Role of Analytics in innovation

Analytics plays a crucial role in modern corporate innovation. The outcomes from analytical models are used to drive new sales processes, to change customer experiences in order to avoid churn, and to identify triggers detecting fraud, risk, or any sort of corporate threat, as well as many other business issues. The knowledge from analytical models is commonly assigned to recognize customer Behavior, to predict an event, or to assess the relationship between events, impacting company actions and activities.

Analytics has three stages.

Stage 1 provides long-term informational insight, helping organizations analyse trends and forecast business scenarios. Data warehouse, data marts, and interactive visual analysis usually support this stage one purpose. Focus is towards identifying trends, historical event patterns, and business scenarios. This analysis is concerned with presenting information about past sales by region, branches, and products and, of course, changes that have occurred over time.

Stage 2 of Analytics maps out the internal and external environments that impact the market considerations, the customers’ Behavior and the competitor’s actions, as well as details about the products and services that the organization offers. How profitable are my products/services? How well have they been adopted by the target audience? How well do they suit the customer’s need? Statistical analyses support these tasks, with correlations, and association statistics methods.

Stage 3 focus is driven by to the company’s strategy. Model development is directed by core business issues such as cross sell, churn, fraud, and risk, and models are also deployed and used once the results are derived. Data mining models that use artificial intelligence or statistics commonly support these types of endeavours. Models are deployed to classify and predict some particular event and to recognize groups of similar Behavior within the customer base for subscribed business change

The three layers of analytics provide a foundation for data-driven innovation, both creating and delivering new knowledge and accessible information. In innovative organizations, access to analytically based answers is fundamental throughout the company. Data is seen as a corporate asset and analytical methods become intellectual property.

Innovation is a wonderful process. It continually evolves, allowing companies to remain ahead of competitors, ahead in the market, and ahead of its time. However, innovation has a price - intangible price—and maybe even a higher price than we could imagine. Innovation demands companies stay at the pinnacle of available technology and be on the leading edge of new business actions. But even more than this, innovation requires people to change their minds.

Innovation demands change. We must take a chance and address a particular situation and put into place something that may never have been tried before. Innovation in the context of new ideas means to try, and sometimes get it right and make things better, and sometimes not. Therefore, innovation is a trial-and-error process, and as such it is also a heuristic process.

Everything changes. The market changes, the consumer changes, the technology changes, and thus products and services must change as well. Analytical models raise the business knowledge regarding what has changed and what needs to change. The new knowledge delivered by analytics is about the company itself, the competitive environment, and the market, but mostly it is about the consumers/ constituents that the company serves

Analytics is geared toward understanding the average, to accurately forecast for the majority, to target most of the population at hand. What companies, analysts, and data miners need to bear in mind is how heuristic this process can be and, as a result, how they need to monitor and maintain all analytical models to reflect changing conditions.