Your Job is Being Redefined

A recent report by McKinsey & Co examines the role of automation in work life. It shows that 45% of work activities overall could be automated. It goes on to say, however, that only 5% of occupations can be replaced with current technology. So few occupations are likely to be replaced with technology in the near future. However, 60% of all occupations could have 30% or more of their activities automated.

This redefinition of jobs extends across the spectrum, from professionals like accountants and lawyers to travel agents (already largely redefined), bank tellers (heavily redefined) and airline pilots (who often steer as little as 4% of a flight.

Job redefinition is a major challenge for people as well as their employers. It calls for constant retraining and continuing change. Clearly adaptability and learning are more and more crucial to success in this evolving world.

Use Big Data Analytics for Marketing Strategy

A report by McKinsey on more than 250 engagements over five years shows that companies that put data at the centre of their marketing and sales decisions improve their marketing return on investment (MROI) by 15 – 20 percent. If this is applied to the estimated $1 trillion in global annual marketing spending, that adds up to $150 – $200 billion of additional value.

This is the primary reason why big data analytics has revolutionized marketing and sales. Analysis of big data can reveal new opportunities for a company. And the companies can tailor their product to customer wishes and beliefs, thus influencing their decision behaviour. In order to make analytics work, it is essential that a company invest on the latest techniques to enable fast analysis on the rapidly expanding pool of big data available to them., such as automated “algorithmic marketing,” an which provides for the processing of vast amounts of data through a “self-learning” process to create better and more relevant interactions with consumers - a kind of combination of data analytics and artificial intelligence. For an interesting take on this point of view, check this link.

How Big Data Analytics can Help with People/Talent Management

Analytics is entering into management at all levels. No longer a geek thing, it has become the fodder of top management. Check out this article on the subject.

How Big Data Analytics Helps Toyota Manage Accounts

For those who wonder how big data analytics is being used to help management, the case of Toyota Financial Services (TFS) serves as an illuminating one. TFS finances the ale of cars to customers and carries a portfolio of about $80 billion worldwide.

During the financial recession, delinquencies rose dramatically. The conventional collection techniques seemed to be consuming a lot of resources with mixed results.

TFS implemented a big data approach under which they collected data pertinent to the customers and applied algorithms to optimize the collection processes.

"their teams collaborated to create a new approach that included multiple technologies to assess individual consumers for their risk. FICO had developed an algorithm that allowed TFS to estimate which customers needed attention and the best way to approach each of them. Other technologies included SAS for statistics and predictive analytics, Oracle software and database software, IBM Pure Data (formerly known as Netezza), Tableau Software integrated into the user interface, Informatica for data integration, VMware for virtualization, and more. The solution relies on multiple technologies from multiple vendors and resides in Toyota Financial Services' many data centers."

The combination of optimization techniques, predictive analytics and prescriptive analytics all combined to yield fairer treatment of their customers, better use of resources and a better collections outcome.

For a more complete rundown, and the source of the above quote, click this link.

Google Analytics vs Spark

In a recent exhaustive, study, Mammoth Data found that Google Cloud Dataflow outperformed Apache Spark in several categories. This is important because both Google and Spark are widely used for big data analytics. Most companies are jumping into the big data world for reasons of competitive necessity.

In its benchmark, Mammoth Data identified five key areas where Google Cloud Dataflow equalled or exceeded Apache Spark:

• Greater performance
• Developer friendly
• Operational simplicity
• Easy integration
• Open-source

For more detail, check out this link.

Data Analytics on iPhones and iPads

iPhones and iPads are not often thought of as useful for data analytics. However, they can be so because some apps are available that simply act as a client under which the data remains on the cloud and the analysis takes place there too. Analyzing data on the cloud is the way of the future, since the idea for downloading is not feasible for much big data and users would have to resort to samples if downloading is used.

Data analytics apps range from simple analysis tools like Google Analytics to WolframAlpha, Statistics Visualizers and Roambi and even some analytics programming languages like Scala and Python. Those who are interested can spend hours experimenting with these tools while learning at the same time. Most are free from the Apple App Store. Some work in conjunction with Siri, thus enabling some StarTrek-like analysis.

For a brief rundown on 10 of these apps, check out this page.