All the hype today is about Big Data and Analytics but many people seem to ignore the fact that if you didn't get the "Small Data" right there is absolutely no way that "Big Data" will add value to your organization - it will just add confusion

1. No Big Data without Small Data
Norman Manley
IT Analyst

2. Big Data - a definition
Big data is the name we give to a collection of data
so large and complex that that it can’t be processed
using traditional IT applications and programs. In
general the volume is at least 1000 times larger than
traditional sources of data.
25-2-2015 © Decision Support Systems 2014 2
“You can have data without information but you can’t have
information without data.”
Daniel Keys Moran – American author

3. Small Data -a definition
Data (small data) is a synonym for facts;
and Merriam-Webster defines it as:
“facts or information used usually to calculate, analyse, or plan something”.
But if the facts are not right then the analysis will be
incorrect and we will make the wrong decisions!
25-2-2015 © Decision Support Systems 2014 3
Invoice Date Customer Country Euros excl VAT VAT Total Invoice Number Payment
22/01/2014 Mondea Netherlands 795.00 166.95 961.95 2014.416 iDeal
24/01/2014 Physter Technology Czech Republic 795.00 0 795.00 2014.417 Visa
27/01/2014 Copenhagen Airports A/S Denmark 795.00 166.95 961.95 2014.421 MC
28/01/2014 Vista Group Finland 575.00 120.75 695.75 2014.423 MC
07/02/2014 Global Information USA 709.01 0 709.01 2014.441 Invoice
14/02/2014 DataPad Inc United States 795.00 0 795.00 2014.451 MC
21/02/2014 Scrip Companies USA 795.00 795.00 2014.464 PayPal

4.  the creation of a consistent, accurate and timely
source of processed data (information) that can be
used to support the decision making process
 the creation of an historic information source which
can be used uniquely as the basis of both
comparative and predictive analysis
 the integration of data from different sources (both
internal and external)
 the creation of “one source of the truth” which we
need as the basis of making better decisions
The goals of working with data
25/02/2015 © Decision Support Systems 2014 4

5. Where does the data come from?
25-2-2015 © Decision Support Systems 2014 5
Small Data - structured
 Internal applications
 Spreadsheets!
Big Data – often unstructured
 Organizational processes:
measurements, websites,
machines
 Communication:
e-mail, reports, presentations
 Social media:
Facebook, LinkedIn, Twitter
 Sensors:
temperature, weather, traffic,
rainfall
 Archives:
old documents, old films

6. Unstructured data - a definition
Unstructured data is not directly accessible in a
database. Examples are various sorts of documents
like Office documents, PDF, XML, email messages,
pictures , videos and sound clips. The contents are
often dates, numbers and other facts but are difficult
to interpret directly with the current technology
25/02/2015 © Decision Support Systems 2014 6
A Letter from the Chairman of IBM
The market for data and analytics is estimated at $187 billion by 2015. To capture this growth
potential, we have built the world’s broadest and deepest capabilities in Big Data and analytics—
both technology and expertise. We have invested more than $24 billion,including $17 billion of gross
spend on more than 30 acquisitions. We have 15,000 consultants and 400 mathematicians. Two
thirds of IBM Research’s work is now devoted to data, analytics and cognitive computing. IBM has
earned 4,000 analytics patents.

7. Big Data is an addition
Big Data is an additional source, not
something that just exists independently
the goal is to complement the existing data
“revenue” from Big Data must have the same
definition as “revenue” from Small Data
quality is just as important; if that is not the
case Big Data is just a lot of Bad Data
25/02/2015 © Decision Support Systems 2014 7

8. What we call things is important
25/02/2015 © Decision Support Systems 2014 8
How much did
I sell?
How much can
I book?
Revenue
= € 100,000 = € 96,422

9. 25/02/2015 © Decision Support Systems 2014 9
Just like most of the other IT analysts I am convinced
that data quality forms a huge risk for our decision
making processes – the problem is that the quality of
the data is so bad that we can’t prove it!
Norman Manley, IT analyst
Data quality – a problem?

10. the files have many different formats which
makes them very difficult to read
it is often unclear what the contents of a
field are (and also what they mean)
privacy is a problem – are we allowed to see
some things and are we allowed to do
anything with the data?
data is often missing (both individual fields
and parts of files)
data is not up to date
Small data – what are the problems?
25/02/2015 © Decision Support Systems 2014 10

11. ETL Process - the basic elements
25-2-2015 © Decision Support Systems 2014 11

12. 25/02/2015 © Decision Support Systems 2014 12
Big Data:
What can we use it for?

13. How useful is big data?
It seems that the 4 engines of
a Boeing 747 generate more
data on a flight to New York
than most companies in do a
year.
The question remains, do we need to save all the
data, for how long, what are we going to do with it
and can we generate “actionable information”?
25-2-2015 © Decision Support Systems 2014 13

14. Very Big Data!
25/02/2015 © Decision Support Systems 2014 14
At up to 500MB per flight this is a huge amount of data

15. 25/02/2015 © Decision Support Systems 2014 15
Big Data successes
Vestas, a Danish wind turbine manufacturer collects data from
35,000 meteorological stations and
45,000 of their own turbines. This allows
them to choose the best locations, in
terms of wind conditions, for placing new
windmills. They expect to collect as much
as 24 petabytes of data (they already
have 2.8 petabytes). The time needed to
analyse the suitability of a new location has reduced form
several weeks to 15 minutes.

16. 25/02/2015 © Decision Support Systems 2014 16
Big Data successes
Los Angeles and Santa Cruz police, together with PredPol
(a software vendor) and a mathematician from the University of
Santa Clara have developed a
system that can predict where
criminal activity will take place,
accurate to an area of 50 m2
. A
combination of historical data
and feeds from “live” cameras is
used to predict where the local police should patrol to prevent
(amongst other crimes) burglary. The number of burglaries has
been reduced by 33% in the last year. This is called “predictive
policing”

17. Conclusions
if Small Data doesn’t work properly then Big
Data has no chance
Big Data in itself has no value - but it does give
us the possibility to generate new insights
the crux is accuracy– bad data quality leads to
information that is even worse
25/02/2015 © Decision Support Systems 2014 17

18. 25/02/2015 © Decision Support Systems 2014 18
"Not everything that can
be counted counts, and
not everything that
counts can be counted."
William Bruce Cameron “Informal Sociology: A
Casual Introduction to Sociological Thinking” 1963