to find compelling propositions for how IoT systems can generate lasting economic value.
  • Installed base of long-lasting industrial equipment and infrastructure: Most industrial infrastructure has long life spans. Companies are thus faced with the alternatives of waiting until current infrastructure wears out or prematurely replacing existing equipment with new equipment fitted with sensors. The good news is that new innovations may emerge to enable the bridging or retrofitting of existing equipment with sensors and actuators that will enable use of IoT systems sooner rather than later.

 

How the Evolution of IoT Will Proceed

The healthcare industry, a subset of the larger IoT industry, has been struggling with the adoption of electronic health records (EHRs) for some time now. The adoption of EHR systems provides a perfect case study of the difficulties of adopting IoT programs in the current technological and economic environments. An examination of the evolution of EHR adoption should thus help inform us as to the expected path of evolution of more general IoT industry systems.

Adoption of EHR Systems

The HITECH Act was enacted in 2009 to encourage industry adoption of electronic healthcare records (EHRs). Incentive payments were offered to providers as a means of offsetting the costs of adoption EMR systems. More importantly, though, from an adoption standpoint, penalties, in the form of a percentage of Medicare reimbursements, were scheduled to be enacted starting in 2015 for physicians who failed to adopt EMR systems and show “meaningful use.”

The HITECH Act set meaningful use of interoperable EHR adoption in the health care system as a critical national goal and incentivized EHR adoption. The "goal is not adoption alone but 'meaningful use' of EHRs —that is, their use by providers to achieve significant improvements in care."

Title IV of the act promises maximum incentive payments for Medicaid to those who adopt and use "certified EHRs" of $63,750 over 6 years beginning in 2011... Doctors who do not adopt an EHR by 2015 will be penalized 1% of Medicare payments, increasing to 3% over 3 years.

Figures 6 and 7 present the adoption paths of EHR systems by hospitals and Figure 8 presents the adoption paths of EHR systems by office-based physicians.

Figure 6

6 hospital emr adoption

Source: ONC Data Brief, “Adoption of Electronic Health Record Systems among U.S. Non- Federal Acute Care Hospitals: 2008-2014”

 

Figure 7

7 hospital emr adoption functionality

Source: ONC Data Brief, “Adoption of Electronic Health Record Systems among U.S. Non- Federal Acute Care Hospitals: 2008-2014”

 

Figure 8

8 physician emr adoption

Notes to Figure 8

  • Physicians adopted a Basic EHR if they reported their practice performed all of the following computerized functions: patient demographics, patient problem lists, electronic lists of medications taken by patients, clinician notes, orders for medications, viewing laboratory results, and viewing imaging results.
  • Any EHR system is a medical or health record system that is either all or partially electronic, and excludes systems solely for billing.

 

Figures 6 – 8 reveal two general themes in healthcare providers’ adoption of EHR systems:

1.  A majority of healthcare providers adopted EHR systems between the time of enactment of the HITECH Act and the time in which penalties for non-adoption were scheduled to begin.

2.  The majority of EHR systems adopted by providers are basic, rather than comprehensive, systems.

So we know that a majority of providers have adopted EHR systems, but how well have they actually fared with them? As with most IoT systems, there are two key issues associated with being able to generate value from EHR systems: (i) interoperability of EHR systems across providers and (ii) providers’ ability to generate meaningful use from the data collected by EHRs.

Let’s start with the state of interoperability in EHR systems. KLAS published a report, “Interoperability 2015,” on EHR systems with the purpose of “providing a clear view of interoperability for interested parties across the nation, including policy makers and national leaders.“ KLAS found that EHRs “are not yet usable or effective” for exchanging “data between systems from different EMR vendors.”

Abstract

Today there are very simple and technically successful ways to exchange patient data between systems from different EMR vendors. Unfortunately, they are not yet usable or effective for physicians. Interoperability is a complicated issue, due to the complex nature of healthcare, the variety of provider use cases, the multi-branded development of healthcare technology, the strict nature of privacy laws, the promulgation of too many incomplete standards, and sometimes ineffective incentives for both providers and vendors. Impacted industry players seem to agree that poor interoperability is overall a result of market immaturity, with no single culprit or character to blame.

