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 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.
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.
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.
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.
Since it first became mainstream, advanced analytics has always been a gamechanger for businesses.
In the last few years, the analytics market has seen considerable growth as brands relied on advanced analytics to ensure the continuity of businesses amidst the fluctuating market changes by capitalising on new market opportunities.
Thanks to this, today, advanced analytics platforms are nothing new to the business world.
That said, with SAS software services, you get an analytics platform like no other.
With its integrated suite built for AI-powered analytics, data management and predictive analytics, it brings you a snapshot of the future changes in the market.
Analytics help you move quickly and decisively amidst these changes, adapting to the fluctuating trends during unpredictable times while navigating the competition simultaneously.
In this post, we take a deep dive into how advanced analytics powered by SAS software services can help you future proof your business.
With SAS software services, you can access data in any format, including SAS tables, Excel spreadsheets and database files, enabling analysts to manage and manipulate them to get the valuable insights that drive business decisions.
Once the data is prepared and organised, you can then employ AI-driven analytics tools to look at variables and trends relevant to your industry and predict the growth, drawbacks, or anomalies in them.
In addition, AI-enabled analytics offered by SAS solutions makes data-retrieval and pattern-identification faster than human operations.
As the insight generation picks up speed, it also accelerates the designing of insight-driven strategies. This way, analysts and business masterminds can come with targeted decisions faster.
Today, with analytics-enabled segmentation, you can manipulate data to identify more insightful customer information.
Going beyond the traditional demographic-based segmentation, now you also get segmentation based on data like consumer lifestyle, social values and relationships.
This information not only reveals current customer preferences but also delivers forecasts about trends in your target audience. The refined preference-based segmentation also lets you perceive your target audience better.
As a result, businesses can shape and tailor their customer experience by offering what they seek, including one-to-one personalisation. According to recent reports, using AI and advanced analytics to optimise the customer experience has enhanced shopping by 49% and spending by 34%.
What is more is that companies that optimise the customer experience through analytics outperform their competitors on key performance metrics, including profit, sales, sales or ROI.
Strategists and decision-makers often come up with business ideas and initiatives to expand the business but deciding if these ideas will work or bring the expected results is a challenging process.
This is where businesses can join human intellect with analytics empowered by artificial intelligence.
With advanced analytics tools such as SAS, you can enhance your risk sensing capabilities, which can help you evaluate the risk/reward ratio of ideas for new ventures.
Moreover, AI-driven analytics can even help you extrapolate data to formulate strategies to mitigate risks of these business opportunities, helping you capitalise on opportunities with a workable contingency plan to tackle potential risks.
SAS software services provide an integrated platform with easily accessible tools to predict future market trends, understand consumer behaviour and make decisions that allow you to reach your future goals.
At Selerity, our advanced analytics desktop can also improve your competitive advantage, maximise the ROI and enable the continuity of your organisation.
If you would like to know more, give us a call.
Our team is ready to help you optimise your SAS analytics experience, letting you control your software with the help of experts.
The energy industry is the driving force behind the modern economy. Every industry relies on the energy industry to function without interruption.
Today, energy consumption has reached an all-time high. According to industry reports, Australian businesses use up to $20.2 billion on electricity every year; this is the equivalent of using 154,439 gigawatt-hours of electricity. Even small businesses may use up to 36,000 kilowatt-hours of electricity per year.
In response to this increasing demand for energy, the energy sector is looking to develop new methods to optimise electricity usage as well as find potential alternatives for energy generation, and big data analytics is playing a critical role in this process.
Here are a few examples of the roles data science plays in the energy sector.
With the growing need for energy, it’s no surprise that some individuals and even businesses may turn to illicit means to get electricity.
In fact, energy theft has become a significant issue for energy companies in recent years. Each year, energy companies lose an average of $89.3 billion to energy theft.
Today, energy companies are using data science to prevent energy theft. Many companies use advanced metering infrastructures that report energy usage, which allow them to observe the flows of energy and identify irregularities.
By keeping an eye on the behaviour of users and comparing these with previous instances of energy theft, energy companies can detect potential bad actors trying to steal from energy grids and take necessary measures to prevent them.
Balancing supply and demand is one of the keys to effective energy management.
When it comes to energy, both high and low demand can lead to many issues, including increased expenses for both consumers and energy companies.
