With the arrival of big data, data science and the internet of things, you could be forgiven for thinking that our sophistication with data is advancing at a phenomenal rate, and from a technical perspective you’d be right. But has our mindset, especially within the data management profession, kept pace with the realities of our digital world? In this thought-provoking session, let’s challenge the conventional wisdom about data management and explore whether we need to start thinking differently. In particular, Does the language of data management serve to clarify or just confuse? Have we developed a coherent set of best practice or a collection of functional silos? Big or small, structured or unstructured, master or transactional – isn’t data just data?

1. The Theory of Everything
Is it Time to Rethink
Image © Thomas Leuthard
The best source of
knowledge is experience
Data Management?

2. Your Presenter
• Founder of Equillian
• Experts in Enterprise Information Management
• Three founding values
– Independence
– Passion
– Knowledge
• Jon Evans
• Information Strategist
• Self-confessed Data Quality geek
• @MadAboutData

4. Image © Thomas Leuthard
The best source of
knowledge is experience
Data or information?

5. W W W. E Q U I L L I A N . C O M
5!
In the blue corner… In the red corner…
The “data-philes” The “info-holics”

6. Data Asset
Data Quality
Data Governance
Data Architecture
…..
…..
…..
And it doesn’t stop there…
W W W. E Q U I L L I A N . C O M
6!
Information Asset
Information Quality
Information Governance
Information Architecture
…..
…..
…..
Is it any wonder our
stakeholders are confused?

7. The quality of our data ultimately affects the quality of our decisions
The Data Value Chain
• Data is the digital representation of objects and events (“raw input”)
• Information is data that has been collated and organised (“data in context”)
• Knowledge is the understanding we derive through interpreting information (“insight”)
• Decisions are the judgements we make based on our acquired knowledge (“outcomes”)
W W W. E Q U I L L I A N . C O M
7!
Data Information Knowledge Decision
is collated
from
is derived
from
is based
on
Raw Refined

8. The quality of our data ultimately affects the quality of our decisions
An Example
• Data is the digital representation of objects and events (“raw input”)
• Information is data that has been collated and organised (“data in context”)
• Knowledge is the understanding we derive through interpreting information (“insight”)
• Decisions are the judgements we make based on our acquired knowledge (“outcomes”)
W W W. E Q U I L L I A N . C O M
8!
Data Information Knowledge Decision
is collated
from
is derived
from
is based
on
21, 6, 2016
Today’s date is
21st June 2016
My summer break is
only 4 weeks away
I’d better book my
flights and hotel

9. Data versus Information
W W W. E Q U I L L I A N . C O M
9!
Data Information
is collated
from
• Raw
• Limited context
• Implicit relationships
• Processed
• Meaningful context
• Explicit relationships
That’s settled then –
information is simply
data that’s been through
a degree of processing
to make it more useful
So at what point does
data become information?

10. W W W. E Q U I L L I A N . C O M
10!
A file containing raw
sales figures
A formatted report
that lists sales figures
ordered by territory
A formatted report
that compares sales
performance across
different territories
Data Information

11. W W W. E Q U I L L I A N . C O M
11!
The Data Continuum
Raw Refined
Data Information
A file containing raw
sales figures
A formatted report
that compares sales
performance across
different territories
A formatted report
that lists sales figures
ordered by territory

12. Two Different Perspectives
W W W. E Q U I L L I A N . C O M
12!
A file containing raw
sales figures
A formatted report
that compares sales
performance across
different territories
A formatted report
that lists sales figures
ordered by territory
It’s all just data, but some
is more processed
It’s all information, but
some is just more usable
The Machine Perspective
The Human Perspective

13. • Humans operate in the
information world
• When we see data, our
brains sub-consciously
convert it to information
It’s time to think differently…
We have two parallel and co-existent worlds
– the data world and the information world
• Machines operate in the
data world
• We use them to store
data so we can readily
access it as information
• Information is simply the human interpretation of data
• To a machine, your sophisticated sales report is just data
• To a human, a set of raw sales figures is still information

14. If a tree falls in a forest
and no one is around to hear
it, does it make a sound?
If data is stored on a
computer and no one ever
sees it, is it information?

15. W W W. E Q U I L L I A N . C O M
15!
Data Information Knowledge Decision
Raw Refined
Information (Continuum) Knowledge Decision
Data (Continuum) Knowledge Decision
Raw Refined

16. W W W. E Q U I L L I A N . C O M
16!
So where does that leave us?

17. Do we need to change our vocabulary?
W W W. E Q U I L L I A N . C O M
17!
INFOVERSITY
Data ?No, let’s just agree that we’re talking about exactly
the same thing from slightly different perspectives

18. Image © D. Sharon Pruitt
The best source of
knowledge is experience
What about unstructured data?

19. What is unstructured data?
• Data that has no structure?
• Documents & reports?
• Audio & video?
W W W. E Q U I L L I A N . C O M
19!
OK, so what is structured data?
• Data that’s stored in
a relational database?
But surely all data has some structure
But all documents are structured to
some degree and even language
follows the structural rules of grammar
But don’t media files conform to defined
standards and include structured meta-data?
But there lots of alternative approaches
to storing data nowadays, and I wouldn’t
describe them as unstructured…
Oh dear, have we confused matters again?

