How important do you think is employee training and upskilling in the process of digitalization of enterprises?
Employee training and upskilling – for both human and digital workers – should be a continuous and ongoing process of learning and growing as organizations mature. As soon as we stop learning and improving our skills, innovation levels begin to tank. So, the concept of employee development is incredibly important.
Within this framework, there are two aspects to this question. First, how do you help people become comfortable working with digital co-workers? Some of this has already started. People are increasingly more comfortable interacting with bots as part of their daily life. Think about how prevalent Alexa and Siri are now. Or, how often we interact with digital workers when we’re trying to get support for a product or service. Second, there is training and upskilling on how we help people think about data and, more importantly, how we bring in context or knowledge into the company and our data. Training will be critical here. A new role that will soon become critical is a Knowledge Scientist. The knowledge scientist’s role will be to gather information and context from across the enterprise while working closely with data scientists, engineers, and business users to make data more useful.
There’s a balance of what digital workers should do and what human workers should do. Machines should be coded to accomplish mundane work while humans can be trained to do higher-level work that is not easily programmable. It’s one of the reasons why we developed the approach of Context-Driven Productivity to help both digital workers and humans by adding context and meaning to their data.
Do you think that with RPA and enterprise automation replacing humans will actually impact the productivity and ROI of enterprises?
It’s challenging to hire and retain good people in the current business environment. In our opinion, it is not about replacing people or putting them out of work. Organizations do not want to get rid of their people; they desperately want to redeploy them into more strategic tasks beyond basic operations. At Ephesoft, we want to remove the mundane and manual tasks and allow them to be creative. We want to help humans be more productive and focus on things digital workers can’t do. Automation isn’t about replacing humans, but reallocating jobs for maximum performance and productivity. In the future, human and digital workers will be working side-by-side with humans being assisted by digital co-workers, which will create both accelerated productivity and ROI.
What do you mean by context-driven digital transformation? Why do you think it’s important?
Context is more than just a piece of data as it is the background in which a task, event, or process takes place. Context includes the meaning and intent surrounding the data. It is the deep, multi-dimensional understanding of data and the relationships between the entities involved. Understanding the context can lead to accelerated insight through AI and machine learning. To be competitive, enterprises need more than data – they need context to enhance productivity and efficiency. We need to start capturing the context. Context provides workers, both digital and human, with a 360-degree view of their data and the background of where that data came from or the event which took place.
When digital transformation projects have context and this full view, organizations have a higher chance of success. Accelerating processes and understanding data with context allows organizations to adapt, grow, and drive customer success. The actual outcomes can come in the form of processing insurance claims faster for natural disaster victims to getting comprehensive healthcare records for patients to prevent giving a loan to a fraudulent company that will create significant risk, loss, and profits. Additionally, Ephesoft’s goal is to improve productivity, which results in better customer experiences, more fulfilling corporate careers, and increased enterprise profitability (operating margins).
How will semantic data applied to processes increase the productivity of the workforce?
The autonomous enterprise of the future, and the digital workers that will run them, will need rich contextual data, which we also call semantic data. The real power and value of digital transformation will only be unlocked by the adoption of a semantic data-driven approach. The future of work will be built on digital workers powered by machine learning, artificial intelligence, and knowledge graphs. It’s not enough for an organization to be “data-driven” anymore; they need to become contextually-driven for real insights and process acceleration. Machines and humans will be able to work together, so optimize productivity, opening the path to an autonomous enterprise journey for the future.
How can firms turn flat data into meaningful dimensional data?
The first step in applying a semantic approach to automation and productivity is moving beyond the documents and understanding the contextual data relevant to a particular process or task. We’ve probably all heard about the term taxonomy. Taxonomy is the process of naming and classifying things such as animals and plants within a more extensive system, according to their similarities and differences. At Ephesoft, we’ve been using the term “Taskonomy” to describe and organize the data around a specific task or process to provide the worker all the contextually relevant knowledge they need to perform the job.
