In a cloud and virtualized environment, information variety and volume are growing geometrically. As such, organizations are struggling to take advantage of cloud, mobile and consumer technologies while connecting to their back-end systems of record. No one person has the time or energy to sort through thousands of files to a) identify records or b) pull metadata for the records. That's where analytics enters the equation. Learn how we used analytics tools and methods to resolve our information migration challenges.

1. Beyond Human Capacity: Using Analytics
to Scale Your Everyday Information
Migration and Interface Activities
Steve Clark
Raytheon Company

2. Introduction
• We’ve all been there:
• Loads of file cabinets
with paper records:
• Organized boxes of
paper:
• Not so organized boxes
of paper:
• Glad to say help is on the
way:

3. Topics
• Background – Problems I’m trying to solve
• Technology – Insight into how the technology works
• Approach – Approach to how I used the tool
• Revelations – Aha moments during the process
• Analysis Results – Effectiveness of the tool
• Recommendations – Lessons to improve results
• Takeaways – Summary of steps to use if using the tool
• Extensions – Other potential uses of the tool
• Contact Info – How to contact me

4. Background
• In Records Management there are a number of problems
challenges that arise that complicate the management of
records. Specifically:
1. Repositories (legacy, archives, targeted for migration, etc)
that contain records and non-records. I’m interested in the
records only so the repository files need to be reviewed by
Subject Matter Experts (SMEs) in order to identify the
records: extremely time consuming!
2. Imaging of existing paper records requires metadata in order
to manage the record: capturing the metadata is a manual
task and is also labor intensive!

5. Technology
• Auto Classification is a semantic technology that can
be used to identify and categorize documents.
• It is a machine learning application which means it is
trained to identify documents primarily through use of
relational keywords.
• Training is an iterative process starting with clues for
record identification. Each iteration generally increases
the probability for accuracy.

6. How the Tool Works
Level 1 Terms Clue Clue Type Score Mod
Statement of Work (SOW) Statement of Work (SOW) Standard 0
Doc Name*=*statement* Metadata 35
Doc Name*=*work* Metadata 20
Doc Name*=*sow* Metadata 50
Doc Name*=*closure* Metadata -10
Doc Name*=*supplier* Metadata -15
Doc Name*=*ssow* Metadata -75
Doc Name*=*jsow* Metadata -75
The clues used
to identify the
item
The score used
to classify the
item
Each hit accumulates
the score. A Score of
50 gets classified
Negative
scores can be
used
The item I’m
trying to identify

7. Approach
• The approach was to utilize the auto classification technology (tool) to determine the feasibility of
identifying records within a specific Engineering document repository.
• Training of the tool is an important aspect of applying the technology. It is typically a one time
(non-recurring) effort that will need tweaking on occasion. The training consists of the following:
– Identify the potential set of records that are likely in the repository. This establishes targets for the training.
In our case we established a Record Work Product List (RWPL) which identifies records within the company.
– Since our target set was Engineering we focused on the identification of 70 Engineering type records.
– Training consists of the identification of key words, phrases, and relationships and associating weighting of
these items in order to classify the document.
– Establish goals for what is good enough: probability to identify records and probability for non-records. My
targets were 85% for records and 95% for non-records.
– Select the training set. The training set should be a representative set of what is expected in the repository
and consist of enough items to establish the targets for all items.
– Perform the training until targets are realized on the training set.

8. Realizations ( )
• The repository I started on for this application had almost a million documents (855,000).
• My first realization was that the documents were all in a proprietary repository and the tool was not able to
directly access items in this repository without developing a connector. So, analyzing documents in the repository
needed customization and I hadn’t time nor budget to develop a connector.
• To get around this issue I had a report generated (in Excel) from the repository that provided me with three pieces
of information: Document Number, Document Title and Document Family (set of 88 types). I was curious to see if I
could use the tool to identify Records based on this limited information.
• From this report I established a training set of 50,000 line items. I used a large set due to the limited information
provided. Note: If using entire documents (vs titles) much less of a training set can be used.
• Training took me 7 passes:
– Set an initial set of “clues” for a set of items. After each run the results were analyzed to determine how many items were
classified and the overall accuracy of the classification
– The item “non-record” was added after the realization that identification of non-records assists in the identification of records
– The goal of the first 4 passes was basically to increase the number of types of Records identified with some emphasis on
accuracy
– The next three passes focused on overall accuracy of the clues. Accuracy is actually more time consuming as it is a manual
process: every item needs to be assessed
– Another realization was that many of the items were not classifiable: Not enough information was contained in the data set to
render a classification (e.g., sometimes the document number was just repeated in the document title field)

