Template/Policy

The Data Integration (DI) Specialist must successfully organize and supervise all DI initiatives. The individual is responsible for managing the flow of data between databases, apps, and Cloud services.

Tool

This tool will help you determine whether or not data integration (DI) is required at your organization, the type of tool needed to support your DI needs, as well as the suitability of deployment methods based on your organization's personnel,...

Note

The progressive movement of business areas into the Cloud is calling attention to the need for stronger data integration (DI) planning in most organizations. Data transaction now need to be made between On-Premise apps, On-Premise and Cloud...

Solution Set

Organizations have been neglecting their data integration needs for years, but now the growing adoption of Cloud applications is complicating the situation even more. This set confronts and explains that challenge, then leads organizations...

Job Description

Data Stewards manage data quality issues by identifying problems that users run into and then putting in place mechanisms to address them. They safeguard data both On-Premise and within the Cloud.

Vendor Research

This discussion document positions Info-Tech Indaba's upcoming big data and analytics research. Data generation globally is growing exponentially, with over 35 zettabytes of digital data being created within ten years, according to industry...

Solution Set

Business Intelligence (BI) has changed in the last ten years. Learn what's new and what could make your BI platform more successful.

Note

Business Intelligence (BI) has changed in the last ten years. Learn what's new and what could make your BI platform more successful.

Vendor Research

This report is designed to aid vendors and solution providers in the sale and marketing of business intelligence (BI) software to enterprises.

Note

Despite the costs and headaches associated with having poor data, most organizations struggle to define and implement a strategy for effectively improving data quality.

Solution Set

This solution set will help you understand the causes of data quality problems and provide strategies to fix the causes of data quality problems, and not just address the symptoms.

Tool

The first step to improving data quality is to conduct an audit of prevalent data quality problems are in the organization. There are five key types of data quality problems, ranging from duplicate data to conflicting data.

Tool

Developing policies and procedures is an important step for improving data quality. IT and business users need to clearly understanding who is accountable for what data. Policies such as refresh cycles and system-of-record are essential for...

Video

Data quality is a significant issue facing many organizations. Despite the costs and headaches associated with having poor data, most organizations struggle to define and implement a strategy for effectively improving data quality.

Note

Many mid-sized organizations live in a state of data chaos. While it's not a conscious decision, many sink themselves further into a messy data architecture by using insufficient tools, methods and processes. Understand the basics of data...

As a business develops, the number of point-to-point integrations grows to become a hopelessly tangled web of integrations. Data integration can bring order to data chaos to increase accuracy and consistency, decrease redundancies, and make...

Enterprises generate a great deal of useless data in the name of Business Intelligence (BI). Business and IT decision makers need to establish the strategic intent behind BI before even considering tool adoption.

Poor data quality can have a serious impact on bottom-line performance and create compliance issues for regulatory reporting. Companies continue to throw software at the problem instead of focusing on business process improvement. Use data...

Data governance is a vital component of any master data management strategy. The creation of a governance program depends upon executive sponsorship and gaining the support of key stakeholders. Develop a clear project strategy from the outset to...

Building or purchasing a Master Data Management (MDM) solution will not solve master data problems on its own. Employee behavior and organizational processes need to be changed first. Assemble a data governance group to drive the organizational...

Service oriented architecture (SOA) promises to provide businesses with flexible applications that can be quickly adapted to meet evolving business. However, successful SOA projects must be built on a solid foundation of quality data. Solve data...

Managing multiple enterprise data sources is painful, especially when they contain conflicting or incomplete information. Steps must be taken to reconcile the data sources and establish a master data set for reporting and analytics. To gain...

Managing enterprise data quality is essential for preventing poor decisions and wasting resources. To assert better management control over data quality, institute a data governance program led by a data quality team.

Getting real value from a business intelligence (BI) application depends on feeding the system high quality data that can be trusted. Improve the return on a BI investment by instituting an analytical master data management (MDM) strategy.

Impact Research

Today's business decision-making is increasingly dependent on Business Intelligence systems. Use this report to learn and apply best practices in designing, deploying, and managing a BI system effectively.

Large data volumes from multiple sources make it increasingly difficult for IT departments to provide their business users with consolidated and accurate data for decision making. Business Intelligence (BI) is useless if it is based on either...