The world of technology moves faster than the speed of light it seems. Devices are updated and software upgraded annually and sometimes more frequent than that.  Society wants to be able to function and be as productive as they can be as well as be entertained “now”.

Software companies must be ready to meet the demands of their loyal customers while increasing their market share among new customers. These companies are always looking to the ingenuity and creativity of their colleagues to keep them in the consumer’s focus. But, who are these “colleagues”? Are they required to be young, twenty-somethings that are fresh out of college with a host of ideas and energy about software and hardware that the consumer may enjoy? Or can they be more mature with a little more experience in the working world and may know a bit more about the consumer’s needs and some knowledge of today’s devices?

Older candidates for IT positions face many challenges when competing with their younger counterparts. The primary challenge that most will face is the ability to prove their knowledge of current hardware and the development and application of software used by consumers. Candidates will have to prove that although they may be older, their knowledge and experience is very current. They will have to make more of an effort to show that they are on pace with the younger candidates.

Another challenge will be marketing what should be considered prized assets; maturity and work experience. More mature candidates bring along a history of work experience and a level of maturity that can be utilized as a resource for most companies. They are more experienced with time management, organization and communication skills as well as balancing home and work. They can quickly become role models for younger colleagues within the company.

Unfortunately, some mature candidates can be seen as a threat to existing leadership, especially if that leadership is younger. Younger members of a leadership team may be concerned that the older candidate may be able to move them out of their position. If the candidate has a considerably robust technological background this will be a special concern and could cause the candidate to lose the opportunity.

Demonstrating that their knowledge or training is current, marketing their experience and maturity, and not being seen as a threat to existing leadership make job hunting an even more daunting task for the mature candidate. There are often times that they are overlooked for positions for these very reasons. But, software companies who know what they need and how to utilize talent will not pass up the opportunity to hire these jewels.

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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.

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.  

Information Technology (IT) tools are here to support your business in the global market. Effective communication is key for IT and business experts to collaborate effectively in search of solutions. Consulting, reaching out for help to a third-party, can bridge the gap between your business marketing experts and IT operations experts, especially with the emergence of big data analytics and its implication on the global market. Having the right consultants equipped with business knowledge and data technology expertise can make a difference.

Your marketing organization is probably familiar with digital tools and conducting global research. Its results can uncover the journey customers take to purchase your products or use your services. It can highlight the pain points and frictions that prevent their experiences with you to be delightful and amazing. Armed with this knowledge and beautiful compelling presentations, marketing executives expect that IT operations leaders will translate these insights into actions.

But people in IT operations are too involved in meeting key performance indicators that have nothing to do with the end customers. Meeting requirements of faster and cheaper don't translate very well into customer satisfaction. A classic breakdown in communication is described in a Harvard Business Review article, “A Technique to Bridge the Gap Between Marketing and IT.” The author goes on to describe how a new CIO at a bank found IT to be focused on the internal organization as their customers, rather than the real end customer. Moreover, no one was looking at the incident reports which clearly showed that incidents were increasing. And nobody looked at what these incidents were doing to the bank’s customers. The startling and scary numbers of incidents were caught and addressed and brought down from 1,000 to 600 or (40%) and later to 450 per week.

Surprisingly, these type of seemingly isolated scenarios are still being discovered within organizations presently, sometimes internally, and through third party insights such as consultants.  By engaging consultants to provide a perspective based on what they’ve experienced before, they can often bring new and innovative ideas or possible challenges to the table that an internal processes probably wouldn’t have been able to see on their own.  Often, third party input can help to provide the translation needed to go from marketing research results into actions that IT operations can understand and make sense in their high-performance culture. When companies understand and use this knowledge to reassess how to improve their customer experiences, they work backward from what customers want to achieve significantly higher improvements. 

IT and business management are more and more being asked to move away from their traditional roles, such as IT being the "technology infrastructure gatekeeper", and instead become enablers across the enterprise of effective collaboration, big data consumers, and key players in driving desired business outcomes. Marketing leaders look to technology as a way to facilitate the customer's journey and his positive experience of it, bring more clients, and meet increasingly higher loyalty goals. They rely on IT projects to enable big data-based behavioral targeting anywhere in the global market. This means projects to analyze search engine results, improve website personalization and optimization, and building of mobile applications for a more personal experience. All these are projects that consultants with their communication, consulting and technical expertise are well prepared to help in order to bridge the expectation gap between IT and other business organizations.

In order to meet these 21st-century business challenges, Information Technology organizations have been transitioning from waterfall stage-gate project management approaches to agile development. The stage-gate method applies a step-by-step approach where waiting, reviewing and approving are required before moving to the next step in the project. Agile management emphasizes collaboration, no decision hierarchies, and few people roles for making quick, customer-focused small changes over time to deliver solutions that delight and amaze customers. Agile development has allowed many businesses to respond quickly to changing customer desires and expectations. But moving to continuous delivery is a struggle requiring focused, dedicated teams that are not well suited to the traditional matrix organization where people are resources whose time must be "chopped" into many pieces and shared among many projects. Agile teams meet frequently as often as daily but never waiting more than a week to do so.

Marketing people are externally focused. IT people are internally focused. The first works with customer emotions. The second works to increase efficiency. Big data analytic tools are used by the first and supported by the second. Consultants can be the glue that helps both come together in effective collaborations that deliver positive business outcomes in both global and local markets.