using machine data analytics to gain advantage in the ... · achieving business value in a number...

©COPYRIGHT 2018 451 RESEARCH. ALL RIGHTS RESERVED.

Using Machine Data Analytics to Gain Advantage in the Analytics EconomyEUROPEAN EDITION

CO M M I SS I O N E D BY

O CTO B E R 20 1 8

A B O U T T H E AU T H O R

N A N CY G O H R I N GS E N I O R A N A LYST, A P P L I C AT I O N A N D I N F R A ST R U C T U R E P E R FO R M A N C E

Nancy Gohring covers application and infrastructure performance for 451 Research, including IT monitoring, application performance management and log management.

2CO M M I S S I O N E D BY S U M O LO G I C

About this paperA Black & White paper is a study based on primary research survey data that assesses the market dynamics of a key enterprise technology segment through the lens of the “on the ground” experience and opinions of real practitioners — what they are doing, and why they are doing it.


B L A C K & W H I T E | U S I N G M A C H I N E DATA A N A LY T I C S TO G A I N A DVA N TA G E I N T H E A N A LY T I C S EC O N O M Y

IntroductionEver since enterprises first embraced software, they have been collecting data for a variety of benefits. Yet organizations are just beginning to realize the tremendous value of that data, particularly in a business intelligence context. Companies have traditionally relied on employees to analyze enterprise application data for business intelligence. However, because the volume of data created and available for analysis has exploded in recent years, it has become increasingly difficult for human experts to identify the most valuable data on which to base their analysis. Also, as the benefits of data-driven decision-making become apparent, businesses are looking to embed the results of analytics across many more of their processes.

The recent emergence of new technologies including self-service analytics, machine learning and automation heralds a revolution toward a concept 451 Research thinks of as pervasive intelligence, where businesses can use their data to insert intelligence across many more business processes – in fact, potentially across all business processes.

We believe that the time value of data is an important factor in achieving pervasive intelligence, enabling a kind of continuous intelligence that’s inserted throughout and across business processes. Many types of data have a shelf life, and the utopian scenario is to compress the time between collecting data and taking the right action to zero. Time becomes increasingly important in an era when customer experience is paramount and where customer loyalty depends on that experience. Intelligent data analytics has the potential to empower businesses to rapidly evolve products and services to meet their customers’ needs.

Businesses that are able to participate in this analytics economy – translating their data into valuable intelligence that gives them competitive advantage – will survive and thrive. Those that don’t will be left behind. Many organizations recognize how crucial enterprise data is and plan to invest in capturing and analyzing it to drive their businesses forward. This is evidenced by 451 Research’s recent Digital Pulse survey where business intelligence was rated the top IT priority for 45% of respondents followed by machine learning and big-data processing.



Figure 1: Top three priorities for IT in 2018 Source: 451 Research’s Voice of the Enterprise: Digital Pulse, Budgets and Outlook 2017 Q: Are any of the following items top IT priorities for your organization in 2018? (Please select up to three.) (n=857)

Today’s software-driven businesses are awash in data that can be collected in big-data engines, analyzed using machine learning and surfaced to enable businesses to participate in the analytics economy. We believe that machine data is a critical source of data. It emanates from the many systems that complex businesses employ and has the potential to contribute to the pervasive intelligence that businesses need to thrive.

We set out to better understand the potential of machine data. Is machine data in fact an important source of fuel in the analytics economy? Do businesses recognize the role machine data can play in driving business intelligence? Are businesses that recognize the power of machine data leaders in their fields?

