Note: This article is updated at McKinsey on Unleashing the Value of Big Data Analytics.
Big Data Analytics and Insights are changing the game, as more businesses introduce automated systems to support human judgment.
Add to this, advanced visualizations of Big Data, and throw in some power tools for motivated users and you have a powerful way to empower the front-line to better analyze, predict, and serve their customers.
McKinsey shares a framework and their insights on how advanced analytics can create and unleash new business value from Big Data, in their article:
Unleashing the value of advanced analytics in insurance
Creating World-Class Capabilities
The exciting part is how you can create a new world-class capability, as you bake Big Data Analytics and Insights into your business.
“Weaving analytics into the fabric of an organization is a journey. Every organization will progress at its own pace, from fragmented beginnings to emerging influence to world-class corporate capability.”
5-Part Framework for Unleashing the Value of Big Data Analytics
McKinsey's transformation involves five components. The five components include the source of business value, the data ecosystem, modeling the insights, workflow integration, and adoption.
|1. The source of business value||Every analytics project should start by identifying the business value that can lead to revenue growth and increased profitability (for example, selecting customers, controlling operating expenses, lowering risk, or improving pricing).|
|2. The data ecosystem||It is not enough for analytics teams to be “builders” of models. These advanced-analytics experts also need to be “architects” and “general contractors” who can quickly assess what resources are available inside and outside the company.|
|3. Modeling insights||Building a robust predictive model has many layers: identifying and clarifying the business problem and source of value, creatively incorporating the business insights of everyone with an informed opinion about the problem and the outcome, reducing the complexity of the solution path, and validating the model with data.|
|4. Transformation: Work-flow integration||The goal is always to design the integration of new decision-support tools to be as simple and user friendly as possible. The way analytics are deployed depends on how the work is done. A key issue is to determine the appropriate level of automation. A high-volume, low-value decision process lends itself to automation.|
|5. Transformation: Adoption||Successful adoption requires employees to accept and trust the tools, understand how they work, and use them consistently. That is why managing the adoption phase well is critical to achieving optimal analytics impact. All the right steps can be made to this point, but if frontline decision makers do not use the analytics the way they are intended to be used, the value to the business evaporates.|
Big Data Analytics and Insights is a hot trend for good reason. If you saw the movie Moneyball you know why.
Businesses are using analytics to identify their most profitable customers and offer them the right price, accelerate product innovation, optimize supply chains, and identify the true drivers of financial performance.
In the book, Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris share examples of how organizations like Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox, are using the power of Big Data Analytics and Insights to achieve new levels of performance and compete in the digital economy.
You can read it pretty quickly to get a good sense of how analytics can be used to change the business and the more you expose yourself to the patterns, the more you can apply analytics to your work and life.