This Whitepaper clearly explains how the Data Governance function plays a key role and which factors are of great importance in successful data management. Also available in Dutch.

1. 1
Auteur
Karel Siemerink
Senior Data
Governance Consultant
Data Governance, the foundation for
building a successful data management
strategy
The importance of data
Data is the lifeblood of nearly all organizations, enabling them to
understand and serve partners and customers better, meet compliance
regulations, get products to market faster and more efficiently, improve
business processes and increase profitability.
Data has become bigger, faster and more diverse, and is used innovatively
to find cost-saving opportunities, uncover risks, improve service delivery and
customer engagement, and grow revenue. Data also lies at the heart of
digital transformation, letting organizations drive changes in their business
models to meet requirements in this new age of the fourth industrial
revolution*. Data is the fuel for process automation. The success of digital
transformation projects depends significantly on accurate, complete and fit-
for-purpose source data.

2. 2
Data Governance:
the foundation for building
a successful data
management strategy
[*The fourth industrial revolution is the name for the revolution in industrialization that we are now in. It describes the blurring of the
boundaries between the physical, digital and biological world. And revolutionizes the way of production by integrating the Internet of Things
(IoT), cloud computing, data integration and other technological developments at the core of production and production systems.]
Organizations execute hundreds of business processes across functions/departments in the course of
a single day, each with a beginning and an end, sharing the critical need for accurate, complete and
fit-for-purpose data to fuel processes so as to accomplish their objectives successfully. Data and
processes need to be integrated and managed, as they depend entirely on each other. Data exists to
enable employees to make good decisions.
The worst that can happen is people making bad business decisions based on data they may
perceive as trustworthy, complete, accurate, and consistent. Poor-quality data or data used in the
wrong context can be worse than no data at all. It can lead to making decisions that are not in line
with the business strategy and critical path. So one of the greatest risks to an organization is poor
data quality. That makes the issue of data quality a top concern. Organizations cannot do anything
important without high-quality data.
Data quality
Most people in organizations feel their data is up to scratch. They do their best to clean up their data,
install software to find errors automatically, and seek confirmation from external sources. The process
is time-consuming and expensive work, and most of the time it doesn’t go well. Even worse, cleaning
up never ends. Even if all the existing data has been cleaned perfectly, the root cause problem of
poor data quality at the source has not been addressed. As companies acquire new data, they will
also acquire new errors that impact the work.
A Gartner study found recently that firms lose an average of $ 15 million a year due to poor data
quality. Gartner has also estimated that as many as 85% of big data projects fail for the same reason.
Although there isn’t a standard list of attributes to define what makes data high-quality or low, a
representative list includes accuracy, integrity, consistency, completeness, validity, timeliness, and
accessibility. In other words: is the data accurate? Does the data match the schema definition? Are
the values consistent if the data exists in more than one place? Is the data complete? Is the data
valid? How recent is the information? Can the information be accessed? Bad data issues usually align
with people, process, and technology. People may not be following policies, processes may seem
correct in upstream locations only to cause problems downstream, technology may not be enforcing
constraints, or it may simply be generating bad data because of bad coding.
Rather than fixing data quality by finding and correcting errors, organizations must adopt a mentality
that focuses on creating data correctly the first time, to ensure data quality throughout the process.
This is step one for organizations that are serious about implementing a data-driven culture
organization-wide, monetizing their data and striving to become efficient. Organizations need to learn
how data can help them to run their businesses more efficiently. Using data intelligently lets them
automate processes, predict when machines need maintenance, and serve customers better.
Companies adopting this approach find that data quality improves quickly.
The scope and scale of data is such that realistically, it cannot all be tackled at once. So where should
you start? What data takes the highest priority and why?

3. 3
Data Governance:
the foundation for building
a successful data
management strategy
Most organizations tend to focus their data-quality efforts, energy and funding in a technology
solution. Although technology is a key critical component in managing data quality, it only represents
about 20% of the challenge. The real challenge is to agree organization-wide on data segmentation,
accountability, standards, controls, decision rights and forcing compliance with validated data
procedures and policies. Employees at every level of the organization should be trained in data
literacy. There is a need to have more people with the ability to interpret data, to draw insights, and to
ask the right questions in the first place. These are skills that anyone can develop, and there are now
many ways for individuals to upskill themselves and for organizations to support them, lift capabilities,
and drive change
The solutions for these challenges can be addressed through a Data Governance function to become
the foundation of an organization’s master data strategy/program. Data Governance assures the
sustainability in time of the data management solution, and will avoid the need for initiating major
future data cleanup and harmonization efforts. It lies at the heart of building a data-driven culture in
the organization.
Data Governance
The principal of governance can be explained through the following example. Imagine an orchestra
comprising 60+ talented musicians, all excelling at playing their own instruments, and gathering to
perform a symphony for a large audience. Everyone understands that if they don’t play the same
symphony, at the same speed and knowing who must play when, the result will not be pleasant for
the audience to listen to. For the orchestra to perform well, all the musicians must play the same
music at the same speed. To ensure those critical requirements are met, the orchestra is led by a
conductor who directs the performance. He unifies the orchestra, sets the speed and shapes the
sounds, and provides instructions to the musicians on their interpretation of the symphony being
performed. The symphony is also documented on a sheet as a language, just like reading aloud from
a book. The symbols used on the sheet represent the pitch, speed and rhythm of the symphony they
convey, as well as expression and techniques used by the musicians playing the piece. Think of the
notes as the letters, the measures as the words, the phrases as the sentences and so forth. The
conductor and the sheet music facilitate the orchestra in conveying a symphony such that the
audience can enjoy the performance immensely.
For an organization to perform well, there are very similar requirements to those in the orchestral
example. An organization needs a clearly-designed strategy and a clear plan for executing that
strategy, to achieve the organization’s objectives. All functional areas of the organization need to be
aligned with the execution of the plan fully, knowing exactly how/when/where to contribute to the
plan’s execution. This will be achieved best through the design & execution of organization-wide
integrated business processes, as well as the definition & implementation of data standards,
architecture, policies and procedures that are aligned fully with these processes. A process & data
governance function and organization will be fundamental to establishing such an environment.
Governance is a well-defined and well-understood structure and process for decision-making,
controls, accountability and the executive management of data and processes. It includes the
determination of data sources, responsibility for integrity, defining requirements for business process
management & development/change, and mechanisms to arbitrate differences amongst stakeholders.
Management is the decisions you make; Governance is the structure for making them. Effective
governance will lead to a data-driven culture within an organization and as a result, high-level data
quality becomes a high priority in the organization.