In Q3 2018, we surveyed 250 executives across the UK, Sweden, the Netherlands and Germany whose companies use machine data in some fashion. Some of the results of the survey surprised us – for example, companies are already using machine data analytics tools for many important business intelligence functions beyond those confined to the IT department. We also gained important insights into the shortcomings that are keeping some businesses from broader usage of machine data analytics. Finally, we discovered that modern businesses – those that have software-centric mindsets, as well as larger enterprises – are doing more than their less advanced counterparts to make the

45%Business intelligence

and visualization

29%Machine learning/

artificial intelligence

28%Big data

processing engines

n=857



most out of the intelligence available to them in machine data analytics tools. In short, we learned that today’s leading businesses recognize machine data analytics as key to helping them move faster, deliver top-notch customer experience and drive more intelligent decision-making.

WHAT IS MACHINE DATA?

Machine data is information that is generated from ‘things,’ delivering insight about processes and activities. Businesses collect machine data from devices such as servers, routers, applications and sensors in order to gain important insight about a range of issues – from whether the device is operating properly to how many end users are interacting with it.

Key Findings

• We found that while security was the number one use case for machine data analytics tools, improved customer experience topped the list in terms of value that machine data analytics brings. This represents an interesting correlation between customer experience and security, where businesses are discovering that the tools they use to comply with security and privacy regulations can at the same time drive improvements in the end-user experience.

• IT operations tops the list in terms of roles that are using machine data analytics, but a surprising array of other roles including security analysts, product managers, developers and customer support workers commonly use machine data analytics tools.

• The adoption of emerging technologies such as containers and microservices is inhibiting the ability to get the data needed for fast decision-making, but perhaps not to the extent that many had thought, suggesting that tools are evolving to meet the needs of modern enterprises.

• Software-centric companies are more likely to integrate their BI and machine data analytics tools, an indication that they understand the business value of machine data.

• Europe leads the US in terms of employing machine data analytics for security use cases and embracing SaaS machine data analytics tools, where US companies are more likely to have a greater number of machine data analytics users and to recognize the importance of machine data in meeting their goals.

• Software-centric companies are more likely to say that a wide variety of users, including head of IT, head of security, line-of-business users, product managers and C-level executives recognize the business value of machine data.



Businesses Recognize the Broad Potential of Machine Data AnalyticsMachine data has long been valued by IT operations teams, which rely on machine data tools to ensure optimal application performance and identify security risks, but the utility of machine data outside of IT traditionally has been less certain. In our survey, however, we discovered that businesses do, in fact, recognize the broader value of machine data. We asked respondents how important machine data was to their companies’ ability to meet their goals. Overwhelmingly, they placed high value on machine data, with 94% saying machine data is either ‘extremely important’ or ‘important’ to their company.

Figure 2: The importance of machine data to companies’ ability to meet goalsSource: 451 ResearchQ. How important is machine data to your company’s ability to meet its goals?

Although IT operations, the typical use case for machine data analytics, still tops the list in terms of ownership and usage of these tools, we were surprised at the wide array of employees in other roles who commonly use machine data analytics – a result that again emphasizes that companies do recognize the many ways that machine data can drive intelligence across the business. Security analysts, product managers and data analysts, some of whom may serve lines of business or senior executives, all appeared at the top of the list of the roles using machine data analytics tools.

54%40%

6%

Extremely important

Important

Somewhat importantn=250



Figure 3: Roles that use machine data analyticsSource: 451 ResearchQ. What roles in your company use machine data analytics tools? (Select all that apply)

We noticed some regional differences in terms of roles that interact with machine data. UK respondents were more likely to have users with data-centric roles in the company – including data, BI and security analysts – using machine data tools than their peers in other European countries. The Germans lag quite a bit behind in terms of CEOs using machine data tools; only 10% of Germans reported that their CEOs access the tools. By comparison, 38% of Swedish respondents and 33% of UK respondents have CEOs that use machine data tools.

Even though IT operations roles dominated among those using machine data analytics, respondents cited security as the number one use case. Given strict data privacy regulations in Europe, including the new EU General Data Protection Regulation, it’s a natural that security is a significant driver for machine data tools in the region. Respondents ranked business insight third – tied with troubleshooting – which may indicate that IT operations organizations are recognizing the value of machine data analytics beyond security, monitoring and troubleshooting. When IT operations teams have better insight into business performance, they can prioritize incident response and improve their ability to support business goals.

