Big Data / FDAAWARE
Rafi Maslaton
President,
cResults the maker of Smart-QC/QA/QD & FDAAWARE
30-SEP-2015
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Agenda
 BIG DATA
– What is Big Data?
– Characteristics of Big Data
– Where it is being used?
 FDAAWARE
– Introduction
– The usage of structure and non-structure information in our industry
to mitigate compliance risk
– Examples
 Summary
2
What is Big Data: A definition
 Big data is a collection of data sets so large and complex that
it becomes difficult to process using on-hand database
management tools. The challenges include capture, curation, storage, search, sharing,
analysis, and visualization. The trend to larger data sets is due to the additional information derivable from
analysis of a single large set of related data, as compared to separate smaller sets with the same total
amount of data, allowing correlations to be found to "spot business trends, determine quality of research,
prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.
(Wikipedia)
 Put another way, Big Data is the realization of greater business
intelligence by storing, processing, and analyzing data that was
previously ignored due to the limitations of traditional data
management technologies (Harness the Power of Big Data: The IBM Big Data
Platform)
 The first organizations to embrace it were online and startup firms, like
Google, eBay, LinkedIn, and Facebook were built around big data from
the beginning.
3
What are the key drivers
2.5 quintillion bytes of data are generated every day! A quintillion is 1018
Data come from many quarters:
 Social media sites
 Sensors
 Digital photos
 Business transactions
 Location-based data
Source: IBM http://www-01.ibm.com/software/data/bigdata/
Facebook handles 40
billion photos from
it’s user base.
Wal-Mart handles more
than 1 million customer
transactions every hour.
Decoding the human genome
originally took 10 years to
process; now it can be achieved
in one week.
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Why Big Data
 Growth of Big Data is needed
 Increase of storage capacities
 Increase of processing power
 Availability of data (different data
types)
 Every day we create 2.5 quintillion
bytes of data; 90% of the data in the
world today has been created in the
last two years alone
 Big data can bring about dramatic cost
reductions, substantial improvements
in the time required to perform a
computing task, or new product and
service offerings
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Three Characteristics of Big Data V3s
 A typical PC might have had 10
gigabytes of storage in 2000.
 Today, Facebook ingests 500
terabytes of new data every day.
 Boeing 737 will generate 240
terabytes of flight data during a
single flight across the US.
 The smart phones, the data they
create and consume; sensors
embedded into everyday objects
will soon result in billions of new,
constantly-updated data feeds
containing environmental,
location, and other information,
including video.
FDAAWARE needs
 Big Data isn't just numbers, dates,
and strings. Big Data is also
geospatial data, 3D data, audio
and video, and unstructured text,
including log files and social
media.
 Traditional database systems were
designed to address smaller
volumes of structured data, fewer
updates or a predictable,
consistent data structure.
 Big Data analysis includes different
types of data
 Clickstreams and ad impressions capture user behavior at millions of
events per second
 high-frequency stock trading algorithms reflect market changes within
microseconds
 machine to machine processes exchange data between billions of devices
 infrastructure and sensors generate massive log data in real-time
 on-line gaming systems support millions of concurrent users, each
producing multiple inputs per second.
6
Common Big Data Customer Scenarios
IT
infrastructure
optimization
Churn
analysis
Fraud
detection
Life sciences
research
Legal
discovery
Social network
analysis
Traffic flow
optimization
Natural resource
exploration
Weather
forecasting
Healthcare
outcomes
Advertising
analysis
Equipment
monitoring
Web app
optimization
Smart meter
monitoring
Source: Microsoft
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Application of Big Data in Risk
Assessments
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Application of Big Data in Risk Assessments
 The examples are based on a platform using structure and non-structure
information to project compliance risk for companies governed by the
FDA.
– Fully integrated risk management platform with FDA CFRs for Drugs,
Biologics and Devices
 By leveraging current and historical 483s/inspections details, recalls,
warning letters etc., and in conjunction with an advanced Projection
Algorithm, assesses specific sites and overall company compliance
related risks (Source FOIA & Internet).
 The usage of non-structured data provides a significant added value,
essential focus areas and critical information to assess the risk for
companies, vendor(s) and supplier(s).
– We will show how Big Data analytics can be used as a practical application to
analyze volumes of data to identify and pin point areas of risks
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Examples of Big Data Analytics
1. Design a new aseptic facility:
– how to avoid making past mistakes related to compliance using an
advanced search engine, which highlights historical observations related
to this area
2. Validation:
– how to provide our team with higher risks areas that should be addressed
and prioritized first
3. My Company Compliance Risk:
– how to leverage some analytics to minimize our compliance risk
4. 3rd Party Audit:
– how to leverage various information sources towards focused audit on site
5. Company Compliance Benchmark:
– how do you compare to your competition
6. Few Examples
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Structure/Non-Structure Date Data Sources
483s & Responses
17,000+
Inspections
40,000+ (2008-2015)
Recalls (Weekly)
Since 2012 (16,000+)
Recall Press Release
(On Going)
Warning Letter
(On Going)
Inspectors History
2,000+ Inspectors
Adverse Events
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





