TOP 5 DATA GOVERNANCE MISTAKES & HOW TO AVOID THEM

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The importance of data in a digital transformation context is known to everyone. Actually getting control and properly governing this new oil does not happen automatically. In this article we have summarized the top 5 Data Governance mistakes and also give you a number of tips on how to avoid them.

1. Data Governance is not business driven

Who is leading your Data Governance effort? If your initiative is driven by IT, you dramatically limit your chance of success. A Data Governance approach is a company-wide initiative and needs business & it support. It also needs support from the different organizational levels. Your executive level needs to openly express support in different ways (sponsorship but also communication). However this shouldn’t be a top down initiative and all other involved levels will also need to be on board. Keep in mind that they will make your data organization really happen.

2. Data Maturity level of your organization is unknown or too low

Being aware of the need for Data Governance is one thing. Being ready for Data Governance is a different story. In that sense it is crucial to understand the data maturity level of your organization.  

There are several models to determine your data maturity level, but one of the most commonly used is the Gartner model. Surveys reveal that 60% of organizations rank themselves in the lowest 3 levels. Referring to this model, your organization should be close (or beyond) the systematic maturity level. If you are not, make sure to first fix this before taking next steps in your initiative. You need to have these basics properly in place. Without this minimum level of maturity, it doesn’t really makes sense to take the next steps. You don’t build a house without the necessary foundations. 
3. A Data Governance Project rather than Program approach

A substantial amount of companies tend to start a Data Governance initiative as a traditional project. Think about a well-defined structure, the effort and duration is well known, the benefits have been defined, … When you think about Data Governance or data in general, you know that’s not the case. Data is dynamic, ever changing and it has far more touch points. Because of this, a Data Governance initiative doesn’t fit a traditional focused project management approach. What does fit is a higher level program approach in which you could have defined a number of project streams that focus on one particular area. Some of these streams can have a defined duration (i.e. implementation of a business glossary). Others (i.e. change management) can have a more ongoing character. 

4. Big Bang vs Quick Win approach

Regardless of the fact that you have a proper company-wide program in place, you have to make sure that you focus on the proper quick wins to inspire buy-in and help build momentum. Your motto should not be Big Bang but rather Big Vision & Quick Wins.

Data Governance requires involvement from all levels of stakeholders. As a result you need to make everyone clear what your strategy & roadmap looks like.

With this type of programs you need to have the required enthusiasm when you take your first steps. It is key that you keep this heart beat in your program and for that reason you need to deliver quick wins. If you don’t do that, you strongly risk losing traction. Successfully delivering quick wins helps you gain credit and support with future steps.

5. No 3P mix approach

Data Governance has important People, Process and Platform dimensions. It’s never just one of these and requires that you pay the necessary attention to all of them.

  • When you implement Data Governance, people will almost certainly need to start working in a different way. They potentially may need to give up exclusive data ownership … All elements that require strong change management.
  • When you implement Data Governance you tilt your organization from a system silo point of view approach to a data process perspective. The ownership of your customer data is no longer just the CRM or a Marketing Manager but all the key stakeholders involved in customer related business processes.
  • When you want to make Data Governance a success you need to make it as efficient and easy as possible for every stakeholder. This implies that you should also thoroughly think about how you can facilitate them in the best possible way. Typically, this implies looking beyond traditional Excel, Sharepoint, Wiki type solutions and looking into implementing platforms that support your complete Data Governance community.



Also in need for data governance?

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DATA VIRTUALIZATION: TOP USE CASES THAT MAKE A DIFFERENCE

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Data Virtualization is definitely on the rise. At its Data and Analytics Summit in London, Gartner was projecting accelerated data virtualization adoption for both first-time and expanded deployments. Besides market analysts, we also see high demand and can confirm this being one of the hottest data solutions. But what are the top uses cases for data virtualization?


Traditional data warehouses entered on repositories are no longer sufficient to support today’s complex data and analytics landscape. The logical data warehouse (LDW) combines the strengths of traditional repository warehouses with alternative data management and access strategies to improve your agility, accelerate innovation, and respond more efficiently to changing business requirements.