Most provider organizations interviewed have multiple connections to outside records through a variety of connection types, but they report frustration with high costs, complex connections, extreme variation in the value of health information exchanges, and unresolved legal/privacy concerns. Vendors and providers are aligned as to what the key issues are that are holding back interoperability, including standards, patient identification, participant willingness, privacy laws, and security.



What Is Interoperability

[I]nteroperability was defined in this report for simplicity’s sake as

The ability of two or more healthcare entities to exchange and incorporate information with precoordination and context such that the information has utility in improving patient care.

Note that this definition includes connections between organizations using the same EMR vendor—what some call “intraoperability.”

As for achieving “meaningful use” of EHR systems, HealthIT.gov reports that as of February 2015, over half of hospitals and as of January 2016, over three-quarters of physicians who have received funding under the HITECH Act have achieved “meaningful use.” Meaningful use for hospitals is defined as “Stage 1: Data capture and sharing.” It is not clear which stage of meaningful use is being reported for physicians, but I assume it is also Stage 1, where stages 2 and 3 are defined as “Stage 2: Advance clinical processes” and “Stage 3: Improved outcomes.”

Based on the current state of interoperability and meaningful use achieved by EHR systems, it appears that it will still be quite sometime yet before EHRs become interoperable and able to generate value beyond data reporting. I also note that two main issues inhibiting providers’ ability to generate real meaningful use from EHR systems six years after the initial mandate  – lack of system interoperability and data privacy and security issues –  are the same barriers that are impeding adoption of general industry IoT systems. This does not bode well for timely adoption in the more general IoT industry.

Differences between EHR Systems and General IoT Systems

There are a few important differences between EHR systems and more general IoT systems that could affect the use of EHR system adoption as being used as a valid guide for predicting the adoption of more general IoT systems.

  • The federal government mandated adoption of EHR systems by healthcare providers, with penalties for failure to adopt and show meaningful use within a pre-specified period of time. General IoT providers face no such mandates. Without a mandate to adopt, the pace of adoption of general IoT systems might lag the rate of adoption of EHR systems.
  • Providers are given specific requirements for the reporting of EHR information. General IoT systems suppliers, on the other hand, will have to figure out on their own which data are important for collection and analysis. Not having clear guidelines for determining which data will be important increases the risks of IoT systems for company adopters. This might decrease the pace of adoption of general IoT systems relative to that for EHR systems.
  • A large portion of the potential value achieved from EHR systems depends on the ability of EHRs to interconnect and share information across providers. In contrast, some value from IoT systems can be generated independently, by particular companies, without having to interconnect to other companies. The fact that IoT adopters will be able to generate some value within their own systems, without having to interconnect to other companies’ systems, decreases the risks of adoption relative to those of EHR systems. This suggests that adoption of IoT systems might advance more rapidly than adoption of EHR systems.

Early Adoption of General IoT Systems

So now that we’ve seen the pattern of adoption of EHR systems by the healthcare industry, what can we say about the initial path of adoption of more general IoT systems?

As previously indicated, participants in the Big Data and IoT markets still face significant hurdles to establishing programs and generating value from them, where one of the bigger barriers to adoption is the lack of industry standards. Currently, there is a standards war that is playing out, in classical form. Colin Nagle provides a nice description of this current state of affairs in “A guide to the confusing Internet of Things standards world”

Google recently announced a new networking protocol called Thread that aims to create a standard for communication between connected household devices.<

If that description sounds familiar, that’s because it is. Thread joins similar collaborative efforts led by the likes of Intel, Qualcomm, GE and others in the race to establish standards for the Internet of Things, which is widely considered the next technology frontier.<

The complexity of these standardization efforts has evoked comparisons to the VHS and Betamax competition in the 1980s. Re/Code’s Ina Fried wrote, “there’s no way all of these devices will actually be able to all talk to each other until all this gets settled with either a victory or a truce.” In the meantime, we’re likely to see some debate among the competing factions.<

“If this works out at all like past format wars, heavyweights will line up behind each different approach and issue lots of announcements about how much momentum theirs are getting,” Fried wrote. “One effort will undoubtedly gain the lead, eventually everyone will coalesce and then, someday down the road, perhaps all these Internet of Things devices will actually be able to talk to one another.”

