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.
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.
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.
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.
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.
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.
When I ask a company representative working in any industry what they want SAS data analytics to do for them, they tend to tell you the same thing—the ability to predict consumer demand.
It’s not hard to understand why.
Whether it is growing competition in the retail industry or the need for value-based healthcare, the ability to anticipate industry trends for the next few years is essential. Among many other benefits, it supports more efficient resource allocation.
This, however, begs another question. Most organisations already use forecast models to predict industry trends—what can SAS data analytics platforms, like SAS Viya, do that their current models aren’t already doing?
The difference lies in machine learning. I have already discussed machine learning in the past but its influence on data analytics platforms is impressive. One reason is the results. This technology allows SAS data analytics models to operate more efficiently and deliver better results to analysts.
In this post, I explain how SAS data analytics can take forecasting to another level. I also discuss the problem with current forecasting models and how SAS platforms resolve this issue.
Traditional forecasting models, like Excel and Enterprise Resource Planning (ERP) systems, are the norm when it comes to forecasting models.
These systems work by analysing a large volume of data to create a picture of where the industry is heading in the next few years. It’s simple, effective, and has worked for several years.
So what’s the problem?
Well, the fatal flaw in traditional forecasting models is in how they function. Most traditional forecasting models assume that markets in the future operate the same way they do in the present—with little to no change. You can see where the problem is because no industry operates in a vacuum.
As the pandemic demonstrated, industries can expand, contract, or completely transform based on unforeseen developments.
One problem could be the limited use of data sources. Conventional forecasting models fail to use data sources beyond the organisation’s internal data sources. This is where SAS analytics provides a huge advantage to users.
SAS data analytics perform a deeper, more comprehensive level of analysis than traditional forecasting models to deliver accurate results.
The main difference between SAS models and conventional models is machine learning. Machine learning algorithms can incorporate additional internal and external sources of information to make more accurate, data-driven predictions.
Thanks to machine learning, SAS data models deliver more accurate readings than their predecessors.
Unlike most forecasting models, machine learning can evolve and become more sophisticated, as you feed them more data too. If you have a business-oriented mind, that means a higher ROI for your SAS data analytics model.
Machine learning engines work with both structured and unstructured data. What does this mean, though? Only that you can draw on sales reports, social media signals, weather forecasts, and marketing poll data. I’m talking about a variety of data sources at your disposal, here. You will have an easier time making connections between the most unlikely sources of data.
In the long run, you will also enjoy more accurate data analysis, which means an easier time predicting what the future will look like. Most organisations already use SAS data analytics as a failsafe against uncertainty, when introducing new products to the market, for example.
SAS data analytics platforms can expand what data analysts do. With previous forecasting models, you could perform ‘’demand planning”—the process of estimating demand at different points in the supply chain. With SAS data analytics, however, you can perform demand sensing.
Demand sensing is an evolution of demand planning. It is a new concept in planning, which captures real-time fluctuations in the industry. Think of it as an evolution of demand planning, given its focus on real-time shifts in data.
SAS data analytics can optimise planning for any organisation. Of course, that is not to negate the other benefits of SAS analytics. These data analytics models can speed up the data analysis process considerably.
The ability to predict industry trends with greater accuracy, however, is crucial. This is because it gives organisations the means to act smarter and more efficiently than before. It means lower operating costs and the more innovative use of resources, which is what every organisation wants to do, no matter what industry they operate in.
Want to know more about SAS analytics? Explore our blog and other resources for more insights.
Automated testing is a huge improvement for SAS data analytics software. SAS offers several benefits to organisations, but if there is one feature it lacks, it is automated testing. SAS algorithms are complex, and those who do not have a background in SAS have a hard time working with SAS code or testing its framework. Automated testing could be the solution the software needs. So, in this blog post, I will be discussing how test automation is a huge win for SAS software.
Testing SAS data analytics software is a challenging prospect for designers, coders and testers without SAS training. The challenge causes two problems: The teams cannot integrate the process and this then results in SAS bespoke test scripts, which leads to delays in project development and deployment. Automated testing solves this problem by streamlining the process. With this method of testing, coders don’t have to go through every line of SAS code or rely on developers to write tests. By streamlining the testing process with automation, it significantly improves the rate of software delivery and improves communication between teams.
Before the days of test automation, SAS developers were forced to take several shortcuts to accommodate testing frameworks. For example, instead of using these frameworks in the development process, they would re-write the same logic repeatedly on different projects even to test new features. Furthermore, not working with a testing framework allows many bugs to accumulate, which is a nightmare for software development. Automated testing addresses some of these issues, by removing the barrier that prevents testers and programmers from testing their bespoke SAS software. With test automation, programmers can create more stable software.
As mentioned before, SAS code can be quiet complex, especially for testers and programmers without a SAS background. Even for those who are familiar with SAS, testing can be a long, arduous process, sometimes with a large team as well. By automating the testing process, SAS analytics software can be tested more frequently, without a large team of testers and programmers. Higher frequency of testing means more stable software and higher quality features.
Unless you have been living under a rock, you would have heard that software development has been shifting from waterfall to agile methodology, where the focus is on professionals with different specialities coming together to deliver software as quickly as possible through subsequent reiterations of the software. Automated testing is the perfect complement for the agile methodology because it allows programmers to test more efficiently and with more frequency, improving the rate of delivery. The improvement in delivery makes test automation an integral part of agile software programming.
Automated testing can lower the cost of developing SAS analytics software. Analytics providers and clients have to pay a higher upfront cost than before. However, the trade-off is a much better testing process where more is done at a faster rate and with a smaller team. While testers are still important, especially for more high-end tasks, test automation tools can do much of the testing without human input. This is exciting news for SAS software because it makes bespoke SAS software more accessible to other organisations, especially when combined with other technologies, like the cloud. Automated testing provides tremendous value to both client and analytics provider because testing is more cost-efficient.
Despite the obvious benefits of test automation, there are some challenges to integrating it into the development process. One reason is configuring test automation – because it is a huge upfront cost. Furthermore, quickly scaling test environments is incredibly challenging, especially when programmers and testers are working in the cloud. Scaling for different test environments proves to be a huge challenge. In some cases, automated testing can lack visibility, especially when different teams are using different strategies for automated testing.
An open testing framework can negate this problem, to some degree. Finally, too many UI tests can break the automated testing. Though these challenges can prove to be a barrier to entry for some organisations, it is only a matter of time before test automation becomes the norm in SAS data analytics software.