What would you say is the main difference between modern business decision-making compared to the past?
In my opinion, the defining aspect of modern decision-making is the integration of data analytics. It is the main difference between the past, the present and the future.
Although decisions made in the past did involve some data, decision-makers were not entirely dependent on what the numbers were saying. Today, it’s a much different story.
Nowadays, we use various tools to make sure the decision-making process is accurate and reliable. SAS analytics is one such tool that is helping modern organisations; SAS, in fact, is the leader in the global data analytics market.
While SAS analytics has seen widespread adoption, organisations require a deep and sound knowledge of the platform to get the most out of their specific deployment. As it happens, SAS training can help teams attain that kind of knowledge.
In this post, let’s explore how SAS training is helping companies create a robust decision-making framework, helping them make more strategic, future-focused decisions.
Although SAS’ data analytics tools have been adopted by many modern businesses, they are not used or deployed in the same way. Every deployment is different since the needs and requirements of one company will not be the same as the next.
That said, data analysts need to have a deep understanding of the entire SAS ecosystem of tools and their capabilities—even if they don’t use all of them—to leverage the full potential of their specific deployment.
SAS training programmes help data analytics professionals learn about the various SAS tools and their capabilities, helping them acquire a deeper, more rewarding knowledge of the popular analytics platform.
Although SAS analytics tools can produce actionable insights at a steady rate, they need high-quality data sets to do so.
That’s why data analytics professionals need to commit to data preparation—the process of cleaning, wrangling, organising and formatting data—to make it ready to be ingested into the analytics infrastructure.
That said, data preparation can be hard to execute and takes a considerable amount of time to complete. In fact, according to recent statistics, data scientists spend 76% of their time preparing data.
With SAS training, data scientists can learn techniques and tricks that reduce the time spent on data preparation and help them focus their efforts more on generating quality and accurate insights to support better decision-making.
Businesses need to manage and maintain their deployment at an optimal level to produce insights at a steady rate. SAS administration helps organisations do that.
While SAS administration may sound simple, it is anything but—administrators need to know how to identify bugs via troubleshooting and what actions to take to address these bugs and vulnerabilities.
Without these capabilities, the effectiveness and accuracy of your SAS analytics platform can be compromised, affecting how useful your decision-making insights are.
Ineffective SAS administration can also lead to lengthy system outages, prohibiting the production of insights.
The good news is that SAS training can help administrators learn critical processes like troubleshooting, bug fixing, and general management of their deployment to ensure smooth operations and reduce system downtime.
Organisations around the world are using SAS analytics to support their data-backed decision-making processes because SAS provides a comprehensive set of tools that produces useful insights.
That said, organisations can optimise their decision-making processes by leveraging the full potential of their deployment with SAS training.
Make the most of SAS training to improve your decision-making capabilities today.
In an era where data has become the new oil, it is paramount to have the right techniques, models and tools for processing the 2.5 quintillion bytes of data produced regularly. Predictive data analytics is a technology that can anticipate future trends. It is an evolution of earlier data analytics models and works by predicting what will happen in the future by analysing historical data, discovering patterns and using that information to draw up predictions about the overall direction of the industry.
Predictive analysis models and findings are powered by machine learning and artificial intelligence. From customer service to social media to FinTech, predictive analysis models are playing a crucial role in driving what you see and when you see it. The technology offers incredible accuracy, making it a reliable tool for businesses across different industries, especially large corporations, working with terabytes of stored data.
We already see predictive data analytics in use – particularly, in service-based organisations. Those who browse the AliExpress product page will notice sections titled, “People who bought this, also bought…” to sell additional products, while platforms, like YouTube, recommend videos based on what users already viewed. Predictive analytics powers these features, and they are very useful because it allows platforms to upsell and cross-sell to customers, boosting sales and revenue. Due to the potential of predictive analytics models, organisations across different industries are investing in predictive analytics to cut costs and boost revenue.
How does predictive analytics actually help in better decision-making?
The amalgamation of an increasingly complicated world, the vast proliferation of data and the pressing desire to stay at the forefront of competition have prompted organisations to focus on using analytics for driving strategic business decisions. Rather than “going with intuition” when maintaining inventory, pricing solutions or hiring talent, organisations are embracing analytics and systematic statistical reasoning to make decisions that improve efficiency, risk management and profits.
From personalising products and services to scaling digital platforms to match buyers and sellers, organisations are using predictive analysis models to enable faster and fact-based, decision-making. In fact, studies show that data-driven organisations that employ predictive analytics not only make better strategic decisions, but also enjoy higher operational efficiency, improved customer satisfaction, along with robust profit and revenue levels. Recent research also shows that data-centered organisations are twenty-three times more likely to acquire customers, six times as likely to retain those customers, and as a result, nineteen times as likely to be profitable.
In an increasingly customer-oriented era, organisations have amassed a wealth of consumer information and data. In order to remain competitive, it is imperative for organisations to use these consumer insights to shape their products, solutions and buying experiences. Research from Mckinsey suggests that organisations that are using predictive consumer behaviour insights strategically are outperforming their peers by eighty-five per cent in sales growth margins and by more than twenty-five per cent in gross margins. Hence, it is important for managers to consider the strategic importance of consumer information.
A telecom company, for instance, can use advanced and predictive analytical models to reduce customer churn and measure the effectiveness of marketing campaigns. Similarly, an online retailer can assess current market share online by seeking answers to questions such as the mix of new and returning visitors, bounce rate and average session duration. Such questions offer crucial insights into the type of content and channels that are likely to have the greatest impact on key consumer segments.
In this volatile environment of data-driven disruption, business managers need to look through two lenses at the same time. Firstly, they have to identify high risk, rewarding opportunities like entering new markets and changing established business models. Secondly, they have to maintain their focus on incorporating analytics into their core business decision-making process. By embedding predictive analysis models into their core strategy, business managers can streamline internal business processes, identify unfolding consumer trends, monitor emerging risks, and build mechanisms for improvement. Driving analytical transformations will, thereby, enable companies to gain a competitive edge and stay at the forefront of digital disruption.
Visit our website for more information on how predictive analysis models are utilised by industry leaders for better decision-making.