Improving The New Data Management Technologies And Leverage

One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Company gathers data by using Web analytics and operational systems, we must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.

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.

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.

Data is very important thing in every business, especially in today’s dynamic world where optimal use of data leads to success in shorter span of time as lots of companies are struggling for truthful and accurate data. These data must be analyzed in exact time and in a proper way so that the decision is more effective, but the data we receive are very redundant and carry lot of space in our system. This creates a challenge for the Analytics people to remove the redundancy and bring out only those relevant data that aids in decision making process. Master Data Management is a solution for such Analyst who wants to eliminate the redundant and inconsistent data of the organization (Vinculum, 2016).

Ever since modern technology has been developed so much in advanced, companies running their businesses has been exceptional with the use of newer updated information technology systems. Essentially it has been a more efficient and productive tool improving businesses significantly with advanced IT with the cost a less amount of spending’s and employers. Companies should be able to recognize that better data management and the analytics has been the key factor to sharpening the business wisely allowing it to grow with more potential in skillset and mindset such a making s smart decisions t, fast in solving and efficient in producing. For instance, Business activity and work

Since 1960 and beyond the need for an efficient data management and retrieval of data has always been an issue due to the growing need in business and academia. To resolve these issues a number of databases models have been created. Relational databases allow data storage, retrieval and manipulation using a standard Structured Query Language (SQL). Until now, relational databases were an optimal enterprise storage choice. However, with an increase in growth of stored and analyzed data, relational databases have displayed a variety of limitations. The limitations of scalability, storage and efficiency of queries due to the large volumes of data [1] [2].

The Fresh Direct has 300,000-square-foot headquarter and 1,500 employees. 8,500 products and 200,000 customers active in every day transaction. So every second there will be numerous data flowing into the company’s center. But the company lacks of a significant information system to deal with those data. They tried to use technology to convert the data to reports of real time information in order to

First a company must understand the relationship between the data they want to keep track of, in the scenario

Abstract— Big data is a very important subject in modern times with the rapid advancement of new technologies for example smartphones, pc/laptops, game consoles, that all in some way gather information that is stored. Big companies are needing a place to not only store all the data that is coming in but to also analyze it for specific purposes and at the fastest speed manageable. There are many different providers out there who provide this service, this paper will talk about one way the company Google handles data using their own special made platform.

Author says that conventional data storage systems (databases) work well with structured data, but crash under heavy workloads. He describes various distributed file systems like GFS (Google file system), HDFS (Hadoop distributed file system), and amazon S3(Simple Storage service). All these file systems handle unstructured data and support fault tolerance by data replication. Specially S3 provides good integration with other amazon services and provides big data processing capabilities to consumers at an affordable cost in a pas-as-you-go fashion. For storing non-structured and semi-structured data, the author provides solutions used in various corporates. He gives examples of BigTable used by Google and PNUTS used by Yahoo. One that caught my eye is the one proposed by Facebook, which is a hybrid data management system. It is hybrid in a sense that it combines features of a row-based and column-based database systems. Upon research I found that this new system actually enhances the performance of both query processing and load balancing [2]. The author then moves on to describe various available cloud vendors. All these Infrastructure as a service (IaaS) providers employ virtualization technologies to maximize

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.

The modern RDBMS advancements are not capable of supporting unstructured information with ideal space necessity. The plan winds up plainly mind-boggling and is henceforth troublesome for designers. The requirement for unstructured information administration is so annoying with conventional RDBMS arrangements (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). Moreover, RDBMS turns out to be an exorbitant answer for creating light-footed web applications with direct information investigation necessities. NoSQL is developing as a proficient possibility in this situation, which connects the issues related with RDBMS innovation. The market development can credit to creative dispatches of NoSQL arrangements, and collective endeavors by NoSQL sellers and clients. The endeavors of organizations, to enhance their market offerings, are creating the request of NoSQL, as a back-end bolster (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). The emergence of agile software development is creating the demand for NoSQL (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). They offer users much more avenues to accept data in many different forms. NoSQL is adaptable as SQL but offers many more uses that can apply to many organizations.

Nowadays, terabytes to petabytes of data that is been stored and transmitted by numerous sources and organizations have realized that these data contain tangible value that has the potential to change the fortunes of a business. Top firms leverage their business through the valuable insights gained through these data to assist them in their decision making process. The huge chucks of data consists structured, semi structured and unstructured data. Organizations have switched their focus more on exploring semi structured and unstructured data that is generated through social media activities, personal media information and geo location data.

Databases are required ot process at the high speed and less delay with the major goal of satisfying the client in terms of performance.

The major of subjects is arranged in data warehouse. For example, inventory, products and customer, to get more benefits form data warehouse it must be integrated by collecting from databases across enterprises, the information for customers should have code or described specifically according to departments. All departments the procedures on database are updated on a regular basis with deletion, insertion and changes. The operations that only be in data warehouse the data can be loaded and the data can be accessed. Once whole data forms and models are recorded, the data can not change. The design of retrospective data warehousing is traditional, and many questions before starting design to be work as required. What we sale? What will be the importance of our debt in the next six months? Where is the best market for a new service? A data warehouse in the World Wide Web is a predictable to save Lawrence Livermore National Laboratories in California a lot of related costs whit clients.