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Best Practices for Using Big Data For Digital Transactions Management

Authors
  • Name
    Shamsh Hadi

When an organization converts its paper-based business processes and transactions that involve filling out forms and collecting signatures and dates, it opens up the possibility of incredible insight into its business operations. This insight into business processes and business transactions not only helps identify what’s working and what needs to be improved it can also predict what to expect. All this is possible with big data. Digitization of our business opens doors for massive amount of structured and unstructured data to be collected, processed and analyzed. Business processes that involved electronic signatures, automation engine and workflows are no exception, rather, they are ideal for big data implementation.

 

In this article we analyze various components of a digital transaction management system including electronic signature and outline some of the best practices for big data application. We organize components of a DTM into categories of insights and outline best practice insights under each. For the purpose of this article, we will only use five user-based categories.

Transaction Templates

How many templates of what type are there.

  1. How many times each template has been used.
  2. How many times each template has been rejected and for what reasons.
  3. How many workflows are there for each type of workflow categories
  4. How many total transactions use some type of template
  5. How many eSign transactions were completed

Document Sets

  1. How many Document Sets are used (by each user, overall)
  2. Are the number of Document Sets sufficient for the company?
  3. How many Document Sets end up having attachments? Size of attachments?
  4. Number and frequency of Document Sets printed, shared, granted access to.

Transactions

  1. How long (min, max, average) has it taken to complete a transaction
  2. How long (min, max, average) has it taken to complete a transaction for each type of work flow.
  3. What is the (min, max, average) length of a bottleneck.
  4. Number of transactions rejected and for what reasons.
  5. What is the (min, max, average) number of signers in the transactions.

Mobile App

  1. Number of users
  2. Number and frequency of log-ins.
  3. Number of scans
  4. Number of users who create new signatures using the ap

Usage Patterns of Signers

  1. Location
  2. Devices used
  3. Type of signatures used
  4. Integrated users, Cloud users, 3rd party app users.

 

And the list goes on. The idea is to identify actionable insights. That’s the beauty and true value of big data. Digital Transaction Management systems are specially ideal for big data applications because they contain extremely large data sets that can be analyzed computationally and can easily reveal patterns, trends, and associations, especially relating to human behavior and interactions.

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