If Government Data Could Fix Traffic Chaos and Protect Our Homes from Flooding, We’d Use It… Right? Wrong!

Viewpoint paper
OpenGovernment
Data
Learn why it will change your digital life forever

Table of contents
1 Evolve and change to a mature standard
2 On to enlightenment
3 Read about its value chain
4 Combine Open Data, Big Data, and IoT
7 Go from Open Data to IoT
8 Get value from your information systems
9 Gain added-value services
10 Incorporate an Open Data engagement model
14 Incorporating business and industry
14 Moving forward
14 About the author
Viewpoint paper | Open Government Data

1
Open Data is the ability to use government data in your
environment—for your profit and others’ benefits. As the
state of Open Data evolves, it becomes more and more
normal for governments to share data and others to use it.
Making it a reliable source of high-quality, consistent data,
available for everyone, to provide better services for others
is its goal.
Open Government Data—a strategic initiative in governments around the world—offers
greater transparency to citizens by sharing government data in a machine-readable format,
for free reuse by others.
Over the last five to seven years, most governments have adopted an Open Data policy, and
many data sets are available on a multitude of websites, such as data.gov. Like any new
technology or trend, Open Data is going through the usual cycles of the Gartner hype cycle
adoption curve.1
However, now’s the time to refocus Open Data from useless inconsistent
data sets—full of errors—to a trustworthy and valuable input source for all kinds of
application and information analytics, including new value-added services for companies
and citizens.
Evolve and change to a mature standard
Get through inflated expectations
It’s clear that Open Government Data is here to stay, as it’s widely backed by senior
government executives.2
In fact, by mid-2015, most governments have adopted an Open
Data policy of their own,3
being driven by the desire to be an open and transparent
government. In Europe, Open Data received a big boost by the updated PSI Directive 20134
that was translated into local laws in the subsequent two years. This directive states that
Open Data will be the default for most publications, with respect for privacy and allowance
for some exceptions to the rule.
In the early days of Open Data, 2008 to 2013, the primary focus was on setting up an Open
Data platform and publishing as many Open Data sets as possible. So, these platforms
started popping up everywhere, as every government level—city, regional, federal, or
continental—wanted its own channel.
Many governments, particularly in the U.S. and UK, invested heavily in them and also in
educating companies and schools on the value of using these data streams. This resulted in
a first generation of citizen-centric applications (apps), such as “find my nearest hospital”
and “where are public bathrooms.” Often these were—and still are—driven by local
contests, such as AppsForX, with X being the name of a city or organisation. They were
useful in raising awareness and understanding the value of these data sets, but the apps
themselves created little business value , so few companies were established, and even less
survived based on this first generation of apps.
Because the focus was on getting as many data sets out there as possible, it was hard for a
company or individual to find the correct data sets, bring them together consistently, and
make some kind of practical use out of them as an application or analysis. Whilst there are
various techniques available, such as Linked Open Data (LOD), to bring these all together,
mostly technical people are fully aware of the value of these techniques—rarely the
individuals who simply want to use the data sets.
1
http://www.gartner.com/technology/research/
methodologies/hype-cycle.jsp
2
http://www.opendatabarometer.org/
3
Ibid
4
http://ec.europa.eu/digital-agenda/en/
european-legislation-reuse-public-sector-
information
If you’re looking for ways to expand your
Open Data intiatives and their impact, become
more influential in creating and stimulating
economical advantage out of this data. This will
help businesses looking to obtain value from
Open Data, either in processing it directly into
apps or services, or by enriching and combining
it with other commercial data and reselling this
in a data-as-a-service model. This evolution
of Open Data is likely to positively impact most
everyone’s daily life.

