The uses of data analytics in biotechnology

Biotechnology is a broad field of biology that leverages various biological systems to develop products that can fundamentally transform the way we do things.

That said, biotech is not a modern concept. It has existed for thousands of years with ancient civilisations using early forms to produce crops and brew alcoholic beverages.

Today, the biotech industry has grown leaps and bounds and has accumulated a considerable amount of scientific data through research. Being in an industry where data is crucial, it’s not hard to see why biotech companies use data analytics.

Modern data analytics tools have enabled biotech researchers to create predictive analytics models and get insights about the most effective ways to achieve their desired goals and objectives.

In addition, biotech companies can use data analytics to help them get a better understanding of their market and predict various situations they may encounter in the future.

In this post, we explore some of the common uses of data analytics in the field of biotechnology.

Genomics

Genomics is a branch of biotechnology that plays a role in developing forensic technologies and identifying how genetic factors may contribute to health conditions.

This branch of biotechnology generally processes large datasets to obtain insights, as researchers have to identify and classify genes from millions of genome bases. Traditionally, this process has been the most expensive and time-consuming.

For instance, The Human Genome Project, a major international effort to map the entire human genome, took thirteen years and billions of dollars to complete.

Today, thanks to modern data analytics, biotechnology companies can decode entire genomes in a much shorter timeline and at a much lower cost than before. 

With data analytics tools, medical researchers can get insights on genetic mutations and gene sequences and use this information to find relationships between genes and the effect of new drugs.

Also, data analytics allow researchers to study the human genome to answer complex medical questions like why some diseases are more likely to affect a certain race of people or why some individuals develop particular illnesses after a certain age.

Data analytics in genomics can also help identify the passing of certain genes within families, which can help find cures for inherited diseases and disabilities.

Agriculture

With data analytics, scientists can conduct studies on different crops on a molecular level to discover ways to achieve the best crop yield.

Data analytics can also help develop GMOs, giving rise to genetically engineered crops that are resistant to diseases and can survive challenging conditions.

Data analytics isn’t just useful for researchers but can also help farmers, as it allows them to study crops and identify the best practices for growing them, determine prices for their harvest and find out the availability of crop necessities, such as fertiliser and tools.

Environmental protection

Biotechnology also plays a critical role in conserving the environment. 

Data analytics can help biotech companies to create products that don’t affect the environment negatively. 

For instance, through data-powered insights, scientists have been able to create alternatives to everything from single-use plastics to bricks using sustainable and biodegradable materials such as mushrooms and other plant-based elements.

The possibilities of data analytics in biotechnology are endless

Data analytics has opened new doors in the field of biotechnology. 

Thanks to data analytics, research and development that took years can now be completed in just a few months and researchers have access to biological, social and environmental insights that can be used to develop better and sustainable products.

If you’re looking to enhance your SAS data analytics experience, our Selerity analytics desktop is designed to help you get the most out of your data.

As SAS managed service providers, we help you manage and optimise your SAS environment. 

Give us a call for more details.

What’s the future of business intelligence with augmented analytics?

Business intelligence tools are a game-changer for businesses across industries, as it allows them to reach higher, further and faster in their industry. Augmented intelligence is one of the latest trends in this area that supplements the operations within BI platforms. 

Augmented analytics integrates technology like machine learning, text mining, artificial intelligence, natural language generation (NLG), natural language processing and automated data processing into business intelligence platforms, improving and substituting the work of data analysts, 

With this latest technology, businesses can streamline their data analytics processes and gain accurate insights faster and more efficiently.

How is augmented intelligence changing the business world?

While artificial intelligence and machine learning have been in use for several years, augmented intelligence enhances the capabilities of these technologies, helping you facilitate growth and generate revenue. 

Machine learning, for example, improves the data preparation process by eliminating tedious and repetitive tasks like cleaning and filtering data and speeding up data retrieval, allowing you to make data-backed decisions faster. 

