Master Data Management for Data-Driven...

IT & DATA MANAGEMENT RESEARCH,INDUSTRY ANALYSIS & CONSULTING

Master Data Management for Data-Driven OrganizationsReport Summary

An ENTERPRISE MANAGEMENT ASSOCIATES® (EMA™) End-User Research Report SummaryJanuary 2016

This research has been prepared for:

Table of Contents

©2015 Enterprise Management Associates, Inc. All Rights Reserved. | www.enterprisemanagement.com


1. Master Data Management for Data-Driven Organizations ........................................................... 1

1.1. Master Data Management: Key to Your Success ................................................................... 2

2. Being Driven By and Mastering Your Data................................................................................... 3

3. Agility Propels Data-Driven Organizations ................................................................................... 5

4. First Step: Fix Key Business Issues ................................................................................................ 7

5. Maturity: Collaboration and Establishing Accountability ............................................................. 9

6. EMA Perspective ......................................................................................................................... 11

7. Methodology and Demographics ............................................................................................... 12

7.1. Research Methodology ....................................................................................................... 12

8. Author ........................................................................................................................................ 12

http://www.enterprisemanagement.com



1. Master Data Management for Data-Driven OrganizationsThe pace of business is moving faster than ever before. Driving this continuing change in pace is data from social, mobile, and other data sources. Organizations leveraging this data are disrupting the way that business conditions and business models are implemented. These organizations are data-driven companies. These companies rely on information on customers, products, and services at a much higher level than traditional organizations to disrupt existing markets and increase their competitive advantage.

Examples of these data-driven organizations can be seen in multiple markets. Netflix is taking customer, product, and connectivity information to revolutionize the video consumption business. Started as a delivery service that turned the DVD and movie rental business on its head, Netflix’s streaming product is now taking the content development and distribution business and making similar disruptions.

Uber takes information on customers, service providers, and locations to turn the relatively stable and highly regulated business of personal transportation away from consolidated providers. The company enables individual customers and service providers to give highly customized services with a higher customer satisfaction rating than was thought possible in the traditional “cab” industry.

Amazon continues its disruption of the online commerce industry by pushing further and further into new markets. With detailed information from customers and products linked with information on supply chains, Amazon continues to push from books to consumer goods to food.

In each of these instances, data-driven organizations are breaking down barriers to new and innovative markets by accessing a higher level of information and leveraging that information at speeds that previous organizations could only dream of. At the core of this disruption is the ability to provide context for viewing habits, passenger and driver quality, and purchase history. All of these concepts require disruptive, data-driven organizations to have access to the most up-to-date metadata on customers, products, and locations, as well as a robust master data management (MDM) practice to leverage that information in the right places and at the right times.

Data-driven organizationNoun

: a company that has either fully integrated data or started with data as the basis for its strategic and operational thinking. The organization uses data and information for finance, marketing, sales, and operations decisions and has built new business models in existing markets using data and information.




1.1. Master Data Management: Key to Your SuccessUtilizing up-to-date information on products lets data-driven organizations capitalize on initial purchases to improve cross-sell/upsell opportunities. With this curated information, a data-driven organization can not only make the most effective offer to a prospective customer, but can also ensure offers are made with the products that are available to and cost-effective for the provider.

The connection between master data management and data-driven cultures lets organizations take a “new” approach to the 360-degree view of a customer. Prior to all of these new mobile, social, and location data sources, the “360-degree” view of a customer was from inside the organization via customer care, enterprise resource planning, and other “inside the firewall” sources. With these new data sources and a strong master data management practice, data-driven organizations can redefine the 360-degree view of the customer to include their opinions about products and services in near-real time. The 360-degree view can include an increased velocity of purchasing decisions from mobile applications and can even determine where customers are interacting with the physical brick and mortar as well as the online and mobile touchpoints.

DIGITAL MARKETING

61.5%

NEW DATA-DRIVEN APPS

48.8%

CUST

OMER EXPERIENCE

52.6%

The graphic above shows the top three business goals for organizations that have a strong approach, or “vital” stance, on the use of master data in their organization —to boost support of Digital Marketing efforts, enhance Customer Experience, and expand into New Data-Driven Applications. These organizations drive their businesses by focusing on the way they can leverage their data assets to provide better products and services to their customers.