7%

15%

19%

21%

22%

24%

26%

28%

28%

28%

32%

34%

37%

45%

56%

SRE

CTO

Customer success

CIO

BI analysts

DevOps

Cloud/application architects

CEO

Customer Support

Data scientists

Developers

Product managers

Security analysts

Data analysts

IT operations

n=250



Figure 4: Use cases for machine data analytics toolsSource: 451 ResearchQ. What are the current use cases for these tools? (Select all that apply)

Businesses Value their Machine Data Analytics Tools but Want to do More with ThemTo better understand the value that users are gaining from their machine data analytics tools, we asked survey respondents to rank the areas where their companies are benefiting the most from machine data analytics. While security has already emerged as an important priority for users of machine data analytics tools – more so in Europe than in the US, where we ran a similar study and found security ranked lower – here we see customer experience emerging as the top benefit of machine data analytics tools. Security ranked second, with more efficient product development tied for first place. We see an interesting correlation in Europe between customer experience and security, where businesses are discovering that the tools they use to comply with privacy regulations can at the same time drive the end-user experience.

There was a fairly even spread among the bulk of the responses, indicating that users believe they’re achieving business value in a number of areas from their machine data analytics tools – including smarter product development, optimized cloud and infrastructure spending, and improved sales and marketing performance.

26%

39%

41%

42%

48%

48%

55%

63%

IoT

User experience

Compliance

Capacity planning

Troubleshooting

Business insight

Monitoring

Security

n=250



Figure 5: The value that machine data analytics brings companiesSource: 451 ResearchQ. Please identify the top three business benefits of machine data. (Percentage of respondents including selection in top three)

Yet, users are keen to do even more. Interestingly, the response regarding improved customer experience also topped the list when we asked about the potential expansion of machine data analytics, with more efficient compliance audits coming in second. This confirms our observation that the combination of customer experience and compliance with security regulations represent strong value for users of machine data analytics tools in Europe. Users want their machine data tools to drive even more insight into the customer experience, which is increasingly important to many businesses, while at the same time helping to ensure compliance.

18%

20%

21%

21%

21%

23%

23%

24%

24%

25%

25%

26%

26%

More efficient compliance audits due to better access to key data sources

Improved customer experience due to the ability to fix issues before they impact customer experience

Improved application uptime due to more efficient troubleshooting of operations issues (MTTI/MTTR)

Better visibility into application performance because of the ability to set and monitor operations KPIs

Improved data

Improved customer experience due to higher quality code releases

Improved business planning because of access to insight into future patterns or cycles

Improved sales and marketing performance due to access to user/customer behavior analytics

Optimized cloud and infrastructure spending due to better insight into usage statistics

Smarter product development based on insight into customer feature usage

Improved security posture because of access to faster and/or more accurate analysis of security threats

More efficient product development (i.e., faster time to market)

Improved customer experience due to deeper insights into customer experience or behaviors

n=250



Figure 6: Potential expansion of use of machine data analyticsSource: 451 ResearchQ. Where do you see the most potential to expand your organization’s use of machine data analytics? (Select all that apply)

There is clearly a lot of interest in the potential of machine data analytics tools, so what’s holding companies back from expanding their usage? Respondents cited a host of capabilities that aren’t available in current tools. The number one capability that users said was lacking in their existing tools was real-time access to data, followed by fast, ad hoc querying. The ability to very quickly find answers is an important capability that supports several use cases around customer experience and that enables continuous intelligence. The speed with which users can access and act on important insight is key to enabling smarter, data-driven decision-making in today’s fast-moving environments.