Challenges include:

  • A data services approach that separates data access from processing, processing from transformation, and transformation from delivery
  • Diverse analytic tools and users
  • Diverse data types and sources including traditional data repositories, distributed processing (big data), virtualized sources, and analytic sandboxes
  • Unified business ontologies that resolve diverse IT taxonomies via common semantics
  • Unified information governance including data quality, master data management, security, and more
  • Service level agreement (SLA) driven operationalization

Data Virtualization provides a virtualization-centric LDW architecture solution. 

With  Data Virtualization you can:

  • Access any source including traditional data repositories, distributed processing (big data), virtualized sources, and analytic sandboxes, both onpremises and in the cloud
  • Model and transform data services quickly, in conformance with semantic standards
  • Deliver data in support of a wide range of use cases via industry-standard APIs including ODBC, JDBC, SOAP, REST, and more
  • Share and reuse data services across many applications
  • Automatically allocate workloads to match SLA requirements
  • Align data access and use with enterprise security and governance requirements

With Data Virtualization you get:

  • One logical place to go for analytic datasets regardless of source or application
  • Better analysis from broader data access and more complex transformations
  • Faster analysis time-to-solution via agile data service development and reuse
  • Higher quality analysis via consistent, well-understood data
  • Higher SLAs via loose-coupling and optimization of access, processing and transformation
  • Flexibility to add or change data sources or application consumers as required
  • More complete and consistent enterprise data security and governance

 


Interested in Data Virtualization?

Would you like to know how Datalumen can also help you understand how your organization can benefit from using Data Virtualization?  Contact us and start our data conversation.

THE GDPR BUSINESS VALUE ROADMAP

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Getting a good understanding of the requirements but also the opportunities and business value is not easy. We designed a GDPR business value roadmap to help you with this and also make you understand what capabilities you need to get the job done.  


1
2
3
4
1

  • How will you understand what in-scope data is used for, for what purpose and by whom?
  • How will you demonstrate how you’re aligning to the principles?
  • Is your approach mostly manual, using interviews, questionnaires & static documentation?
  • Is your approach inaccurate, time consuming, resource consuming, out-of-date –or all of the these?


2

  • Do you understand where in-scope data is across your organisation and how it is shared?
  • How will you demonstrate you understand the size & shape of the data problem across domains and data subjects?
  • Is your approach mostly manual, using interviews, questionnaires & static documentation?
  • Is this approach inaccurate, time consuming, resource consuming, out-of-date –or all of the these?

3

  • How will you capture, manage and distribute consents across channels and business units?
  • How will you demonstrate you have captured the lawfulness of processing across all in-scope data sources?
  • Do you have anything in place already? Or are you planning on extending existing preferences capabilities?

4

  • How will you put protections and controls around identified in-scope data?
  • Can you demonstrate you have relevant control over the relevant in-scope data?
  • Are you planning to manually apply controls? Or apply masking, deletion & archiving solutions as required?
  • Will this approach give you a holistic view around the protections & controls you have in place?





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The Datalumen privacy policy can be consulted here.

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Have a look at our GDPR or Data Governance, 
contact us and start our Data Conversation.



TIBCO NOW 2020 – KEY DATA MANAGEMENT NEWS

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Last week TIBCO NOW 2020, a first-ever all-digital TIBCO NOW Connected Experience, was organized. A 3 day event stacked with the latest and the greatest about the TIBCO end-to-end offering. According to TIBCO close to 6000 attendees from over 80 countries and representing almost every industry tuned in. The main theme was TIBCO’s intention to propel Sustainable Innovation with technology frameworks, supported by proven solutions, best practices and techniques that support customers’ need for continual business reinvention.


Day 1-2-3

Day 1 featured two special guests, Jen Sey, SVP & CM for Levi Strauss, and Charlotte Yarkoni, CVP of Commerce & Ecosystems at Microsoft. Both executives spoke about how they use innovation and data to meet customers where their needs are.  