2. Being Driven By and Mastering Your DataEMA recently performed primary end-user research on the implementation of master data management practices in relation to data-driven organizations and their cultures. This research focuses on how data-driven organizations are taking a renewed look at the practices associated with master data management to effectively implement their disruptive business models and drive their revenues up and their costs down while improving their overall customer experience and satisfaction.

Data-driven strategies within respondent organizations have been banded together into the following segments to detail different levels of organizational culture.

• Vital – These data-driven cultures view the use of data as being paramount to how they drive revenues and customer satisfaction. These respondents replied that their data-driven strategy is currently adopted and a vital part of our business.

• Important – These organizations have integrated data-driven strategies into their culture, but have not taken it to the level of those identified as vital. These respondents identified it as being currently adopted and a somewhat important part of our business.

• Adopted or Researched – These organizations are still working out how to implement data-driven strategies in their organization. These respondents indicated it is currently adopted; it is planned for adoption in the near future (3-6 months); or it is being researched for adoption in next year (6-12 months).

The EMA respondent panel was asked how they evaluate their own data-driven strategies across a continuum. This provides a breakdown of this segmentation to see where participating organizations fit along the continuum detailed above.

“How would you describe the extent to which your company is using a data-driven organizational strategy?”

0% 5% 10% 15% 20% 25% 30% 35% 40%Percentage of Respondents

Vital

Important

Adopted or Researched

38.2%

28.4%

33.3%

Data-Driven Culture

Data- Driven CultureVital

Important





Almost 40% of respondents identified that data-driven organizational strategy is vital to their success. This is closely linked to the number of respondents who have a proactive approach to the master data management practice. Survey participants were asked:

“How would you describe the extent to which your company is implementing master data management practices and technologies for customer-facing applications?”

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%Percentage of Respondents

Vital

Important


41.2%

28.4%

30.4%

Master Data Management Strategy

MDM StrategyVital

Important


The levels of master data management practices and technologies being implemented within an organization have been banded together into the following categories.

• Vital – These are companies where master data management strategies are an important part of their data management practices and are key aspects of how they address the domains of MDM. In these organizations, master data management is identified as currently adopted and a vital part of their business.

• Important – These organizations have a significant investment in MDM. However, they are still working toward the vital level. These respondents identified their master data management strategy as being currently adopted and a somewhat important part of their business.

• Adopted or Researched – This level of MDM strategy is similar to that of an adopted data-driven culture with organizations dabbling in their MDM practices. These respondents indicated it is currently adopted; it is planned for adoption in the near future (3-6 months); or it is being researched for adoption in next year (6-12 months).

Vital data-driven cultures and strong MDM practices are closely linked both in concept and in response rate in the EMA survey. Organizations with vital data-driven cultures believe in the use and proliferation of data to further their business models and create new opportunities. They believe that without access to the latest information on MDM domains such as customer, product, and region, their organizations will only achieve limited success with their data-driven initiatives.




3. Agility Propels Data-Driven OrganizationsFocused, data-driven cultures have much higher requirements to achieve an effective master data management strategy than other organizations. Data-driven cultures embrace the usage of data sources that previously were unfamiliar to organizations. They use multistructured data sources as well as data sources from outside of the corporate firewall. Data-driven organizations will also incorporate this information into a larger number of customer-facing applications in comparison to organizations with a lower focus on data-driven concepts. The net effect will be to not only increase the types of information the organization receives, but to also vastly increase the numbers of touchpoints where those data sources can make a difference with customers. Finally, these varied and numerous data sources will be pushed out to these customer-facing applications more frequently than organizations that only pull data from inside the corporate firewall.

As part of the EMA research, panel respondents were asked:

“From which of your data sources do you integrate information for your master data management practices associated with customer-facing applications?”

By looking at this response in relation to the segments associated with data-driven cultures, it is clear that, for data sources outside the corporate firewall, an overwhelming majority of the responses are from organizations with vital data-driven cultures.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Percentage of All Mentions

Operational application data (e.g., point of sale, customercare, CRM, supply chain)

Data from customer touch points (e.g., feedback fromsurveys, data from call centers, outbound campaigns, etc.)