2
Now, there’s nothing fundamentally wrong with this approach. In fact, it’s typical for any new
technology on the Gartner,5
hype-cycle curve, especially in the early phases when an
initiative is mostly (new) technology driven. And governments should certainly continue to
produce Open Data, but the quantity of data sets is clearly not the only parameter that will
make this a long-lasting business model.
Recently, governments started questioning the failure rate of start-ups and survival of
added-value services with positive longer-term business potential. Because of this, since
2014, governments shifted away from publishing high-volume supply- and quantity-driven
data sets to developing industry partnerships and providing demand- and quality-driven
data sets.6,7
Governments realise Open Data’s real value will come from industry and the
added value that companies can bring to the public. This makes it much more efficient for a
government to have an open dialogue with industry partners to align the production of data
sets to the services each industry wants to produce.
This approach also means governments need to be a trustworthy source of publishing
information. This leads to early discussions on how governments should become service-
level agreement (SLA)-driven to Open Data users. This type of SLA should contain basic
guarantees that the government publishing entity will provide the data, keep it correct, and
update or enhance it going forward.
On to enlightenment
Recently, there has been increased interest from the Eurpean Union (EU) Digital Agenda to
provide standards to all EU countries to make Open Data sets more consistent. The EU
launched a great deal of material to standardise publishing and harvesting of Open Data sets
amongst member states. Various initiatives—defining standard data models, vocabularies,
and metadata—are taking place to realise reuse of data sets across government publishing
entities. Signs are good that the quality and consistency of information will increase by
leveraging these models and standards.
Despite the existence of many Open Data platforms, there remains considerable resistance
at the department executive level inside governments to engage and publish Open Data, or
allow its reuse by other governments and countries. This is mainly due to lack of awareness
and engagement.
So, all too often, many governments fall back on publishing classical printed reports in which
much statistical data is printed in a graph format. Because of governments’ reluctance to
publish Open Data, for example, journalists must often resort to scanning printed reports or
retyping their content in order to make an analysis in the press. So, suddenly, one starts to
question the correctness of the information—the fact that parts of the published data may
be owned by others, the data could be wrongly interpreted, and many more reasons come to
the surface. These are all fears of the unknown and attributes dealt with in basic change
management. Embracing Open Data is therefore more than a mere technique; it’s also a
significant cultural shift to greater information openness and transparency.
New legislation comes into effect in 2015, for all EU member states, that will make Open
Data much more the default and standard way of publishing data. This will drive an even
wider adoption of Open Data—at all government levels—and herald an increase in
publishing new Open Data sets. Following are the principles that are part of the new EU PSI
20138
directive, which will be the norm for local legislation:
• Open Data by default
• Quality and quantity
• Usable by all
• Data released for improved governance
• Data released for innovation
• Technical annex
5
http://ec.europa.eu/digital-agenda/en/
information
6
https://www.bestuurszaken.be/open-data-
dag-vlaanderen
7
http://www.opengovpartnership.org/country/
united-kingdom
8 http://ec.europa.eu/digital-agenda/en/
information

3
As the early adopting countries of Open Data, the U.S. and UK launched a Digital Government
Partnership in early 2015 to “use digital services to better serve citizens and businesses, and
to build a stronger digital economy.”9
This partnership further enhances their commitment to
publish Open Data sets as one of the cornerstones of their open government approach.
There are still a lot of subject areas where Open Data is not yet widely accepted and where
the debate still rages, or is just starting. Movements, such as Open Access—the publication
of research funded by governments, as Open Data10
is still in the early stages of becoming
the norm, with some countries more advanced than others in this. Of course, Open Data
tends to threaten traditional approaches and commercial ecosystems.
So, Open Data is undergoing a transformation at this very moment. It is evolving from a
technology-inspired initiative into a cultural shift as Open Data becomes the default for
governments around the world. As such, this transformation closely matches the original
objectives of facilitating more open and transparent government by using modern IT
technologies to disclose government information. The whole movement is evolving from
simply producing data sets to creating added-value services using Open Data and other data
streams. It will increasingly become a sub-stream of government information that can be
merged with other information to achieve economic value for companies or benefit individuals.
So, Open Data is becoming a given and will not disappear. And all of the hype around it will
likely fade away in the near future as it become natural for a government to have quality
data sets available.
Open Data is following the logical stages of the Gartner hype-cycle curve, and the next
phases are on the horizon and will be taken seriously as an available source of information in
analytics and added-value services.
Read about its value chain
DeloittehaspublishedaninterestingoverviewvaluechainmodelofhowOpenDatacanbeapplied.11
9
https://www.whitehouse.gov/blog/2015/01/16/
us-uk-digital-government-partnership
10
http://ec.europa.eu/research/swafs/index.
cfm?pg=policylib=science
11
https://www2.deloitte.com/content/dam/
Deloitte/uk/Documents/deloitte-analytics/
open-growth.pdf
Figure 1. Deloitte Open Data value chain model
Source: Deloitte