Moreover, by removing technical barriers, augmented analytics makes data more accessible to employees across departments, which may otherwise require IT expertise or mature data management skills to leverage.

With augmented intelligence, you can leverage ML to comprehend complex data about your industry and organisation and identify patterns in user preferences, allowing you to deliver a personalised customer experience.

Using machine learning and natural language generation lets you automate data analysis and deliver better insights and findings to decision makers.

In addition, Natural Language Processing allows you to interactively manage data with the help of text-based and voice-enabled technologies. 

NLG takes this a step further by delivering interactive alerts and insights about business performance.

What does this mean for the future of your business?

• Faster operations with accelerated insights

The backbone of strategic business decisions is data-driven insights. 

By integrating augmented analytics into BI platforms, you can improve data accessibility, helping you provide relevant data to the right person at the right time in an understandable manner to support the decision-making process.

Also, the whole process can streamline the data analytics pipeline and accelerate the data analytics process. With less time spent on finding and analysing data, analysts can focus more on strategic tasks and less on tedious and repetitive tasks. 

• Make business decisions based on accurate, unbiased insights

While analysts are highly trained and skilled professionals, they can be limited by personal opinions. 

In contrast, machine learning, supplemented by the latest BI tools operates with minimal human interferences, making it highly unlikely for the algorithms to be affected by human biases. 

These augmented analytics tools deliver unbiased insights, giving you a complete picture of the market situation. Using these insights, you can make informed decisions that are not influenced by confirmation bias.

• Highly efficient automated operations

Automating operational tasks like data preparation, data discovery, and statistical analyses can improve efficiency in repetitive operations that need highly specialised skills.

Automation also makes insights that would otherwise necessitate a large time and energy from your technical team accessible and visible to your analysts. 

With automated insights more comprehensively delivered to BI users than ever before, you can assess your business performance, identify opportunities, and understand how your brand competes in the marketplace. 

Augmented analytics: the next big leap in data analytics

Augment analytics is now a favourite tool of businesses due to its capacity to democratise analysis and simplify the job for your team. 

Today, business revenue is driven by quick and efficient analytics. It can help your company move forward amidst the competition. 

Your team can work with more up-to-date and relevant insights without having to go through the traditional drawn-out procedure to gain answers needed for strategising. 

If you want to know more, don’t hesitate to give us a call. Our team at Selerity is ready to help you upgrade your SAS analytics experience. 

What’s the connection between AI and advanced analytics?

Advanced analytics models have been driving critical decisions in organisations since the dawn of the century. 

With 2020 bringing significant changes to market conditions and companies having to navigate trade and supply chain disruptions, sudden fluctuations in demand, and new risks, the role of analytics in business decision making is more critical than ever in this new normal. 

In fact, according to industry analysts, the compound annual growth rate of the advanced analytics market is expected to hit 21.9% by 2027. 

With analytics models, businesses today can formulate informed decisions based on data-driven forecasting. 

These data analytics models deliver insights into trends and patterns regarding employees, buyers, and competitors using multiple data sources like emails, instant messages, CRM applications, and social media.

With the rise of artificial intelligence, however, there is also growing scepticism about the efficiency of analytics against AI algorithms. 

Will AI replace advanced analytics models or, is it more efficient to use AI to enhance the performance of analytics? 

Understanding the role of artificial intelligence in data management

Artificial intelligence technologies can perform tasks like reviewing records, running tests and providing insights based on the data. 

Today these technologies are taking over many business processes, with approximately 15% of enterprises using AI technologies in their daily operations. 

Artificial intelligence also allows you to leverage virtual assistants or bots, machine learning and machine vision, test analysis, deep learning and natural language processing, to get more nuanced insights.

Integrating these AI-powered technologies with analytics tools can bring you quality insights faster, enhancing your overall enhanced data management experience. 

How can artificial intelligence enhance your analytics model? 

Using AI-powered technologies like machine learning to enhance business analytics can deliver a more streamlined data collection process, as they have the potential to make the data acquisition and preparing process more effective, accurate and convenient. 

