5 Data Management for Analytics Best Practices

Data is at the heart of virtually every organization – backing up decisions, supporting operations and feeding into analytics. But sometimes data is hard to access. Sometimes it’s dirty. Other times, it’s simply not in a format that can be used for analytics – analytics that could unveil crucial insights for your business. Why not manage your data in a way that keeps it clean and ready for analytics? Don’t miss out on opportunities that might be hiding in your data.

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Data Management: Why It’s So Essential & The Basics of Data Preparation

Why is data management essential?

For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytic purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility.

50-80

%

OF OVERALL DEVELOPMENT TIME

Most data scientists spend 50 to 80 percent of their model development time on data preparation. That cuts sharply into the time they could have spent generating insights.

The Basics of Data Preparation

Data scientists and business analysts often know ahead of time what data they want to profile or visualize prior to preparing and modeling it. But what they don’t know is which variables are best suited – with the highest predictive value – for the type of model being implemented and the variable being modeled. Identifying and accessing the right data are crucial first steps.

Data Management for Analytics: Five Best Practices

Before you can build an effective model, you’ll need consistent, reliable data that’s ready for analytics. That’s where our five data management for analytics best practices can help.


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01: Simplify

02: Strengthen 

03: Scrub 

04: Shape 

05: Share 

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YOU'RE RIGHT!

Data that’s managed properly can easily move between systems and is captured at the appropriate time (in batch, while streaming, etc.). This data is made usable through validation, standardization or enrichment, and it’s available to everyone who needs it to do their jobs – not just technical users.

NICE TRY!

Data that’s managed properly can easily move between systems and is captured at the appropriate time (in batch, while streaming, etc.). This data is made usable through validation, standardization or enrichment, and it’s available to everyone who needs it to do their jobs – not just technical users.