23%

26%

27%

28%

29%

30%

30%

32%

32%

32%

33%

34%

35%

Improved application uptime as a result of more efficient troubleshooting of operations issues (MTTI/MTTR)

Improved customer experience due to higher quality code releases

Optimized cloud and infrastructure spending due to better insight into usage statistics

Smarter product development based on insight into customer feature usage

Improved business planning because of insight into future patterns or cycles

Better visibility into application performance because of the ability to set and monitor operations KPIs

Improved security posture because of access to faster and/or more accurate analysis of security threats

Improved sales and marketing performance due to access to user/customer behavior analytics

Improved customer experience based on the ability to fix issues before they impact customers

Improved data-driven business decisions because of the ability to set and monitor business KPIs

More efficient product development (i.e., faster time to market)

More efficient compliance audits due to better access to key data sources

Improved customer experience as a result of deeper insights into customer experience or behaviors

n=250



Ease-of-use issues came in third. Many businesses have a select few experts who must run analytics and build reports for individual users who don’t have the expertise to use complicated tools. This creates a gate that prohibits a wider audience of users from gaining value from the data. Machine data analytics tools that are easier to use allow more people across the organization to benefit from them.

We noticed some regional differences in this question, with respondents from the UK most likely to cite a lack of self-service and ease of use compared to their peers. Respondents from Germany were significantly more likely to say they lacked real-time access to data, at 48%, compared to the others including Sweden, where only 24% of respondents complained about this. The Germans were also most likely to say that their tools lacked advanced analytics that surface performance problems or alert against KPIs.

Figure 7: Capabilities that are lacking in current toolsSource: 451 ResearchQ. What business value capabilities are lacking in your current machine data analytics tool? (Select all that apply)

We also learned that the management of machine data analytics tools is a notable barrier to broader usage. We asked respondents about the barriers to broader adoption within their organizations, and while cost topped the list, other responses related to management problems closely followed. Nearly a quarter (20%) of respondents said that developers don’t have the time to collect relevant machine data that would appeal to more people, 19% said that open source tools require too much management overhead, and 19% said that those responsible for machine data analytics tools don’t have the time to manage them. These are the types of challenges that we see result in companies moving from open source software and on-premises deployments to SaaS tools.

24%

25%

28%

29%

31%

32%

34%

37%

Ability to view performance against important KPIs, such as user experience

Insight into experience of individual users married with information about the users (i.e., experience prioritized by high-value customers

Visibility into relationship between revenue and performance problems

Useful front-end visualization tools

Advanced analytics that automatically surface performance problems or alert against KPIs

Self-service and easy to use so that anyone in the company can create reports

Fast, ad hoc querying

Real-time access to data

n=250



Europe Lags – and Leads – Compared to the US in Some Notable WaysWe conducted the same survey in the US and found some notable differences that indicate that respondents in the US are more likely to use and understand the value of machine data analytics. For instance, 36% of US respondents have more than 100 users interacting with machine data at least once a week, while in Europe, only 21% of respondents have that many users (we surveyed a similar split of companies by number of employees in the US and Europe). Likewise, 64% of US respondents said that machine data is extremely important to their company’s ability to meet its goals, with 54% of European respondents saying the same.

We think that the gap is due to US businesses being more likely to have a software-centric mindset. Software-driven businesses tend to recognize ways that machine data – collected from those important software products – can deliver important insight, both about software performance and business performance. We found that 64% of US respondents said most of their company had software-centric mindsets, while only 40% of European respondents said the same.

Figure 8: Notable differences between US and European respondentsSource: 451 Research

36%

64%

64%

74%

21%

40%

54%

48%

100+ users in your company interact with machine data at least once a week

Most of the company has a software-centric mindset

Machine data is extremely important to your company's ability to meet its goals

Machine data tools are deployed on-premises

US Europen=250 n=250



However, Europeans are ahead of their US counterparts by a couple of important measures. Our survey found that European respondents are leading the way in terms of recognizing the value and utility of machine data analytics in security use cases. When we asked respondents about current use cases for their machine data analytics tools, security topped the list among Europeans (see Figure 4), where security came in second (after monitoring) in the US. In addition, when we asked who recognizes the business value of machine data, head of security ranked last among US respondents, but fifth (of eight) among Europeans. Our survey found that Europeans are embracing machine data analytics for security use cases and then recognizing ways to drive additional use cases, including to support customer experience imperatives.