When applied to business operations, AI-driven analytics can deliver micro-targeted insights like customer-product matches and upcoming purchases, allowing you to design and implement highly targeted campaigns and maximise your marketing ROI. 

Here are some of the ways how AI-powered analytics can enhance your processes;

• Handling labour shortages

Switching to AI or integrating AI with analytics can help you meet the labour shortages you may experience in your organisation. 

While you focus on bridging the labour gap, your AI-driven analytics model can cover for you, meet the market demand faster and execute fool-proof campaigns without bottlenecks. 

• Data categorisation

AI-driven classification models can categorise data and make it easier to access, retrieve and analyse data to get predictions. 

Increased digital activity within organisations has created an influx of data that can get difficult to manage if you don’t have the right resources. When classified, data is easier to store and backup. 

With clustering, data management is even more convenient and efficient. 

Clustering models sort data into different groups based on similar properties making it easier to retrieve historical data and make a decision based on insights they provide. 

• Forecasting

With forecasting models applied to data, you can predict future events, including how many customers will convert, how many visit your store in a given time or how much sales to be expected.

AI is likely to enhance the performance of advanced analytics tools

Artificial intelligence integrated with advanced analytics makes most of your operations—like identifying market trends and testing assumptions—autonomous. What you get by integrating artificial intelligence in your analytics tools are enhanced analytics capabilities.

You can access tools and resources to assimilate data and make strategic, data-driven decisions that ensure financial security, increased sales and improved productivity with AI-driven analytics.

If you want to know more about AI-enhanced analytics, don’t hesitate to give us a call. Our team at Selerity is ready to help you optimise your SAS Analytics experience.

How can data analytics help asset management companies make better investment decisions?

Data analytics has transformed the way industries across the world function; it has introduced new avenues for predicting changes in the market, making better decisions, designing better work structures, and so much more.

Today, data analytics has become a staple in many industries and analytic techniques like machine learning, big data analytics and artificial intelligence have become common in the decision making process.

Asset management companies, in particular, are leveraging data analytics to help people make the best use of their investments and grow their finances over time. 

In the last two years, Australian asset management companies have invested $1.03 trillion on behalf of their clients despite the pandemic situation, and this figure is expected to grow in the coming years.

Data analytics has played a major role in this uptick in investments, and in this blog post, we explore how data analytics helps asset management companies make these important decisions.

Data analytics improves data acquisition

To make better investment decisions, asset management companies require large amounts of data, which is abundant nowadays thanks to technologies like IoT and the internet. 

That said, making sense of this large pool of data and leveraging that to make investment decisions can be a complex process with legacy analytics methods.

With data science and modern data analytics platforms, however, asset management companies can assimilate the data and use the insights gained about the market conditions to make informed investments.

AI and big data deliver better investment insights

Sometimes, asset managers make investment decisions solely on stock price fluctuations and their clients’ prospects, and these decisions may not always be very accurate.

By using the power of big data analytics and artificial intelligence, asset management companies can gain valuable insights by analysing unstructured and structured data.

Along with techniques like machine learning, AI can pick out useful information about investment markets and create summaries and steps that asset management companies could take when they make their investment decisions.

Data analytics improves client profiling

Client profiling is a vital part of any asset management company. 

Through client profiling, asset managers can get a clear understanding of the preferences, expectations, and requirements of their clients, allowing them to formulate investment strategies to meet these requirements.

With data analytics, asset managers can use data from different client segments to identify characteristics that make them different, making the client profiling process more accurate and efficient.

With good client profiling, an asset management company can make more specialised financial decisions for their clients.

It enhances risk management capabilities

Risk management is a crucial part of asset management. 

Failure to understand investment risks and execute strategies to mitigate them can lead to major financial repercussions for the company and its clients.

The usual approach to investment risk assessment is through identifying standard deviations in share prices by using legacy tools like spreadsheet. This approach, however, is not very accurate for understanding risks as it does not take into account every market variable.

