Editorial
We’ve heard industry pundits and solution vendors evangelize the benefits of digital transformation initiatives.
Get a mortgage by applying for a loan and submitting tax and ID forms via a mobile phone. Submit an auto accident claim — even provide supporting photos and schedule a car rental — all from a mobile phone.
Digital Everything
However, we’ve already seen what digital transformation can do through the disruption of many consumer services. Think of Blockbuster Entertainment’s physical stores versus digital streaming on Netflix, and taxi services versus ridesharing apps like Lyft and Uber.
From a B2B standpoint, recall the move from on-premises 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.
Digital transformation ultimately means digitizing processes for improved process transparency and accuracy, ease and speed of transaction, customization and satisfaction from the customer experience.
The concept has become so mainstream that by the end of this year, more than 70 percent of the Global 500 companies will have a dedicated digital transformation or 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, according to IDC. Unfortunately, IDC also believes at least one-third of these leaders will fail to clear these digital transformation hurdles.
Overcoming Digital Transformation Obstacles
Ultimately, there are three core components hindering the success of digital transformation initiatives.
1. Good Enough Isn’t Enough
At the heart of the digitization revolution is the ability to accurately and quickly automate the capture of information from any source.
It started with automated mailrooms and expanded to other units of the enterprise including accounts payable, human resources and customer relationship management. Intelligent capture is the first step in a digital transformation process, but organizations are still using OCR solutions from the beginning of this century where the extraction of data is static and housed in a repository.
It is 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.
Learning Opportunities
Webinar
Apr
30
Supercharge Contact Center Efficiency with Generative AI
Learn practical strategies to reduce post-call documentation time by over 8 minutes.
Register
Conference
May
1
Qualtrics X4 Salt Lake City 2024
Register
Webinar
May
2
Unleash AI for Crisis Management: Transform Threats into Triumphs
Learn how Sprinklr Insights provides real-time insights on current events, trends, and brand crises to empower your team.
Register
Webinar
May
9
Driving Customer-Centricity in the Financial Services Industry
Discover how financial organizations are leveraging generative AI technologies.
Register
Conference
May
9
An Irish Roundtable in the Valley: The Economic Impact of AI on the World of Work Stanford 2024
Exploring the Impact of AI On the World of Work with Irish diaspora leaders and tech business champions from the North of Ireland
Register
Conference
May
13
Gartner Marketing Symposium/Xpo Conference London 2024
Register
2. Not Having Context to 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.
In some cases, the knowledge gained through analyzing captured data could actually become a product or service. IDC estimates that by 2019, 40 percent of IT projects will create new digital services and revenue streams that monetize data. This could include custom or context-driven products, services and upsell offers.
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 to make smarter business decisions quicker.
3. Waiting for the C-Suite to Mandate the Change
A managing director at PwC recently 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, especially among midmarket organizations.
A key part of the effort is automating manual business process through intelligent data capture, classification and extraction to feed new or rapidly evolving customer-facing business processes. 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.
Data Capture Drives Digital Transformation
For digital transformation to be successful, it has to be a holistic view of how the enterprise services the needs of its customers.
Information coming into an organization from multiple channels contains essential data that drive business processes and enable automation. With paper and electronic documents both widely used for customer interactions, it’s clear that smarter data capture is essential to smarter business processes.
Learn how you can join our contributor community.
