Why analytical models are better with better data


man focusing on why analytical models are better with better dataMost enterprises employ multiple analytical models in their business intelligence applications and decision-making processes. These analytical models include descriptive analytics that help the organization understand what has happened and what is happening now, predictive analytics that determine the probability of what will happen next, and prescriptive analytics that focus on finding the best course of action for predicted future business scenarios.

The common denominator of all analytical models is data. And, as the TDWI Best Practices Report Improving Data Preparation for Business Analytics explained, regardless of the model used, analytics can only be as good as the underlying data. Analytics based on poor-quality data can lead to bad business decisions. For example, geographical profiling of customers based on inaccurate postal address data provides a false impression of where the most valuable customers live and could drive bad business decisions about where to focus marketing efforts.

The report cited users of business intelligence and analytics tools lamenting how they spend considerable time searching for the right data only to find that the data is flawed or not prepared to meet their specific business requirements. And with business requirements increasingly calling for fresher data to satisfy operational business intelligence and near-real-time analytics, users and applications need current and frequently refreshed data for data discovery and to test and score new analytical models. However, as the report noted, often the fresher the data the lower its quality. Sometimes this is due to not having enough time to run data quality processes, forcing users to feed raw data that has not been properly prepared into analytical models.

Enterprises trust analytics to help them make better, data-driven decisions. The quality of analytical results is highly dependent on data preparation, and data quality is a cornerstone of well-prepared data. Regardless of what analytical model is employed, analytical models are better with better data.

Download the full TDWI report


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Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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