Disruptive technologies such as hand-held devices, cloud computing and social media are rattling the foundations upon which traditional businesses are built. Enterprise customers have grown smarter at ensuring the latest technological trends work in their favor. Everyone is trying to zero in on their core competencies by employing commodity services to run their business.

Likewise, enterprise application vendors need to zero in on their core competencies and enhance more value to the businesses of their clientele by leveraging standards-based commodity services, such as IaaS and PaaS, provided by leaders in those segments (e.g. Amazon EC2, Google Cloud Platform etc.).

What else enterprises need to do is learn to adopt new and emerging technologies such as cloud, utility and social computing to build on them to penetrate new market avenues.

New small and medium-sized entrants into the market are constantly challenging enterprises given their ability to rapidly turnaround and address the requirements of the customers in a cost-effective manner. Additionally, these new advancements also affect how enterprises create, deploy, and manage solutions and applications. If you take the example of Force.com, for instance, you find that it’s a common war zone for enterprise application vendors to furnish SME markets with their applications, with the new entrants mostly having an edge.

There are a lot of articles you will find on the internet that talk about the tenants of having a successful professional career. From soft-skills to job relevant skills, there is an unending list of the do’s and don’ts for establishing a great career. However, a successful career in information technology commands some specific efforts and focus. As a result, it is critical to focus on these 4 key tenants that can help you establish a promising and successful career in Information Technology.

·         Be Multi-lingual– This is the analogy of Steve Job’s famous quote ‘Stay Hungry, Stay Foolish’ as it applies to Information Technology. Gone are the days when you could train yourself on a specific programming language say Java or C++ and code your way to a successful career. The best programmers of today and tomorrow are pushing the limits and becoming experts in one of more languages. Knowing more than one programming language instantly makes you more employable since you can add value to multiple projects that require different languages. If you need proof, IT professionals knowing more than one language can attract a salary premium of £10,000 . Additionally, there is no telling how dynamic technology is and by being open to constantly learning new languages you will position yourself to get technology jobs that did not exist a few years ago

·         Go Beyond the ‘How’, Focus On ‘Why’: A common theme with most information technology professionals is their ability to figure out the HOW or, in other words, applying their technical know-how in achieving the solution to a problem. This is especially true when you are working for a service based IT organization where your key job is to develop a solution for the client’s business problem. Yes, you are and will get paid to be good at the ‘How’ but to advance a career in IT; it will help you immensely to also start focussing on the ‘Why’. This stems from a famous quote by Einsten “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it”. In essence, spend time in understanding ‘Why’ are your trying to solve the problem before you get down with figuring out the ‘How’ part. The reasons for developing this mindset are two-fold. One, you will instantly distinguish yourself from thousands of other IT peers who are content with the ‘How’ part. Two, there is a good chance that you want to get ahead in your career not only as a programmer but as a system architect or a business solution consultant. This is where the habit of asking the right questions pertaining to why a certain IT solution is requires will help you build the right solution.

·         Focus on the impact and results (Financial impact):This may not apply to IT professionals who are early in their careers but is paramount for senior IT professionals. For the most part, IT departments are required to make sure that the systems and the solutions function as desired and help the business run efficiently. In other words, the key metric for success for most IT professionals is being extremely good at technology, languages and Quality Assurance. However, the times are changing! No longer is the Chief Information Officer (CIO) in charge of making IT decisions. With organizations closely guarding the ROI of their investment in technology, CIOs are increasingly required to be cognizant of the financial benefits of technology so that they can justify the spending on IT. No wonder than that CFOs are increasingly pressurizing CIOs to get their act together

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.

Welcome to the career field of Business Intelligence. Business Intelligence is a concept that involves a certain level of interaction within an organism, analytically and dynamically, to come to business solutions which implement better, more effective and timely decision making. These solutions are reached by establishing an understanding of the right kinds of user data: what is going well, what is going wrong, taking and monitoring certain actions, previously unknown trends, and patterns, and improved collaboration. When all of this data is taken into account, the entire decision-making process, within a business, will inevitably improve. As an Oracle BI developer, there are specific skills which will drastically make your job easier and results more effective. As time goes on and technology changes, the list is constantly being updated. The following are skills an Oracle Business Intelligence Developer might need to know or learn in 2019.

Communication