Having the right data at the right place, at the right time, can help you discover market opportunities, improve quality, speed up production or delivery, and help you outmaneuver the competition. The right data can help you do so more efficiently – thus more profitably. The problem with these seemingly simple goals is that they get a lot of lip service without driving action.
The Information Age has ushered in an era of Big Data to support business decision making.
Big Data knows everything and wants to tell you all about it so you can make better business decisions. But Data Mining provides such a rich opportunity that it is easy to fall into the trap of analysis paralysis. While traditional data aggregators continue to collect ever more time tested data points around consumer preferences and social trends that influence markets, from ever larger and more segmented audiences, the Internet of Things (IoT) adds a staggering amount of new data. Sooner or later we face the question of how to parse that data to inform intelligent action.
Albert Einstein famously said, “the more I learn, the more I realize how much I don’t know.”
Clouds of bits and terabytes, event reports, and moving average projections based on standard deviations can obscure the vision of managers trying to land meaningful projects on time and within budget.
Marketing has become a data-driven hard science built on psychology, sociology, behavioral studies, and metrics like time on page, bounce rates, click-through rates, A/B testing and other techniques supported by google analytics data.
Things like Google Analytics, Time On Page, Bounce Rates, Click-Through Rates (CTR), Abandoned Carts, Returning Users, Artificial Intelligence, Predictive Modeling, and Remarketing Ad Strategies have changed the game.
Newer Kanban-style manufacturing control systems with goals of Just in Time (JIT) inventory management for lean manufacturing have given way to even more sophisticated Enterprise Resource Planning (ERP) systems.
End to end Value Chain Engineering, Process Control, Agile Project Management techniques, the Internet of Things (IoT), and sophisticated Electronic Data Interchange (EDI) systems have been implemented to create more, better products delivered to the market faster and at a lower cost.
Data Science is an emerging field that can be applied to everything from marketing to value chain and even human resources. Minute details are collected at every moment and fed into a centralized pattern matching, categorizing, and decision tree system that can be parsed using algorithms to help produce new and actionable insights. If you don’t have this kind of talent in house, then it might be a good idea to obtain contract help from specialists.
Which theories should be tested first? How many variables should we consider? How do we prioritize them? Should we be more concerned with size, speed, rate of acceleration, or location? In the race to acquire data and analyze it, don’t forget that it takes smart people to ask the right questions, measure the right things, and introduce logic and segmentation to drive action.
Better questions produce better answers. What do stakeholders actually want? Which segments are moving fastest? Where is the market really headed and why? What action steps can we take right now to get there ahead of the competition?