How to do effective big data processing, analytics

Many organizations have invested millions of dollars in big data, big data analytics, and hired data analysts, only to feel frustrated. There is no denying that they are now getting more and better data. Their analysts and analytics are also top-notch. But managers still seem to have the same types of ideas and arguments about the business as in the past, except that they are using much better data and analytics than before. The final decisions may be more data-driven, but the organizational culture still feels the same. As one CIO recently told me, "We can do real-time analytics now that I couldn't have imagined five years ago, but the impact is still far from what I expected." ? What's going on? After Fortune 1000 hosted several Big Data and Big Data Analytics conferences and spent a lot of time assisting organizations that seemed satisfied with the return on their analytics investments, a clear "data heuristic" emerged. Organizations with mediocre to moderate analytics results use big data and analytics to support decision making; organizations with good Return on Analytics (ROA) use big data and analytics to drive and sustain behavior change. The better data-driven analytics aren't just incorporated into existing processes and review sessions, they are used to create and encourage different types of conversations and interactions. "We don't do analytics or business intelligence until management has identified that they want to change and know exactly what the behaviors are that are being impacted," said one CIO at a financial services company. "Improved regulatory compliance and better financial reporting are easy outcomes to obtain. But that just means we use analytics to do what we're already doing better than before." ? The real challenge is insight, and using big data and analytics to improve problem solving and decision making can mask a reality in organizations that new analytics often require new behaviors. Company personnel may need to share and collaborate more; departments may need to set up different or complementary business processes; and managers and senior executives may need to ensure that existing incentives don't undermine the growth opportunities and efficiencies that analytics can bring. For example, a medical supplier integrating analytics on the "most profitable customers" and "most profitable products" must completely re-educate both the business and technical support teams, both of which are designed to "interrupt" and "disrupt" the business. disturb" and "educate" customers about higher value-added products. The company understands that these analytics should not just be used to support existing sales and service practices, but should be viewed as an opportunity to drive new types of facilitative and consultative sales and support organizations. Ironically, the quality of big data and analytics is less important than the purpose of the analysis. The most interesting tensions and debates have always revolved around whether organizations get paid the most for using analytics to make established process behavior better, or to change the behavior of company personnel. But the general *** understanding is that the most productive conversations focus on how analytics can change behavior, not solve problems.? "Most people in our organization do better in history class than in math class," a consumer product analytics executive told me. "It's easier to get company people to understand how new information and metrics might change the way they do things; it's harder to get them to understand the underlying algorithms. ...... We learned the hard way that 'over-the-wall' ( over-the-wall) data and analytics is not a good way to get our internal customers value from their work." ? Getting the right answers, or even asking the right questions, turns out not to be a major concern for companies with high ROA. There's no denying that the questions, the answers, of Data & Analytics are important. But what is more important is how these questions, answers and analytics align (or conflict) with the behavior of individuals and organizations. Sometimes, even the best analytics can trigger counterproductive behavior.

The above is a small share of how to conduct effective big data processing, analysis? The relevant content, more information can be concerned about the Global Green Ivy share more dry goods