Data Analysis And Data Processes

Data can be stored in data warehouses. However, they need data cleansing to standardise the data.

24) Before it can be loaded into the data warehouse, operational data must be extracted and

Extraction, Transformation, and Loading processes are responsible for the operations taking place in the back stage of a data warehouse architecture. In a broader aspect, initially the data is extracted from the source data stores which could be On-Line Transaction Processing or Legacy system, files of any formats, web pages or any other documents like spreadsheets or text documents. In this step, only the data which is different from the previous execution of ETL process (newly inserted, updated) gets extracted from the sources. Next, the extracted data is sent to Data Staging Area where the data is transformed and cleaned. Finally, the data is loaded to the central data warehouse and all its counterparts e.g., data marts and views. (Kabiri & Chiadmi 2013, p.1)

There might be multiple source systems or legacy systems that need to be considered as part of the data conversion and migration and it is expected to understand a reasonable portion of the intended use of each of these underlying source systems. One of the other activities in the agenda is to give a deep dive in to the different stages of migration and the connectivity between the stages of Extract, Transform and Load. Introduction to various environments that host or stage the data is also part of the plan.It is also anticipated to have introduction to the data flow and conversion from source to

Data Cleaning: The dataset consist of set project number and effort multipliers which further is segmented into a set of 15 parameters, development effort and line of code. Relevant information will be fetched from the initial dataset and dataset will be converted into a subset which will help us to get relevant results.

A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.

The edit check programs in the DVP help in cleaning up the data by identifying the discrepancies.

Data integrity refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be Attributable, Legible, Contemporaneously recorded, Original or a true copy, and Accurate (ALCOA).

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

One of the biggest changes that come with the GDPR is keeping data storage reviewed regularly and letting customers know the purpose for which you will use their data, and how long you will keep that data. This cumbersome task can be viewed as a huge marketing plus-point when you realise that without this aspect of data cleansing, systems quickly become blocked up with old, incorrect, completely useless data.

Within an enterprise there are various different applications and data sources which have to be integrated together to enable Data Warehouse to provide strategic information to support decision-making. On-line transaction processing (OLTP) and data warehouses cannot coexist efficiently in the same database environment since the OLTP databases maintain current data in great detail whereas data warehouses deal with lightly aggregated and historical data. Extraction, Transformation, and Loading (ETL) processes are responsible for data integration from heterogeneous sources into multidimensional schemata which are optimized for data access that comes natural to human analyst. In an ETL process, first, the data are extracted from

In today 's organizations, basic descision making procedures and day by day operations frequently rely upon information that is put away in an assortment of information stockpiling frameworks, arrangements, and areas. To transform this information into helpful business data, the information commonly should be consolidated, purified, institutionalized, and compressed. For example, data may be changed over to an alternate information sort or different database servers may store the vital information utilizing diverse patterns. Dissimilarities like these must be settled before the information can be effectively stacked to an objective target. After the plan and improvement of data warehouse as per the business prerequisites, the way toward combining the information into the information stockroom from different sources is to be thought of. Extract Transform Load (ETL) procedures are basic in the achievement of the Data Warehousing ventures. The way toward extricating information from one source (extract), changing it as per the outline of the data warehouse(transform) and stacking it into data warehouse (load) constitute ETL. As it were, ETL is the way toward extracting information from different information sources, changes it according to the prerequisites of the target data warehouse and effectively stacking it into the data warehouse (database). In the transformation procedure data is institutionalized to make it perfect with the target database along with data purifying

Duplicate data is defined as the existence of data in several records which is also known as redundancy. The definition has different interpretations. Data warehouse contains voluminous data for mining and analyzing it for better decision making process. In any data warehouse, data comes from n number of sources and hence the result is increase in data and the duplication of data. In order to clean the data, data preprocessing is done which includes data cleaning, data integration, reduction etc. which attempt to clean the data and make the process of decision making much easier.

There are many ways of cleaning data using many tools that help in formatting, removal of unwanted parts of data. Here I will make an effort to demonstrate a method of extracting, cleaning of data files using Visual Basic and evaluating its usefulness. This report comprises certain topics that I have studied during the course of my masters program which includes few concepts of data warehousing, data management and data analytics. These topics cover different ways of data manipulation such as extraction, transformation techniques, loading of data using SQL queries (creation of tables, insertion of values and checking their normal forms), creation of a data warehouse, evaluating its usefulness by measuring several factors, applying data mining techniques to analyze data in a better way that will lead to improved understanding of business and importance of analytics on business data.

Increasingly, banks and financial institutions are beginning to realise the potential in properly utilised data. The transition from paper based operations, to fully automated systems and processes have grown the financial industry by leaps and bounds. However, access to such powerful tools are not without challenges. In order to reap the benefits of data analysis, one must ensure that the transformation of the data from end to end is understood, and the complete lineage can be tracked.