With new data analytics tools, asset managers can create stress models for stock market performance and company operations, allowing them to test different scenarios that simulate various market conditions. 

With the help of these scenario-based analytics, asset managers can optimise their risk management processes and make stress-free decisions.

Improve asset management decisions with data analytics

Data analytics has enabled asset management companies to serve their customers better by making better and more effective investment decisions. 

Through data analytics, asset managers can get a better understanding of their client requirements and how market conditions can affect their investment decisions, helping them optimise their investments to meet client requirements.

If you are working in asset management and are looking to enhance your SAS data analytics experience, check out our Selerity Analytics Desktop. 

As SAS Managed Service providers, we are dedicated to helping you optimise and manage your SAS environment. Get in touch with us for more information.

Why SAS analytics for containers is a game-changer in data collection

The advent of container technology is revolutionising software development and data analytics because these software units allow you to run applications on different platforms, including desktops, physical servers and virtual servers. 

Containers present a consistent interface that allows developers to easily migrate software to different environments making the work easier for your IT teams and analysts. 

While container technology does benefit many development workflows, what does using SAS analytics for containers mean in data collection? 

Today, you can implement and manage your SAS analytics tools within containers, making it easier to manage critical business data in a unified platform. 

In this post, we explore how SAS for containers is transforming data management processes across the globe. 

Adoptable options for more than one software container versions

One of the primary advantages of using SAS Analytics for containers is the flexibility it offers to the users. 

SAS Analytics has the tools to help you deploy the SAS Viya platform on Docker and Kubernetes.

You can even get support for Docker deployment on SAS 9 with the SAS Recipe, a set of instructions that lets you access the traditional SAS software depot, extract the software components to perform a Docker image build, and create dedicated containers.

This approach enables the customisation of the operating system to the way you desire for building the Docker image and running the container. 

With an integrated platform, developers can access various tools they can deploy in different contexts. For data analysts, this means readily available predictions and insights delivered without bottlenecks. 

Analysts get self-access to data platform

In most traditional cases, data analysts rely on the expertise of the IT team to access the data hubs. 

With container-based data science platforms like Domino Data analysts, analysts have access to the SAS platform without assistance or approval from the IT team. 

Once the container image for SAS is deployed, anyone in your analytics team can access and use it to gain insights faster. 

Unlike the conventional SAS platforms, SAS for containers has a wide range of container images that include a combination of SAS and open-source software—analysts can easily leverage a range of tools necessary for data collection, examination and analysis.

You can easily upgrade to a new version of the software

Another benefit of using SAS Analytics for containers is that you don’t need to worry about new updates. If you want to upgrade to a new container, you can create a new image with the latest version and use it to build an updated container.

This feature gives you the chance to upgrade your software without disrupting other users, eliminating the need to wait for weekends or pausing your operations midweek to deploy new updates. 

Additionally, you can also test the new container and deploy it when it is stable and optimised. It is also possible to keep multiple container images for different versions; users can test their code against different versions for stability and efficiency before deployment.

Moreover, you can implement just the required SAS software, analytical models, and supporting code in the form of a small execution engine bundled into a lightweight container.  

Establish a high-end data collection process with SAS for containers

Data collection is the heart of business strategizing. 

That’s why optimising the data collection process using SAS analytics for containers guarantees an efficient data analytics pipeline, helping you establish simplified and streamlined business operations within your corporation. 

With our range of managed processes, we can help you access and establish a seamless and sophisticated SAS experience. 

If you would like to know more about how you can transform your corporation with better data collection, don’t hesitate to give us a call to learn more about utilising SAS analytics for containers. 

How is healthcare data analytics changing post-COVID-19?

With over 130,000 COVID-19 cases in Australia, the healthcare industry has been working diligently to find new ways to curb the spread of the disease and ensure better health for the population. 

As a result, the healthcare industry has become more reliant on data analytics than ever before.

While data analytics has always played some part in the healthcare industry, after the COVID-19 pandemic, the data analytics landscape for the healthcare industry has broadened, and new avenues for big data analytics have come to light.

