A Project On Acquiring And Sharing Data Within The Cmmi Research Community

Through informational interviews with seven industry experts and a thorough literature review, the team explored the concept of “big data” and generated key insights which will guide the Federation’s approach as the organization develops its members’ data analytics capacities. Additionally, the team identified a clear business case for implementing data analytics at CDCUs using strategies appropriate for the level of resources within each individual organization. The team also developed a set of survey questions for the client to use when gauging the level of interest and capacity within any individual CDCU.

The call to action is clear for EBP and evaluation strategies. To that end, the ten (10) steps applied for framing a simulation informatics infrastructure in the academic environment are presented in this graphic as an approach to outcomes for process evaluation in setting the stage for simulation integration.

In your business, you have your own big data challenges. You have to turn heaps of data about various entities into actionable information. The reporting needs of institutions have evolved from simple single subject queries to data discovery and enterprise-wide analysis that tells a complete story across the institution. While the volume, variety and velocity of big data seem overwhelming, big data technology solutions hold great promise. The way I see it we can use this as one of the biggest asset for the company. We have the capacity to see patterns recounting in real time across complex systems. Huron is marshalling its resources to bring smarter computing to big data. With the Huron big data platform, we are enabling our clients to manage data in ways that were never thought possible before.

Big Data is garnering great recognition for its data-driven decision making methodology. Right from data acquisition where there is a flood of data available, we need to make effective decisions about usage of data. Privacy, scalability, complexity and timeliness are the problems that hinder the progress of Big Data.

Therefore, the consecutive sections discussed the definition of big data, tools for analyzing big data, data mining, knowledge discovery, visualization and collaborative

Big data is not as new as many people believe it to be. It is actually a concept that has been around for almost a century. It is just the “same old data marketers have always used, and it’s not all that big, and it’s something we should be embracing, not fearing” (Arthur). In 1944, Fremont Rider “predicted that the amount of data in the world would increase exponentially” (Hopp). Rider was right on target with his prediction seventy years ago. Data has grown much greater than he probably could have ever imagined back then.

Big Data is becoming more meaningful with the ever more powerful data technologies, which enable us to derive insights from the data and help us make decisions. Big Data also creates new courses and professional fields such as the data science and data scientist, which are aimed at analyzing the ever growing volume of data. Some might think this exaggerated because data analysis, after all, not a new invention. However, we might all agree that the progress of digitization associated with the generation of ever larger amounts of data have totally changed the ways we deal with data.

Big Data is a term for very large amounts of formal and informal information that can be analyzed to find trends and patterns. The information can be about anything, but it needs to be processed in a way that will give it value and relevance. It can come in multiple formats and from different sources such as large databases, electronic records, social media, mobile phones, apps, wearable devices such as pedometers, and others. Different data sets are combined and contrasted in different ways to give perspectives and insights about a topic. It can be used in a seemly endless number of ways and people are discovering new ways to use it all the time, some of them entertaining. The largest areas of use include those relating to consumer behavior and choices, business procedures, healthcare, science and research, and law enforcement. People are also discovering its use in their personal lives as well for things like buying a home, dating, fitness routines and travel.

Firstly, the main problem is deciding which data should be selected. The data, explaining customers’ desires and needs, is important to be collected while most of the enterprises are confusing about what data they should concentrate on. A recent Gartner report (2014) stresses that 64% of firms raced to plan or launch a Big Data project, though they did not have enough professional knowledge yet. To understand what customers need through Big Data possibly turns into the core of companies’ target. The large data volumes and different varieties of data lead to data complexity.

Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become. Why? More data may lead to more accurate analyses. More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies.

Five years ago, few people had heard the phrase ‘Big Data.’ Today, it’s hard to go an hour without seeing it implemented practically in our daily life. The promise of a highly accurate data-driven decision-making tool is an attractive lure for any organization in any industry. However, big data is not without its own problems.

According to the Government Construction Strategy document in 2011, the “Government will require fully collaborative Level 2 3D BIM (with all project and asset information, documentation and data being electronic) as a mini¬¬¬¬¬mum by 2016.”

Tom Davenport, an author specializing in business intelligence, analytics and business process innovation, defines big data in his recently authored book “Big Data at Work: Dispelling the Myths, Uncovering the Opportunities” as “The broad range of new and massive data types that have appeared over the last decade or so.”

The term big data came into the picture to refer the big volumes of information’s both the companies and governments are storing. The data may be where we live, where we go, what we buy and what we say etc. all will be recorded and stored forever. More than 90% of data is generated in the past 2 years only and this volume is increasing day by day and doubling for every two years. In this world, the organizations are using the data generated by us and no one knows what they are doing with the collected data. Big data is defined as a lot of structured and unstructured data from different sources, such as E-commerce websites, online transactions, social networks, medical records, internet search indexes, banking and financial services, scientific searches, weblogs, and document searches and so on. Big data also can be described by four V’s: Volume, Velocity, Variety and finally Value.

Big data is certainly one of the biggest buzz phrases in it today. The term ’Big Data’ appeared for first time in 1998 in a Silicon Graphics (SGI) slide deck by John Mashey with the title of ”Big Data and the Next Wave of InfraStress” [9]. -Combined with virtualization and cloud computing, big data is a technological capability that will force data centers to significantly transform and evolve within the next five years. Similar to virtualization, big data infrastructure is unique and can create an architectural upheaval in the way systems, storage, and software infrastructure are connected and managed. Big data is an amalgam of large and varieties of data sets including structured data, semi structured data and unstructured data so it’s beyond the capability of traditional tools to capture, store, process and analysis of big data. It is true that big data have capability of unlocking new sources of development in many fields but at the same time researchers are being confronted challenges with big data. This paper reveals the various challenges faced with big data and opportunities realized with big data. Keywords: Big data, Challenges, Opportunities, Security Issues.