In addition, perhaps because of stricter data privacy and security regulations in Europe that ensure cloud providers deliver compliant services, the European respondents were far more likely to use SaaS machine data tools than their US counterparts. While 74% of the North American respondents said they deploy their tools on-premises, only 48% of Europeans said the same.

Larger, Software-Centric Businesses Lead the PackSurprisingly, the adoption of modern technologies is not holding back broader usage of machine data analytics as much as we expected. We asked whether the adoption of new technologies such as cloud, containers, microservices, CI/CD tools and even serverless environments made it more difficult to get the data needed for fast decision-making, and 53% of respondents said no. Only among users of CI/CD tools were respondents more likely to say that the adoption of those technologies made it more difficult to get the data they needed for fast decision-making.

Because we commonly hear that new technology adoption makes it difficult for businesses to get visibility into their technology environments, we think this response could indicate that some machine data analytics tools are beginning to enable better insight into complex environments. We have seen tools evolve in order to collect the granular data required in application environments built on technologies such as containers and microservices. Still, 47% of survey respondents said that the adoption of modern technologies does make it harder to get the data they need for speedy decision-making, indicating that a notable portion of the market is still underserved in this regard.



Figure 9: The adoption of new technologies and the ability to get data for fast decision-makingSource: 451 ResearchQ. Has the adoption of this/these technologies made it more difficult for you to get the data you need for fast decision-making?

Among respondents who said that most of their company has a software-centric mindset, we found some clear trends indicating they are more advanced in terms of their use of machine data analytics than those from companies that are less software-centric. Respondents from mostly software-centric companies are more likely to say that a wide variety of users, including head of IT, head of security, line-of-business users, product managers and C-level executives recognize the business value of machine data. These software-centric businesses understand the potential value of data analytics across the business.

NoYes

CI/CD tools (Chef, Puppet, etc.)

Serverless technologies (i.e., AWS Lambda and

Azure functions)

MicroservicesContainers and/or container orchestration tools

(Docker, Kubernetes, etc.)

49%46% 44%

52%51% 54%

56%

48%n=91 n=91 n=91 n=75



Figure 10: Machine data business value recognition, by software-centricitySource: 451 ResearchQ. Who in your company recognizes the business value of machine data? (Select all that apply)

In addition, respondents from companies where most people have a software-centric mindset are more likely to already be integrating their BI tools with their machine data tools. Such integrations indicate that users are marrying the machine data they collect from operations with other sources of company data – potentially about revenue and customer activity – likely in ways that deliver business-critical intelligence. Because BI tools are typically employed by line-of-business users such as product managers and top executives, the marrying of BI and machine data tools reflects advanced usage in ways that are designed to help achieve high-level business goals.

56%

61%

51%

54%

49%

51%

50%

65%

34%

29%

40%

36%

39%

29%

40%

22%

10%

8%

5%

10%

9%

15%

6%

6%

1%

3%

4%

1%

5%

4%

6%

Head of IT

DevOps professionals

Operations professionals

Head of security

Security administrators

Line-of-business leaders

Product managers

C-level executives

Most of the company has a software-centric mindset Half the company has a software-centric mindsetLess than half the company has a software-centric mindset Only a few discreet teams are software-centric

n=250



Figure 11: The integration of machine data and BI tools at software-centric companiesSource: 451 ResearchQ. Do line-of-business users in your company currently integrate machine data and BI tools?