The pandemic has resulted in a dynamic environment that keeps delivering new revelations related to the pandemic and a multitude of new healthcare options for keeping people safe.  Healthcare data analytics models have to change and adapt rapidly to keep up with this dynamic environment.

In this post, we explore how healthcare data analytics has changed post-COVID-19 and what this could mean for the future.

Availability of new data

The healthcare industry has become increasingly reliant on the use of IoT technologies such as wearable sensors and monitors that help keep track of COVID-19 patients and to monitor the health of individuals who are suspected of having the disease.

These devices collect and transmit an ocean of data, which—with the help of data analytics—healthcare professionals can use to gain insights that help identify areas of improvement in healthcare facilities 

For instance, with the help of advanced algorithms and artificial intelligence, medical professionals can have better insights into the logistics involved in deciding which patients need treatment more urgently and determining the most effective ways to treat them.

New collaborations and more data sharing

Businesses across industries quickly realised that the war against COVID-19 can’t be won by fighting alone. 

As a result, many industries formed alliances to find solutions to bring the effects of the pandemic under control. The health industry itself started working with organisations from different industries for this very reason.

For example, by partnering with a virtual drug discovery platform provider, healthcare professionals and institutes like Harvard Medical School were able to use data analytics to compare the efficacy of drugs against COVID-19 proteins, which helped find new treatment options.

With these collaborations, the healthcare industry is receiving large amounts of data, which can fill the gaps in their understanding of the current pandemic situation and future approaches to healthcare.

The pandemic had made it clear how critical collaborations are for the healthcare industry to leverage its data analytics capabilities.

Telehealth has become more dependent on big data analytics

Telehealth was offered as a convenient alternative to traditional healthcare systems, allowing people to connect with medical specialists remotely.

Today, telehealth has become a common standard due to social distancing laws. Even in a post-COVID scenario, telehealth is used by many people because of its convenience.

Due to this, there is an urgent need to improve the capabilities of telehealth platforms, and Big data analytics has become a crucial tool in this process.

Healthcare analytics systems use big data to analyse patient information for a more accurate diagnosis. 

Big data can also help improve communication between telehealth providers and patients, making telehealth more intuitive and user-friendly.

Data analytics in healthcare has changed forever

Data analytics once played a moderate role in healthcare, but post-COVID, it has evolved and opened new opportunities for improving treatments, diagnosis, and relationships with patients. 

If you work in the healthcare industry and are looking to leverage your data analytics capabilities, our Selerity analytics desktop is what you’re looking for. 

This is the ultimate platform for managing your SAS ecosystem and enhancing your SAS experience.

Get in touch with the Selerity team for more information.

Optimising data exploration and analysis with SAS data management solutions

Business data is a valuable resource for every contemporary company. 

This information holds insight into factors like patterns in customer behaviour, avenues for cost savings, and offers a chance to accelerate and optimise business progress. 

Today, 83% of organisations see data as an integral part of their business strategy. 69% of organisations, however, have noted that inaccurate data has reduced the quality of their work. 

Mitigating the risks posed by inaccurate data is the reason why companies need optimised data exploration and analysis that allows them to leverage reliable data for operational enhancement. 

When you operate with analytics software like SAS, you get more than just data analysis tools. It also helps you access and organise your data more strategically, which means this data can also be leveraged more effectively. 

Keep reading to find out how SAS data management solutions offer one of the best platforms for data exploration and analysis. 

Access data across multiple platforms

Storing data in one accessible and centralised location (the cloud or Hadoop) makes it easier to create more seamless operations. 

With SAS, you can access your data from wherever it is stored, without having to change the data location. This means that business analytics and data scientists will have access to more data across multiple sources, formats, and structures. 

This software allows you to integrate your work with other data flows, all while scheduling and monitoring the process using SAS technologies.

Owing to its integrated system, it also shortens the time taken to perform key processes. With database technologies, for example, you can analyse your data within the database itself. 