PDF/IMAGE/TXT
Processing the 483
Questionnaire, site profile
Algorithm
Reports/KPI
Remediation Plan
Reduce Compliance
Exposure and
Business Risk
Data Flow - Overview
 Reports / KPI
 Processing the 483







Severity
Keywords
Context
Warning Letters
Areas’ Allocation
Quality Systems
….
 Algorithm
 PDF / IMAGE
TXT - Confirmation
Upload
Fully Linked to 21 CFR
Manage Remediation
Projects
 Questionnaire, site profile
Project
Progress
Report
 Remediation Plan
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
Designing an Aseptic facility
 Ability to search ALL historical 483s where Aseptic processing was cited, including
information such as companies, industries and content
Source: FDAAWARE
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
Designing an Aseptic facility
 Ability to search ALL historical 483s where Cleaning was cited, including information
such as companies, industries and content
 Learn from structured/non-structured DB about areas in which other companies were
cited to avoid costly mistakes
Source: FDAAWARE
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
Designing an Aseptic facility
 What are the information sources are used?
483s PDF file (Non-Structured)
Code of Federal Regulations (Non-Structured) Title 21Food and
Drugs

Converting a non-structure information that is available via FOIA to a
value added input into the design stage
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
Validation Key Word
 Ability to search ALL historical 483s where Validation was cited, including information
such as companies, industries and content
Source: FDAAWARE
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
Validation Key Word
 How to leverage non-structured information that is available
via FOIA to prevent us from making the same mistakes others
made
 Advanced search engine provides compliance, validation and
engineering teams the ability to focus on details that are easily
overlooked
Source: FDAAWARE
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
My Company Top Risks - Executive Dashboard
 High Risk areas are highlighted on the TOP
Source: FDAAWARE
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
3rd Party Audit
 Using structure and non-structure information to assess our vendors’ risks
Source: FDAAWARE
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
Company Compliance Benchmark
 Using structured and non-structured information to compare various selected
companies in various compliance related areas
Source: FDAAWARE
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
Source: FDAAWARE
Few Trends - Example
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
Source: FDAAWARE
FDA Auditor Statistic
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Summary
 In Today’s complex and costly compliance space, companies must
leverage all information sources (Structured and Non-Structured)
to better align with the latest compliance requirements
 Leveraging some of the new Big Data tools can facilitate this
process and enhance a company’s ability to manage their
compliance related risks
 FDAAWARE leverages various information sources to better equip
companies with analytics needed to manage risk, prepares
companies for inspection and focuses their compliance team on
areas that have been identified by the FDA as critical
 When facing limited cost, resources and time, the usage of
technology, an advanced algorithm and analytics, is crucial in
order to minimize risk
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THANK YOU
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