4
Deloitte distinguishes five actors:
• Suppliers—Organisations that publish their data via an open interface, letting others use
and reuse it.
• Aggregators—Organisations that collect aggregated Open Data and sometimes other
proprietary data, typically on a particular theme, and find correlations, identify efficiencies,
or visualise complex relationships.
• Developers—Organisations and software entrepreneurs that design, build, and sell
web-based, tablet, or smartphone applications for individual consumption.
• Enrichers—Organisations—typically larger, established businesses—that use Open Data
to enhance their existing products and services through better insights.
• Enablers—Organisations that facilitate the supply or use of Open Data, such as the
AppsForX competition initiatives.
This value chain clearly illustrates that each government (aka supplier) is the first point in the
value chain, after which many other players provide added value by enriching the data or
encapsulating it into application programming interfaces (APIs) for programmers.
Combine Open Data, Big Data, and IoT
Access a new world of value-added services
Since 2013, the focus of Open Data has increasingly shifted away from simply producing and
publishing data sets to using them for analytical or added-value purposes, such as doing
something with these data sets. This means Open Data is maturing, and companies are now
seriously considering using these sets in their analytics environments—for internal
purposes or to create value-added services for external data users.
Today, there are enough available tools, methods, and websites to position Open Data as a
form of Big Data.12
And by using real-time sensors, along with Internet of Things (IoT)
concepts, even greater value can be obtained from it. Arguably, a new technology-driven
hype cycle could be starting here; regardless, this usefully highlights some new possibilities
in using Open Data for new valuable services in real time. One example that can be found,
already in many countries, is using Open Data to divert traffic based on real-time information.
There is a real revolution going on in this space. Big Data techniques such as analysing
and visualising data can now be applied to make sense out of the many thousands of data
sets each government (or country) publishes, even in real time and for sensor-based
information streams.
12
http://opendata-tools.org/en/data/
Figure 2. The data revolution
(Open)
Sensor
Data
(Open)
Big Data
Open
Data
Let’s look a little bit closer at how these ecosystems can actually coexist, strengthen each
other, and bring value to companies and individuals.
Link Open Data to Big Data—One small step
Now, it’s only a small step from Open Data to Big Data. One may not directly see the link, but
think about weather forecast data as an example. This data tends to be high in variety and
velocity, and certainly big in terms of volume if unprocessed and coming directly from, say,
a satellite source. As such, there is a convergence between private Big Data sets and public
Big Data sets, and the intersection shows an interesting area where these can be joined up
and analysed.

5
In Figure 3, Joel Gurin, Open Data Now website13
owner, made it very clear that there is much
added value in joining up these data sets.
The Guardian newspaper goes one step further and defines Open Data14
as:
• Big Data that’s not open is not democratic.
• Open Data doesn’t have to be Big Data to matter.
• Big, Open Data doesn’t have to come from governments alone.
• But, when a government turns Big Data into Open Data, it’s especially powerful.
The added value not only comes from government institutes, but also from bringing public
and private data sets together. At first, one may be inclined to think this will present huge
challenges in aligning the data sets to join or query them. But, there are a lot of techniques
that can facilitate this process.
In the recent past, analytical and visualisation tools were very focused on a static database
model to create some sort of meaning out of the database. IT teams spend considerable
time transforming and cleansing data before loading it into a data warehouse, ready for use
and analysis. This approach, however useful, is not very agile and usually only works on
structured information and predefined queries or parameters. Fortunately, new techniques
can speed up this lifecycle and make it much more dynamic.
Techniques like LOD have made it possible to join Open Data sets from around the world,
simply by using the uniform resource identifier (URI) of each data set. This makes analysing
data interesting, as it’s independent from the source or format from which it originates. All
you need to know is the URI. And as technology matures, more and more dynamics are
coming out of these tools, enabling additional unstructured data sets to be analysed
automatically. These new features generate a new dynamic and new possibilities.
With LOD, we can make the jump to the semantic web. According to Tim Berners Lee,15
if
Open Data sets are the pieces, and the semantic web is the whole of all these pieces, it is a
part—not the whole—of Web 2.0.16,17
Of course, there are many pieces that all link up
together to form the semantic web, as depicted in Figure 4. For the purposes of this
document, we will focus on Open Data only.
13
http://www.opendatanow.com/
14
http://www.theguardian.com/public-leaders-
network/2014/apr/15/big-data-open-data-
transform-government
15
https://nl.wikipedia.org/wiki/Tim_Berners-Lee
16
http://www.w3.org/DesignIssues/Semantic.html
17
http://www.techrepublic.com/article/an-
introduction-to-tim-berners-lees-semantic-web/
Figure 3. The value of joining data sets
Source: Joel Gurin, Open Data Now website