Human-generated documents (e.g., email, application formdocuments)

External augmentation data (e.g., demographic orpsychographic)

Social media data (Twitter, Facebook, forums, etc)

35.2%

24.5%

34.7%

18.2%

17.9%

29.6%

30.6%

24.5%

21.2%

21.4%

35.2%

44.9%

40.8%

60.6%

60.7%

Master Data Management Data by Data-Driven Culture

Data- Driven CultureAdopted or Researched

Important

Vital

More than six of 10 responses from the EMA survey panel in both external augmentation data and social media data were from those respondents in vital data-driven cultures. This is in stark contrast to other levels of data-driven strategies that favored the more familiar data sources from within the data center, such as operational application data, information from customer platforms, or human-generated documents.

Customer-facing applicationsNoun

: applications that directly interact with customers or revenue-generating business partners. These applications will have online and mobile aspects to meet the needs of the customer. These applications can include inbound or outbound marketing, ordering systems, customer support, and product information platforms.




Data-driven organizations within the vital category also leverage data more broadly across various platforms to provide a more comprehensive look at their customers. These organizations seek additional data sources to be included in their customer-facing applications. The EMA research panel was asked:

“How many customer-facing applications need consistent master data such as customer, product, and/or location data?”

Organizations with vital data-driven cultures represent over 60% of the responses associated with “more than 25” customer-facing applications that are being served with master data.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Percentage of Respondents

1 to 5

6 to 10

11 to 25

More than 25

52.4%

36.4%

26.3%

11.1%

19.0%

31.8%

31.6%

27.8%

28.6%

31.8%

42.1%

61.1%

Number of Projects by Data-Driven Culture


Important

Vital

Organizations less focused on data-driven concepts have a lower number of customer-facing applications. Those who have “only” adopted or researched data-driven concepts for their organization represent just over 50% of those with 1-5 customer-facing applications being supplied with master data.

With the number of applications, it can be assumed that there are economies of scale in the implementation of master data on those customer-facing platforms. With more customer-facing applications being served by the master data management practice, organizations will develop a level of maturity that comes with the use of best practices and the lessons learned from implementations. These best practices include how to apply data cleansing and quality techniques with lower levels of effort and move away from manual processes. They also include how to best update and manage external information to support those customer-facing platforms. These lessons can be banded into segments that define levels of master data management maturity. EMA has defined these segments.

• Learning – Organizations that are putting the knowledge and skills to make an effective master data management practice into place. These learning organizations are managing 1 to 5 customer-facing applications as part of their MDM practice.

• Maturing – Companies with 6 to 10 customer-facing applications in their MDM practice are maturing into a broader master data management challenge. These organizations have moved beyond the initial steps of servicing those platforms.

• Robust – Organizations with 11 to 25 customer-facing applications or more than 25 have built a level of experience where improvement in MDM practices and strategies come naturally. These robust organizations develop finer-grained controls, staff skills, and techniques as compared with their counterparts.




The EMA respondent panel was asked about the frequency of updates:

“How often do you update the master data associated with your customer-facing applications?”

When these maturity segments are associated with the frequency of updates of master data, robust organizations have overwhelmingly taken these skills and abilities and used them to update their customer-facing applications on a daily basis, if not more often.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Percentage of Respondents

Greater than Daily

Daily

Multiple times per day

24.6%

19.0%

52.2%

19.0%

36.4%

23.2%

61.9%

63.6%

Project MaturityLearning

Maturing

Robust

Not surprisingly, the relatively low experience of learning organizations limits them to less frequent updates of their master data. In fact, there are no respondents who identified themselves as learning who updated their customer-facing applications multiple times per day.

4. First Step: Fix Key Business Issues Master data management practices generally start with the best intentions. For instance, management may ask for better information to improve work with customers. MDM programs might begin with members of the IT department being proactive on data quality and data stewardship activities. In each of these instances, an organization will have specific business drivers or objectives to meet with this program. Respondents in the EMA data-driven master data management survey were asked about their business objectives for their master data management practices:

“Which of the following business drivers are behind your organization’s need for data management practices associated with customer-facing applications?”

Not surprisingly, the aggregate answers associated with master data management practices focused on the initial steps of customer experience management and customer loyalty or compliance and privacy management. These two answers represent nearly 19% and just over 15%, respectively.