4. 4
Data Governance:
the foundation for building
a successful data
management strategy
Executive sponsorship from the organization is key and critical for governance to be effective. Data-
driven culture starts at the (very) top. Companies with strong data-driven cultures tend to have top
managers who set an expectation that decisions must be anchored in data – that this is normal, not
novel or exceptional. They lead by example and strongly endorse and encourage enterprise-wide
commitment to a data governance program. With Executive Management buy-in, an investment can
be agreed for monetary, technology and staff resources.
Obviously not all data is equal, so organizations shouldn’t try to govern all data equally. Focus first
and govern properly the least amount of data with the greatest business impact. Data that only
impacts part of the business can initially be governed less, and data used by the fewest number of
people, departments or processes should be governed least well. This will help focus the limited
resources on the data that matters.
There is no ‘one size fits all’ approach for building a successful data governance function and
organization. The design and implementation will depend significantly on the organizational culture,
DNA and business critical path. However, the minimum requirements for building a pragmatic and
sustainable data governance function & organization, are four building blocks:
1. Organizational bodies
It’s important to have strong decision-making bodies in place, that are empowered and accountable
for the activities with the data governance program. Decision rights and accountabilities should be
assigned at multiple levels across the organization. This could consist of a data governance council, a
data governance office or a stewardship community to facilitate the management of people,
processes and tools for the administration of a data governance program emphasizing transparency,
reporting, the presentation of meta data, and accountability.
2. Rules & Rules of engagement
Data goals, scope, ownership, segmentation, rules, definitions, controls, decision rights, policies &
procedures, life-cycle practices, tools and technology, quality measurements etc.
3. Integrated processes and technology
Data and processes will need to be managed in a fully integrated manner as they are entirely
dependent on each other. Changing processes might require data to be adjusted, and by the same
token changes in data might impact the processes this fuels. Data creation, maintenance and
retirement processes will need to be designed so that data is created, maintained and retired correctly
the first-time, meeting data-quality dimensions, procedures and policies. Technology plays a key
critical role in automating these processes with workflow, ‘track and trace’ and audit track
functionality.
4. Support the business strategy
Obviously, the data governance function needs to be fully aligned with the organization’s strategy and
critical path. With reporting, data analytics and compliance playing a huge role in the success of
today’s organizations, strong data governance becomes more vital than ever.

5. 5
Data Governance:
the foundation for building
a successful data
management strategy
A lot of additional information and details about data governance & data management can be
obtained from Data Management Reference Models such as DMBOK, for example. Such reference
models contain generally-accepted best practices and references for each data management
discipline (knowledge areas). Each knowledge area identifies:
a. Activities, processes and best practices
b. Roles & responsibilities;
c. Deliverables and metrics;
d. Maturity models.
It should however be realized that though these reference models provide value-added information
about the different requirements (the what) by knowledge areas, they do not provide clear guidance
on how to implement these successfully in an organization.
Data governance in short
It is obvious that poor data quality is a great risk for organizations. Especially in today business
environment where data is one of the most important bloodline of an organization. To be able to
create a corporate data-driven culture that support business goals optimally, a data governance
function is a critical requirement that forms the fundament for a successful data strategy.
The successful setup and implementation of a data governance function heavily depends on the
adoption and hands-on support from executive management. They must be ambassadors for the
program and support and facilitate it throughout the entire company.
It is important to realize that technology is only an enabler for a successful data strategy. The
outcomes of the strategy for people, processes and data should have the main focus.
When introducing a data governance function, it makes no sense to try to boil the ocean. First focus
on the smallest amount of data with the greatest business impact. The implementation of data
governance is an evolution, not a revolution. S: So take people along in the process and train them to
improve creation, maintenance and interpretation of data. In this way, there is a basis for organizing
all building blocks, from organizational units, clear guidelines and rules, integrated processes and
technology, to alignment with the business strategy.
At Tentive we have the experience and expertise to support your organization in envisioning and
building a pragmatic data governance function and data management strategy that is aligned fully
with your organization’s strategy and critical path.