This article, titled Analytics 3.0, explains how the third generation intelligent technology has evolved from two phases. These articles include both before and after big data. This third innovation in intelligent data should provide an integrated solution to large amounts of data, analyzing business performance and service quality, as well as the ability to analyze products and services. Analytical is now a key part of the strategy. This transformation requires companies to be able to identify new problems and then respond using enhanced functions and visibility (Davenport 2013). New datasets and administrative options will make the 3.0 version more robust. It also requires modified analytics, new systems and technologies, as well as enhanced analytics. Slide 3 shows the application of analytics to the sport business. Slide 8’s comments confirm that the article describes the change in corporate culture. Analytics will evolve to include greater data processing capabilities and collaboration, as well as software and hardware upgrades.
This article examines the development of data analytics, from the first era through the third generation. Organizations were less efficient in the first era due to better data collection. Big data characterized the second era and required more accurate evaluation techniques. Analytics is now in the 3.0 age. All businesses have access to data. Author describes how Beta has been augmented in its capabilities. Davenport (2013) mentions Schneider Electric and Bosch Group as examples of companies who have successfully adopted 3.0 analytics in order to meet their customers’ needs. Davenport finally examines the conditions for 3.0 analysis. Technology’s new age will allow firms to use multiple data sources. Schneider, for instance, uses sensors to analyze logistical variables such as fuel supply and fuel location. Analytics 3.0 incorporates both modern and old methods for superior management.
The most important lessons in this article are focused on technology’s relevance and ongoing development. This article focuses on the need to ensure that enterprises are constantly looking for ways to evaluate their data and provide value. As a way to boost many activities, technology will keep evolving. Davenport (2013) demonstrates how technology’s ongoing development and analytic effects frontline staff.
It is important that employees are prepared to adapt to changes. Davenport (2013) said that analytics 2.0 and 2.0 provided too much data on worker movements. However, analytics 3.0 will not provide enough information for employees to feel comfortable. Change is known to have a significant impact on workers. Particularly in light of technological advancement, it is important to prepare employees.
Technology can improve the efficiency of companies, this is the third lesson. Here are some examples of companies that use analytics to increase productivity. No matter the type of business they work in, technology has been a way to increase productivity. Analytics has expanded beyond the internet and data firms to include other business types. Businesses can capitalize on technological advances through constant technological innovation.
