Data Quality is the buzz word in the digital age.
What is data quality and why is it so important?
“Data quality” is the term that is probably hidden but plays an important role in many streams. Data plays a vital role in acquiring a market place, especially in enterprise data management stream.
Data Quality Examples
Following are some examples which emphasize the need for data quality.
- A customer shouldn’t be allowed to enter his age where he
Data Quality – Overview
Corporates have started to realize that Data accumulated over the years is proving to be an invaluable asset for the business. The data is analyzed and strategies are devised for the business based on the outcome of Analytics. The accuracy of the prediction and hence the success of the business depends on the quality of the data upon which analytics is performed. So it becomes all the more important for the business to manage data as
Data Generation, Analysis, and Usage – Current Scenario
Last decade has seen an exponential increase in the data being generated from across traditional as well as non-traditional data sources. International Data Corporation (IDC)report says that, data generated in the year 2020 alone will be a staggering 40 zettabytes which would constitute a 50-fold growth from 2010. The data generated per second has increased to 2.5 Quintillion bytes and with the advent of latest innovations like the Internet of Things; it
Web Services Overview:
Web Services are services available over the web that enables communication and provide a standard protocol for communication. To enable the communication, we need a medium (HTTP) and a format (XML/JSON).
There are two parties to the web services, namely Service Provider and Service Consumer. A web service provider develops/implements the application (web service) and makes it available over the internet (web). Service Provider publishes an interface for the web services that describes all the attributes of
MongoDB is an open-source document- oriented schema-less database system. It does not organize the data using rules of a classical relational data model. Unlike other relational databases where data is stored in columns and rows, MongoDB is built on the architecture of collections and documents. One collection holds different documents and functions. Data is stored in the form of JSON style documents. MongoDB supports dynamic queries on documents using a document based query language like SQL.
This blog post
This blog post is the final part of the Data Warehouse Migration to AR series. The second part of the blog post series Data Warehouse Migration to Amazon Redshift – Part 2 details on how to get started with Amazon Redshift, the business and technical benefits of using AR.
1. Migrating to AR
The migrating strategy that you choose depends on various factors such as:
- The size of the database and its tables
- Network bandwidth between
Data cleansing and standardization is an important aspect of any Master Data Management (MDM) project. Informatica MDM Multi-Domain Edition (MDE) provides reasonable number of cleanse functions out-of-the-box. However, there are requirements when the OOTB cleanse functions are not enough and there is a need for comprehensive functions to achieve data cleansing and standardization, for e.g. address validation, sequence generation. Informatica Data Quality (IDQ) provides an extensive array of cleansing and standardization options. IDQ can easily be used along with
Apart from the better understanding on data, we need to pay more attention towards the basic statistics as it is the key concept of driving the data to develop interactive visualizations and convert tables into pictures.
The rapid rise of visualization tools such as Spotfire, Tableau, Qlikview and Zoomdata, has gained immense use of graphics in the media. These tools currently hold an ability to transform the data into meaningful information with proper standard level principles in the statistical
This blog post is the second part of the Data Warehouse Migration to AR series. The first part of the blog post series Data Warehouse Migration to Amazon Redshift – Part 1 details on how Amazon Redshift can make a significant impact in lowering the cost and operational overheads of a data warehouse.
1. Getting Started with Amazon Redshift (AR)
Since Redshift is delivered and managed in the cloud, it is mandatory to have an Amazon Web
IBM InfoSphere Master Data Management Collaborative Edition provides a highly scalable, enterprise Product Information Management (PIM) solution that creates a golden copy of products and becomes trusted system of record for all product related information.
Performance is critical for any successful MDM solution which involves complex design and architecture. Performance issues become impedance for smooth functioning of an application, thus obstructing the business to get the best out of it. Periodic profiling and optimizing the application based on the findings