Data Management, Data And Information Quality For Big Data?

Big data is used to refer to availability of massive volume of data, both structured data and unstructured data, (Viktor & Kenneth, 2014). The data is so large and grows exponentially that processing it using traditional techniques is difficult. The data is not only massive, but also diverse and fast changing. Organizations, therefore, require modern techniques, infrastructure, and advanced personnel skills to address it efficiently. In simple terms, the volume, variety, and the velocity of the data is too great.

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 has many challenges and opportunities associated with it, which necessitates us to rethink on aspects such as data management in order to attain desirable outputs. The next generation of BDA lies in its data management and its associated systems, principles and platforms. This will indeed make Big Data in creating a new wave of technological advancements.

Cloud computing has really shifted the whole landscape of how companies make their databases now, and with this has brought the concept of big data.

Big Data is all around us, from creating our anniversary videos on Facebook and giving Netflix recommendations to utilizing all cellphones as weather stations to improve predictions (source 4) and combining it with machine learning for interesting insights. (source 5) All this data needs to be processed, creating a multitude of jobs in the data science field. Companies with products relying heavily in internet are investing heavily in Big Data, companies such as Uber, Spotify, Google and IBM to name a

Before talking about Hadoop and Mark Logic, it is very important to understand Big Data. What is big data, what’s the consequence and how it is linked with Hadoop and Mark Logic? “Large set of data, unstructured and structured which is created everyday over the internet via different devices is known as Big Data”. For example: “if the user has 7 accounts and creates multiple files in each account he has already created large set of data of his own “Big Data is generally described in terms of the three Vs:

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 an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

A Big Data system is comprised of a number of functional blocks that provide the system the capability for acquiring data from diverse sources, pre-processing (e.g. cleansing and validating) this data, storing the data, processing and analyzing this stored data, and finally presenting and visualizing the summarized and aggregated results. The rest of this article describes various performance considerations for each of the components shown in Fig 1. Refer: Appendices

Big data is defined as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making (Gartner IT Glossary, n.d.). IBM added a term Veracity as the fourth V to describe the unreliability characteristic of data in certain areas (Gandomi & Haider, 2015). Big data comes from various sources such as text, social media websites, images, audios, videos, e-commerce transactions, mobile devices, GPS signals, and sensors to collect climate data (Gunelius, 2013).

Big Data is defined by three characteristics: volume, velocity, and variety. Volume is the size of the data stored, and is measured in terabytes, petabytes, or exabytes. Velocity is the rate at which data is generated. Variety refers to the types of data, such as structured, semi-structured, or non-structured [Mahmood13]. Structured data is data that typically resides in a

1. Hadoop: Hadoop is the Apache open-source programming structure for working with Big Data. Developed with the basis of Google technology, it was implemented by Yahoo. It is versatile and complex for a “fits-all” solution.

Financial Services are of 4 types – Banking Service, Foreign Exchange Services, Investment Services and Insurance. The three key attributes of Big Data are – Volume, Variety and Velocity.

Data itself is useless, until it is mined and transformed into a valuable source of knowledge discovery. Due to its conversion into useful information, data mining has become the leading source being used in many fields worldwide. “Data mining is based on complex algorithms that allow for the segmentation of data to identify patterns and trends, detect anomalies, and predict the probability of various situational outcomes.”[1] Many organizations from healthcare to multimedia and more are relaying on data and getting developed through the use of it. Regardless of how, data warehouse changed its rhythm and dimension in terms of measurements such as: variety, volume and velocity. Today, one can see the current trends of data mining in different fields such as social networks, healthcare and businesses. As data mining is giving the opportunity for those fields to get advanced, "Big Data" is also opening up new doors within itself as the new trends emerge.

Big Data is one of the most discussed concepts in the business world today. The concept of Big Data is one that has been increasingly debated over the past few years by many different kinds of business industries. One industry where this discussion is prominent is the accounting profession. Big Data will be defined more in-depth later on, but in shorter terms, Big Data refers to the exponential sum of data available in our world today. This amount of data is beyond the traditional analysis that can be computed by human power. When Big Data is converted and used to its full potential, it could have a material impact on the accounting industry as we know it. Companies today must stay on top of the Big Data trend if they wish to