As a result of easy data access and monitoring, your authorised users can perform data exploration and analysis without relying on your IT team for data provisioning. With tools like the built-in business glossary, users will also have a more comprehensive understanding of the processes they handle—improving productivity and ensuring smooth operations across the board. 

Benefit from straightforward data governance 

When you have to move data from the location where it is stored to another for management purposes, it disconnects the sourced and managed data. This makes it difficult to govern your data, especially big data. 

When data movement is minimised, it’s easier to initiate data governance processes and policies. It will also let you maintain the quality, privacy, and security of your data without disruptions.

The integrated, end-to-end event designer in the SAS data management platform helps you build and edit data processes with ease. This will enhance your efficiency when it comes to the governance of metadata related to administrative and business operations. 

Easy data governance also lets you reuse data management techniques. Your company can deploy these flexible rules and maintain consistent standards for your data management.

Strategise data-driven plans with reliable insights

40% of business strategies fail due to inaccurate data. To lower this risk, it is essential to have accurate data and insights. 

SAS data management brings you a platform with built-in auditing tools to monitor and process data, ensure high-quality data, and maintain transparency. As a result, you can seamlessly extract reliable data that is ready for visualisation, analysis, and operational use. 

The integrated tools further optimise your analytics by cleansing your data, removing invalid data, and giving you reliable data for more accurate strategising. Features like SAS Visual Analytics let you explore data visually, find new patterns, and publish reports on both web and mobile devices. 

Rolling out a new business strategy needs careful planning that SAS solutions can support by making it easier to extract and explore data, and lower inaccuracies and inconsistencies so you can make swifter, data-driven decisions. 

SAS data management solutions enhance your operations  

Effective data exploration and analysis are vital for any business to gain a sustainable competitive edge. It also reduces security risks, increases productivity, improves responsiveness, lowers data loss and heightens cost-efficiency. 

By partnering with Selerity, you can access the right resources for an optimised experience with SAS Analytics tools. A SAS ecosystem that’s managed by Selerity will promote data quality and extract accurate, reliable insights so you can handle your data with ease on one platform. 

Don’t hesitate to contact our team to learn more about how you can enhance your data management through SAS data management solutions. 

Data analytics in urban planning: What’s the future of the industry?

In recent years, we have seen a significant increase in the rate of population growth across the globe.

While it took more than 123 years for the global population to reach two billion from one billion, it only took another 94 years to grow by another five billion people. Experts estimate that we may see the global population reach eight billion as soon as 2022.

In addition, over the last few decades, more people are also moving to urban areas. It’s estimated that by 2050, about 66% of the global population will be dwelling in urban areas. 

This mass migration to urban environments has put a major strain on urban infrastructure, including living arrangements, public transport, and other community facilities.

What this means is that today, the urban planning sector is facing significant challenges in building cities that can support the surge in population. Data analytics has emerged as a crucial tool for urban planners to build safer, cleaner, and more productive urban environments in this scenario.

In this post, we explore how data analytics is transforming the present and future of urban development.

The role of data analytics in modern urban planning

• Data analytics helps urban planners build more efficient cities

Over the past decade, people have become very dependent on the Internet of Things (IoT) devices—smartphones and other similar technologies have become a part of daily life.

This has given urban planners access to an ocean of data, which they can use to gain crucial insights into the usage of city establishments, public transport, and urban living arrangements, allowing them to build better infrastructure.

In addition, urban planners can leverage predictive analytics and artificial intelligence to run simulations that give an accurate representation of how proposed urban developments will affect the lives of millions of city dwellers.

For instance, by using predictive analytics, urban planners can create virtual models of transportation infrastructure and simulate traffic conditions to gain insights into how the new system will affect traffic conditions across the city, allowing them to design public transport systems that minimise traffic congestions and make travelling more convenient.

• Improves communication between urban dwellers and developers

An age-old problem urban planners have faced is understanding the issues faced by city dwellers.

Fortunately, with the influx of new technology and IoT, urban planners can engage with the public more effectively and use data analytics to get better insight into their needs and biggest issues.

