The document by Stefan Urbanek discusses data quality, defining dimensions such as completeness, accuracy, credibility, timeliness, consistency, and integrity. It highlights the importance of measuring data quality throughout the data lifecycle, including extraction, transformation, and loading (ETL) processes. Additionally, it presents methods for data cleansing and the use of structured data formats to improve data processing and reporting strengths.

1. Data Cleansing
What about quality?
Stefan Urbanek
stefan.urbanek@gmail.com
@Stiivi March 2011

2. Content
■ Introduction
■ What is data quality?
■ E and T from ETL
■ Summary

3. http://vestnik.transparency.sk

4. Brewery
analytical data streams
&
Cubes
online analytical processing
github/bitbucket: Stiivi

5. Quality

6. What is data quality
?

7. Dimensions
■ completeness – data provided
■ accuracy – reflecting real world
■ credibility – regarded as true
■ timeliness – up-to-date
■ consistency – matching facts across datasets
■ integrity – valid references between datasets

8. completeness

9. all
none
better
0%
25%
50%
75%
100%
20
05
-3
20
05
-5
20
05
-7
20
05
-9
20
05
-1 1
20
06
-1
20
06
-3
20
06
-5
20
06
-7
20
06
-9
20
06
-1 1
20
07
-1
20
07
-3
20
07
-5
20
07
-7
20
07
-9
20
07
-1 1
20
08
-1
20
08
-3
20
08
-5
20
08
-7
20
08
-9
20
08
-1 1
20
09
-1
successfully processed? 20
20
09
09
-3
-5
20
09
-7
20
09
-9
20
how many % of the field is filled and
09
-1 1
20
10
-1
20
10
-3
20
10
-5
20
10
-7
20
10
-9
Quality measure
completeness: 55%

10. type 1 type 2
+

11. all
none
better
0%
25%
50%
75%
100%
20
05
-3
20
05
-5
20
05
-7
20
05
-9
20
05
-1 0
20
05
-1 2
20
06
-3
20
06
-5
20
06
-7
20
06
-9
20
06
-1 1
20
07
-1
20
07
-3
20
07
-5
20
07
-7
20
07
-9
20
07
-1 0
20
07
-1 2
20
08
-3
20
08
-5
20
08
-7
20
08
-9
20
08
-1 1
20
09
-1
successfully processed? 20
20
09
09
-3
-5
20
09
-7
20
09
-9
20
09
how many % of the field is filled and
-1 1
20
10
-1
20
10
-3
20
10
-5
20
10
-7
20
10
-9
Quality measure
completeness: 88%

12. reconstruction: 5€
temperature: 32˚C
accuracy

13. timeliness

14. Auto-measurable
■ completeness – easily
■ accuracy – somehow
■ credibility – not-so
■ timeliness – easily
■ consistency – yes
■ integrity – yes

15. What does that mean:
“high quality data?”
?

16. 85%

17. appropriate for given
purpose

18. attach quality report

19. Quality Measurement
for accuracy and transparency

20. ■ why to measure?
■ when to measure?
■ where to measure?

21. from staging to analytical data
from source to staging data analytical model
since 2009 description
Download Parse Load source Cleanse Create cube
staging clean data
raw sources HTML files YAML files contracts table
(staging)
from source to staging data
2005-2008
REGIS (SK "unknown" fact table
organisations) suppliers map dimension tables
Load source
Download Parse 08
08
YAML files
raw sources
2005-2008
search index
Pre-process
Create
search index
One HTML per
Large HTML files
Procurement dimension tables search index
(one per year)
Document
dimension index
keep intermediate results for auditability

22. from staging to analytical data
from source to staging data analytical model
since 2009 description
Download Parse Load source Cleanse Create cube
staging clean data
raw sources HTML files YAML files contracts table
(staging)
from source to staging data
2005-2008
REGIS (SK "unknown" fact table
organisations) suppliers map dimension tables
Load source
Download Parse 08
08
YAML files
raw sources
2005-2008
search index
Pre-process
Create
search index
One HTML per
Large HTML files
Procurement dimension tables search index
(one per year)
Document
dimension index
insert probes at appropriate places

23. like unit testing:
1. write probes
2. set data quality indicators
3. pass data through

24. SQL
PostgreSQL
yml database
table
YAML directory coalesce
values
{x:.2%}
+ 15.00%
data audit threshold formatted
printer

25. field nulls status distinct
------------------------------------------------------------
file 0.00% ok 100
source_code 0.00% ok 6
year 0.00% ok 6
donor_code 0.00% ok 2
receiver_name 1.25% fail 10363
receiver_address 13.29% fail 9979
receiver_ico 13.53% fail 5813
project 0.01% ok 28370
program 0.00% ok 29
subprogram 11.60% fail 177
project_budget 14.48% fail 9487
requested_amount 88.73% fail 1356
received_amount 9.32% fail 2179
contract_number 13.29% fail 28627
contract_date 57.88% fail 1425
source_comment 99.93% fail 9
source_id 89.52% fail 814