Day 2 built upon the discussions of why sustainable business practices and data-centric tools are critical to very practical discussions about how organizations can build a foundation for sustainable innovation. ”Data is the common element,” explained Dan Streetman, TIBCO’s CEO. “How data connects systems, people, and devices. How we manage, unify, and bring order to that data and ultimately how we build on that data foundation to make faster, smarter decisions is how you build a framework for sustainable innovation.”

Day 3 was all about delivering on the promise of sustained innovation and was led by TIBCO’s Head of Customer Excellence, Jeff Hess. He explained that it all starts with putting your customers first. You need to constantly ask “How can we make life better for our customers?” It’s a powerful clarifier in a world of competing priorities. By asking this question every day it’s the only way to ensure you can deliver on the promise of sustained innovation.


What’s new in the TIBCO data management corner?

TIBCO is an end-to-end software vendor and TIBCO Now 2020 was used to showcase all of the newer existing but also upcoming technology. Given our partnership and our field of expertise as a data management & data governance solution provider, we will zoom into what’s new in the TIBCO Unify domain and the most interesting data management topics.

TIBCO Any Data Hub.
One of the bigger announcements was the launch of TIBCO Any Data Hub framework. This is basically a host of technologies and proven methodologies to help customers sustain innovation. Being hyperconnected, using artificial intelligence and data science and empowering everyone in an organization with data is how you can innovate. Supporting TIBCO’s “better together” strategy, the TIBCO Any Data Hub framework supports tighter integration across its portfolio, including integration with TIBCO Cloud Integration, TIBCO Cloud Mashery, TIBCO Cloud Metadata Management and TIBCO EBX. This enables customers to build out data-as-a-service architectures and an all-in-one approach for data definitions, one data catalog, one point of access, one security model and one governance system. However it’s an open framework so it can also quiet easily hook into your existing landscape and get setup with the data management components you already have in place.

TIBCO Data Virtualization.
A core component of TIBCO’s Any Data Hub is TIBCO’s market-leading data virtualization platform. TIBCO Data Virtualization latest 8.3 release supports now a significantly expanded set of data adapters. With TIBCO Data Virtualization, you can now connect to over 300+ data sources including support for streaming sources such as Apache Kafka, OSIsoft, and OPC Unified Architecture. In addition, it is now available on the Microsoft Azure Marketplace, along with the current AWS Marketplace offering and has a Dockerfile option. Besides info on the new 8.3 version we also received insight in the roadmap and what TIBCO intends to implement in the future TDV.

TIBCO EBX & Cloud Metadata.
Next to that there was also the necessary focus on the key Master Data Management (MDM) component, TIBCO EBX (former Orchestra Networks MDM that got acquired), which still manages to dominate the MDM market. Linked to that and better integrated is the second generation of the recently introduced TIBCO Cloud Metadata and step by step starts to extend the platform capabilities in the meta data management domain.

Ready for your TIBCO NOW binge-watching?

TIBCO NOW 2020 has finished, but be aware that you can still watch everything on-demand through October 9 2020. TIBCO is leaving registration open after the event, so if you haven’t already, you have one last chance to register. 

Sessions worthwhile checking out:
  • Empower Your Business with Any Data from Any Location at Any Speed. Watch ▶
  • Build a virtual data layer that integrates your enterprise data silos. Watch ▶
  • Govern All Your Master and Reference Data. Watch ▶
  • Discover, Catalog, and Govern All Your Enterprise Metadata. Watch ▶
  • OPG: Billion-dollar Nuclear Programs on Time and on Budget with MDM. Watch ▶
  • What’s new about TIBCO EBX & TIBCO Cloud Metadata. Watch ▶

Did you miss any of these sessions? Do you have a specific data question? Would you like to have a briefing or a demo for your team? Let us know and we are happy to setup a one-on-one.

Contact us




CDO EXCHANGE 2020 – KEY TAKEAWAYS

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Given the circumstances we all need to face nowadays, CDO Exchange 2020 was organized last week in an adjusted online way. Regardless of the non-traditional approach, this still turned out to be an interesting forum for leaders active in the data domain (CDO – Chief Data Officers and others). The event was chaired by Bloor Research Brian Jones.