Energy companies, therefore, need an efficient demand response strategy to find the perfect harmony between supply and demand and data analytics solutions can help in this process.
With real-time management solutions and applications, energy companies can monitor the metrics of energy usage and adjust the energy supply to meet the demand.
Power outages are a common problem many businesses have to face.
Although power outages have become less common over the years, they can still happen due to several unexpected events and leave thousands of people without power and bring business operations to a halt.
For instance, the state of Texas in the USA suffered a major power outage recently due to inclement weather conditions that lasted for several days.
To tackle this, energy companies are now using data science and other forms of data analytics to enhance outage detection and prediction. Using these solutions, energy companies can gain insights on the effect of weather on power grids and potential outages in specific areas
Using this data, energy companies can predict outages by identifying the metrics and their threshold values and detect the cause of outages.
Once the causes have been identified, energy companies can take measures to keep their energy flow in check, and warn people of potential blackouts.
By the end of the day, energy companies are still companies, and they rely on their customers to turn a profit. For every energy company, the needs and requirements of its customers are a priority.
Energy providers can get valuable data on their customers regarding their behaviour and energy usage patterns, which can then be used to find meaningful relationships between power supply and customer demand and customise services and recommendations for their customers.
While data analytics have always been present in the energy sector, energy providers have come a long way from using static models and algorithms for data analytics.
With new real-time data analytics solutions, the data science applications for the energy sector are almost endless.
At Selerity, we offer SAS managed services that are designed to enrich your SAS data analytics experience by optimising and managing your SAS environment.
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.
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.
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.
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.
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.
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.
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?
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.
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;
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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 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.
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.
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.
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.
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.
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.
The fast pace of the market and the competition make business management unpredictable unless you have the right tools like data-driven business intelligence (BI).
By providing insights into market patterns, buyer behaviour, and other economic factors, business intelligence can help you make better decisions to improve business performance. BI tools allow you to explore large datasets and leverage them as a resource to gain useful insights.
By leveraging BI tools, you can enjoy improved efficiency, fraud identification, better product management, improved brand image and more.
While there are many BI tools in the market, SAS has always been a leader in data analytics. With AI-driven platforms that provide you with an extensive range of tools to enhance your data analytics capabilities, SAS can help you streamline your business processes.
Here are the reasons why you should choose SAS analytics business intelligence and data management for your brand.
While traditional data analytics tools can deliver quality insights, most often than not, they fail to deliver these insights to all parties in the decision-making process.
That said, with SAS, you can overcome this challenge and improve information access across your business functions.
One of the key features of the SAS business intelligence suite is the ability to easily integrate with MS Office tools like Excel and Outlook.
Through this integration, you can distribute information and exchange important insights with others involved in the decision-making process. Storyboard and narrative creation features available in the platform assist with presenting data to decision makers in an understandable manner.
In addition, all these tools access data through metadata representations, making it easier for everyone involved in decision making to receive quality insights and orderly create action plans.
Navigating a data management system and analytics tools is not always straightforward unless you are well-equipped with the knowledge of information technology. Most of the time, you will have to rely on IT pros when managing your data, making the whole process time-consuming.
With the SAS business intelligence platform, you have access to integrated tools that perform multiple functions like analytics and reporting, making it easier to navigate. This also allows you to access and manage data, make decisions and draw inferences without relying on IT professionals.
With visual data analytics, you will also have valuable data represented in graphs, charts, and other visuals, making information and insight gathering convenient and comprehensive.
Additionally, the Business Intelligence app gives you 24/7 access to business functions with devices such as smartphones—you can monitor your business from anywhere, anytime.
SAS analytics business intelligence and data management ensures accuracy and high precision in functions like predictive and descriptive modelling, forecasting, simulation, and experimental design.
As a result, you can leverage SAS to build an effective analytics strategy and formulate data-driven decisions to improve your marketing, accelerate your operations, or enhance the customer experience.
The focus on consistency and standardisation of data also allows you to avoid erroneous or false data that could lead to wrong decisions that can endanger your business.
Today, the business environment is more challenging than ever before. You need the right tools to survive and succeed in this landscape, and SAS Analytics helps you do that.
Here at Selerity, we are committed to providing you with a seamless SAS experience through our range of managed services.
Don’t hesitate to contact our team to learn more about SAS analytics business intelligence and data management.