Another core concept to understand is the power of linked data or semantic web. The Semantic Web is about making links between datasets that are understandable, not only to humans but also to machines.
Once you have the taskonomy or a semantic model representing the task or process you need to automate and have identified data relationships, we can begin to turn our flat data into context. This is where we use machine learning to extract data from unstructured and semi-structured data; we classify data; we use deep learning to pull out the entity’s and tag them following our semantic model, and we create the links to related data and produce a knowledge graph.
Knowledge Graphs allow us to represent the context with high accuracy, understandability, and, most importantly, reuse. Once a knowledge graph is created, it can continue to scale and connect more information and data to build a complete understanding of the entire set of data. Ephesoft has developed a new platform that will address all of these challenges.
Do you think that being data-driven is sufficient to bring in the digital transformation?
I recently read a report from NewVantage Partners on a survey they did concerning how big data and AI were driving digital transformation. They looked at some Fortune 1000 companies, and what they found was a bit surprising. Despite all the hype and investment in becoming data-driven over the last few years, only 31% said they were data-driven, and that number has been going down. Could the underlying approach to becoming “data-driven” be flawed? Could organizations be struggling because they continue to apply a legacy mindset and approach to data acquisition and storage?
Being data-driven isn’t enough. The current state is that people don’t have the data they need to be productive and get their jobs done. Ironically, however, organizations are drowning in data. We’ve never had access to more data than we do right now. The problem is it’s all generic flat data, missing the context of any specific process.
Reports show that 50% of RPA and 70% of digital transformation projects fail to deliver improved productivity. They fail to free up human resources to work on the innovations that can keep an organization alive. As a result, many enterprises are just running in place. This concludes that it is essential to go beyond being “data-driven” – you need to incorporate context to make your digital transformation initiatives successful.
What are the pre-requisites to bring about digital transformation in enterprises?
In our push to become data-driven and ultimately, context-driven, we first capture data and attempt to transform it into computable bits that we can use fuel automation and derive insights. However, the fundamental challenge with how we capture and represent information outside the human brain is that it is never quite accurate. We always lose some of the meaning, relationships, and context. Despite all the advancements in technology, organizations are still trying to model and store the information based on how we’ve historically stored data. We need to abandon the structure-centric approaches of the past and embrace a meaning-first or semantic-centric approach to data.
Therefore, the pre-requisites must start with the need and mindset for change and a better way of working, which is automated. Most organizations that need this type of technology often have both electronic and physical high-value, high-volume documents that are often labor-intensive. This could be invoices, insurance claims, mortgages, healthcare records, transcripts, or financial documents, for example. A project team and budget must be in place, as well as existing knowledge of current processes. Each digital transformation project is unique and may have different goals, which will dictate the level of productivity and technology needed.
“It is essential to go beyond being “data-driven” – you need to incorporate context to make your digital transformation initiatives successful.”
Kevin Harbauer, CTO, Ephesoft Inc.
Kevin Harbauer is the Chief Technology Officer at Ephesoft and brings 20 years of diverse software development, management, operations, and implementation experience. He has proven success in building and leading high-performing product development and IT organizations. Similarly, he has helped organizations adopt innovative technologies and practices, including DevOps, Continuous Delivery, Software-as-a-Service, cloud-based, cybersecurity initiatives, and data-driven operations.
Previously, as the CTO and CISO for Healthwise, he delivered measurable growth and advanced Healthwise’s mission by aligning product and technology strategies with the company’s corporate vision. In addition, he provided thought leadership around the development, presentation, and implementation of the Healthcare interoperability standards and was an early adopter of cloud-based infrastructure in the healthcare sector. Before his time at Healthwise, Kevin was Director of QNXT development at QCSI and TriZetto, where he led a large development organization and was responsible for delivering industry-leading products to the health care payer market. Kevin achieved a BS in Computer and Information Science at the University of Maryland College Park, an MBA from the University of Phoenix and completed an executive education program from the Sloan School of Management at MIT.