Customer experience management andcustomer loyalty

Compliance and privacy management

Marketing, sales, and/or services operationalefficiency

Better understanding of customer (360 degreeview)

Turn data into actionable information at thecustomer touch points

Internal transparency allowing any user toaccess customer data

Establish competitive advantage against peers

18.7%

15.4%

14.8%

14.2%

12.7%

12.4%

11.8%

Business Drivers

However, when segmentation of master data management strategies is applied, there are significant differences between those organizations with stronger thoughts on MDM practices and those with a less significant focus.


Better understanding of customer (360 degreeview)

Turn data into actionable information at thecustomer touch points

Internal transparency allowing any user toaccess customer data

Establish competitive advantage against peers

19.1%

16.7%

14.6%

25.6%

27.7%

31.0%

34.1%

20.5%

53.2%

52.4%

51.2%

53.8%

Business Drivers by MDM Strategy

MDM StrategyAdopted or Researched

Important

Vital

Vital level organizations represent over 50% of the responses for better understanding of customer (360-degree view); turn data into actionable information at the customer touchpoints; internal transparency, allowing any user to access customer data; and establish competitive advantage against peers. The highest percentage of mentions for vital level organizations is in the establish competitive advantage against peers response.




5. Maturity: Collaboration and Establishing AccountabilityMoving from the business drivers associated with master data management practices to those associated with a technical or operational perspective, organizations with stronger data-driven cultures look at the drivers for master data management practices differently than those with a lower focus on the use of information. Organizations starting on their data-driven practices, or “dabbling” in those areas, are mainly concerned with the first steps of master data management. This is because those organizations need to develop the business and technology platforms associated with building a strong MDM framework. Those with more robust approaches to data-driven strategies are implementing further down the maturity path of MDM practices. The EMA research panel was asked about technical drivers within their current data management practices:

“How do you view the following technical/operational drivers for data management practices associated with customer-facing applications?”

The responses in association with the data-driven culture segmentation show that EMA panel respondents starting their data-driven strategies are more focused on the data quality and control stage of the master data management continuum. This is a much more technically focused and addressable concept that takes place as part of a data integration or data cleansing process.


Data quality and control

Improve productivity of business users

Speed in time to implementation

Accessibility of customer data by theoperations

Reusability of data across channels andapplications

Empowerment of business users for datagovernance and stewardship

Technical agility to connect to and accessnew/additional data sources

37.1%

24.0%

25.0%

19.6%

22.0%

11.1%

20.0%

24.2%

26.0%

22.9%

34.8%

36.6%

33.3%

28.6%

38.7%

50.0%

52.1%

45.7%

41.5%

55.6%

51.4%

Technical Drivers by Data-Driven Culture


Important

Vital

However, organizations with a vital approach to data-driven culture tend to have more of a focus on the delivery aspects of their practices, with over 55% focusing on the empowerment of business users for data governance and stewardship and 52% focusing on speed in time to implementation. With the penetration of information into a data-driven business, these business stakeholders have the knowledge of both the customer-facing applications being updated with master data, as well as the quality of the information.




Data-driven cultures require a strong commitment to their data management initiatives. These organizations develop frameworks around their master data management practices and put resources in place to manage their initiatives. To identify the level of involvement and responsibility for data-driven organizations and their data management strategies, the EMA research panel was asked:

“Who has the end responsibility for your organization’s data management practices associated with customer-facing applications?”


Business analysts from marketing,sales, finance

Data stewards

12.8%

10.3%

25.6%

31.0%

61.5%

58.6%

MDM Ownership by Project Maturity

Project MaturityLearning

Maturing

Robust

Within robust organizations, 61.5% of respondents give these roles to business analysts from marketing, sales, and finance, and 58.6% give them to data stewards. Both of these roles are data-knowledge intensive and require an understanding of how this data relates to the business. Use of these roles is less prevalent within maturing organizations that look to enterprise data architects and IT-focused data analysts, with both at almost 43%.

Dedicated roles also differ depending on the size of the organization, as larger enterprises can dedicate more resources to their master data management practices. The following chart highlights these differences based on company size.