Since standards have not yet been established in the IoT industry (Postscapes provides a nice layout of the various standards and protocols currently available for the IoT industry), early adopters face more risk than companies that choose to enter the market later, after standards have been established.

Generally speaking, new entrants into a market that involves standards face two options:

1.  New entrants may establish their own, proprietary standard(s) and accept a larger piece of a potentially smaller market pie. This is the riskier path.

2.  New entrants may adopt current industry standards and compete for a (smaller) piece of a much bigger pie. This is the less risky path.

Noteworthy is the fact that, as per Metcalfe’s law, companies will generally face greater value potential from larger IoT systems. This suggests that for most companies, conforming to industry standards (or planning to conform once standards have been established) might end up having greater value potential – by eventually being able to connect to more other things – than establishing a proprietary standard. As such, perhaps the best path for early adopters to take for their initial efforts would be to use protocols that are as adaptable or system-neutral as possible, and then migrate to whichever standards become established later down the line.

Which industries are early adopters most likely to draw from?

A Big Data program lies at the heart of any IoT program. Datamation reiterates this concept in, “Why Big Data And The Internet of Things Are A Perfect Match”:

Big Data capacity is, in essence, a prerequisite to tapping into the Internet of Things. Without the proper data-gathering in place, it’ll be impossible for businesses to sort through all the information flowing in from embedded sensors. What that means is that, without Big Data, the Internet of Things can offer an enterprise little more than noise.

This implies that early adopters of IoT programs will fall into one of two groups:

1.  Companies that already have a Big Data program either up and running or in the works.

2.  Companies who don’t currently have a Big Data program, but who have the most to gain from an IoT program.

It’s difficult to find information on actual implementation rates of Big Data programs by industry. However, there is information available from McKinsey that indicates which industries have the most to gain from implementing Big Data programs. Presumably, industries with the most to gain have been the first to adopt. Figure 9, taken from Rasmus Wegener and Velu Sinha, “The value of Big Data: How analytics differentiates winners,” classifies companies by (i) the ease of capturing value from Big Data and (ii) the potential magnitude of value to be captured. Figure 9 implies that the companies most likely to already have Big Data programs are those in industries with large bubbles and/or those in industries located in the upper-right hand portion of the diagram, including manufacturing, healthcare, information, and finance and insurance.

Figure 9

9 ease data by industry2

Source: Rasmus Wegener and Velu Sinha, “The value of Big Data: How analytics differentiates winners”

McKinsey also provides estimates of the expected economic impact of IoT programs by industry, as presented in Figure 10. Figure 10 indicates that “Factories,” or companies that generate products and services, have the most to gain from IoT programs; so, companies in this industry might be early adopters. McKinsey defines Factories as follows:

We define our factories settings as dedicated, standardized production environments. This includes facilities for discrete or process manufacturing as well as data centers, farms, and hospitals. Indeed, the standardized processes in all these settings provide an opportunity to apply the same type of process improvements that IoT enables in a manufacturing facility.

Figure 10

10 distrn benefits by industry

Source: McKinsey Global Institute, “The Internet Of Things: Mapping The Value Beyond The Hype”

Why Be an Early Adopter?

Given the risks involved with establishing an IoT program sooner rather than later, many companies would probably be inclined to wait until standards have been set before jumping in. This begs the question as to why companies would choose to be early adopters of IoT programs. There are several reasons for doing so.

Big Data Companies Already Have a Head Start

Companies who already have Big Data programs have already established much of the infrastructure needed to make IoT programs successful. They can leverage their Big Data advantage to be early adopters of IoT and further increase their profit potential.

