Gauging demand for a product has always been a challenge for enterprises. Historically, future demand has been predicted by collecting data related to past sale and demand, as well as the gut feeling of the managers and decision makers. Getting these figures right is an art, and this art can have a serious impact on the bottom line of a business. In addition to future demand, enterprises are also interested in knowing customer behavior as well as events that are likely to take place in the future. To do so, a suitable combination of tools and techniques needs to be used to leverage the current data in order to predict future business events.
Analytics, as the term is commonly used, consists of techniques that can be used for the discovery and communication of meaningful patterns in data. It is a multi-dimensional discipline used to gain valuable knowledge from data. Insights from data are used to recommend action or to guide decision making as per the business context. The quality of analytical results depends not only on having a large volume of data but also on the algorithms chosen, as well as the quality of the data itself.
Traditional analytical tools claim to have accurate and up-to-date information related to the enterprise, but that data tells what has already happened. Such post-facto insights are not sufficient for enterprises to maintain competitiveness. Enterprises would like to know more about the future — well in time — so as to take informed decisions.
Predictive analytics encompasses a variety of machine learning techniques that analyze current and historical facts to make predictions about the future. The core element is the predictor, a variable that can be measured for an individual or entity and can be used to predict future behavior. For example, a credit card company could consider age, income, credit history, and other demographics as predictors to determine an applicant’s capacity to pay. Historical data is analyzed to develop a model that can contain multiple predictors and these models are used to forecast the probability of future events.
Enabling Predictive Analytics
To benefit from predictive analytics initiatives, you need a well-defined roadmap that aligns implementation with business needs. While many enterprises have a well-defined business intelligence roadmap, defining a similar roadmap for predictive analytics is a difficult task.
To enable predictive analytics in the enterprise, consider the following points:
Empower a C-level champion. Having buy-in from the senior leadership of the enterprise helps imbibe the culture of using data-driven methods to reach decisions across the enterprise.
Create a cross-functional team. One of the keys to success is having people who understand the tools and techniques and can apply them skillfully as per the business context. Predictive analytics makes demands on business as well as IT functions. Advanced statistical techniques are needed to synthesize qualitative and quantitative data into actionable insight.
Identify and prioritize compelling business cases. A compelling business case is equally critical for a successful enterprise-wide adoption of predictive analytics.
Understand and prepare the data. Data understanding is a preparatory activity, and in many cases, its value is underestimated. Selection of the appropriate data source is crucial to getting the desired result.
Choose the right set of tools and techniques. The choice of analytics tools depends on the volume, velocity, and variety of information as well as the desired insights. Enterprises can benefit by putting in place a collection of best-of-breed tools rather than using one single tool for all activities.
Form a robust integration and deployment strategy. In order to explore a huge volume of data quickly and uncover latent needs and wants, these initiatives have to be closely aligned with operational and decision-making systems.
Establish a synergy with other technologies. Predictive analytics can be considered a branch of cognitive intelligence, which deals with the development of computer systems modeled after the human cognitive systems. Cognitive intelligence algorithms span several different branches of computer science and mathematics, including machine learning, pattern recognition, predictive analytics, natural language processing, expert systems, and agent-based systems. To maintain synergy, predictive analytics initiatives need to have a well-defined interface with the other constituents of cognitive intelligence.
Stay tuned for next time, when we'll be discussing AI-enabled applications and going out of business.