6
An interesting dimension of the semantic web is that artificial intelligence processes can now
be applied—getting the web to do some thinking for us—to published Open Data sets. This
is less “science fiction” than it sounds. In the last couple of years, many new semantic
algorithms have come to life that can interpret data and match them with similar data sets.
So, these algorithms can derive what fields are, for example, an address in a particular data
set, and join them up with similar fields in other data sets. These algorithms are all based on
underlying standardised data models and brought together in ontologies. Several
organisations are publishing standardised data models, so there is a definite advantage and
need for this.
One example is Dublin Core,19
where lots of standard models are defined and published. If
analytical tools implement these standard data models and apply the semantic web
algorithms, then a lot of the joining up and interpretation of data sets can be automated. If a
data set is further encapsulated in an RDF stream, it can be queried using SPARQL. This
brings even more options to the table of an analytical tool, such as, querying the right
information from any Open Data set that is encapsulated in RDF using SPARQL, instead of
loading the whole data set into the application.
One example of how this can be applied to your website or application is Open Calais.20
With
this, the Open Calais Web Service automatically creates rich semantic metadata for the
content you submit—in well under a second. Using natural language processing (NLP),
machine learning, and other methods, Calais analyses your document and finds the entities
within it. But, Calais goes beyond classic entity identification and returns the facts and events
hidden within your text as well. So, by applying semantic web methods to Open Data, a lot of
information can be brought consistently together to be analysed, and even automatically
joined up.
Now, apart from the characteristics of volume, variety, velocity, and vulnerability—as with
Big Data, there are advanced analytical and visualisation Big Data methods that can also be
easily applied to Open Data. Many new open source platforms are popping up, providing
tools and mechanisms that make visualisations and analytics on Open Data sets much easier
for companies and individuals. These platforms all use the same principle: Collect any
number of Open—and closed—Data sets and let the platform produce a visualisation on it.
So, getting meaning and value out of these Open Data sets has never been easier than today.
A good overview of available data and visualisation sites can be found at http://opendata-
tools.org/en/visualization/.
Bringing this all together, Open Data is the raw material. By applying LOD and semantic web
techniques, you can get more automated alignment and meaning out of this data. It can be
joined up in visualisation and using analytical toolsets, enabling much faster and better
interpretation of massive amounts of data. And because of this, Open Data deserves its new
status as a valuable (extra) input stream in any analytical programme.
18
http://upload.wikimedia.org/wikipedia/
commons/f/f3/Semantic_Web_Stack.png
19
http://dublincore.org/
20
http://www.opencalais.com/about
Figure 4. Pieces forming the sematic web18
Source: Wikimedia

7
Go from Open Data to IoT
There is only a small next step from analysing static data sets to dynamic real-time sensor
data and IoT. Governments are sending out more and more sensor data, such as weather
stations, water levels, pollution levels, traffic density, and security cameras, as real-time
Open Data sets. This lets companies create added-value services in real time by
incorporating these real-time streams in their analytics platform or tool.
Some companies, such as IBM, Google™, Microsoft®, and Cisco, were quick to realise the
value in combining Open Data and IoT, creating platforms to take in various Open Data sets—
static or real time, produced traditionally or via sensors. These companies have launched
various Open Data platforms that collect and analyse these information streams and let
users create new services to the public—paid or free.
These platforms all share a similar approach, in which:
• Information from sensors is collected and published on the platform. Governments play a
vital role in helping publish data from public sensors. Of course, security and privacy
concerns are taken seriously.
• A user logs onto the platform, connects various—sensor and other—data sets together for
analysing and viewing the information in near real time.
• The user produces a visualisation or application with this data that can be shared with the
world. The decision to make this a paid model is up to the user.
Advanced visualisation
Visualise the data sets and bring to public
Deliver (new) services to citizens or companies
Open Data
Many data streams from various websites all over the world
Big Data
Bring these data sets together and congregate them—ﬁnd
meaning out of the (automatically/semantic/ontology-based)
BI 2.0
Applying DWH BI techniques to analyse the data sets
Government
Partners
Partners
Virtualisation/
services
BI 2.0
Big Data
Open Data
Figure 5. Best practice approach to extract value from Open Data sets
Figure 6. IoT sensor data inputs
Traﬃc
information
Bridges
Waterways
Weather
station
Water levels
Air pollution
IoT XX%

