If you’re contemplating investment in an automated data processing solution that will better equip your business for intake of unstructured data, you’ve already made an important commitment to a stronger foundation for your team. 

It’s no secret that digital data processing is playing a bigger and growing role. Every day in almost every business, critical information is received in email, images, PDFs, spreadsheet, presentations, and other documents. According to Christine Taylor’s report at Datamation, that unstructured data makes up 80% and more of enterprise data, and is growing at the rate of 55% and 65% per year. The decisions, strategies, and everyday responsibilities of employees hinge on access to the critical information within those files. Yet, extracting the data that’s needed is a precarious process at best. 

Even with some automation in place, operational professionals are losing hours manually searching through documents, identifying pertinent information, and cutting and pasting to get the contents into a usable form. The writing on the wall is clear - to increase capacity for growth, to empower institutions for long-term success and operational efficiencies - better solutions are a must.

But you’ve recognized that already, and that’s why you’re here. Any business contemplating a brighter future, with automated data processing ease, there are two potential paths forward: 

  • Build an unstructured data extraction and automation solution from the ground up
  • Partner with an intelligent data automation vendor who has an end-to-end solution ready to go

Build versus buy. Here are 5 things to consider before you pick your path: 

   1. People

Building an intelligent automated solution requires AI and machine learning and other advanced cognitive technology skills. You’ll need data scientists, data engineers, data analysts, and IT pros to manage infrastructure, updates and maintenance. If you build in the cloud, you’ll need an expert there, too. If these professionals aren’t already on your payroll - with bandwidth in project load to spare - seeking a vendor who’s staffed up and ready might be a good way to go. 

   2. Tools and software

To build an intelligent document processing solution, your highly trained team of experts will need the right tools for the job - tools that adequately support the following functions: 

  • Data extraction
  • Data storage
  • Data aggregation
  • Workflow automation
  • Authentication
  • Integration with enterprise tools and applications
  • Support for common file types (PDF, email, images, spreadsheets, etc.)
  • Data ingestion and export
  • Machine learning models
  • Model maintenance frameworks
  • Data labeling
  • Infrastructure management (cloud, on-premises, or hybrid)
  • User experience (UX)
  • Graphical user interface (GUI)
  • Security and privacy
  • Compliance and auditing
  • Automated testing
  • User acceptance testing

Overwhelmed? If so, working with a data automation vendor will ensure these are already built into the system. If not? Perhaps you’ve got what you need for production in-house.

   3. Time and scalability 

One of the biggest considerations to ponder in deciding whether to build or buy your data processing system, is how much time your organization has to spare. From staffing, to needs assessment, to design, to prototyping, to development, to testing and deploying a solution, it can be months and years until a from-scratch solution is functional. Can you afford to keep your current processes for unstructured data that long? 

Scalability comes into play, too. Perhaps you only have a one-document use case you’d like to automate right now. A solution for that may be easy enough to build by yourself. But how long until you want to tackle another form of unstructured data? And how long until a new bottleneck trainwrecks your team’s ROI? An end-to-end data processing toolset that’s scoped for flexibility may be a smarter, and certainly faster, way to go.

   4. Maintenance

Once you’ve built your own system, your work doesn’t stop there. Consider, too, the ongoing support requiring dedicated work by your operations, IT, and data science teams. That’s a lot of person-hours, needed month after month. Do you have the bandwidth to spare?

   5. Cost

Building an in house system takes resources. People - tens of thousands of hours from technical personnel. Tools - a broad array of technologies along with licenses, training and integrations. Hardware - infrastructure needed to run your system ongoing. Maintenance and support - ongoing operational expenses are standard for any system you make. 

If a predictable cost structure and price far more affordable on the average business’ budget is more in line with your priorities, a vendor solution may be the way to go. 

Whether you stay in house or go outside, an investment in your unstructured data solution is a smart choice for your business, your people, and your customers. How you get there is simply a matter of your priorities, goals, resources and time. 

Want to review each of these considerations in more depth? Read our ebook for more.

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