The event opened with a strong focus on data program fundamentals like stakeholder management and business case creation.  The second part was all about AI and data driven value creation including the necessary attention on the important data ethics topic.

Key Takeaways


  • No data program or initiative without a purpose. Sounds like basic but so true. It’s not the first time that that a data initiative is kicked off because it’s innovative but at the end doesn’t address any real business need.
  • In order to be successful you need to inform business leaders at all levels. Not just C-level but all leadership in your business stakeholder community. Of course, these business leaders also need to be open to listen. Reading tip: Also have see our post about MDM business case building.
  • Correctly translating business needs is important but also understanding the right priorities is key. What are my real burning data platforms?
  • In the AI, ML, …. Basically the data science context, we are happy to see that a substantial number of business leaders managed to gain understanding of the most important principles of data science. Pay the necessary attention to this in your organization and make sure that you also remove the data  & data science ‘language barrier’.
  • Don’t forget the company politics. In some organizations you will need to cope with individuals attempting to sabotage constructive change if it was ‘not invented here’ or ‘owned by us’. This point of attention was valid before, but unfortunately is still there and definitely present in the context of data programs.
  • Ethics is not about a choice, it’s an obligation. Data can be powerful and can potentially deliver huge value. Besides this potential, it also comes with a duty of care. In an era where customer centricity is vital, you need to make sure that your data management is objective, trustworthy and transparent to your customers and other stakeholders.
  • Ethics carry a cost. However the cost of not doing things right is much higher. Think about the overall and longer term reputationally and commercially cost.



Do you have a data management question or require some level of support with a data initiative in your organization? Feel to reach out to us and schedule a free sync session.

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SUMMER READING TIP

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Summer is here and the longer days it brings means more time available to spend with a ripping read. That’s how it ideally works at least. We selected 3 valuable books worth your extra time.

 

The Chief Data Officer’s Playbook

The issues and profession of the Chief Data Officer (CDO) are of significant interest and relevance to organisations and data professionals internationally. Written by two practicing CDOs, this new book offers a practical, direct and engaging discussion of the role, its place and importance within organisations. Chief Data Officer is a new and rapidly expanding role and many organisations are finding that it is an uncomfortable fit into the existing C-suite. Bringing together views, opinions and practitioners experience for the first time, The Chief Data Officer’s Playbook offers a compelling guide to anyone looking to understand the current (and possible future) CDO landscape.

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Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility

Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, the first book ever written on the topic of data virtualization, introduces the technology that enables data virtualization and presents ten real-world case studies that demonstrate the significant value and tangible business agility benefits that can be achieved through the implementation of data virtualization solutions. The book introduces the relationship between data virtualization and business agility but also gives you  a more thorough exploration of data virtualization technology. Topics include what is data virtualization, why use it, how it works and how enterprises typically adopt it. 

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Start With Why

Simon Sinek started a movement to help people become more inspired at work, and in turn inspire their colleagues and customers. Since then, millions have been touched by the power of his ideas, including more than 28 million who’ve watched his TED Talk based on ‘Start With Why’ — the third most popular TED video of all time. Sinek starts with a fundamental question: Why are some people and organizations more innovative, more influential, and more profitable than others? Why do some command greater loyalty from customers and employees alike? Even among the successful, why are so few able to repeat their success over and over? 
 
People like Martin Luther King, Steve Jobs, and the Wright Brothers had little in common, but they all started with Why. They realized that people won’t truly buy into a product, service, movement, or idea until they understand the Why behind it.  ‘Start With Why’ shows that the leaders who’ve had the greatest influence in the world all think, act, and communicate the same way — and it’s the opposite of what everyone else does. Sinek calls this powerful idea The Golden Circle, and it provides a framework upon which organizations can be built, movements can be led, and people can be inspired. And it all starts with Why.

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Summer Giveaways

We’re giving away 50 copies of ‘Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility’.  Want to win? Just complete the form and cross your fingers. Good luck!


Winners are picked randomly at the end of the giveaway. Our privacy policy is available here.