8
So, now it’s time for various industries to consider Open Data as an input stream as a
valuable and trustworthy add-on source of (sometimes real-time) information. Companies
can then use analytics, Big Data, and sensor data techniques on government-published Open
Data sets.
Get value from your information systems
Set up governance in an Open Data landscape
Most articles on Open Data tend to focus on the practical use of it once the data set is
published on an Open Data platform. However, it really all starts with unlocking data from
source systems and bringing it—in a consistent way—to the Open Data platform. As such,
concern for quality should originate in the back office, where traditional systems, databases,
and products hold that information, usually in a non-normalised way. Figure 7 shows how
the lifecycle of Open Data—from source to publication—could look.
21
http://opendataforum.info/images/Open_
Data_Handboek_20141119.pdf
22
http://www.dama.org/content/body-
knowledge
Whilst this paper’s author was writing a chapter for the Open Data handbook of the Flemish
Government,21
it was discovered that Open Data follows about the same patterns as any
data set that is loaded into a data warehouse, and that the same techniques remain valid:
• Data needs to be extracted from source systems in much the same way as it’s done for
data warehouses.
• To make sure data is consistent with standard data models and that semantic web
techniques can be applied, much attention should go into transforming the data and
preparing it for publishing.
• It is essential to load cleaned and consistent data into a data warehouse. This can also be
done when publishing data on an Open Data platform, including metadata provisioning.
As such, organisations can usually reuse most of their existing extra, transform, and load
(ETL), and data warehousing tools. In fact, reusing them is highly preferred, as it automates
the whole process, so updated data can be published by running the complete script.
It is strongly advised to also use all of the master data management (MDM) techniques with
Open Data. There are good models available, such as DMBOK.22
Certain governments, like
the UK and U.S., have gone to great lengths to provide standards, but all too often these are
not sufficiently mandatory, which results in a lot of publishing variations.
It is recommended that governments create or enhance their existing MDM plan by including
Open Data as a new channel for publishing data. However, data is often created at entity
level rather than enterprise, so it’s essential to make sure every entity does this at its own
level, before sharing data across entities.
It might even be a good idea for the publishing government to engage in a sort of SLA that
commits to providing quality data aligned with other agencies, which is constantly kept up to
date. Only with such a commitment can you be sure that Open Data will be used by companies.
Use governance in an Open Big Data landscape
With the arrival of Big Data analytics, the desire for consistency and quality in data sets will
only increase, so good governance will be necessary.
Figure 7. Open Data lifecycle
Search
Harvesting
Linked Open Data
Database
Document
Data set
Application
Extract.
transform,
publish
Open Data
platform
Open
Data
Government/publishers responsibility Users
Unlocking data from source systems Publish and (re)using Open Data

9
Gain added-value services
There is still a lot of debate on what an added-value service really means. It’s the ultimate
one million dollar question. At present, many consulting companies and the EU (the EPSI
platform) have issued reports about the economical return that publishing Open Data will
trigger. For example, McKinsey reported back in October 2013 that “Open Data can help
unlock $3 trillion to $5 trillion in economic value annually across seven sectors.”23
Figure 8
provides a clear picture of the sectors in which citizens can anticipate added-value services.
23
http://www.mckinsey.com/insights/
business_technology/open_data_unlocking_
innovation_and_performance_with_liquid_
information
Figure 8. Open Data’s potential value by industry
It’s clear that value is out there, but how do you unlock it? In Figure 9, a model is presented to
keep in mind when using Open Data.
Clean Query Visualise Publish
Your Open Data
Added value for citizens
Figure 9. Added value of Open Data
Getting meaning from Open Data may appear to be the most magical of all achievements,
but analytical tools have matured beyond simple reporting and can now look for meaning
automatically and present results in a highly visual way. Of course, interpretation is still
up to the human who is looking at this data, but much more number crunching is being
done automatically.

