Data quality challenges are often why organizations are reassessing AI and BI projects. Here are four best practices to help you do data prep efficiently.
IBM is reporting that data quality challenges are a top reason why organizations are reassessing (or ending) artificial-intelligence (AI) and business intelligence (BI) projects.
Arvind Krishna, IBM’s senior vice president of cloud and cognitive software, stated in a recent interview with the Wall Street Journal, “about 80% of the work with an AI project is collecting and preparing data. Some companies are not prepared for the cost and work associated with that going in. And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.” 
Many businesses are not prepared for the cost and effort of data preparation (DP) when starting AI and BI projects. To compound matters, hundreds of data and record types and billions of records are often involved in a project’s DP effort.
However, data analytics projects are increasingly imperative to organizational success in the digital economy, hence the need for DP solutions.
Data Quality Testing Skills Needed For Data Integration Projects. Data integration projects fail for many reasons. Risks can be mitigated when well-trained testers deliver support. Here are some recommended testing skills.
The first step is to understand what is data governance. Data Governance is an overloaded term and means different things to different people. It has been helpful to define Data Governance based on the outcomes it is supposed to deliver. In my case, Data Governance is any task required for.
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‘Data is the new science. Big Data holds the key answers’ - Pat Gelsinger The biggest advantage that the enhancement of modern technology has brought
We need no rocket science in understanding that every business, irrespective of their size in the modern-day business world, needs data insights for its expansion. Big data analytics is essential when it comes to understanding the needs and wants of a significant section of the audience.