RABOBANK GIVES CUSTOMERS ANIMAL & PLANT NAMES TO ADDRESS GDPR REQUIREMENTS

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The Dutch bank Rabobank has implemented a creative way of using customer data, without having to request permissions. If you are one of their customers and they use your data with internal tests to develop new services, there is a chance that you will get a different name. With special software data is pseudonymized and they do so with Latin plant and animal names.

Your first name will become i.e. Rosa arvensis, the Latin name of a forest rose, and your street name i.e. Turdus merula, the scientific name of a blackbird. It is a useful solution for the bank to be somehow in line with the General Data Protection Regulation (GDPR) that takes effect on the 25th of May. When developing applications or services, analyzing data or executing marketing campaigns based on PII (Personally Identifiable Information) type of data, companies require to have an explicit consent. In order to be able to do this after May and without getting your consent, the bank uses data masking / pseudonnymization techniques.

 

Explicit consent & pseudonymization

With the new privacy law the personal data of citizens are better protected. One of the corner stones of the GDPR is the requirement to get an explicit consent and linked to that the purpose. Even with a general consent, companies do not get a carte blanche to do whatever they want to do with your data. Organizations must explain how data is used and by whom, where they are stored and for how long (more info about GDPR). Companies can work around these limitations if they anonymize / pseudonymize this PII type of data because they can still use and valorize this data but without a direct and obvious link to you as a person. You as a person become unrecognizable but your data remains usable for analysis or tests.  


Why scientific animal and plant names?

‘You can not use names that are traceable to the person according to the rules, but suppose it is a requirement to use letters with names, you have to come up with something else,” explains the vendor that delivered the software. “That’s how we came up with flower names, you can not confuse them, but they look like names for the system. Therefore, it is not necessary for organizations to change entire programs to comply with the new privacy law”.° 

Note that data anonymization/ pseudonymization technology does not require you to use plant and animal names. Most of this type of implementations will convert real to fictitious names and addresses that even better reflect the reality and perhaps better also match the usage requirements (i.e. specific application testing requirements). Typically substitution techniques are applied where a real name is replaced with a another real name.

 

Take aways

Pseudonymization vs anonymization

Pseudonymization and anonymization are two distinct terms that are often confused in the data security world. With the advent of GDPR, it is important to understand the difference, since anonymized data and pseudonymized data fall under very different categories in the regulation. Pseudonymization and anonymization are different in one key aspect. Anonymization irreversibly removes any way of identifying the data subject. Pseudonymization substitutes the identity of the data subject in such a way that additional information is required to re-identify the data subject.  With anonymisation, the data is cleansed for any information that may be an identifier of a data subject. Pseudonymisation does not remove all identifying information from the data but only reduces the linkability of a dataset with the original identity (using i.e. a specific encryption scheme). 

 

Pseudonymization is a method to substitute identifiable data with a reversible, consistent value. Anonymization is the destruction of the identifiable data.

 


Only for test data management?

You will need to look into your exact use cases and determine what techniques are the most appropriate ones. Every organization will most likely need both. Here are some use cases that illustrate this: 


Use caseFunctionalityTechnique
Your marketing team needs to setup a marketing campaign and will need to use customer data (city, total customer value,  household context, …).Depending on the consent that you received, anonymization or pseudonymization techniques might need to be applied. Data Masking
You are currently implementing a new CRM system and have outsourced the implementation to an external partner.Anonymization needs to be applied. The data (including the sensitive PII data) that you use for test data management purposes will need to transformed to data that cannot be linked to the original.  Data Masking
You are implementing a cloud based business application and want to make sure that your PII data is really protected. You even want to prevent that the IT team (with full system and database privileges) of your cloud provider has no access to your data.Distinct from data masking, data encryption translates data into another form, or code, so that only people with access to a secret key or password can read it. People with access but without the key will not be able to read the real content of the data. Data Encryption
You have a global organization also servicing EU clients. Due to the GDPR, you want to prevent  your non-EU employees to access data from your EU clients.Based on role and location, dynamic data masking accommodates data security and privacy policies that vary based on users’ locations. Also data encryption can be setup to facilitate this. Data Masking
Data Encryption
Your have a brilliant team of data scientists on board. They love to crunch all your Big Data and come up with the best analysis. In order to do that, they need all the data you possibly have. A data lake also needs to be in line with what the GDPR specifies. Depending on the usage you may need to implement anonymization or pseudonymization techniques.Data Masking

 

Is Pseudenomization the golden GDPR bullet?