20. W W W. E Q U I L L I A N . C O M
20!
A database table
containing monthly
sales figures
An extract of the
monthly sales figures
saved as an Excel file
The monthly board
report, which shows a
table of monthly sales
figures on page 6
Structured Unstructured

21. W W W. E Q U I L L I A N . C O M
21!
The Data Continuum
Firm structure Fluid structure
Structured Unstructured
A database table
containing monthly
sales figures
The monthly board
report, which shows a
table of monthly sales
figures on page 6
An extract of the
monthly sales figures
saved as an Excel file

22. The Data Continuum
W W W. E Q U I L L I A N . C O M
22!
RawRefined
Firm Fluid
A database table
containing monthly
sales figures
The monthly board
report, which shows a
table of monthly sales
figures on page 6
An extract of the
monthly sales figures
saved as an Excel file
A file containing raw
sales figures
A formatted report
that compares sales
performance across
different territories

23. W W W. E Q U I L L I A N . C O M
23!
RawRefined
Firm Fluid
A database table
containing monthly
sales figures
The monthly board
report, which shows a
table of monthly sales
figures on page 6
An extract of the
monthly sales figures
saved as an Excel file
A file containing raw
sales figures
A formatted report
that compares sales
performance across
different territories
Which of these would you want to avoid
falling into the hands of a competitor?

24. So is there such a thing
as unstructured data?
Or are we just managing
a continuum of data?
Maybe we should ask a
couple of our employees…

25. Bill (Database Administrator)Betty (Records Manager)
I look after my company’s documents and
files – I don’t really get involved with data
When I joined, we kept lots of our
information on paper…and still do…
…so I quickly learned the importance of
only keeping the stuff we really need
I look after my company’s databases – I
don’t really get involved with documents
When I joined, we only had one server
and I kept a close watch on disk space…
…but now hardware is so cheap, if we run
out of storage, we just buy more!
Images © Thomas Leuthard

26. It’s time to think differently…
Images © Thomas Leuthard

27. We’ve created
an artificial and
unhelpful separation
of structured and
unstructured data
We need to cross-
pollinate our skills
and knowledge to
bring them back
together again
Just because storage is
cheap doesn’t mean we
can do without a retention
and archiving strategy
Just because we use
SharePoint doesn’t mean
we can avoid defining
our document standards
Images © Thomas Leuthard

28. W W W. E Q U I L L I A N . C O M
28!
Data Information Knowledge Decision
Raw Refined
Information (Continuum) Knowledge Decision
What is knowledge?
• “the understanding we derive through interpreting information”
• “information and skills acquired through experience or education”
• It’s the documented (and undocumented) “learnings” that tell us how
to run our business
• Includes methods, procedures, best practice, patents, trade secrets…

29. W W W. E Q U I L L I A N . C O M
29!
Data Information Knowledge Decision
Raw Refined
Information (Continuum) Knowledge Decision
Knowledge Management is so often the poor relation of
Data Management
• “it’s all in people’s heads so there’s nothing to manage”
• “we have a folder somewhere that contains all the
procedures”
• “we don’t really have a formal approach – everyone
looks after their own stuff”
How well do you manage the
knowledge in your organisation?

30. W W W. E Q U I L L I A N . C O M
30!
Data Information Knowledge Decision
Raw Refined
Information (Continuum) Knowledge DecisionDecisionInformation (Continuum)
Data (Continuum) Knowledge Decision
Raw Refined
DecisionData (Continuum)
Solution: just manage it in the same way
as your other information assets

31. Image © Jenny Downing
The best source of
knowledge is experience
Where does big data fit in?

32. n. data
big data
Image © Eric Fischer

33. The Data Continuum
W W W. E Q U I L L I A N . C O M
33!
RawRefined
Firm Fluid
The data continuum isn’t limited to 2 dimensions
– we could include volume, velocity, variety…

34. It’s time to think differently…
Ultimately, it doesn’t matter whether our data
is big or small, firm or fluid, raw or refined
In all cases we need…
• Good definitions so we understand what we’re dealing with
• Clear processes for managing it through its lifecycle
• Sophisticated techniques for exploiting its value
• Strong governance to ensure it’s being treated as an asset
• A robust technical infrastructure to support all of the above

35. W W W. E Q U I L L I A N . C O M
35!
Data
Architecture
Management
Data
Development
Data
Operations
Management
Data
Security
Management
Reference &
Master Data
Management
Data
Warehousing
& Business
Intelligence
Management
Document &
Content
Management
Meta-Data
Management
Data Quality
Management
Data
Governance
DMBoK wheel courtesy of DAMA International