IT- focused data analysts

IT-focused project manager

Business analysts from marketing,sales, finance

Line of business executives

Enterprise data architects

Line of business project managers

Data stewards 10.3%

42.9%

47.9%

41.0%

44.4%

31.4%

45.7%

37.9%

55.1%

50.0%

53.8%

47.2%

68.6%

51.4%

51.7%

5.1%

8.3%

MDM Ownership by Company Size

Company SizeMid-sized (under 500 headcount)

Large (500 to 5000 headcount)

Enterprise (over 5000 headcount)




Sixty-eight percent of enterprises leverage their information-centric resources by assigning data architects to fulfill this role, with IT-focused data analysts at 55%. In most cases, midsized companies fall at the other end of the spectrum and select data stewards or line of business executives, while large organizations do not have a centralized strategy and leverage different resources based on their corporate cultures and master data management initiatives. Additionally, the larger the organization, the more likely they are to allocate resources to these programs and provide resources based on their initiative requirements.

6. EMA PerspectiveData-driven organizations depend on master data management strategies and practices to be effective in their new and innovative business models and to meet the needs of their customers by managing the ever-increasing complex data landscape. Master data management strategies within data-driven organizations are important to empower automated and customized applications that drive increased customer satisfaction and revenues. For example, the use of accurate and up-to-date information in a data-driven organization’s customer relationship management (CRM) platform allows for marketing and sales efforts based on the latest information on customers. Without this up-to-date information on customers, organizations miss opportunities for positive customer interactions and create inconsistent touchpoints with their customers. Too many misdirected messages, either via electronic communication or via direct content, limit the effectiveness of the targeted messaging. This highlights the value proposition of developing a framework that targets customer-facing applications through master data management initiatives.

Data-driven organizations with a vital master data management focus leverage many customer-facing data sources multiple times a day to make sure they have the proper visibility into their customers’ habits. For organizations less mature within their data-driven architectures, their move towards this vital approach will enhance their ability to meet their customer demands by leveraging customer and product data more efficiently.

Companies like Netflix, Uber, and Amazon provide a glimpse into what organizations are doing right with their data. As information complexities increase and more multistructured data sets become essential to customer-related business operations, data-driven organizations will need to follow in the footsteps of these types of organizations to ensure customer-related success.




7. Methodology and Demographics7.1. Research MethodologyEMA and Talend crafted the data-driven master data management survey that is the basis for this report. Before the survey was conducted, Talend was provided with a copy of the survey instrument. However, sponsors had no direct involvement in or influence on the survey creation, survey contents, survey execution, or any of the subsequent evaluation and analysis of the results for this report.

For this research, EMA invited prequalified business intelligence (BI) and information technology (IT) professionals to complete an extensive web-based survey. These respondents were further qualified based on their responses to the following questions.

• What is your primary role in the usage and/or management of big data applications/technology within your organization?

• Which of the following best describes your company’s primary industry?

• How would you describe the extent to which data-driven initiatives have been adopted within your business/organization?

• How would you describe the extent to which master data management initiatives have been adopted within your business/organization?

Respondents who failed to qualify on these questions were rejected. As a result, all respondents (in addition to being independently prequalified through the initial invitation process) self-identified as being active participants with a working knowledge of current data-driven and master data management (MDM) data management practices within a company that is presently researching, planning, or implementing big data strategies.

A total of 102 business and technology professionals responded to an invitation to provide their insights on data-driven and master data management (MDM) data management practices. To offer a balanced enterprise view of the subject, the respondent pool was also restricted. Technologists represented 55% of respondents. Business stakeholders were 45%.

8. Author

John Myers Enterprise Management Associates


About Enterprise Management Associates, Inc.Founded in 1996, Enterprise Management Associates (EMA) is a leading industry analyst firm that provides deep insight across the full spectrum of IT and data management technologies. EMA analysts leverage a unique combination of practical experience, insight into industry best practices, and in-depth knowledge of current and planned vendor solutions to help EMA’s clients achieve their goals. Learn more about EMA research, analysis, and consulting services for enterprise line of business users, IT professionals and IT vendors at www.enterprisemanagement.com or blogs.enterprisemanagement.com. You can also follow EMA on Twitter, Facebook or LinkedIn.

This report in whole or in part may not be duplicated, reproduced, stored in a retrieval system or retransmitted without prior written permission of Enterprise Management Associates, Inc. All opinions and estimates herein constitute our judgement as of this date and are subject to change without notice. Product names mentioned herein may be trademarks and/or registered trademarks of their respective companies. “EMA” and “Enterprise Management Associates” are trademarks of Enterprise Management Associates, Inc. in the United States and other countries.

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