Recently I've found myself part of the trend of ordering "easily prepared meals" via the web. On the surface it seems simple. You click the meal you want on a website and a couple of days later all the ingredients arrive. You just have to do the rest. As simple as 1,2,3 – right? Not really. On a few occasions, I've noticed that an important step is missing – how to actually cook the food. Imagine prepping all these ingredients and then finding there are no directions about temperature or how long to cook the food. After a ton of work, you're left with nothing more than well-mixed food.

At this point, you're probably wondering how this relates to data preparation. Well, imagine spending time preparing your data only to realize that there is no easy method for moving it to reporting or analysis. Data scientists and business users often face this dilemma when using their existing data prep tools. All preparation – no simple link to reporting and analytics capabilities. For lack of a better analogy, it's like not having a simple way to cook and consume the data you've prepared. Organizations are left with nothing but well-mixed data.

Self-service data prep needs grow with the volume of data

The analytics space has seen more growth in recent years than at any other time in history. Driven by organizations' ability to capture and consume more data, self-learning algorithms are finally starting to realize their potential. But with more data comes the requirement to manage it properly – enter self-service data preparation. We often hear about the 80/20 rule: 80% of an analyst’s time is spent getting data ready, and only 20% of their time is spent gathering insights. Self-service data prep tools can be a life saver for these individuals, and for the IT staff that supports them. While self-service data prep continues to grow in importance, it's essential to have a solution that lets you manage data well beyond the preparation stage, as quickly and easily as possible.

Why SAS is different

With capabilities like data cleansing, data lineage, data governance and data provisioning, SAS Data Preparation has the rich features data scientists expect along with the simplicity business users need. For many data prep tool vendors, that's where the value ends. But with SAS, it's just the beginning.

With just one click, SAS Data Preparation users can work inside of SAS Visual Analytics and/or SAS Visual Data Mining and Machine Learning. If going directly from well-managed data to analysis is important for your organization, SAS has you covered. For example, by using SAS Data Preparation with SAS Visual Analytics, you can:

• Understand and share what’s happening with dynamic visuals. SAS Visual Analytics lets users create interactive reports and dashboards by querying and preparing data from multiple sources in a self-service manner.
• See the big picture and underlying connections. Interactive discovery lets you examine all opportunities hiding in your data. Discover why something happened, and identify critical drivers to make better decisions.
• Drive business results with data-backed insights. With easy-to-use predictive analytics, business analysts can assess possible outcomes and make better, data-driven decisions – no programming required.

Using SAS Data Preparation along with SAS Visual Data Mining and Machine Learning, you'll be able to:

• Quickly deploy predictive models. SAS provides automatically generated score code in multiple programming languages for all your machine learning models.
• Boost the productivity of your analytical teams. With support for the entire machine learning pipeline, a variety of users can build and expand upon sophisticated models to get highly accurate results – all in a single, collaborative environment.
• Solve complex analytical problems faster. SAS eliminates the need to load data multiple times during iterative analyses. Analytical model processing time is now measured in seconds or minutes rather than hours – so you can solve problems faster than ever.

 From data prep to analytics: A retail example

Let's look at an example of a major European retailer that took advantage of the easy link SAS provides between data preparation and analytics. With no visibility into their store, customer and stock keeping units (SKUs) performance, this retailer lacked a method to properly track and replenish store inventory. The retailer needed to have a process to quickly manage data and an analytics program to predict and ensure that the right mix of product inventory was available at the right location for its customers. With a SAS data preparation solution in place that quickly linked model and analytical development, the retailer found it could better manage 300,000 SKUs. This resulted in $77 million in sales growth, because of having the right products at the right store at the right time. Data preparation combined with analytics was the answer.

Want to learn more about how your organization can go from data prep to analytical insights in just a few clicks?