10
Incorporate an Open Data engagement model
If you are operating in a government entity and want to publish Open Data, there are a lot of
guidelines and handbooks available that mainly focus on establishing a platform and using
techniques like LOD to publish and harvest data sets. However, this is just the tip of the iceberg.
In this section, the focus is on getting the cultural and governance aspects right from day
one, as this will largely determine the success of any Open Data programme. For that, we
propose the steps covered in Figure 11 as a kind of Open Data engagement model (ODEM).
Step 1. Engage more
First of all, it’s important to define and set a strategy for deploying Open Data in your entity.
In the early days, the five-star model advised all governments—as a first step—to “just
publish it.” This indeed resulted in a quantity-led approach to Open Data; but also had an
adverse effect, as many companies do not want to use data that is not consistent or cleansed
properly before publishing.
It may seem logical and applicable in all important government programmes to adopt a
strategic approach, but the early days of Open Data clearly showed the programme was
driven by technically inspired people who primarily wanted to demonstrate that linking data
sets in a semantic web was possible. Although initially this was acceptable, we have now
moved beyond this phase on the Gartner hype cycle.
Developing a strategy means getting buy-in from all levels to engage with Open Data and
adopt a quality approach. It comes down from the top echelons, with the government itself
providing a framework for all entities to legally commit to Open Data. It also comes from the
ground up, with all agencies committing to publishing data sets in a consistent way with
other agencies. This means alignment and agreements will have to be created, shared, and
sometimes even enforced.
Figure 10. A Vision (Intel)—Improving life with Open Data as one of the input streams*
Better life for all
Sensor Data
Big Data
Open Data
Intelligent
government
Digital
services
*Loosely based on the Intel® Intelligent Systems Framework
Figure 11. Open Data enagement model
Engage
more
Stimulate
innovation
Educate
more
Steer
correct

11
Most governments will state they have the necessary laws and commitments in place.
However, there’s still a lot to be done at entity level to get the right leadership to
acknowledge that Open Data will not threaten or increase their work and services. Rather, it
will complement and help realise a truly digital government.
After a commitment is obtained at all levels and governance put in place to start publishing
Open Data, the first step is to create an Open Data Master Plan that brings together most—if
not all—information streams in an entity and defines which ones can be published as Open
Data. There are still some restrictions that apply to Open Data such as privacy, so existing
data classification schemes should be mapped against identified Open Data candidates.
Apart from identifying existing information streams, there must be valid instructions for
newly created applications or information streams; they should be logical to identify these
new streams with or without an Open Data marker. As such, it will become natural to define
Open Data sets from any change in existing or new applications functionality.
A next important step is to align your Open Data sets with standard-defined data models for
common objects like citizens, addresses, opening hours, budget, and service descriptions,
amongst others. There are many standards available, but various entities are choosing their
own data models, which makes it hard to interpret similar Open Data from different entities.
Luckily, semantic web techniques can come to the rescue; but, try and fix this before
publishing instead of letting algorithms guess (wrongly) about what elements it holds.
Readers who are familiar with MDM, data quality processes, and various existing data
validation processes should feel very comfortable, as these processes are entirely valid for
Open Data, and even more so as the scale of users is far larger than at the entity level.
At this point, you should determine whether you need an Open Data central team to
safeguard all the quality attributes for all entities. The more consistency that is built in by
applying standard data models, the more companies can start using all similar, joined-up
data sets.
Government entities often focus only on publishing their own Open Data. However, they can
also use Open Data from other entities or countries for internal purposes. An agency that
uses Open Data from other entities can make its analytics even more powerful, certainly in
an international context, when its local government results can be mapped onto those of
other similar countries.
When creating Open Data sets, it’s essential to automate as much as of the data extraction
as possible, transform it to a standard data model, and publish it on an Open Data platform.
In the business intelligence (BI)/data warehouse (DWH) areas, ETL tools facilitate automation
and the same tools and techniques can also be used for Open Data. This has the added benefit
of automatically scheduling Open Data sets updates, keeping the Open Data up to date.
Strategically, it is essential to learn from your experiences and improve the base—every
time, every day. This is not a one-time exercise; it is an ongoing process of discovering and
adapting your information needs to the reality of changing applications, data models, and
company requests. So, engaging at all levels is essential to Open Data success in your
government entity.
Apart from publishing data, governments must be open to feedback from individuals or
companies that use this data. Many government entities fear this feedback loop, as they
think only negative comments will be received. Nevertheless, being an open and transparent
government, which is the goal of Open Data, also means being responsive to customer
feedback, responding adequately to all quality issues.
Step 2. Stimulate innovation
Many governments believed that the success of Open Data would come naturally and that
many companies were waiting for the data to arrive so that they could start building apps on
it. Nothing less is true these days, as many governments start to realise it’s not enough just
to provide data sets without guidance; they must stimulate organisations to create
innovative services. In the early days, there were many hackathons and contests to get
people interested in creating the initial wave of services. Although they did create an initial
buzz, many of these applications did not survive or become sustainable. That is because the
people who attended these data contests were mostly students and not companies that
have a particular business model in place.
Engage summary
• Develop a strategy to deploy Open Data in
your entity—focus on cultural and governance
aspects as much as technical ones.
• Make an Open Data master plan and maintain
it; consider an MDM approach.
• Always go for quality and consistent
information; the rest will not be used anyway.
• Learn from your experiences and improve at
the base.
• React to users’ feedback.
• Start reusing Open Data from other agencies in
your analytical environment.
• For every new data stream in the back office,
check whether it can be replaced by using or
creating an Open Data stream.
• Automate the ETL process as much as possible.
• Do not delay; start now.
• Be a trustworthy partner by regularly updating
your data—increase frequency, validate
quality, and updated metadata.

