ContextAPP EXECUTIVE SUMMARY:

By most estimates, up to 90% of enterprise data is unstructured and never analyzed (Marr, 2019); 70% in the form of text (e.g., emails, documents). By the end of 2021, more than 80% of organizations will fail to develop a consolidated data security policy across silos, leading to potential noncompliance, security breaches, and financial liabilities. (Davis, 2019) 71% of enterprises struggle with managing and protecting unstructured data (Rizkallah, 2017), and leading Robotic Process Automation vendors estimate that ~50% of their workflows still begin with a document. In addition, many organizations fail to leverage their data in a manner that produces insights into their processes, leading to potential loss of revenue or efficiencies.

Context – Unified Approach to Extracting, Normalizing and Analyzing Unstructured Data

BUSINESS AND TECHNICAL CHALLENGES:

There are few turn-key, out-of-the-box solutions that organizations can use to solve their problems. Most tools require custom development and API integrations that take months to implement and may not lead to a well-integrated solution. Once the data is extracted, organizing and normalizing different data structures can be daunting for most organizations and lead to cost overruns and delays. Data analysis can be challenging because the data is not organized in a single place, leading to siloed Analytics.

Lastly, training custom models to extract relevant information requires significant investments and resources towards efforts that lead to little to no gain for organizations.

As of 2020, 80% of AI/ML projects have failed for reasons

that can be summarized into the 4 points (MV, 2020):

1) Challenging to get AI Projects off the ground

2) Non-AI data tasks take the longest

3) Integration between technologies

4) Custom Development is time-consuming