Data analytics also allows urban planners to share their insights with city dwellers and encourage them to participate in designing efficient cities by giving ideas on how to improve certain aspects of urban living, like public transport and waste disposal.

• Improves resource management

The growth of a city or any urban area depends on how resources are managed.

Through big data analytics, urban planners can get accurate insights on how city resources are being used and use these insights to allocate resources to areas where they are most needed.

What data analytics holds for the future of urban planning

Today, many developers across the world are developing smart cities that can support the needs of millions of city dwellers while delivering significantly better gains in many areas such as law enforcement and transportation.

In fact, data analytics has already helped urban planners to convert 280 cities into smart cities, and more may come in the coming decade.

Data analytics will also be a major driving force in the development of carbon-neutral urban environments in the future, as many developed and developing countries are trialling plans to reduce the carbon footprint of cities.

Leveraging data analytics is the best way forward for urban planning

With the boom in urban population, effective urban planning is key to ensuring urban areas are safe and sustainable. Leveraging the capabilities of data analytics will be critical to the creation of effective city development plans.

If you’re looking to find new ways to leverage data analytics in urban planning, try out the Selerity analytics desktops to optimise your SAS experience. Get in touch with our team for more information.

How SAS for data analysis delivers useful insights faster

Accurate data is an important resource when it comes to doing business in the modern age. Having swift access to this data allows you to stay competitive in the industry. 

As a statistical software used for data analytics, SAS is one of the best platforms that can help you improve and evolve your business. With features like innovative analytics, data management tools, and business intelligence software, SAS is a platform that is trusted around the world.

Whether it’s navigating through challenges in the market or deploying new strategies that guarantee optimised results, SAS provides useful insights for faster and better decision-making. 

What are the benefits of using SAS? 

SAS Analytics comes with many tools such as dashboards, predictive analytics, and real-time analytics that let business owners explore and utilise data that is relevant to their businesses. 

With the help of SAS, companies can extract useful and accurate data and leverage information to improve their business strategically. SAS also allows you to gain insight into business operations by making it easier to analyse and comprehend big data. 

With detailed and accurate data, you can ensure better decision-making and better business performance overall. 

How can SAS provide faster insights?

With its many, integrated tools, automated analytical functions, and intuitive navigation interface—using SAS for data analysis has become a method of extracting useful information on a swift timeline. 

• Easy navigation 

As a platform utilised in business operations, the speed you gain from using SAS for data analysis is not only a result of its accurate functions. 

The easy navigation offered to users allows teams to solve complex issues that were too time-consuming or impossible to solve accurately before. Among the experts who use SAS for their business operations, it is also known for providing high-performance tools built for professional analytics. 

Combined with its user-friendly interface, these tools make it easier to navigate and operate, fast-tracking the process of gathering useful insights. 

The tech-driven analytics functions also perform faster and more accurately than traditional, manual processing. As a result, you will not only be saving time on data gathering but also cutting down on the time you would have spent rectifying errors with more manual processes. 

• Accurate analysis and comprehension of data 

Using the Visual Analytics feature offered by SAS, you can access insights into new data sources. Users can also create visual representations of data that make data analysis faster and easier to understand. 

On the other hand, SAS Contextual Analysis lets you identify emerging issues in the industry, buying patterns, and trends in the market in unstructured data without requiring prior knowledge of its contents.

The advanced analytics tools featured in SAS let you not only measure your success, but also identify the threats to your business.

With accurate information provided by prescriptive and descriptive analytics on your side, you can mitigate risks and initiate strategies that can help you overcome potential challenges.

• Swift automated text analysis

The automated processes introduced by SAS also support the delivery of faster insights. Processing data with the help of technology makes the data extraction and analysis process more efficient and less prone to errors. 

The automated text analysis feature can assess textual data that is collected from portals such as social media, customer calls, or comments—data that cannot otherwise be accurately assessed until the development of a manual taxonomy. 

