Artificial Intelligence (AI) is changing the way companies do business at all levels. Not only is it at the cutting edge of innovation, it is also the creator of new types of businesses. How does AI add value to businesses? It adds value by using data analysis and Machine Learning (ML) to create and automate processes. The extent that AI and ML impact business is growing exponentially.
Difference Between AI and ML
When talking about AI, it’s really important to create a clear understanding of exactly what we mean, as it’s a term that’s often used but less often defined.
AI, from a technical perspective, is a technique which enables machines to mimic human behaviour. A subset of AI is ML which involves the use of mathematical and statistical algorithms that enable machines to learn from data. Kilian O’Carroll, Data Scientist at ProCogia explains: “From a technical standpoint, it is ML that is the foundation for much of Data Science; whereby specific problems are solved with specific algorithms, based on, for the most part, big data. We are not at the stage where the same computer program can be used one day to predict house prices and then the next day to predict cancer rates.” Both problems would require the algorithms to learn different patterns from distinct sets of data. In addition, the specific choice of algorithm may need to be different. “As Data Scientists, we build models using ML to predict specific measurable outcomes. We teach machines to pick out patterns of behaviour which they can do without any intuition or emotion that we [humans] would attach to things. By applying ML in a careful and correct manner, Data Scientists strive to remove this bias, and lend mathematical rigour to decision making.”
Understanding and Growing Your Business with Data Science
Data Science is vital for today’s growing businesses. Along with an acute understanding of your business, Kilian believes that Data Scientists should be embedded in any business team working closely with the domain experts. “We work with the business to ask, if we can predict something via data science how useful is that information going to be to the business? From a data side, we assess what data we have available to us and how trustworthy that data is. For example, when analyzing a product, we focus on areas such as customer feedback in order to highlight different cohorts (clusters) of users that may use a product differently, or to highlight pain points that customers are experiencing.”
Sentiment Analysis
Data Scientists collaborate with domain experts within any given industry to predict all kinds of outcomes. For example, pulling data from an app or a website such as Google Reviews allows Data Scientists to understand levels of customer satisfaction and carry out ‘sentiment analysis’, which is a branch of Natural Language Processing (NLP), to extract the underlying sentiment from reviews. ProCogia has created a data analytics tool for Product Managers, Market Researchers, and App Developers called Appsedia which converts social chatter into actionable insights. Gathering this type of intelligence drives change, allowing businesses to flex to the feedback of differing ‘clusters’ of sentiment.
Customer Segmentation and Personalized Recommendations
Data Scientists use customer segmentation to get a deeper understanding of differing cohorts of people. For example, by analyzing household energy usage throughout the day, it might be possible to estimate which users likely have electric cars based on a high overnight usage fingerprint. Grouping people by different behaviours – also referred to as clustering – opens up a better understanding of any particular cohort; what age they are, what entertainment they like, what disposable income they might have etc. It is these insights that are so valuable to advertisers who can target their campaigns with personalised content. Identifying how similar some groups of customers are and how distinct other cohorts are, allows organizations like Netflix the opportunity to make film recommendations to similar minded clusters of its customers (i.e. with the same viewing profile) and to personalize their adverts to best suit individual viewers too. This type of system is referred to as a Recommendation Engine, and is the driving force behind many of the most successful tech companies we utilize and interact with today.
Feature Importance
Data Scientists will often rank ‘feature importance’, which involves understanding what features of a particular product or service matter the most to the customer. This information drives business focus, allowing decisions to be made to develop certain features and to discard others.
Historical Trends/Mitigating Risk
Data experts are always interested in looking back over past data; understanding historical data allows Data Scientists to predict the future with a certain level of confidence. Using data predictions can be lifesaving. For example, take an airline, if certain tolerances are breached, an aircraft will be in danger of engine failure. Using predictive and prescriptive analytics, these types of catastrophic events, can be predicted and avoided.
Anomaly Detection
Another way of mitigating risk using data is anomaly detection, which highlights outliers in data. This process is often automated as machines are much more efficient and accurate than humans at sifting through mountains of data and monitoring any changes. On a less technical note, automated anomaly detection can greatly enhance an analyst’s quality of life at work, as it removes the need to manually check through multiple charts on a regular basis, as well as reducing their concern that certain data issues may be missed. In today’s corporate world, AI does not only add value to businesses, it is essential for the success of any business.