12
Countries like the U.S. and UK learned that Open Data will only be used if the government
stimulates industry to discover Open Data, its usability, and economic value before they will
engage and make investments for building apps or new services. The Flemish Government
took a different approach in subsidising selected government entities to propose an Open
Data programme that will publish Open Data sets. A number of projects were selected that
have the best possible chance of published data sets being picked up by companies and
turned into services or apps. A subsidy is approved per project when data sets are published
and used by industry.
So, continue with the initial innovation contests, but increase focus on companies or
industries that present a business plan for long-term data use. This will immediately
generate corporate concerns about reliable partnerships with government entities, and raise
questions about whether data is correct and up to date. The solution is to provide a contact in
the government entity, ready to react together on any data issues. This puts governments in
unusual territory; for the first time, they may have to subscribe to a business SLA when
delivering Open Data. This should be seen as a positive move, likely to trigger more
consistency in data models and raise the quality of released data.
New (technically inspired) hypes are emerging on the horizon. This time, these are
techniques for IoT and real-time sensor data, based on Open Data principles. In response,
governments should not only stimulate the institutionalisation of Open Data, but also
actively engage with universities and leading-edge companies that want to take this one
step further. After all, it’s only the start of accepting Open Data as a normal way of getting
meaningful government data and creating services that help citizens learn new skills,
navigate safely on the roads, and prepare for severe weather conditions. And these are just a
few of the many new services that will be based on IoT and sensor data.
It’s best to work in a more cooperative and collaborative way with academic institutions and
corporations to unlock the first data sets that companies want to use in their new services.
This will deliver the best results in new added-value service delivered to citizens, based on a
continuous update of government data.
Step 3. Educate
From 2011 to 2014, the UK government spent a lot of time and resources on educating
school students about Open Data. The purpose was to stimulate the younger generation to
get acquainted with and start using it. This proved an interesting approach, as when the
younger generation is made accustomed to Open Data, it becomes normal for these
upcoming citizens to use and publish it.
More education is needed. Citizens and companies around the world require more
understanding of it. Not so much on the technical aspects, such as LOD and URI, rather on
the practical uses of Open Data in creating new applications and services.
How far should governments go? Should they invest in applications and services themselves,
even as innovators and thought leaders? Most likely, governments should restrict their
activities to publishing Open Data and let the market do its work. However, shining the
spotlight on good use cases by listing them on an apps website, like the U.S. government
initiative apps.gov, can stimulate the market so long as this doesn’t give preference to any
specific company or application.
In addition to educating citizens and companies, internal government personnel need
educating. One might think more about how to set up an Open Data programme, as the data
can also be a source of information for other government entities, such as, internal reuse of
Open Data. So all government entities need to be made aware of how to read and process
Open Data sets. Selected internal government data analysts should also receive training.
These people should be made aware of the various—even open source—platforms that
exist for producing visualisations and infographics with Open Data.
As data quality remains a prime aspect of Open Data, any education on MDM techniques is
not wasted effort. Delivering good, reliable data sets is essential for this information to be
trusted and used by industries and companies.
Stimulate innovation summary
• Engage with communities of interest to use
your Open Data—or understand why they
won’t.
• Think ahead about applications using IoT and
sensor data.

