A project manager acts as the primary link between business and technical teams. A project manager is responsible for maintaining the project schedule, developing project estimates, working with external teams and tracking project issues. The project manager belongs to either the technical team or the project management office (PMO). The project manager works with business teams, technical teams, business counterparts, testing resources, vendors and infrastructure teams.

A project manager is often challenged with diagonally opposite views from the business side and technical side. A project manager’s success depends on balancing the needs and emotions of both sides.

Understanding the Requirements
A project manager must familiarize with the project’s requirements as defined by the business or product managers. This will help you understand the business vision behind the project. You will need this knowledge while negotiating with the technical teams.

Understanding the Technical Landscape
A project manager must also understand the technical systems, resource skills and infrastructure capabilities available for the project. Business teams come up with expectations that are sometimes beyond the capabilities of the technology team. It is the responsibility of the project manager to understand the technical capabilities available to the project.

Walkthrough of Business Requirements
This is a critical step in the project delivery process. The project manager must invite members from the business team, technical team, testing team, infrastructure team and vendors. The project manager must encourage the various stakeholders to ask questions about the requirements. Any prototypes available must be demonstrated in this meeting. The project manager must find answers to all questions resulting from the requirements walkthrough. The project manager must get the final version of the requirements approved by all stakeholders.

Managing Conflicts in Timelines and Budgets
All project managers will face the conflicts arising from shortened timelines and limited budgets. Business teams typically demand many features that are nearly impossible to deliver within short timeframes. The project manager must work with business and technical teams to prioritize the requirements. If the project is executed in a product development organization, then the project manager could utilize agile methodologies to deliver projects incrementally. In this case, the project manager may be required to act as a scrum master to facilitate scrum meetings between various stakeholders.

The Art of Saying “No”
As a project manager, you may be forced to say “no” to demands from both business and technology teams. However, it is important to create a win-win situation for all parties when you are faced with conflicting demands. You can work with the stakeholders individually before bringing all parties together. Most stakeholders prefer to work together. The success of a project manager depends on how effectively he or she can bring out the best in everyone, driving everyone towards a common goal.

Finally, the job of a project manager is not to satisfy the demands from all corners. The project manager must identify the essential deliverables that will meet the business needs, with a solid understanding of what is possible within the limits of technology.

Related:

Smart Project Management: Best Practices of Good Managers

Is Agism an Issue in IT?

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.

As the cloud buzz is getting louder with every passing day you are tempted to take the big leap into the cloud but may have restrained yourself paranoid by ad infinitum cloud security discussions floating on the web. No one can deny the fact that your data is the lifeblood your business. So, undoubtedly its security is of paramount importance for survival of your business.  As cloud computing is a paradigm shift from the traditional ways of using computing resources, you must understand its practical security aspects.

Is Cloud Computing Safe?

There can’t be a binary answer (Yes or No) to this question. But with my experience and as an authority on the subject I can tell you that technologies enabling Cloud services are not in any way less secure than the traditional or on-premise hosting model.  Also, with the evolution of technology, the cloud providers are getting matured and almost all the providers are offering built-in security, privacy, data backups and risk management as a part of their core service.If you are not a big IT company then you must ask yourself:

·         Can an on-premise solution or a traditional hosting provider match the same level of standard security and privacy requirement as provided by the specialist cloud provider whose core competency lies in providing state of the art security and privacy?

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.