This document discusses how Hadoop can help solve challenges with key corporate data known as "small data". Small data refers to structured data that is critical to main business activities. It discusses issues with small data like multiple sources and definitions causing inconsistencies. It proposes using Hadoop to iteratively detect errors and inconsistencies in small data to allow normalization. Normalization combines subject knowledge and rules to make small data more consistent and meaningful for analysis. The document argues Hadoop provides a flexible, fast environment for data analytics that can help address requirements around understanding, preparing, and maintaining small but critical corporate data.

1. Hadoop Powered
Corporate Data
How to Produce and Manage Meaningful Data and
Analytics
Dr. Geoffrey Malafsky
Phasic Systems Inc.

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Governance
Warehouse
Analytics
NoSQL Streaming
BI
Integration
Architecture
Modeling
Big Data Hadoop Velocity,
Volume,
Variety
Veracity

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Governance
Warehouse
Analytics
NoSQL Streaming
BI
Integration
Architecture
Modeling
Big Data Hadoop Velocity,
Volume,
Variety
Veracity
What does this
really mean for
my corporate
data?
Disruption

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Organizational Issues
Technology Issues
Business Issues

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Are we discovering new knowledge?
Are we analyzing business and
operations for decisions, audit,
compliance, consolidation?
Are we fulfilling required reports?

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Veracity, Meaningful
Does it matter?
Topic Should Does
BI Yes Sometimes
Required Reports Yes Sometimes
Audit Yes Yes
Compliance Yes Yes
Consolidation Yes Sometimes
Marketing Yes Sometimes
Financial Yes Yes but….
Decision Making Yes Yes but….

7. TechLab by InsideAnalysis
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Normalizing Corporate Small Data With Hadoop and Data Science
By Dr. Geoffrey P Malafsky
In part one of this discussion series (Hadoop for Small Data), I introduced the idea that Small Data is the mission-critical data management challenge. To
reiterate, Small Data is “corporate structured data that is the fuel of its main activities, and whose problems with accuracy and trustworthiness are past
the stage of being alleged. This includes financial, customer, company, inventory, medical, risk, supply chain, and other primary data used for decision
making, applications, reports, and Business Intelligence.”
I am excluding what I call stochastic data use cases which can succeed even if there is error in the source data and uncertainty in the results since the
business objective is getting trends or making general associations. Most Big Data examples are this type. In stark contrast are deterministic use cases,
which I am focusing on here and in the next TechLab in September, where the ramifications for wrong results are severely negative. This is the realm of
executive decision making, Accounting, Risk Management, regulatory compliance, security, to name a few.
Corporate Small Data is
structured data that is the
fuel of its main activities
Data Normalization combines
subject matter knowledge,
governance, business rules,
and raw data to make it
meaningful.

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Hadoop was created to handle extraordinarily
large and constantly changing data sets. It is a
very well-engineered software framework and
set of tools for distributed storage and cluster
computing. But, can it help solve the intractable
challenges with key corporate data ?

9. The Challenge of Corporate Small Data
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multiple sources multiple definitions multiple copies
variable structures
different data values
hidden conflicts in data
definitions
which to use
different model types &
standards
more storage more data flows
Many DW & marts different ETL
complex dependencies
conflicting
business rules
analyses restricted
by inconsistencies

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An example of embedded errors that defy traditional tools and methods. Two
authoritative data systems have many occurrences of conflicts, errors, and
quantitative discrepancies. Finding these has been too difficult with common tools.
But, using small Hadoop cluster (this is Corporate Data not Big Data) allows us to
iteratively detect, learn, adjust. Once detected, investigated, and understood we can
find just the one answer from business needed to correct.

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FPDS FPDS-WAWF FPDS-WAWF-GDUNS

12. Requirements for Data Analytics
1. Data must be understood
2. The right definitions must apply at the right time for the right user
3. Data’s lineage and provenance must be clear
4. Data integrity must be preserved
5. Data must be accurate, consistent, complete, timely, unique and valid
6. Data and system access must be secure
7. Data must be provided in multiple arrangements to meet different user needs and analytical
processing requirements
8. Data must be prepared and tracked to support meaningful analysis for different user needs
9. Data processing must be flexible to adapt to new knowledge and discoveries on data already
being used
10. Data must be normalized using authoritative or best known sets of codes, lookup values, and
source adjudication knowledge and rules
11. High speed, low maintenance techniques and tools are needed to be cost and time effective
12. Lifecycle audits and data maintenance must be performed including maintaining and
documenting data from raw source to intermediate transformed to full normalized
13. Use Common data models that align, correct, and semantically unify data from multiple
sources to enforce meaningful and consistent analysis
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14. An Example of Hidden Business Rules and Logic
• If (DELIVERY_ORDER=NULL) v_piid = CONTRACT else v_piid =
DELIVERY_ORDER
• If ( x1='0') v_modification_number = '0‘ else v_modification_number =
x2
• where x1: if (ACO_MOD=NULL) x1 = x3 else x1 = ACO_MOD
• where x3: if (PCO_MOD=NULL) x3='0‘ else x3=PCO_MOD
• where x2: if (x4=NULL) x2='0‘ else x2=x4
• where x4: x4= LTRIM(x5)
• where x5: x5=x1
• essentially this first tries to use ACO_MOD, and if this is NULL then it tries
to use PCO_MOD and sets = '0' if these are NULL
• If (DELIVERY_ORDER=NULL) v_idv_piid = y1 else v_idv_piid = CONTRACT
• where y1: y1 = REF_PROC_INSTRUMENT with all '-' characters
removed
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key business logic as buried in a database stored procedure (condensed)

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Flexible, Fast, Adaptive, Multi-Tool Data Analytics Environment

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Hive Impala SQLServer
FPDS Hadoop Query Times Text Field (secs)
Text Parquet Parquet Partitioned

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Parallel Jobs in Hadoop

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