We also found that larger businesses, which may be more sophisticated in terms of making data-driven decisions, have a greater expectation that their machine data analytics will deliver real-time results so that they can quickly make important decisions.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Most of the company has a software-centric mindset

Half the company has a software-centric mindset

Less than half the company has a software-centric mindset

Only a few discreet teams are software-centric

Yes No Don’t know n=86



Figure 12: Business size and the expectation of real-time resultsSource: 451 Research Q. What is your expectation for your machine data analytics to support speed of decision-making, by company size?

ConclusionMarket disruption and customer demand are driving transformation in most sectors, putting significant pressure on businesses to move fast in order to best serve customers and stay ahead of the competition. New technologies that enable big data and sophisticated analytics are becoming key tools in support of these business imperatives. Machine data analytics tools are becoming a key component in the strategies of modern businesses that are emerging as leaders in their sectors.

Our survey showed that organizations across industries recognize the value of machine data in a business context, which sets them up to participate in the analytics economy in a way we think will be key in gaining competitive advantage. Software-centric companies are on the leading edge of this, discovering ways that machine data can drive pervasive intelligence: real-time machine data analytics is becoming a continuous intelligence source for organizations; they are embracing machine data to inform insight into user behavior and compliance; and organizations are expressing interest in expanding their use of machine data in important ways, including to further improve customer experience. Similarly, forward-thinking companies – those that have a strong software-centric mindset – tend to be more advanced in their use of machine data analytics, with line-of-business users who integrate machine data and BI tools, for instance.

32%

37%

42%

48%

400 - 1,000 employees

1,001 - 5,000 employees

5,001 - 10,000 employees

> 10,000 employees

REAL-TIME (I.E., REAL-TIME DASHBOARDS ON TV SCREENS)

n=99



Businesses that wish to move in the direction of these leaders will need machine data analytics tools with important capabilities:

• Machine analytics tools must be self-service and easy to use so that professionals across the business can use them to drive intelligence into their decision-making processes.

• Users must be able to retrieve analytics from machine data analytics tools quickly to obtain the intelligence needed to enable agility.

• Machine data tools must harness sophisticated analytics that embrace machine learning in order to automatically present important intelligence to users.

• Users should be able to integrate their machine data tools with other important enterprise systems, such as business intelligence products, to get the most out of the machine data they collect.

It’s clear that machine data plays an important role in enabling the kind of pervasive intelligence that we believe will be required for businesses to thrive in today’s competitive marketplaces. Those that recognize this are already getting ahead. Those that fail to embrace the potential will be eclipsed by the competition.

Survey DemographicsTo get an accurate read on the market’s adoption of machine data analytics tools, we surveyed a cross section of users from 250 companies. We heard from businesses of all sizes, with 26% of respondents working for companies with 10,000 or more employees.

Figure 13: Company size based on number of full-time employeesSource: 451 ResearchQ. Approximately how many full-time employees work at your company?

26%

26%22%

14%

12%

400 - 1,000 employees

1,001 - 5,000 employees

5,001 - 10,000 employees

10,001 - 50,000 employees

50,000+ employees

n=250



We also heard from respondents from a wide array of sectors including technology, manufacturing and government. Because some of these industries, such as healthcare and financial services, have particular demands around compliance and security, our survey tapped into a broad representation of important needs across the market.

Figure 14: Primary industries of companies included in surveySource: 451 ResearchQ. What is the primary industry of your business?

The largest number of respondents belong to an IT operations business unit. Given that we asked respondents whether their company uses machine data tools in any fashion and eliminated those who said no, the relatively high percentage of IT operations pros is a reflection of the business persona that typically plays some role related to machine data analytics tools. The fact that respondents in product, database admin, BI analyst and data science teams also appeared in the survey is interesting in itself, and points to the wide usage of machine data tools.

16%

16%

14%

12%

11%

10%

9%

5%

4%

2%

1%

IT/Tech/Software

Manufacturing

Financial Services

Government/Education

Pharmaceutical/Healthcare

Retail

Professional Services/Consulting

Leisure/Hospitality

Telecommunications

Cloud Computing Provider

Media/Content n=250



Figure 15: Business units that respondents belong to within the companySource: 451 ResearchQ. What business unit do you belong to in the company?

