HR Digital Transformation - A Business State of Mind (Part 3 of 6)

It should not be a surprise that digital technologies have been used widely in business for over four decades. They have become especially present with the evolution of the Internet, High-Speed Broadband networks, and Cloud-based applications. So, what are some of these technologies? Here are some terms to become familiar with, what they mean, and how they are leveraged.

Process Automation – Basically, any business process or task that was previously done by a human and is now done by a computer and application. In order to accomplish this in even a basic way, the task must be replicable and driven by a standard rule or set of rules. Something as simple as summing a column of numbers in a Microsoft Excel Worksheet is a form of automation. The function had to be programmed. As technology has advanced, we have been able to make the Summing program richer, by making the data attributes conditional and then having the ability to provide the parameters for each condition. For example, only sum positive numbers, or only sum numbers when a person's last name starts with "B." The capability has advanced to such a state that, with proper guidelines, the computer can build the program on its own. This is called Robotic Program Automation (RPA) which is principally automation of the automation.

RPA vs RDA - Over time, a distinction has evolved with RPA. This is "Attended" and "Unattended" RPA, which is distinguished by whether a person is involved in the automated process.

• RPA refers to an unattended process. Examples would be scheduled processes that run in the background for a business application such as file creation and subsequent export to another database.
• RDA refers to Robotic Desktop or Device Automation, and the process is attended, meaning that there is human interaction with or throughout the execution of a business process. A typical example would be completing an online form, which based on the data entered by a human, directs the system to flow in various directions or provide feedback to the human such as an error message or request additional input. An excellent example would be voice commands with a device through Amazon Alexa Echo.

Machine Learning – This is combining sensor data digitally captured from the operation of a mechanical component as input to an RPA task. A program is constructed to execute a specific task when the data collected meets a true or false condition. A common example is that once a car has reached a specific mileage; the system will alert the driver that it's time for service. In advanced scenarios, similar data can be used to initiate or schedule a maintenance service call, helping service organizations better plan their service demands.

Bio-Metrics – With respect to business, this is the incorporation of biological inputs or outputs to drive process automation. Common examples of inputs are fingerprint, optical scans, and facial recognition to securely identify a user in the process. For output, a car's computer may alert the driver with a warning vibration if the driver is not staying in within the lane markers. For input and output, using voice and audio to interact with a smart assistant.

Internet of Things or IoT – This is the concept of integrating devices with the ability to tap into the Internet, often referred to as making the device "Smart," allowing remote communications and control. Good examples are various smart devices in a home that can be remotely controlled to receive a command from a smart phone, such as controlling lights, locks, security systems and thermostats in the home or having the system send an alert to a smartphone to answer the door with a video and voice. A more advanced example would be a military drone operated from half-way around the world. It means being "connected" to the world-wide network and devices at all times.

Chat Bots and Digital Assistants – A ChatBot is an integrated guide for an application to assist the user with navigation and decision support. Typically, the interface is pop-up assistance that usually requires a typed response. They are called Chat Bots, because the experience resembles texting with a human person, but is actually a simulated interface programmed to respond to keywords related to the situation. With newer technologies such as Amazon Alexa or Google Assistant, which can be integrated with business applications, the keyboard is substituted with a voice command. Both technologies can be called digital assistants and are a bridge to full AI.

Artificial Intelligence or AI has been a controversial topic ever since the movie 2001 a Space Odyssey in the 1960s. AI is the science of building a machine that can fully replicate human thinking and behavior, capable of self-learning and possibly free will. On a more practical level, it's about a computer that is designed to recognize patterns collected from sensors over time so that it can anticipate or predict an effect based on contextual circumstances. Sounds scary, but this technology is already being used in everyday life. The programming that is now going into autos to produce a driverless car is a recent example. Unmanned robotic exploration of the planets in the Solar System, such as robots like the Mars Rover are too far away to send minute-by-minute commands as in the drone example above. So, these devices have been programmed with RPA AI capability to recognize, through sensors, certain conditions to allow the Rover to carry out its mission. It also uses sensor data to create new ways to approach various problems on its own without human instruction. It adapts by using data to build its own process.

Nano Technology is the science of molecular robotics. It is typically about building a molecular machine programmed with a single task. The microscopic machine is referred to as a Nano Bot. A Nano Bot can work as an individual bot, but Nano Bots with the same task are usually deployed in mass acting in concert to produce a Macro effect. Nano Technology is used to alter things at a molecular level. There is a lot of optimism about applying Nano technology to medicine and microelectronics. In business, this might be applied to increase the battery life of a mobile device or miniaturize the size of sensors tied to ML or AI.

Predictive Analytics - Considering all the previous technologies described, they all have one thing in common…Data. All these technologies are collecting or producing data needed to perform a task. At some point, there is enough data collected over time for the machine to record trends and then to be able to develop cause and effect scenarios. To be precise, there are so many data points needed to be able to predict a future outcome, that only recently have computers been able to crunch the numbers. But the use of predictive analytics is becoming pervasive. Probably the most common example is shopping online, where the system is predicting of what products a shopper is more likely to buy. Or surfing Netflix, which over time, starts to make recommendations based on the subscriber's viewing patterns. However, when it comes to thousands of employees working across a large international organization, the number of data points is much more complex. Organizations are already using this technology to make critical business decisions, and soon that same power will be in every employees' hands.

Summary

The single common aspect of all these technologies is DATA. Data is collected and leveraged in every case. The quality of the data being collected or produced is paramount to the effectiveness of automation and a consistent result. Effective Digital Transformation depends on quality data. This is an important concept to remember as we progress through this series.

In part 4 of the series, I will address how Data is being used in amazing ways to change the way companies and their employees engage. To make this happen, I predict that every medium to a large company in the future will have a Chief Data Scientist role. At a minimum, they will employ a person as a Data Scientist or at least have someone schooled in data science on staff who will be responsible for making sense out of all the data points collected by company technology. They will also be a key part of the design of new systems and the configuration to purchased technology. Most modern applications already capture data and provide analytics within the application's function. However, to truly drive analytics, tools have been developed and are evolving that capture data from multiple sources, including applications, machines, user-experience, and more. These are tools of the data scientist.

I look forward to your comments and extensions of this discussion. I am sure I will learn something from the feedback.

About Author

Steve Bradley is Former​ VP and Director for the Cloud HCM Practice at Birlasoft. He has over 20 years of HCM technology experience – and founder of two HR service companies, SystemLink and Learn2Perform. He is a frequent speaker at conference events and advisor to organizations of all sizes on the topic of HR Digital Transformation, HCM Technology and HR Organizational Change in a technology age.