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Data preparation is, to use TechTarget’s definition, “the process of gathering, combining, structuring and organizing data so that it can be analyzed as part of data visualization, analytics and machine learning applications. The components of data preparation may include pre-processing, profiling, cleansing, validation and transformation.”
It’s the challenge of extraction and transformation of data from all corners of an organization ready for analysis and visualization — and the resulting business benefits — that we focus on here. It’s a challenge that presents itself to every business on the path to digital transformation and unlocking insight from data.
It would be easy to think that a technical process such as data preparation would be best carried out by trained, technical consultants and IT department staff. But research shows that this causes more problems than it solves. The results of TDWI Research’s survey on this approach, for example, were startling. “Half of the respondents spent more time cleaning the data than using it,” GCN reported. “Most (73%), spent at least 41% of their total time preparing the data for analysis. Little more than a third spent between 61% and 80% of their time preparing data, and 8% spent nearly all of their time on data preparation.”
Half of the respondents spent more time cleaning the data than using it…Most (73%), spent at least 41% of their total time preparing the data for analysis. Little more than a third spent between 61% and 80% of their time preparing data, and 8% spent nearly all of their time on data preparation.”
TDWI Research, Improving Data Preparation for Business Analytics
ZAP Data Hub automates all the steps of data preparation required for accurate and productive Business Intelligence analysis. A software-based system, it not only reduces the time and expense of a manual labor-based approach, it also vastly reduces the chance of human error. And as part of a wider, automated data management process it ensures that all elements of data preparation automatically adhere to an organization’s data security and data governance protocols.
ZAP Data Hub’s self-service data preparation tools are also intentionally user-friendly and quick to learn, thus allowing users from all departments—not just IT—to engage in the process and integrate the right data, the right way, into their Business Intelligence reports and analysis.
Bloor Research would agree that such an automated, self-service software-based approach is the key to efficient data preparation. “It allows (all business units) to prepare and perform ad hoc analyses without being reliant on IT,” they explain. “There are also major benefits for IT, not just in being able to monitor what users are doing but also in reducing IT workload.”
TDWI Research also support this solution to the challenge of data preparation and point to such “innovative software technology and methods aimed at accelerating, if not automating, processes necessary to support business analytics” as the key for businesses, ranging “from small organizations using spreadsheets and visual discovery tools to large enterprises trying to improve data quality and delivery, for a variety of uses including BI and advanced visual analytics (for whom) data preparation difficulties are a major concern.”
Pointing the way to the automated, self-service data preparation and data management software ZAP has developed, GCN lists 10 steps to speed the journey from data prep to insight. Not least points, three, four, five and six:
1. Cut the time needed for data preparation
2. Use data catalogs, glossaries and metadata repositories
3. Automation makes it easier
4. Use technologies and methods to increase repeatability
5. Consider self-service data preparation solutions
6. Integrate self-service data preparation
7. Create a flexible structure for analytics.
8. Ensure self-service data preparation
9. Include data preparation in data governance objectives
10. Create centers of excellence
“Use technologies and methods to increase repeatability so that scripts, workflows and other elements can be reused,” GCN recommends in point three, which aligns with the self-service and pre-packaged data preparation modules within ZAP. And, to expand on points five and six, their recommendation mirrors ZAP’s proposition: “evaluate new technologies enabling non-technical users to prepare data themselves” and “integrate self-service data preparation with self-service Business Intelligence and visual analytics.”