Consider for a moment what your organization would be able to achieve if everyone had access to data and everyone and everything was accurate. Often times, organizations, whether they recognize it or not, do not appreciate scenarios where an individual makes an immediat decision with the available information. Unfortunately several situations in those scenarios could make that outcome go bad:
- Incomplete data at the time of the decision-making
- Incomplete options
- Impact of the chosen option is not considered via a multi-staged process
Can a human Subject Matter Expert (SME) process the necessary level of information to cure these three pitfalls?
No, but what you can do is make data your SME. With data, you can make machine learning algorithms “intuitively reusable” by continually training the decision-making model. Data can help us understand decisions from best past actions (i.e. what worked before) and enable computer algorithms to provide us with a way forward based on what outcomes we wish to see. This is an iterative process that presents to you the best decision every time.
An overlooked aspect of data as your SME is being able observe many more possible use cases of data. Observation by humans is an intrinsic activity of our social interaction in the workplace. Often times, a human SME is generally attributed as an expert in his or her role because of his or her creativity and ability to handle various situations, which leads to a balanced reaction to the situation. And while some detractors would love to discuss at length the creative ability of computers, I’ll happily point them to the method of “monkey see…monkey do.” That being said, if we can provide our machine with excellent role models to follow, then the data becomes a huge set of samples of what to do or what not do. Therefore, we seek options by observing a collection of best practices and let the machine see the data for what works or does not work. Within the digital process automation (DPA) practice, Pega provides the ability to read assignments, recognize actions, and utilize continued enhancements such as event strategy rules and natural language processing. Our average human SME has probably trained tens, if not hundreds, of people to interpret data and make decisions to influence outcomes; a data SME can do the same.
Finally, and what is probably the most interesting aspect of using your data as your SME is the ability to process moves as a chess champion would. For any given configuration on a chessboard, a player must consider all moves and countermoves in order to determine the most beneficial result. In the world of business, most “intelligence” starts, and ends, with one move of the metaphorical chess game. What data as a SME can do is provide the journey that we want a customer to take. We can then examine the moves and countermoves in that relationship. Our ultimate goal is to achieve the winning move – described as the ultimate business outcome. Seriously, who cares about taking a pawn? But if taking that pawn opens the lane for a checkmate, that move suddenly makes sense.
Too many organizations are thinking about only one move at a time without considering the move-countermove bond between a business and the customer. While chess is a classic game of competition, games of a cooperative nature also exist in which each player is unaware of the next player’s move, but together they must work to achieve the common goal. Data as your SME is the best chance for enabling that cooperative game between your business and your customer. With data – participating in real-time, learning in real-time, knowing the options available, and having the depth of knowledge to see moves several interactions from now becomes a breeze. You gain a huge competitive advantage against companies that are looking at just processing from a “what can I do right now” mindset.