We’ve seen how digital transformation can disrupt an industry. Think of Blockbuster Entertainment’s physical stores vs. digital streaming on Netflix, and taxi services vs. Uber’s ridesharing app. From a B2B standpoint we saw the move from on-premise CRM applications like Oracle and Siebel to cloud-CRM Salesforce.com, or dedicated corporate data centers moving to Amazon, Google and Microsoft public and hybrid cloud hosting.
According to IDC, by the end of 2017 over 70 percent of Global 500 companies will have a dedicated digital transformation/innovation team, and by 2020 all enterprises’ performance will be measured by benchmarks in customer engagement, digitization of new and traditional offerings, operational efficiency and organizational agility. Unfortunately, IDC also believes at least one-third of these leaders will fail to clear digital transformation hurdles. There are three core reasons hindering their success.
Settling on good enough
It’s not good enough to automate the capture of information. Today, all sorts of data forms need to be captured, extracted and analyzed ranging from proof of delivery, proof of income, proof of ID, new account forms, claims forms and many more. Once digitized, the data must be classified, extracted and verified to support and integrate with downstream processes to action information. Intelligent capture is the first step in a digital transformation process, unfortunately, many organizations are still using OCR solutions from the beginning of this century when the extraction of data is static and housed in a repository.
Not understanding data
Data needs to be understood within the context of the customer’s need and cross-referenced with the company’s pre-set rules and policies to make better business decisions. However, not all data is nicely packaged in preset forms. The challenge with unstructured data (think handwritten notes and social content) is it requires a more sophisticated, linguistic-based approach for capturing, classifying and extracting then injecting intelligence into business processes.
By using natural language and deep semantic processing at the sentence, paragraph and document level, unstructured and semi-structured data can be used by knowledge workers to extract value and understand meaning and relationships between entities in a single document or across a corpus of documents. This provides unprecedented insights in order to make smarter business decisions quicker.
Line-of-business managers not empowered
A managing director at PwC listed ten ways organizations can succeed with digital transformation. Not surprising, the top three included having C-level support and direct involvement with the strategy. While that is ideal, line-of-business owners are increasingly responsible for reassessing existing processes as part of the digital transformation initiative. Additionally, depending on the size of the company and the maturity of the initiative, IT will continue to play a role in qualifying vendors ahead of C-suite involvement.
It’s clear that smarter data capture is essential to smarter business processes. intelligent process automation reduces costs and cycle times, but more importantly, enables digital transformation of customer-facing processes. To be successful, an organization must start with an intelligent foundation, where context and content deliver a better understanding of the company’s data. Click to learn more.
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