9. Training Results
• Seven runs were made on the training set of 50,505 items:
• The classification percentage was monitored because not all of the items were
considered classifiable:
• Targets were actually achieved after the sixth run but one additional run was
made to slightly enhance accuracy.
CLASSIFICATION PERCENTAGE 1/11/2017 1/13/2017 1/16/2017 1/18/2017 2/15/2017 2/28/17 3/16/17
# record work products 54 57 64 66 72 70 70
# records classified 13227 17755 27490 32404 34291 35086 34834
# non records classified 0 6091 6623 8621 9732 10390 10791
percentage classified 26.2% 35.2% 54.4% 64.2% 67.9% 69.5% 69.0%
CLASSIFICATION ACCURACY 1/11/2017 1/13/2017 1/16/2017 1/18/2017 2/15/2017 2/28/17 3/16/17
# record work products 54 57 64 66 72 70 70
# records classified 13227 17755 27490 32404 34291 35086 34834
# non records classified 0 6091 6623 8621 9732 10390 10791
non-record accuracy ----- ----- ----- ----- 94.8% 98.3%
overall classification accuracy ----- ----- ----- ----- 91.0% 95.1%

10. Auto Classification Percentage
• Detailed analysis was completed on the 6th run to
determine the overall classification accuracy:
2/28/17 CLASSIFICATION ANALYSIS counts PERCENT
classified correctly 33550 66.4%
classifyable 7928 15.7%
incorrectly classified (considered fixable) 1539 3.0%
unclassifyable 5484 10.9%
blank - not analyzed as classifyable or not 2004 4.0%
Total 50505 100.0%
CLASSIFICATION ESTIMATION counts PERCENT Totals
estimated classifyable 44202 87.5% 748719
estimated unclassifyable 6303 12.5% 106772
Total 50505 855491

11. Auto Classification Results
• The auto classification training clue set was run on the
remaining set of 804,954 items consisting of 8 batch
runs of ~100,000 each :
• Overall the results achieved on the larger set (67.6%)
was consistent with the training set (69%) within -1.4%
CLASSIFICATION PERCENTAGE batch 1 batch 2 batch 3 batch 4 batch 5 batch 6 batch 7 batch 8
# record work products 70 70 70 70 70 70 70 70
# records classified 66644 66703 66445 66311 68700 69131 68016 71431
# non records classified 21307 21109 21131 21065 21841 22044 21706 22828
unclassified 33355 33296 33554 33688 31299 30868 31983 32454
percentage classified 66.6% 66.7% 66.4% 66.3% 68.7% 69.1% 68.0% 68.0%

12. Recommendations
• If you choose to use document titles for classification purposes:
– Refrain from using just numbers or cryptic abbreviations (use
standard)
– Standardize or eliminate certain shorthand type notations (e.g., appr
or appv for approved)
– Be more rigorous when selecting the document type (inaccuracies
here cause classification errors)
– Ensure spelling is correct
– If document numbering could be standardized this would also assist
the accuracy (e.g., notices of revision were sometimes prefixed with
NORxxxx)

13. Takeaways
• Identify the potential set of records that are likely in the repository.
Modulate the set of records such that you can achieve the desired
targets.
– Use retention policy as a guide.
• Establish goals for what is good enough: probability to identify
records and probability for non-records.
• Select the training set. The training set should be a representative
set of what is expected in the repository and consist of enough
items to establish the targets for all items.
• Ensure that the documents are searchable especially if in *.PDF
format.

14. Extensions
• The auto-classification technology can be used to
extract metadata from documents:
– Especially forms that have designated information
partitioning;
– Documents that follow standard headers, paragraphs or
that follow certain section titles and topics.
• We also did some work where the tool can be used to
screen for personally identifiable information (pii) on
documents.

15. Extensions
• Example of a form where yellow shaded represents metadata
extracted from a form:
Author Name Employee Number
John Doe 123456
POC John Doe
Employee Number 123456
POC Mail Stop / Location 123456
POC Telehone 333-555-4444
Date 12/12/2012
Business Unit Corp
Author Funnctional Organization or Region Technology
POC Cost Center 66655
Author / Publication Information
Point of Contact

16. Extension Results
• Record metadata was extracted from these forms into a
spreadsheet and used the spreadsheet to bulk upload the
items into the record repository complete with metadata.
– Alternatively this would have been a fully manual process.
• We noted that in some cases a field we needed was left
blank which led us to have the form change the field to a
required field.
• Another repository had 5000+ documents all of which had
a form as part of the document set.

17. Contact Info
Steve Clark
Raytheon Company
Company Record Manager
781-522-5151 (o)
339-227-7678 (c)
Steven_f_clark@raytheon.com