Early Adoption Might Create an Initial Competitive Advantage

Companies may become early adopters of IoT programs so as to gain advantages over their competitors. Yet, efficiencies generated early on from IoT programs will later be competed away as more companies adopt IoT systems. McKinsey Global Institute notes this in “The Internet Of Things: Mapping The Value Beyond The Hype”.

Early adopters may have an opportunity to create competitive advantage (through lower operating costs, the chance to win new customers, and greater asset utilization, for example), but later adopters may be able to gain those benefits at a lower cost. As IoT applications proliferate, investing in IoT is likely to become “table stakes” to remain competitive.

However, early adopters who continue to innovate or otherwise find novel ways to benefit from their IoT programs might maintain their advantages over later-adopting rivals. More from McKinsey:

Companies that use IoT in novel ways to develop new business models or discover ways to monetize unique IoT data are likely to enjoy more sustainable benefits.

Later Adopters Risk Losing Out to Early Adopters

As the uptake of IoT programs gains momentum, companies that put off adoption risk permanent loss of competitive advantage, both from old rivals who are earlier adopters of IoT systems, as well as from new entrants into the industry. Historically, early adopters and new entrants have been able to steal away not only market share, but the most profitable market share, from rivals who are slower to adopt new technologies.

 

What Will Be Important for Success in IoT?

Use of High Quality Technology and People

As illustrated in Figure 1, IoT requires simultaneous use of semiconductors, telecom networks, and Big Data. All three components must work together effectively and efficiently to provide the best IoT outcomes. The ability to extract maximum value requires using the highest quality hardware, software, data, processes, and personnel.

Rasmus Wegener and Velu Sinha reiterate this point in “The value of Big Data: How analytics differentiates winners”:

Leaders build up their analytics capabilities by investing in four things: data-savvy people, quality data, state-of- the-art tools, and processes and incentives that support analytical decision making … to build a high- performing analytics machine, you need to do all four well. Success in each capability depends on strength in the others.

More from Charles McLellan, “The internet of things and big data: Unlocking the power”:

The intersection of the IoT and big data is a multi-disciplinary field, and specialised skills will be required if businesses are to extract maximum value from it. Two kinds of people will be in demand: business analysts who can frame appropriate questions to be asked of the available data and present the results to decision makers; and data scientists who can orchestrate the (rapidly evolving) cast of analytical tools and curate the veracity of the data entering the analysis pipeline. In rare cases, the business analyst and the data scientist may be one and the same (valuable) person.

Engineering of Companies Around Data

For companies to be successful in the new era of data, they will have to reengineer their enterprises to adapt to the continually changing environment. WEF makes this point in “Industrial Internet of Things: Unleashing the Potential of Connected Products and Services”:

Real time for today’s Internet usually means a few seconds, whereas real time for industrial machines is often sub-millisecond. The engineering rule of thumb dictates that a 10x change in performance requires an entirely new approach, not to mention the 100x change that the Industrial Internet will likely need.

Newly redesigned companies must learn to base decisions on hard analytics, rather than on instincts or gut feelings. From EMC and IDC, “Executive Summary: Data Growth, Business Opportunities, and the IT Imperatives” :

There is an abundance of technical solutions, and successful early adopters. But organizations must adapt – and adapt fast, given that the digital universe more than doubles every two years. The foundation of that adaptation will be in the datacenter, but the rest of the organization, the one filled with people, tradition, culture, and habits, must also adapt.

Studies unanimously concur that companies who become data-driven significantly outperform their peers. Specific examples from the literature include the two cites below.

From Rasmus Wegener and Velu Sinha, “The value of Big Data: How analytics differentiates winners” :

We found that only 4% of companies are really good at analytics, an elite group that puts into play the right people, tools, data and intentional focus. These are the companies that are already using analytics insights to change the way they operate or to improve their products and services. And the difference is already visible. These companies are:

• Twice as likely to be in the top quartile of financial performance within their industries

• Three times more likely to execute decisions as intended

• Five times more likely to make decisions faster

Leaders build up their analytics capabilities by investing in four things: data-savvy people, quality data, state-of- the-art tools, and processes and incentives that support analytical decision making … to build a high- performing analytics machine, you need to do all four well. Success in each capability depends on strength in the others.