36. W W W. E Q U I L L I A N . C O M
36!
Data
Architecture
Management
Data
Development
Data
Operations
Management
Data
Security
Management
Reference &
Master Data
Management
Data
Warehousing
& Business
Intelligence
Management
Document &
Content
Management
Meta-Data
Management
Data Quality
Management
Data
Governance
Governance
Exploitation
Management
Definition
Infrastructure
DMBoK wheel courtesy of DAMA International

37. Round is the perfect shape for many things…
W W W. E Q U I L L I A N . C O M
37!
but unfortunately, data management isn’t perfect

38. The DAMA Square?
W W W. E Q U I L L I A N . C O M
38!
Data Governance
Data Architecture
Management
DataOperationsManagement
DataSecurityManagement
DataDevelopment
Reference &
Master Data
Management
Data Warehousing &
Business Intelligence
Document &
Content
Management
Meta-Data
Management
Data Exploitation
Data Management
Data Definition
Data Governance
Data Infrastructure
Data Quality
Management
I like the symmetry of the wheel, but
prefer the transparency of the square

39. W W W. E Q U I L L I A N . C O M
39!
Data Governance
Data Architecture
Management
DataOperationsManagement
DataSecurityManagement
DataDevelopment
Reference &
Master Data
Management
Data Warehousing &
Business Intelligence
Document &
Content
Management
Meta-Data
Management
Data Exploitation
Data Management
Data Definition
Data Governance
Data Infrastructure
Data Quality
Management
We need to
formalise our data
quality policies,
standards, roles
and responsibilities
We need to
ensure the data
definitions and
data quality rules
are documented
We need to identify
the structure,
location and flow of
data to guide our
data quality efforts
Enablers
Improved data quality
leads to more accurate
insight and better
decision making
Beneficiaries
High quality master and
reference data helps to
improve consistency
and simplify integration
Data Quality
in Context

40. Image © D. Sharon Pruitt
The best source of
knowledge is experience
Is our strategy working?

41. Military Strategy
“The planning, coordination, and general
direction of military activities to meet
overall political and military objectives”
W W W. E Q U I L L I A N . C O M
41!

42. Data Strategy
“The planning, coordination, and general
direction of data activities to meet
overall business objectives”
W W W. E Q U I L L I A N . C O M
42!

43. Developing a Data Strategy
• Step 1 – Conduct a data management maturity
assessment
• Step 2 – Determine that the overall maturity is extremely
low (just like everyone else)
• Step 3 – Endure a painful meeting with senior execs
where the full enormity of the challenge is laid bare
• Step 4 – Agree some short term tactical fixes, because
anything else is deemed too difficult
• Step 5 – Return to your desk, cry into your coffee and
carry on as before
W W W. E Q U I L L I A N . C O M
43!
Sound familiar?

44. It’s time to think differently…
A strategy…
• is a plan of activities to get
us from the current state to a
desired future state
• is often clouded by negative
perceptions of our starting
point
• can sometimes turn into
nothing more than a series
of short-term tactical fixes
• ideally needs to be driven
by a high-level business
vision
A vision…
• is a picture of our desired
future state created by the
business
• focuses on our future
aspirations, rather than our
current issues
• takes a long-term view,
which can be broken down
into interim milestones
• provides the basis for a
more detailed strategy
aligned with our business
Don’t start your strategy until you’ve developed your vision

45. Developing a Data Vision
• Step 1 – Engage with key stakeholders to understand the
future role of data from a business perspective
• Step 2 – Analyse the business drivers, opportunities and
challenges to identify a set of underlying themes
• Step 3 – Switch negatives to positives and craft a description
of a brighter future that aligns with each of themes
• Step 4 – Present your themes to senior execs using imagery
and metaphors to bring your vision alive
• Step 5 – Agree that it will take sustained effort to realise the
vision and carve each theme into interim milestones
• Step 6 – Keep the vision at the forefront as you develop it
into a fully fledged strategy
W W W. E Q U I L L I A N . C O M
45!

46. Images © Jenny Downing

47. Image © D. Sharon Pruitt
The best source of
knowledge is experience
Is it time for a rethink?

48. Information is
simply our interpretation
of the complex world around us
But in our rush to push the limits of achievement and
embrace the possibilities, we’ve confused our thinking
and created artificial barriers where none should exist
At the end of the day, it doesn’t matter whether data
is big or small, firm or fluid, raw or refined, created
by man or created by machine – data is just data
Our technology might have advanced, but
our goal remains the same – a unified
approach to governing, defining,
managing and exploiting
our data
Digitising information and using computers
to store it as data have helped us achieve things
we never thought were possible even 10 years ago

50. Training changes the way people act
Education changes the way people think
W W W. E Q U I L L I A N . C O M
jon.evans@equillian.com
@MadAboutData