How SAS analytics can simplify data analytics

SAS analytics

Data is a critical asset for businesses, especially when it comes to predicting risks, capitalising on trends and staying ahead of consumer behaviour. As a result, we gather large amounts of raw data every day and process them to produce useful insights that can help us make better decisions.

Modern data analytics, however, can be very complicated. 

In the past, data collection was done through simple means, such as surveys or questionnaires. While these types of data gathering methods are still possible, the majority of data collection and data analytics operations today involve the use of computers and advanced software tools like SAS.

With SAS Analytics, analysing and processing data can be faster and more reliable for both individuals and organisations alike.

In this post, let’s take a look at how SAS analytics can simplify data analytics.

Managing large databases is easier

One of the primary drawbacks of traditional data analytics tools is their inability to process large amounts of data. As a result, the insights produced by traditional means tend to be less accurate or the insights are not available when we need them.

Modern data analytics platforms like SAS, however, are great at processing extensive data lakes in a fraction of the time, since they rely on automated systems and algorithms.

The SAS data analytics platform also vastly reduces the need for human intervention, which means there is a lower probability of errors in the process. All of these abilities culminate in a data analytics platform that produces accurate insights at a predictable rate, supporting data-driven decision making.

It streamlines data preparation

Modern data collection operations collect all sorts of data such as sales figures, personal information, consumer preferences and more from different sources. With traditional data analytics tools, identifying and preparing all the different types of data requires considerable human involvement.

Data preparation also takes up a large portion of data scientists’ time. In fact, according to Forbes, data scientists tend to spend more than 75% of their time on data preparation.

Fortunately, with SAS Analytics’ inbuilt data preparation tools, organisations can automate data preparation processes such as wrangling, cleaning, formatting and organising, saving a considerable amount of time.

SAS is flexible

Data file formats are notoriously hard to standardise since statistical tools like Excel, Stata and SPS use proprietary file formats, which add complexity to traditional data analytics tools.

SAS, on the other hand, is capable of reading all kinds of data files, including the ones mentioned above. Additionally, SAS can convert the data from other statistical packages into its own SAS format, making them easier to access.

SAS is also updated frequently by its developers to ensure that it can identify new types of data and read new kinds of data files.

SAS is scalable

Scalability is the main benefit of SAS analytics compared to traditional data analytics tools. As established before, traditional tools lack the capability to process large amounts of information, at least when it comes to their regular deployment status.

In order to process large data lakes, organisations need to scale the data analytics process to encompass all available data. While this may sound simple, it is anything but when it comes to traditional tools.

Organisations need to hire more data analysts and invest in expensive hardware to scale to meet requirements.

SAS analytics platforms, on the other hand, can be scaled up and down seamlessly thanks to cloud integration, which also requires less computing power to operate.

With SAS, data analytics is easier than ever!

We’ve come a long way from the days where we had to pour over stacks of books and files flowing with raw data and having to calculate and get insights on our own. 

Thankfully, we now have advanced analytics tools like SAS to make data analytics much simpler and more effective.


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