A long-held belief in the field of analytics is that only 20% of a person’s time is spent analyzing data – the remaining 80% is devoted to preparing data. It's a sobering statistic to say the least. Even more so as the need to get insights from data in real time rises in importance. To fight fraud, understand customers and evolve to using artificial intelligence, organizations can no longer afford to rely on the IT department to handle data preparation as business users sit and wait.

Enter self-service data preparation

Thus has been the promise and reward of self-service data preparation tools from SAS. Being able to transform, blend, shape, cleanse and standardize data – in a business-user-friendly, self-service environment – helps organizations “flip the script” on how much time it takes to perform data preparation and gain business-critical insights.

Interested to see what’s new in the space and learn how to use data preparation to improve your business? Read the following five articles to get started:

• The 5 D’s of data preparation. Delve into the details of what data preparation involves and discover the ins and outs of what it takes for organizations to succeed.
• Data preparation without analytics: Just well-mixed data. Learn about blending data preparation with analytics, and why the two are such a perfect match.
• Self-service data preparation: The self stands for value! IT alone can’t handle all of the tasks required for preparing data. Learn how data preparation gives control to the business.
• Data preparation and the analytics life cycle. Discover the role data preparation needs to play throughout the analytics life cycle – why it matters and how it works.
• 3 ways to prepare data for analytics: How to choose the best option. There’s more than one way to prepare data for analytics. Find out which method is best suited to your purposes.

The status quo is no more

Despite the time savings organizations are experiencing from data preparation, there’s still a push to do more in even less time. Machine learning and artificial intelligence capabilities – traditionally considered only in the realm of analytics – are now a key part of what makes data preparation most valuable.

The latest release of SAS Data Preparation takes much of the leg work out of preparing data for analytics. AI and machine learning techniques can scan an organization’s data, then provide transformation suggestions. Users have the option to accept the suggestions and complete the transformations with a simple click of a button. No advanced or complex coding needed. Now, data experts can be liberated from mundane, redundant transformation tasks and get their analytics results to market faster.