This allows you to analyse the data, its characteristics, and the relationships within it faster and utilise this information to uncover relevant patterns. 

SAS for data analysis—faster insights and accurate results

Every business is trying to get ahead in the industry by using every tool and resource they have at their disposal. This means that you don’t just have to be accurate and efficient with your processes but also faster. 

Using SAS for data processing and analysis lets you have it all. 

What makes it quicker is not just the speed of delivery, but also the advantage of having accurate information that is easy to comprehend and makes your decision-making processes easier than ever. 

How can big data analytics help businesses overcome cybersecurity challenges?

Cybercrime has plagued the internet since its inception and over the years, cybercriminals have been becoming craftier, creating more complex cybersecurity threats.

Businesses are one of the main targets of cybercrime. In Australia, cyber attacks cost businesses approximately $29 billion every year and many are left crippled and unable to recover.

Fortunately, modern businesses have bolstered their defences against cyber attacks through cybersecurity infrastructures. Despite this, however, cyber threats are always evolving, becoming more aggressive and complicated, forcing businesses to keep updating their cybersecurity measures frequently.

While big data analytics have helped businesses improve many of their functions like sales, customer service and workload management, it can also help them improve their cybersecurity infrastructure.

Here, we’ll take a look at how big data analytics can help businesses overcome the challenges of cybersecurity.

Analysing historical data 

Historical data is a treasure trove of information for businesses. Just like finding patterns in sales and consumer buying patterns, businesses can use big data analytics to discover meaningful connections between past and present cyber attacks and find ways to make their cybersecurity stronger against these cybersecurity threats.

Big data analytics on historical data can also allow businesses to predict future threats. By analysing statistical information, businesses can identify patterns that deviate from the norm, helping them predict an imminent cyber attack.

Businesses can also pair risk assessment with quantitative data analysis to predict its susceptibility to potential cyber attacks, and using the insights they gain from this analysis, they can develop countermeasures against future attacks.

A great way for businesses to leverage their big data analytics is by incorporating machine learning; with machine learning, businesses can develop new responses to cyber attacks using the information collected and analysed from past attacks.

Monitoring work activities

Research has shown that many data breaches in businesses may have actually been carried out by an employee, though in most cases, cybercriminals may gain access into a company’s network by hacking an employee’s account and posing as their victims. 

Most of the time, an employee might not even be aware that their account was hacked.

With big data analytics, businesses can monitor workflows to detect any suspicious activity in their company network. For example, the business can analyse data based on employee movements during work hours and the data they accessed or changed during their work tasks to check for any usual behaviour, like opening confidential files without authorisation.

When suspicious logins and unusual activities on the company’s network are detected, the business can take action by identifying the threat and stopping it before it happens.

Improving intrusion detection 

Identifying possible weaknesses in a business cybersecurity infrastructure in real-time is very difficult; using big data analytics, businesses can automate this process and analyse logs, workflows and events to identify any usual activities and irregularities in data.

Cybersecurity breaches are becoming more complex and due to this, intrusion detection systems such as NIDS (Network Intrusion Detection Systems) have become more powerful and sophisticated in detecting cyber threats. 

Real-time big data analytics can be used to enhance these systems and give them a more comprehensive way to detect possible threats and put a stop to them before they gain access to a business network.

Identifying important incidences

Big data analytics can be used to analyse historical data and use the insights gained to improve a business cybersecurity infrastructure. The problem, however, is finding data on the relevant cybersecurity-related incidences.

Historical data, even that of a small business, can be vast, so finding the most important cybersecurity data from this sea of raw information can be tricky. 

With big data analytics, businesses can filter and categorise data so that it’s easier to identify important cybersecurity-related data from the past and their relationships with other relevant historical data.

Keep your business safe from cyber threats with big data analytics

Big data analytics has opened new avenues for businesses all over the world. From marketing to cybersecurity, the number of things businesses can improve with big data analytics is endless.

Keep your business ahead of the competition and safe from cyber threats with help from big data analytics.