Pseudonomization or anonymization can be one aspect of a good GDPR approach. However, it is definitely not the complete answer and you also will need to look into a number of other important elements:

  • Consent Mastering

    Key to the GDPR is consent and the linked purpose dimension. In order to manage the complete consent state you need to make sure that this information is available to all your data consumers and automatically applied. You can use consent mastering techniques such as master data management and data virtualization for this purpose.

  • Data Discovery & Classification

    The GDPR is all about protecting personal data. Do you know where all you PII type of data is located?  Data discovery will automatically locate and classify sensitive data and calculate risk/breach cost based on defined policies.


    Data Discovery & Classification

  • Data Register

    A data register is also a key GDPR requirement. You are expected to maintain a record of processing activities under your responsibility or with other words you must keep an inventory of all personal data processed. The minimum information goes beyond knowing what data an organization processes. Also included should be for example the purposes of the processing, whether or not the personal data is exported and all third parties receiving the data.

    A data register that is integrated in your overall data governance program and that is linked with the reality of your data landscape is the recommended way forward.




° Financieele Dagblad

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EUROPEAN RETAILERS ARE MISSING 35% OF SALES DUE TO LACK OF PRODUCT INFORMATION

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The holiday season is the most important sales moment of the year. Nevertheless, a Zetes study reveals that retailers miss about 35 percent sales due to products not being immediately available or the lack of product information.

A quarter of consumers leave a store without actually buying anything if they do not immediately see the product that they are looking for or if that is not immediately available. It is one of the conclusions of a market research conducted by supply chain expert Zetes. The study specifically focused on buyer behavior during the annual peak period between November and January and analyzed both physical and online retail. 120 European retailers and over 2000 consumers were interviewed for this study in the January 2018 timeframe.

Time is money

The study states that stores miss 35 percent of sales due to the unavailability of products. The main reason for this is the expectation of the customer: more than in the past, customers will simply leave a store if they do not immediately see a product and they do not bother to talk to a shop assistant. 

If customers do however approach a shop assistant, they expect to receive more information about the product availability within two minutes. A rather limited window of opportunity especially if you know that the study also calculated that 51 percent of shop employees need to go to a cash desk to obtain the necessary information, and that 47 percent also needs to check the warehouse to verify the availability of a product. Both actions cost time and time is very expensive during the peak period. The study also reveals that 62 percent of retailers do not have access to real-time product data.  

Return Management

Also deliveries and returns typically cause extra problems during these busy months. It is common knowledge that people tend to buy quicker when they are sure that they can possibly return a product. The processing of returned parcels also causes problems. 26 percent of retailers indicate that they are having problems during the peak, so that only 39 percent of the returned goods are available for sale within 48 hours.  

Conclusion

“A lack of visibility of data is the core of these sales problems during the holidays,” the report states. “Consumers want choices, and they want to be informed. Instead of a general “not available” message, a retailer has a much greater chance of securing sales by telling the customer that a product will soon be back in stock and delivered within three days or will be available for click & collect.” There is still a significant room for information management improvement with direct sales optimization as a result. 

More information
https://www.zetes.com/en/white-papers

Also in need for real-time product information?

Would you like to know how Datalumen can also enable you to get real-time product information?

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GARTNER SURVEY FINDS CHIEF DATA OFFICERS ARE DELIVERING BUSINESS IMPACT AND ENABLING DIGITAL TRANSFORMATION

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By 2021, the CDO Role Will Be the Most Gender Diverse of All Technology-Affiliated C-level Positions.

As the role of chief data officer (CDO) continues to gain traction within organizations, a recent survey by Gartner, Inc. found that these data and analytics leaders are proving to be a linchpin of digital business transformation. 

