Creating New Systems For Revolutionize Industry Workflows

Data and information management is a huge growth area. But it's not just data management creating new job opportunities, its gathering, analyzing, storing and securing the data as well.

With the amount of information and moving data, the development and use of these systems is imperative to stay ahead, innovative, and secure as a company. With such an overload of different information it would be nearly impossible for an individual to organize or recognize different patterns that a developed system can. Although, wise people are needed in order to create and develop them and without their knowledge and insight the systems can never be created successfully in the first place. People, information, and information systems are all an important parts of the puzzle to analyze data and create new management information systems that enable new ways to stay ahead of

Please describe your aptitude and motivation for graduate study in Data Science, including your preparation for this field of study and your future career goals. Please be specific about why the UC Berkeley MIDS program would be a good intellectual fit for you.

These valuable experiences have advanced my understanding of the intricate process models, subtleties of workflow design, supply chain, business management and developed subject matter expertise on middleware technologies. However, possessing rudimentary understanding on the business fraction of complex information problems, I believe my work in the domain of process modeling is fragmentary and have much to gain in the vast discipline of information systems. I now aspire to be closely associated with business process design, the factors which influence the ever changing market and play a decisive role in devising complex business strategies for future

Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet certain goals.

This is one of the greatest challenge that is acting as a key barrier in widespread adoption and pervasive use of the tools fostering for development and progress in this field of study. The real time of the big data analytics would require some of the unique features and special computing powers and potentials (Gantz, 2012). Tools have to made specially advanced so as to incorporate process terms in real time (Chen, 2012). Every business oriented organization should be transformed into information centric (Kaisler, 2013) to focus upon the real time data analysis in terms of both input and output.

Finman views this SLA as a groundbreaking venture to harness the diverse array of IT borne customer demands and opportunities that cannot be met by adhering to traditional paradigms. Datanal utilizing sophisticated data-mining software developed by Minertek will recognize and integrate common IT characteristics from disparate operations, programs, procedures, and products. This enables the customer to reduce or eliminate duplicate, parallel systems and to achieve economies of scale and open new opportunities.

Carlson Shared Services (CSS) uses a centralized data processing model to meet business operational requirements. The central computing environment includes an IBM mainframe, over 50 networked servers from Hewlett-Packard and Sun Microsystems. In this centralized data processing unit all the servers and the mainframe had their separate disc storage. This dispersed data storage scheme had the advantage of responsiveness; that is, the access time from the server to its data was minimal. However, the data management cost was very high. There had to be backup procedures for the storage on each server, as well as management controls to reconcile data distributed throughout the system. RapidInsight framework enable organizations to integrate real-time, streaming data, to gain real-time actionable insights, integrate and harmonize data from social media sites, public datasets, and industry reports with data from enterprise systems (Fulton, 2015). The RapidInsight framework gives analyst the ability to analyze data in real-time to respond quickly situations in which businesses tend to encounter. A thorough look at the entire business operation, which includes, combining data from external and internal data sets, reduces time to market for implementing real time data to insight. A factory set up is in place to reduce the time and cost involved with

Perform a time in motion study of all parts of the current production workflow process

In an uber globalized market of today, companies are faced with challenges in each and every step of their business. Our analytics and research services are geared towards giving those companies that extra edge over the competition. We process and analyze terabytes of data and break down all the fuzz and chatter around it to give our customers meaningful insights about their competition and the market they are engaged in.

Companies are facing a highly uncertain environment due to financial-crisis, global economy, and dealing with high-competitiveness. The value encompassing the culture of leveraging people, process, data and technology. The shared core-values and priorities of an organization has becoming essential for intelligent enterprise (Stubbs, 2014) . As Evan Stubbs described in his book (Stubbs, 2014), we need skilled employees who are involved in applying business analytics, comfortable leaders with uncertain environments and planning for unknown unknowns. Process must be linked to achieve an outcome and strongly defined, further to create specific steps that can be automated. Not less important – data and measure process for future analysis and decision-making process; convert simple data to valuable information for executives. And the technology enables to produce assets.

The industrial use of databases is advancing to the point where modern and fast solutions are required in most industries. Plattner (2011) states that "Sub-second response time and real-time analytics are key requirements for applications that allow natural human computer interactions. We envision users of enterprise applications to interact with their software tools in such a natural way, just like any Internet user interacts with a web search engine today by refining search results on the fly when the initial results are not satisfying" (Plattner, 2011). Keeping up with the South River hospital and pharmacy 's growth requires keeping up with this database technology.

David Whitenack discusses how Go, a new programming language invented by Google can be used to overcome common struggles data scientists face such as: building ‘production ready’ applications, applications or services with inconsistent behavior and difficulties in integrating data science development in an engineering company. Go alleviates these problems while still being productive in performing data science. And then he discusses that Go has a data science ecosystem which enables users to perform basics like data gathering, cleaning, organizing as well as machine learning. Nicolas Seyvet and Ignacio Mulas Viela explain the how the telecom industry can handle the “explosion of data” by using data analytics. They apply two data analytics models: Kappa and a self- training Bayesian model, on a use case using a data stream originating from a telco cloud-monitoring system. These models help the user understand the principles behind the two models, how an end-to-end analytics project is carried out in the telecom industry and finally main challenges in these two analytical implementations.

“The integration of advanced analytics into a transactional database is revolutionary. Today a majority of advanced analytic applications use a primitive approach of moving data from databases into the application tier to derive intelligence.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.