26. E and T from ETL
E as Extraction

27. HTML Documents

28. Ceci ne sont pas des données

31. html
body
div id=#page
div id=#page
div id=#container
div id=#main
div id=#innerMain
div (anonymous)
div (anonymous)
table tbody
tr td
tabletbody
tr td
table trtd
tbody
tabletd value
√tr

33. Now: you parse!
3 seconds
*non-technical explanation follows

34. <SPAN class=podnazov>More information
</SPAN>

35. ?

36. <SPAN class=podnazov
style="TEXT-TRANSFORM: uppercase">o
</SPAN>
<SPAN class=podnazov>dkaz na&nbsp;projekt
...

37. ?

38. <SPAN class=podnazov
style="TEXT-TRANSFORM: uppercase">o
</SPAN>
<SPAN class=podnazov>dkaz na&nbsp;projekt
...

39. <SPAN class=podnazov
style="TEXT-TRANSFORM: uppercase">o
</SPAN>
<SPAN class=podnazov>dkaz na&nbsp;projekt
...
here is a subtitle
and it should be in upper-case:
o
And here is another subtitle:
dkaz na (non-breaking space) projekt
much better
here is a label: Odkaz na projekt

40. “Structured”
spreadsheets
error prone
more work needed

41. ✓ structured file format

42. 1
2
4
3
5
(1) image & title
(2) repeating groups of columns
(3) padding rows/columns
(4) removed redundancy for readability
(5) colored cells

43. 1
2
3
(1) header with row padding
(2) multi-row logical cell
(3) broken pattern

44. 1
2
(1) multi-row cell
(2) more values in a row

45. why?
source id
itemid
file format parser data extraction
class id
item
amount
class
item
amount
class
why not? amount
“structured”
file
raw data

46. E and T from ETL
T as Transformation

47. Basic pattern
slightly more technical

48. source lists and maps
?
+
target
?
diff
?
target

49. SELECT ...
EXCEPT
SELECT ...
*in PostgreSQL, not in MySQL

50. sta_vvo_vysledky
sta_regis
- -
map_suppliers
1
unknown suppliers
? Slovensko
+
2
+
tmp_coalesced_suppliers_sk
-
sta_suppliers
+
3
new suppliers

51. Script or manual?
script

52. Script or manual?
script
■ recurrent processing (weekly, monthly,...)
■ huge amount of data
■ one-time processing
■ small amount of data

53. appropriate tool
for given task

54. balance

55. from staging to analytical data
from source to staging data analytical model
since 2009 description
Download Parse Load source Cleanse Create cube
staging clean data
raw sources HTML files YAML files contracts table
(staging)
from source to staging data
2005-2008
REGIS (SK "unknown" fact table
organisations) suppliers map dimension tables
Load source
Download Parse 08
08
YAML files
raw sources
2005-2008
search index
Pre-process
Create
search index
One HTML per
Large HTML files
Procurement dimension tables search index
(one per year)
Document
dimension index

56. Brewery
data streams

57. Data Sources Data Targets
CSV file
relational database
data stream
processing
Google Spreadsheet
report
X
remote Excel Spreadsheet URL
processing streams

58. data row data row data row
data source data target
value value value value
id id id
item item item
class class class
amount amount amount
data source data target
data record data record data record
id value
item value
class value
amount value

59. Sources
X
SQL
CSV file XLS file SQL query mongo DB
yml
Google spreadsheet YAML directory row list record list

60. Targets
yml
SQL
CSV file SQL table mongo DB YAML directory
{x:.2%}
<html> 15.00%
HTML table formatted printer row list record list

61. Record Operations
+
!
append distinct aggregate merge (join)
!x
? ? n
sample select set select data audit numerical statistics*

62. Field Operations
A→B
re + +
field map text substitute value threshold* derive*
abc
+
string strip consolidate value histogram/bin* set to flag*
to type

63. +
SQL
? <html>
SQL

64. yml nodes = {
"source": CSVSourceNode(...),
"clean": CoalesceValueToTypeNode(),
"output": DatabaseTableTargetNode(...),
"audit": AuditNode(...),
"threshold": ValueThresholdNode(),
"print": FormattedPrinterNode()
}
connections = [
("source", "clean"),
("clean", "output"),
SQL
("clean", "audit"),
("audit", "threshold"),
("threshold", "print")
]
+ ... # configure nodes here
stream = Stream(nodes, connections)
stream.initialize()
{x:.2%} stream.run()
15.00%