Data acquisition, ingestion, preparation and consumption are major challenges for organizations today. We deploy Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Data Engineering platforms to automate ingestion and consumption of the data you need.

Data Challenges

Many organizations are seeking to automate inefficient manual processes that ingest and extract data  to get it ready for consumption and use by downstream business operations and systems. However, there are many inherent challenges, and few common ones include:

  • Data sources may be internal and external, which require different security considerations

  • Data is often in many different formats (e.g., PDF, spreadsheets, etc.) and may come in via different channels (e.g., email, shared drives, secure web locations, etc.). Examples of data being ingested by companies include Financial and Regulatory reporting, Social media data (e.g., Twitter and Facebook), Web Clickstream and log data, Document repositories, Telemetry data from IOT devices

  • Placement of data fields can vary from one period to the next, including errors such as missing fields

  • Some documents, such as legal contracts, may include footnotes, which vary from one contract to another, and can be placed either at the bottom of a page, or at the end of the document
  • Dependency on specific people who either have special knowledge, or have written notes on how to deal with discrepancies and special situations, or even the need to handle things in a special way that may be unique to the business

  • Immature data management processes that necessitate improvisations or special instructions that vary from one period to the next

There are other numerous challenges inherent to this process, and building the right automation capability, while achievable, requires a great deal of thought and expertise.

Finding the Optimal Solution

Some of the most common challenges companies face in identifying the right solution include:

  • The abundance of marketing hype, biased reports from “experts” and vendors that create confusion and promise a panacea for all types of data ingestion and extraction processes
  • Lack of organizational expertise and depth in understanding some of the newer technologies, such as Optical Character Recognition (OCR), Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) which are often the basis for many available solutions
  • Fact is, that all of these (and other) approaches can work, but the success depends on their fit for a particular situation

What You Should Expect from an Optimal Solution

  • A perfect solution to automate your data ingestion process 100% and with 100% accuracy is unlikely
  • However, you should be able to automate 70-95% of your process, and significantly reduce manual labor, errors and costs
  • A truly competent AI/ML based solution should not require continuous, ongoing manual maintenance as data source formats and content placements change

We have the expertise to look beyond the hype, and identify and implement best-fit platforms which are built using true AI, ML, and Semantic Engineering architecture, to successfully ingest, digitize and extract data from a wide variety of structured and unstructured data sources, using multi-dimensional open-source and proprietary algorithms with built-in domain, geographic and industry sector expertise.

Beyond ingestion, we help our clients prepare, enrich, transform, store and consume the data, and apply AI, ML, NLP, and Advanced Analytics (including Descriptive, Diagnostic, Predictive and Prescriptive) to transform manual operations into intelligent, straight-through Smart Operations.

Are you a victim of hype? Here are 4 ways you can test if a platform is truly built using Artificial Intelligence, Machine Learning, and Natural Language Processing:

A true AI/ML/NLP platform learns like a human, by training and reinforcement learning, so

  1. Continues to work when input formats change, or data appears on different pages or places
  2. No need to define pre-built templates to read inbound documents or other data formats
  3. Reads and processes footnotes (even when the footnotes appear in different places)
  4. Performs data ingestion including digitization, extraction, ontology-driven and second-source validation, auto-tagging of data and metadata, and use of knowledge graphs for identifying and storing information and relationships

Most solutions such as those embedded with RPA, document management and workflow tools will fail these tests, leaving in relatively expensive, still manually-intensive and error-prone processes.

Let us collaborate with you to leverage AI/ML in the most optimal manner for your business.