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Predictive Analytics in the Retail Industry

Introduction

 

Figure 1: Predictive Analytics

 

Predictive analytics is the utilization of information, factual calculations and machine learning methods to distinguish the probability of future results in light of chronicled information. The objective is to go past recognizing what has happened to give a best appraisal of what will occur later on. Predictive analytics is utilized as a part of numerous divisions for future expectations with the help of machine learning

Big Data Analytics and augmented patient care

Why Big Data in Healthcare is so required?

 

Figure 1: Need for Big Data Analytics

 

“Data analytics” refers to the practice of taking masses of aggregated data and analyzing them to draw important insights and information contained in it. This process is increasingly aided by new software and technology that helps examine large volumes of data for hidden information that can help us in many areas and healthcare is one

How to enhance your Business Value using Data Quality (DQ) Tools

This blog post lists out some of the core concepts  of Data Quality assessment, perception changes in data quality, data quality management over the years, use of data quality tools to parse, standardize and cleanse data.

 

 

What is Quality Information?

There has been a change in the conventional focus on data usage with regard

MDM Project Management in Emerging Economies (Africa)

Introduction

Inter and Intra business communications turn out to be perpetually mind boggling, the need to successfully manage time and assets winds up the key. All things considered, and coupled with huge interest in infrastructure and service, the interest for project management (PM) aptitude in Africa has expanded significantly over a wide assortment of Industries. Africa is a market of middle class consumers which is expected to reach 1.1 Billion by 2060 and will need to cater for maintaining the

Leveraging Event Manager for Key Data Changes Orchestration

In a connected world, data should travel fast, in fact on a real-time fashion, to serve the purpose – enabling business to thrive. Data that is not available in time impacts business. Events that matter to business, that effects critical data changes, should orchestrate data changed sync up with connected systems that are the backbone of the business.

Statistics show that 200 businesses change addresses, 150 business telephone numbers will change or be disconnected, 5 Supplier/Vendor will go through rebranding

IICS and Data Solutions on the DX Platform

Overview

The move to the cloud is fully in force, and therefore the variety of organizations’ integrated knowledge in hybrid environments has multiplied two-fold. Nearly three-quarters of respondents’ integrated knowledge in hybrid and cloud environments claimed poor knowledge quality in cloud services, restricted API access, company security and compliance policies as being among the key problems in implementations. The largest issue among all was an absence of data and skills contained inside their organization’s IT departments on the way to

Planning for Big Data Success

Introduction

Most organizations are either starting or have already started on their Big Data journey.  As most other technology hypes, Big Data has also followed the hype cycle and there was a drop in interest in past years after the initial frenzy.  What we are seeing now is the second round of influx in interest around Big Data after the initial peak some years back. With increasing maturity in Big Data products and business use cases, we are inclined to

Big Data Challenges and Solutions

Introduction

Big Data has empowered organizations to inspect substantial volumes of structured and unstructured data. Big Data augments decision making, by delivering data and conclusions from the projected valuable information. Organizations are presently in a situation to consolidate their data with the acquired large data sets such as geospatial data. Client sentiments can be observed and changes in client conclusions can be effectively distinguished through the scouring of online information.

Data is an asset and becomes a liability when you

Informatica MDM – Fuzzy Matching

This blog touches upon the basics of Informatica MDM Fuzzy Matching.

Informatica MDM – SDP approach

A master data management (MDM) system is installed so that the core data of an organization is secure,  is accessible by multiple systems as and when required and does not have multiple copies floating in the system, in order to have a single source of truth. A solid Suspect Duplicate Process is required in order to achieve the 360 degree view of an entity.

Automate Data Quality with Informatica IDQ

Data Quality – Overview

Data Quality is the process of understanding the quality of data attributes such as data types, data pattern, existing values, and so on. Data quality is also about capturing the score of an attribute based on some specific constraints. For example, get the count of records for which the attribute value is NULL, or find the count of records for which a date attribute does not fit into the specified Date Pattern.

Managing your Data Quality

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