How SAS data preparation can optimise your data analytics
Do you know which process manufacturing lines have in common?
Manufacturing lines require high-quality material that’s ready to be used in production. The process they have in common, then, is preparing raw material to make it suitable for value-added production.
This analogy applies to data analytics as well.
Imagine data analytics platforms as manufacturing lines. In this case, the raw material would be the high-quality data required to churn out the right products, which, for us, is actionable analytics.
Raw data can’t be ingested directly on the analytics platform, however, as it may contain errors and discrepancies.
Raw data needs to be cleaned and enriched in a process known as data preparation. According to Forbes, data scientists spend more than 76% of their time preparing and organising data using a variety of tools.
While there are many data preparation solutions out there, the SAS data preparation platform remains one of the most popular options.
In this post, we explore how SAS’s data preparation platform can optimise data analytics platforms to produce high-quality, actionable insights.
SAS data preparation increases your efficiency
Modern businesses collect vast amounts of data every single day. According to recent findings, organisations now collect more data in a single month than they did in the entire decade between 2002-2012.
This data needs to be enriched using data preparation techniques such as wrangling, cleaning, correlation and formatting. Without these processes, analytics platforms can’t produce accurate and actionable insights as the raw data can generate errors and discrepancies in the process.
That said, traditional data preparation techniques can only process less than 10% of the collected data, which means 90% of the raw data is either wasted or takes an unnecessarily long time to be processed, hampering efficiency and output.
SAS data preparation, however, can generate codes to handle cleaning, wrangling, correlation and formatting automatically, eliminating these challenges from the get-go.
Data preparation can increase the accuracy of analytics platforms
As we’ve already established, raw data can create errors and inaccuracies in the data analytics process if ingested directly.
That said, data prepared through traditional techniques can also create errors in the code, leading to inaccurate insights since traditional methods rely on manual processing, which not only requires a long time to complete but is also subject to human errors.
Modern data preparation tools rely on computer-generated codes that are free of this issue. These codes are designed to better identify anomalies, null values and duplicate entries compared to older techniques.
Data preparation supports a migration to cloud services
In recent years, more and more organisations are migrating their assets, processes and analytics infrastructure to the cloud to facilitate a collaborative work environment.
Unfortunately, integrating raw data into the cloud analytics platform is easier said than done, as organisations still need human resources to integrate this data into the cloud analytics environment.
SAS data preparation, however, is designed to be self-serviceable, meaning you don’t need a team of data scientists to carry out this process!
With this platform, organisations can clean, wrangle, structure, and format data from data lakes and enter it into cloud data analytics deployments without much human intervention.
Make the most of SAS data preparation tools to optimise your analytics platform
Data analytics platforms are great at arming organisations with the insights they need to make strategic, far-reaching decisions.
That said, analytics platforms are somewhat like manufacturing lines. They can’t produce high-quality insights without data that has been prepared to be ingested.
Fortunately, with SAS data preparation, organisations can enrich vast amounts of data without too much human intervention and feed it into analytics platforms.
Optimise your data analytics deployment with the right data preparation tools today.