We also largely heard from professionals who are closely involved with their machine data analytics tools. Just over two-thirds (67%) of respondents either make the purchasing decision or make recommendations for their company’s machine data analytics tools, and the rest reported that they are users of the tools.

42%

14%

12%

11%

8%

5%

4%

3%

1%

IT operations

Product

Security

Database admin

Developer

DevOps

Data science team

BI analyst

SRE n=250



Figure 16: Respondents’ roles regarding machine data analyticsSource: 451 ResearchQ. What is your role with machine data analytics within your organization?

39%

28%

32%

I make purchasing decisions

I make recommendations for my organization

I use it

n=250



Machine Data Analytics Platform as a Service Drives Continuous Intelligence

The age of digital services is putting pressure on companies to move with speed and agility, which means companies today are relying on analytics like never before. This shift is defining a new Analytics Economy, in which companies will win or lose based on how well they consume, and act upon, insights from analytics to drive faster innovation, better customer experiences, and increase data-driven decision making across their entire organization.

To address this shift, Sumo Logic believes companies now require an analytics platform that is continuously elastic, scalable and secure to handle the seasonality, unexpected events and threats common in today’s digital business cycle. Sumo Logic’s cloud-native, machine data analytics platform, delivered as a service, gives customers access to real-time, continuous intelligence. Built as an elastic, scalable, secure, cloud-native service, the platform ingests, parses and correlates machine data – logs and time-series metrics – to provide a unified, real-time view into a company’s modern application stack. Advanced analytics via machine learning algorithms also surfaces outliers and anomalies automatically. With Sumo Logic, companies can now successfully compete in the Analytics Economy, turning traditional cost centers, like operations, security and user behavior analytics, into engines that drive business value.

About Sumo Logic

Sumo Logic is a secure, cloud-native, machine data analytics service, delivering real-time, continuous intelligence from structured, semi-structured and unstructured data across the entire application lifecycle and stack. More than 1,600 customers and 50,000 users around the globe rely on Sumo Logic for the analytics and insights to build, run and secure their modern applications and cloud infrastructures. With Sumo Logic, customers gain a multi-tenant, service-model advantage to accelerate their shift to continuous innovation, increasing competitive advantage, business value and growth.

CO N T E N T F U R N I S H E D BY

About 451 Research451 Research is a preeminent information technology research and advisory company. With a core focus on technology innovation and market disruption, we provide essential insight for leaders of the digital economy. More than 100 analysts and consultants deliver that insight via syndicated research, advisory services and live events to over 1,000 client organizations in North America, Eu-rope and around the world. Founded in 2000 and headquartered in New York, 451 Research is a division of The 451 Group.

© 2018 451 Research, LLC and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication, in whole or in part, in any form without prior written permission is forbidden. The terms of use regarding distribution, both internally and externally, shall be governed by the terms laid out in your Service Agreement with 451 Research and/or its Affiliates. The information contained herein has been obtained from sources believed to be reliable. 451 Research disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although 451 Research may discuss le-gal issues related to the information technology business, 451 Research does not provide legal advice or services and their research should not be construed or used as such.

451 Research shall have no liability for errors, omissions or inadequacies in the informa-tion contained herein or for interpretations thereof. The reader assumes sole respon-sibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice.

NEW YORK1411 Broadway New York, NY 10018 +1 212 505 3030

SAN FRANCISCO140 Geary Street San Francisco, CA 94108 +1 415 989 1555

LONDONPaxton House 30, Artillery Lane London, E1 7LS, UK +44 (0) 203 929 5700

BOSTON75-101 Federal Street Boston, MA 02110 +1 617 598 7200

using machine data analytics to gain advantage in the ... · achieving business value in a number...

Documents