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.  

When it comes to running a start up, leaders need to make sure that their key players are motivated. This has been seen with many companies. Back in the 1970's it was found with the inspiration and diligence of the late Daniel Nigro when he formed Kleer-Fax. More recently it was seen in David Khasidy, the founder and recently retired president of SunRay Power Management, the most dynamic green energy leader in the US today.

The question is, what is it that great leaders like David Khasidy and Daniel Nigro do that make the difference? How do the most vulnerable companies (start ups) break the mold and become a part of our everyday lives?

It starts with their mission and vision.

Create a Strong Mission and Vision

There are many reasons why start ups fail. For one, they usually lack the capital to last through the lean times. Secondly, they often don't have the tolerance for setbacks that occur. Lastly, they do not have a long-term plan, also called a mission.

When a business has a strong mission, the team knows it and their focus toward their work and service to others within and without the company reflects that. To complement that, the shorter term vision of the company needs to be present as well.

This can even be seen in sole proprietorships with no employees, such as when Brian Pascale started his law practice. His vision was to find justice for his clients while his mission was to build upon a career that had already set precedents in the area of tort law.

As his practice has grown, new staff members can sense the vision and mission he exudes.

Encourage Ownership of Projects and Processes

Start ups need to inspire and motivate their employees because they need to know that they are not only a part of something important, but that their contributions mean something.

What won't happen if they are not there? What contribution do they make, and what are the consequences of them not fulfilling their part of the work?

By encouraging ownership in projects, team members can find that the work they are doing is not only important for the organization, but that they are going to be a big part of what makes it happen. The alternative is that they feel replaceable.

Offer Incentives That Keep the Company Competitive

When team members embrace the mission and vision of the company, and then take ownership for the company's success, they are going to need to be justly rewarded.

This could include flexible schedules (for those who don't need a stringent one), use of an account at a nearby takeout place, or even the potential for ownership as a result of a vesting program.

The incentive everyone is looking for more immediately, though, is cash. When the company takes in more revenue as a result of the efforts of those on the team, rewarding them can go a long way not only in making them feel appreciated, but in encouraging them to bring in more business.

Members of a start up team are usually very talented, and commonly underpaid. However, if they believe they are going somewhere, it will make a big difference.

Related:

Good non-programmer jobs for people with software developer experience

Once again Java tops C as the number one sought after programming language on the internet.  According TIOBE Programming Community Index for February 2013 and five search engines: Google, Bing, Yahoo!, Wikipedia, Amazon, YouTube and Baidu, Java regained its position after being bumped by C in May 2012.

Despite the recent urging by the U.S. Department of Homeland Security of computer users to disable or uninstall Java due to a flaw in Runtime Environment (JRE) 7, Java, has increased its market share of all languages by (+2.03%) in the past six months. The jump in Java’s popularity does not come as a surprise as the Android OS claims massive success in the mobile space.  The top twelve programming languages listed in the index are:

1.  Java
2.  C
3.  Objective-C 
4.  C++
5.  C#
6.  PHP
7.  Python
8.  (Visual) Basic
9.  Perl
10.  Ruby
11. Java Script
12. Visual Basic.NET

Also rising, Python and PHP which are competing to becoming the most popular interpreted language.

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.