13
Note that education should be an ongoing process, not a one-off. Information sessions
should be repeated at least twice a year. Since not every entity is yet operating at full speed,
evangelising and technical guidance remain a necessity.
Step 4. Steer and correct
Open Data is not a single, finite initiative. It’s just started to impact governments, making it
necessary to learn, steer, and correct Open Data activities adequately. That’s why
governments need to keep an open approach to managing Open Data issues and adapt to
any situation—innovation, new standards, new data sets, and critics.
In the last few years, many themed platforms were established; examples include
healthcare, transport, and food platforms. Typically these are created by communities of
interest, nonprofit organisations, or EU-funded research projects. They all facilitate using
Open Data around one particular subject and offer a joined collection of data sets published
by governments, often on an international scale. Techniques such as LOD and URI are being
fully exploited to bring relevant data sets together and offer a collection of data to the user.
In a way, a form of data brokering has been established, which is perfectly in line with the
definition of Open Data—it enables organisations to enrich data or bring data sets together
for the public’s benefit.
Educate summary
• Educate your internal personnel on how to use
Open Data for improving citizen services.
• Give information sessions about how to
publish and use Open Data sets.
• Repeat these education steps frequently.
Actively communicate with various industries or communities of interest to facilitate use of
Open Data. Go one step further—provide scheduling information to these organisations.
This helps companies know when certain data sets will become available. Also listen to
companies when they describe their data needs; this will help trigger creation and delivery of
relevant Open Data sets.
Until now, many governments were supply driven, meaning they decide what data sets to
open and when. This resulted in a large quantity of data sets rarely being used. Only by
becoming more demand driven and publishing data sets that will actually be used, can
governments succeed in creating impact. This requires good cooperation with companies,
aligning data sets to defined needs.
Steer and correct summary
• Trigger communities to use your data, and give
them ideas.
• Help build community or themed platforms,
and continue to use standards.
• Become demand driven.
Figure 12. Linked Open Data approach
Added
value
services
Government
Media/
data broker
Enterprises
Citizens/
customers/
knowledge/
workers
01101
00111
11001
01101
00111
11001
01101
00111
11001
Figure 13. Aligning government data sets to defined needs
Past Future
Community of technical believers Culture: Think reuse from the beginning
Supply -driven Demand-driven
• Governments unilaterally decide
what data sets to publish
• Driven by numbers of data sets, not by
quality and consistency of information
• Governments seeking active
partnerships with industry
• Publish quality data sets and be a
trustworthy partner
• Tell us what you need and we will
make it available

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This is an HP Indigo digital print.
Incorporating business and industry
So far, this paper has focused on governments, suggesting ways to set up and run Open Data
programmes, but let’s not forget industry and community interests. Without the companies
and citizens that use Open Data, there would be no applications or services based on these
data sets.
So, how should industry and communities use Open Data? No matter which industry or
community of interest you operate in, it’s essential to get familiar with the concepts of Open
Data and investigate whether these data sets can be used as an extra source of information
in your analytics programmes. Statistical information about demographics or society trends
can prove very valuable for marketing research and positioning a (new) product to the right
category of people. Weather and traffic information can be very useful for transport
companies to plan or adjust work schedules and avoid idle time on the road.
Look for added value in your application. Try to find an app that people want and will pay for
such as services that make their life better—avoiding traffic, getting children into the best
schools, renting homes in a green area.
Moving forward
Open Data is here to stay. It is our strong belief that the initial buzz will fade away over the
next few years, as it will become normal for governments to publish Open Data in a
consistent and high-quality way. However, we are not there yet, and our best advice for
governments is to use an ODEM approach to become a trustworthy source of information to
the world. Only then, will the digitisation of governments become real, at least on the output
side, with results made available in a machine-readable format. As demonstrated, there’s no
shortage of technical methods or platforms, but a natural flow of data inside government
applications needs establishing.
About the author
Yves Vanderbeken
Yves Vanderbeken is an HP Enterprise Services account chief technologist and lead
enterprise architect for the Flemish government and local governments (Flanders). Since
2011, Vanderbeken has been a core team member of the Flemish government’s Open Data
team. He defined the technical strategy for setting up the Open Data platform and
extracting, transforming, and publishing information in a consistent manner. He is the
co-author of the Open Data Handbook published by the Flemish Government in February
2014. Vanderbeken coaches and advises various departments on their Open Data Master
Plan and how to disclose data from their source systems to the Open Data platform,
ensuring data quality and consistency across data sets.
If you are in an industry or community
that wants to use Open Data:
• Get familiar with Open Data concepts.
• Investigate what data you could use in your
internal analytics.
• Engage with your government to explain what
types of data you would like to use.
If you are a developer or a company
looking to develop services with Open
Data:
• Engage with your government and ask for
relevant data.
• Check the visualisation platforms, upload
Open Data sets, and publish your work.
• Cross and link the data sets you obtain.
© Copyright 2015 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. The only
warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein
should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein.
© 2012 Google Inc. All rights reserved. Google and the Google Logo are registered trademarks of Google Inc. Microsoft is either a registered
trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Intel is a trademark of Intel Corporation in the U.S. and
other countries.
4AA6-1379EEW, September 2015

If Government Data Could Fix Traffic Chaos and Protect Our Homes from Flooding, We’d Use It… Right? Wrong!

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