The third annual Gartner Chief Data Officer survey was conducted July through September 2017 with 287 CDOs, chief analytics officers and other high-level data and analytics leaders from across the world. Respondents were required to have the title of CDO, chief analytics officer or be a senior leader with responsibility for leading data and/or analytics in their organization. 

“While the early crop of CDOs was focused on data governance, data quality and regulatory drivers, today’s CDOs are now also delivering tangible business value, and enabling a data-driven culture,” said Valerie Logan, research director at Gartner. “Aligned with this shift in focus, the survey also showed that for the first time, more than half of CDOs now report directly to a top business leader such as the CEO, COO, CFO, president/owner or board/shareholders. By 2021, the office of the CDO will be seen as a mission-critical function comparable to IT, business operations, HR and finance in 75 percent of large enterprises.” 

The survey found that support for the CDO role and business function is rising globally. A majority of survey respondents reported holding the formal title of CDO, revealing a steady increase over 2016 (57 percent in 2017 compared with 50 percent in 2016). Those organizations implementing an Office of the CDO also rose since last year, with 47 percent reporting an Office of the CDO implemented (either formally or informally) in 2017, compared with 23 percent fully implemented in 2016. 

“The steady maturation of the office of the CDO underlines the acceptance and broader understanding of the role and recognizes the impact and value CDOs worldwide are providing,” said Michael Moran, research director at Gartner. “The addition of new talent for increasing responsibilities, growing budgets and increasing positive engagement across the C-suite illustrate how central the role of CDO is becoming to more and more organizations.” 

Budgets are also on the rise. Respondents to the 2017 survey report an average CDO office budget of $8 million, representing a 23 percent increase from the average of $6.5 million reported in 2016. Fifteen percent of respondents report budgets more than $20 million, contrasting with 7 percent last year. A further indicator of maturity is the size of the office of the CDO organization. Last year’s study reported total full time employees at an average of 38 (not distinguishing between direct and indirect reporting), while this year reports an average of 54 direct and indirect employees, representing the federated nature of the office of the CDO design. 

Gartner CDO Survey Results

Key Findings

CDO shift from defense to offense to drive digital transformation

With more than one-third of respondents saying “increase revenue” is a top three measure of success, the survey findings show a clear bias developing in favor of value creation over risk mitigation as the key measure of success for a CDO. The survey also looked at how CDOs allocate their time. On a mean basis, 45 percent of the CDO’s time is allocated to value creation and/or revenue generation, 28 percent to cost savings and efficiency, and 27 percent to risk mitigation. 

“CDOs and any data and analytics leader must take responsibility to put data governance and analytics principles on the digital agenda. They have the right and obligation to do it,” said Mario Faria, managing vice president at Gartner. 

CDO are responsible for more than just data governance

According to the survey, in 2017, CDOs are not just focused on data as the title may imply. Their responsibilities span data management, analytics, data science, ethics and digital transformation. A larger than expected percentage of respondents (36 percent) also report responsibility for profit and loss (P&L) ownership. “This increased level of reported responsibility by CDOs reflects the growing importance and pervasive nature of data and analytics across organizations, and the maturity of the CDO role and function,” said Ms. Logan. 

In the 2017 survey, 86 percent of respondents ranked “defining data and analytics strategy for the organization” as their top responsibility, up from 64 percent in 2016. This reflects a need for creating or modernizing data and analytics strategies within an increasing dependence on data and insights within a digital business context. 

CDO are becoming impactful change agents leading the data-driven transformation

The survey results provided insight into the kind of activities CDOs are taking on in order to drive change in their organizations. Several areas seem to have a notable increase in CDO responsibilities compared with last year:

  • Serving as a digital advisor: 71 percent of respondents are acting as a thought leader on emerging digital models, and helping to create the digital business vision for the enterprise.
  • Providing an external pulse and liaison: 60 percent of respondents are assessing external opportunities and threats as input to business strategy, and 75 percent of respondents are building and maintaining external relationships across the organization’s ecosystem.
  • Exploiting data for competitive edge: 77 percent of respondents are developing new data and analytics solutions to compete in new ways.

