In Part I of our white paper, we introduced the importance of “Quality” in artificial intelligence ( AI ) and machine learning ( ML ) to optimize business processes, increase revenue, and improve overall customer experience. At Cerebri AI, we group quality into four key disciplines:
These core disciplines must be mastered to deliver the best insights from AI/ML on a consistent basis. This whitepaper will focus on “Data.”
Cerebri AI helps large enterprises and organizations make better decisions by systematically using the computational power hosted in the cloud or on-premise high-performance clusters to apply methods and techniques at-times referred to as artificial intelligence (“AI”), or machine learning (“ML”).
In using machines to help make better decisions, we must systematically generate better insights ( data science ) to make predictions ( machine learning ) and ultimately taking the best actions ( artificial intelligence ) to achieve the desired results. We generally use the term AI to cover all three aspects of making decisions using machines.
Quality is always essential in any decision-making process. When using a systematic approach like that outlined above to make customer experience (“CX”) decisions, quality really, really matters. A “bad” insight or prediction in taking “customer” actions may result in losing a customer, maybe even forever. Customers are almost always costly to acquire, easy to lose, and can be impossible to re-acquire.
Have you ever wondered why they call it Data Science? As humans, we like to organize complex concepts into patterns and logical flows. It makes it easier for us to think through problems, explain them to others, and get the answers we are looking for.
“Data comes first, not science.”
Data is the energy source fueling AI technology. How many times have you heard the expression, “garbage in, garbage out.” This couldn’t be more true than in the discipline of AI. AI can help us make better decisions, but decisions based on flawed or insufficient data are almost always wrong. Using flawed decisions to drive your CX strategy could result in missing critical opportunities, alienating customers, or worse.
Data quality usually means
In AI modeling, it also means
As previously mentioned in Part I of this series on Timing, the speed at which data is made available for AI modeling ( think about how fresh is your data ) matters in making the best predictions. The next level of data speed is the transformation from batch processing of data to streaming. Omnichannel CX is increasingly about digital commerce. Digital operates at streaming speed, a fire hose relative to the assembly line of batch data processing.
How CX-data can and should be managed in the digital world is the subject of its own white paper, so we will be brief here. Sufficient to say, high-quality CX would mean that a customer’s clickstream on a website should be analyzed in real-time, and their behavior would be compared to the journeys of like customers Action and offers, as appropriate, would be determined and actioned in-time across channels. Most large enterprises are a ways away from being able to do this other than by using simple, less effective rules-based decision engines.
Near real-time processing is where everything is headed, and will stay that way for the foreseeable future. That will mean merging streaming and batch data to score customers and recommend NBAs and NBOs. The Cerebri CVX 3 platform includes data intake technology that will allow for this down to the data record level, which gives the enterprise a strategic CX advantage.