Being treated like a twelve year old at work by a Tasmanian-devil-manager and not sure what to do about it? It is simply a well-known fact that no one likes to be micro managed. Not only do they not like to be micro managed, but tend to quit for this very reason. Unfortunately the percentage of people leaving their jobs for this reason is higher that you would imagine. Recently, an employee retention report conducted by TINYpulse, an employee engagement firm, surveyed 400 full-time U.S. employees concluded that, "supervisors can make or break employee retention."

As companies mature, their ability to manage can be significant to their bottom line as employee morale, high staff turnover and the cost of training new employees can easily reduce productivity and consequently client satisfaction.  In many cases, there is a thin line between effective managing and micro managing practices. Most managers avoid micro managing their employees. However, a decent percentage of them have yet to find effective ways to get the most of their co-workers.  They trap themselves by disempowering people's ability to do their work when they hover over them and create an unpleasant working environment. This behavior may come in the form of incessant emailing, everything having to be done a certain way (their way), desk hovering, and a need to control every part of an enterprise, no matter how small.

Superimpose the micro manager into the popular practice of Agile-SCRUM methodology and you can imagine the creative ways they can monitor everything in a team, situation, or place. Although, not always a bad thing, excessive control, can lead to burnout of managers and teams alike.  As predicted, agile project management has become increasingly popular in the last couple of decades in project planning, particularly in software development.  Agile methodology when put into practice, especially in IT, can mean releasing faster functional software than with the traditional development methods. When done right, it enables users to get some of the business benefits of the new software faster as well as enabling the software team to get rapid feedback on the software's scope and direction.

Despite its advantages, most organizations have not been able to go “all agile” at once. Rather, some experiment with their own interpretation of agile when transitioning.  A purist approach for instance, can lead to an unnecessarily high agile project failure, especially for those that rely on tight controls, rigid structures and cost-benefit analysis.  As an example, a premature and rather rapid replacement of traditional development without fully understating the implications of the changeover process or job roles within the project results in failure for many organizations.  

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

Information Technology is one of the most dynamic industries with new technologies surfacing frequently. In such a scenario, it can get intimidating for information technology professionals at all levels to keep abreast of the latest technology innovations worth investing time and resources into.

It can therefore get daunting for entry and mid-level IT professionals to decide which technologies they should potentially be developing skills. However, the biggest challenge comes for senior information technology professionals responsible for driving the IT strategy in their organizations.

It is therefore important to keep abreast of the latest technology trends and get them from reputable sources. Here are some of the ways to keep on top of the latest trends in Information Technology.

·         Subscribe to leading Analyst Firms: If you work for a leading IT organization, chances are that you already have subscription to leading IT analyst firms notably Gartner and Forrester. These two firms are some of the most recognized analyst firms with extensive coverage on almost every enterprise technology including hardware and software. These Analyst firms frequently publish reports on global IT spending and trends that are based on primary research conducted on vendors and global CIOs & CTOs. However, subscription to these reports is very expensive and if you are a part of a small organization you may have issues securing access to these reports. One of the most important pieces of research published by these firms happens to be the Gartner Hype Cycle which plots leading technologies and their maturity curve.Even if you do not have access to Gartner research, you can hack your way by searching for “Gartner Hype Cycle” on Google Images and you will in most cases be able to see the plots similar to the one below

Like me, I believe most people go about their business never to give a serious thought about their assumed private correspondence when using Gmail to email friends, colleagues and business associates.  As it turns out, your daily banter may not be so private after all.  A recent article in Fortune Magazine, “Judge Rejects Google Deal Over Email Scanning” caught my attention and an immediate thought dominated my curiosity…Google email and scanning scam.  

In essence, the article describes Googles’ agreement to change the way it scans incoming messages so that it no longer reads emails while they are in transit, but only when they are in someone's inbox! So, what exactly does that mean? Judge Koh, a San Francisco federal judge, said she's not so sure about that. Her ruling claims the settlement does not provide an adequate technical explanation of Google's workaround, which involves scanning in-transit emails for security purposes, and then later parsing them for advertising data. The judge also proposed a legal settlement to pay $2.2 million to lawyers, but nothing to consumers.

My interest in this story is not so much about the proposed settlements or the specific details about how Google or any of the web giants settle claims based on vague legal language. It is however, more about the naiveté of myself and perhaps many others that never question how the email scanning process really works. I wonder, do most of us really care that Gmail uses contents of our mail to display targeted ads?