CDO are diverse and tackling a wide array of internal challenges

Gartner predicts that by 2021, the CDO role will be the most gender diverse of all technology-affiliated C-level positions and the survey results reflect that position. Of the respondents to Gartner’s 2017 CDO survey who provided their gender, 19 percent were female and this proportion is even higher within large organizations — 25 percent in organizations with worldwide revenue of more than $1 billion. This contrasts with 13 percent of CIOs who are women, per the 2018 Gartner CIO Agenda Survey. When it comes to average age of CDOs, 29 percent of respondents said they were 40 or younger. 

The survey respondents reported that there is no shortage of internal roadblocks challenging CDOs. The top internal roadblock to the success of the Office of the CDO is “culture challenges to accept change” — a top three challenge for 40 percent of respondents in 2017. A new roadblock, “poor data literacy,” debuted as the second biggest challenge (35 percent), suggesting that a top CDO priority is ensuring commonality of shared language and fluency with data, analytics and business outcomes across a wide range of organizational roles. When asked about engagement with other C-level executives, respondents ranked the relationship with the CIO and CTO as the strongest, followed by a broad, healthy degree of positive engagement across the C-Suite. 


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FORRESTER DATA VIRTUALIZATION MARKET Q4 2017 UPDATE

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Forrester Research recently published The Forrester Wave™: Enterprise Data Virtualization, Q4 2017 report. The firm profiled 13 vendors in this report. The last Wave on this topic was published a while ago,  March 2015, with 9 vendors. Here is an overview of what has changed in the last two-and-a-half years.

Data Virtualization Market has Expanded

According to this Forrester report, the enterprise data virtualization market has expanded along multiple dimensions – customer adoption, more industries, more use cases, new players, and acquisitions.
 

  • More customer adoption – Forrester states customer adoption of data virtualization has been gaining momentum. In 2017, Forrester profiled 2,106 global technology decision makers for its Data Global Business Technographics Data and Analytics Survey, and found that “…56% of global technology decision makers in our 2017 survey tell us they have already implemented, are implementing, or are expanding or upgrading their implementations of DV technology, up from 45% in 2016.”
  • More industries – Forrester states that in its early years, data virtualization was primarily used in financial services, telecom, and government sectors. In the last 5 years, however, Forrester has found significant adoption of DV in insurance, retail, healthcare, manufacturing, oil and gas, and eCommerce verticals as well.
  • More use cases – Further, Forrester found that among the customers who have been using data virtualization, the deployment has increased from single-use case, primarily customer analytics, to a broader enterprise-wide use involving multiple use cases such as internet of things, fraud detection, and integrated insights.
  • New players – In the 2017 Enterprise Data Virtualization Wave report, four new vendors have been included implying, in our opinion, expanding data virtualization market.
  • Acquisitions – In signs that the data virtualization market is maturing, TIBCO Software recently acquired Cisco Information Server, thus entering the data virtualization market.

We think all these data points are significant indicators that the data virtualization market is a healthy, growing market that is reaching maturity.

Data Virtualization Poised for Further Growth Pushed Forward by Leaders

Forrester expects the data virtualization market to grow further “because more enterprise architecture (EA) professionals see data virtualization as critical to their enterprise data strategy.” It says that these EA Pros are looking to support more complex data virtualization deployments. To satisfy such needs, the leaders featured in the report provide high-end scale, security, modeling, and broad use case support with their mature product offerings. “The leaders we identified offer large and complex deployments, and they support a broader set of use cases and more mature data management capabilities,” Forrester says. It is worth noting that four of the past five leaders retained their Leaders positions, while one vendor slipped into the Strong Performers.

Read the Complete Report

The Forrester Wave: Enterprise Data Virtualization, Q4 2017 is a must read for enterprise architecture (EA) professionals. According to Forrester, “Enterprise data virtualization has become critical to every organization in overcoming growing data challenges. These platforms deliver faster access to connected data and support self-service and agile data-access capabilities for EA pros to drive new business initiatives.”

Click here
to download
the complete report.



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