From Andrew McAfee and Erik Brynjolfsson, “Big Data: The Management Revolution” :

The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results. In particular, companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.

Ability to Efficiently Utilize Massive Amounts of Messy Data

IoT is about collecting massive amounts of data, both structured and unstructured, from a variety of sources. Companies must be able to figure out how to extract value from these enormous amounts of data, as Bob Violino describes in “The 'Internet of things' will mean really, really big data”:

The biggest hurdle facing organizations considering IoT deployments will be knowing what to do with the massive amounts of information that will be gathered.

At the same time, companies with efficient IoT programs will learn how to cull the data appropriately so they end up collecting, storing, and processing only the tiny fraction of data that actually provide real value. According to EMC and IDC in “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things,” the especially valuable data constitute a mere 1.1% (22% x 5%) of the digital universe.

The key is to find the part of the digital universe that is richer than others.



In 2013, only 22% of the information in the digital universe would be a candidate for analysis, i.e., useful if it were tagged (more often than not, we know little about the data, unless it is somehow characterized or tagged — a practice that results in metadata); less than 5% of that was actually analyzed. By 2020, the useful percentage could grow to more than 35%, mostly because of the growth of data from embedded systems.

?Of the useful data, IDC estimates that in 2013 perhaps 5% was especially valuable, or "target rich." That percentage should more than double by 2020 as enterprises take advantage of new Big Data and analytics technologies and new data sources, and apply them to new parts of the organization.



But the data needed to support business transformation in the era of the Third Platform is much messier. It tends to be unstructured, diversely formatted, of uncertain accuracy (and sometimes uncertain origin), of unpredictable value, and often flowing into repositories and demanding attention in real time.

Ability to Continually Find New Ways to Extract Value from Information

Successful IoT programs are not just about having connected devices or having “a sea of data.” Rather, the real value in IoT programs is being able to extract value from all the data generated by those connected devices. Konica Minolta describes this notion in more detail in, “The Genius of Things: Driving Real Value from the Internet of Things”:

A sea of data from connected devices simply gives rise to an ocean of noise. Instead, the outcome relies on the quality of the data available and your business’s ability to analyse it. To deliver real value, you will need to be able rapidly translate both structured and unstructured information from a myriad of sources into usable business intelligence.

A significant part of being able to extract real value from data is the need to customize IoT programs to meet the specific needs of each company. Bob O’Donnell emphasizes this thought in “IoT Will Drive Tech Outside of IT”, where his “customer”:

[T]he need for even more specialized and focused knowledge about each customer’s [i.e. company’s] business and how IoT can potentially drive benefits that are specific to that business is going to be essential for success.

During the initial stages of IoT adoption, companies will use data to improve the efficiency of their processes (see Figure 4). However, once enough companies in an industry have achieved this level of efficiency, doing so will transition from being a generator of competitive advantage into being a minimum requirement for industry success. Deloitte iterates this concept in “The Internet of Things Ecosystem: Unlocking the Business Value of Connected Devices”:

While cost-reduction and efficiency efforts can be valuable to a firm, the returns diminish over time, and the value is often competed away as competitors implement similar efficiency improvements.

After process efficiency has become necessary for mere survival, companies will have to rely on finding new ways to extract value from their IoT programs, in particular through innovative processes. Thomas H. Davenport reiterates this in “Competing on Analytics”:

At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the last remaining points of differentiation. And analytics competitors wring every last drop of value from those processes.

IoT promises to bring trillions of sensors, ten of billions of connected devices, and trillions of dollars to the global economy. Companies will spend billions of dollars establishing IoT programs in an attempt to generate value from the oncoming tsunami of data. The companies that will succeed in riding the wave will be those that (i) understand the importance of using high quality hardware, software, data, processes, and personnel; (ii) understand the importance of becoming data-driven organizations; (iii) are able to efficiently process massive amounts of messy data; and (iv) are able to continually find new ways to improve their processes and enhance the value they provide their customers.

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