Bridging the Human-Computer Data Gap



Accessibility of new technologies means significant upsides in both customer experience and productivity, however Data is a major stumbling block.


New technologies within the fields of advanced data analytics, cognitive computing / artificial intelligence and visual recognition will create innovative commercial opportunities for firms that can bind those capabilities with their existing strengths. With the increasing availability of cloud applications, these new capabilities are no longer exclusively available to the largest or technically advanced companies.
Against the backdrop of "being digital" businesses are increasingly seeking commercial applications of these technologies in order to enhance their customers' experience as well as to reduce costs through intelligent automation.
Examples include...
Improving customer experience:
  • Help find relevant products / services within companies or in the market.
  • Help manage finances, budget, household bills and find the cheapest utility providers based on predicted usage.
  • Help identify fraud by reconciling my receipts to my credit card statement.
Cost Saving Propositions through automation:
  • Data Entry & form processing
  • Matching invoices, financial reconciliation
  • Legal contract review and compliance reporting
We are now in a world where the crucial barrier to leveraging these new technologies isn’t necessarily the upfront cost or complexity of managing the software / hardware rather the ability to gain mastery of the underlying data that is required to drive these applications. Something you cannot buy off-the-shelf.

Based on where we are today, to achieve aspirations of automating key customer facing and operational tasks, we need a bridge between the digital and dynamic human world.

Everyday new documents and spreadsheets are being created, businesses have different invoice format, every department has a different spreadsheets and databases. In addition, within most organisations today, significant amounts of data still exists in physical form (printers and filing cabinets are still critical pieces of office furniture). Even when documents are in digital form (word documents), they are not always machine readable (e.g. a document scanned as a PDF).
So far most companies have only been able to digitize forms and computerize processing of very repeatable tasks. It takes time to create new screens in applications and database fields, this process is unable to keep up with the pace of business change.
As data / information management and digitization capabilities evolve the degree of automation available in dynamic commercial environments will also increase.
Getting this right will bring:
  • More agile software applications due to more flexible data structures.
  • The ability to develop deeper customer or organizational insights by tapping into unstructured data and linking it to structured data.
  • More real-time / on demand insights (dynamically tapping into new data sources such as IOT sources).
  • Ultimately lower barriers and incremental costs of future automation.
Consider the example of automating the process of invoice matching for a finance department; instead of defining within an OCR tool which fields must be captured, we simply feed it multiple versions of the an invoice from a certain supplier and it can figure separate out the data fields vs the data itself. Additionally applications can further work out which general ledger account the value of that invoice needs to be entered into based on what’s happened to similar invoices in the past.
In the near term, manual intervention will be required to curate and correct automated actions, however the burden of design and preparation for automation is dramatically lowered through machine learning techniques.

To push the extent of human tasks we can automate, we will need to master a number of new techniques and change a few old ones in the way we manage data.

As data volume grows within organisations in both volume, variety and velocity; using and managing data within organisations will be more like using and managing data on the internet. Don’t seek to sort of control it, think about harnessing what's relevant for you.
This means:
  • Access to organisational data needs to be opened up, data silos inhibit processing of tasks which span multiple business units.
  • We will need more flexible database structures (and physical places to store data) that business users are able to design, use and control.
  • More collaborative ways of managing data (definitions and descriptions) that is the responsibility of business users. (As opposed to centralized classification schemes maintained by technical experts.)
  • We will need the ability for machines to reliably infer meaning from natural language text (i.e. summarizing data, think of automatically adding hashtags)
  • More accurate OCR (optical character recognition) capabilities that includes the ability to translate hand writing to machine readable data.
  • We need to employ AI / machine learning techniques that can automatically classify data and cross reference data between documents and structured data sources.

Hey I'm not reading anything here that I don't already know!

Some of you are thinking that there's nothing new here and that is exactly my point. Most of the software is available today.
We use to have to go out and buy expensive scanning solutions or document management systems, now it's available in the cloud. If there's functionality you need, there's likely an API you can call to obtain it or there will be one available shortly.
The data only the other hand, is created by people. Often inconsistently, with differing purposes and meanings over time.
Over the last 30-40 years, we've only been trying to force people to conform to processes that machines understand. We are now at a time where we can start bringing machines closer to understanding people.
This post represents my personal views. Let me know your thoughts in the comments.
Image source: https://www.cs.rochester.edu/courses/212/spring2017/

Comments

Popular posts from this blog

I'm an Australian Business, What's Digital?

Strategy Meets Design Thinking