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.
Ever since the dot.com revolution, businesses have been exposed to more data than ever before. Today, modern businesses collect more data in a month than they did in the entirety of the 2000s.
That said, having access to vast amounts of data will become meaningless if a business doesn’t utilise it to create meaningful insights and make decisions that enhance its business functions. The business intelligence function helps businesses do just that.
Business intelligence spans technologies, processes, procedures that collect, integrate, analyse, and present data generated by businesses and their customers. Unlike traditional data analytics tools, BI tools present insights in a more coherent manner.
With the right BI tools, businesses can leverage detailed insights that can help them make decisions regarding sales, marketing, product development, customer service and more.
Although traditional BI tools are positioned to provide insights on historical interactions or current happenings, with the integration of predictive analytics—which leverages historical data, statistical algorithms, and machine learning—BI solutions can give businesses a peek into the future. They do this by identifying opportunities and allowing businesses to be agile and proactive toward future developments.
In this post, let’s explore the role of predictive analytics in business intelligence, and how businesses can leverage it to optimise their operations.
Optimising service delivery is one of the primary applications of predictive analytics in business intelligence. Businesses can create a better customer experience by studying past behaviours and preferences, and customising their service offerings to better suit the particular needs of each customer.
eCommerce websites like Amazon and eBay recommend products customers are likely to buy based on their past purchases and current searching behaviour. Netflix uses a similar approach to recommend new movies and TV shows to their subscribers based on their watchlist.
In short, businesses can improve their customer experience by leveraging predictive analytics.
As long as businesses have existed, fraud has coexisted. This has led to significant financial losses to many businesses across the globe. A recent study revealed that, in 2019, fraud cost the global economy over $5 trillion—a figure that is expected to grow with the boom in digital interactions.
That said, not all industries are affected equally by business fraud—some industries are inherently more at risk than others. The insurance industry, for example, loses $80 billion annually, and in the UK, banks lost $620 million due to fraud in 2019.
Businesses need a robust solution to fight fraud, and business intelligence combined with predictive analytics could be that solution.
Unlike traditional fraud prevention methods, which rely on reactive measures to limit the damage caused by fraudulent practices, predictive analytics in business intelligence helps businesses identify potential fraud and proactively police their service delivery channels to prevent these transactions.
Today, businesses have a wealth of information about their customers’ purchasing behaviour and preferences. With this information, predictive analytics can deduce the probability of a customer buying a product, which can, in turn, help businesses focus their marketing efforts on customers with a higher likelihood of purchasing their products.
Take ads on YouTube, for example. If a user’s watch history suggests that they are interested in learning about digital security, they will see at least one advertisement that markets VPN services the next time they are on YouTube. This is enabled by predictive analytics algorithms identifying the user as a potential customer for VPN services.
Predictive analytics can also help businesses keep the news cycle going during off-season months. Smartphone manufacturers, for example, identify months in which phone sales can slump due to loss of press, and release minor refreshes, new colours, or software updates to the existing models to keep the headlines talking about their products. Think of Apple releasing new colours halfway through the lifecycle of their latest iPhone model.
The cutthroat nature of the modern business landscape requires businesses to be on their toes to stay ahead of the competition. With business intelligence tools powered by predictive analytics, you can always stay one step ahead of your rivals.
Modern businesses collect astronomical amounts of data every single day. According to certain studies, businesses now collect more data in a single month than they did across the entire 2000s.
Data collection at this level is not unwarranted either; businesses, today, rely on this data to make critical managerial decisions and improve the services and products they supply to the market, among other objectives.
To make sense of the collected data, modern organisations use a plethora of data analytics tools, but in most cases, these tools are incredibly complex and not too user-friendly. Certain solutions prove to be an exception, however; Microsoft’s Power BI tool, in particular, simplifies data analytics while retaining advanced analytics capabilities.
Due to its ease of use and advanced analytics capabilities, the analytics platform is gaining popularity, and data scientists are beginning to incorporate it into their workflow.
In this post, we explore how Power BI simplifies data analytics and how data scientists can leverage its capabilities to achieve better analytics performance.
Although many analytics platforms can produce analytics insights using data lakes, these are often not presented in an understandable format due to a lack of integration between the analytics engine and visualisation tools.
Microsoft’s Power BI predictive analytics platform, on the other hand, has visualisation tools integrated at the analytics engine level, meaning that the platform can present insights that are understandable even to non-data-savvy professionals.
The visualisation tools built into the platform are also customisable, which is helping data analysts present data their own way.
In addition to inbuilt visualisation tools, the Power BI community makes a wide range of advanced data visualisation templates available on a near-constant basis, including heat maps, decision trees and correlation plots, allowing data scientists to present hyper-specialised and nuanced business intelligence insights.
Moreover, Power BI also incorporates natural language searches, which means decision-makers can use the platform to get the information they need using English phrases—there’s no need to learn syntax.
Although the core principle of the Power BI predictive analytics platforms is simplicity, they also cater to advanced data analytics requirements.
One of the more advanced analytics-oriented features available is the integration of R—an open-source programming language primarily used in data mining and statistical applications.
R currently has over 7,000 plugins and scripts that enable advanced data manipulation, machine learning, data modelling and visual analytics. With engine-level integration, the BI platform allows data scientists to incorporate R language visualisations and insights directly into a standard insights dashboard.
Power BI’s in-built capabilities already allow for advanced techniques such as data slicing and hierarchical analysis, but with R script integration, data scientists can produce more advanced predictive models using machine learning and data smoothing.
One of the main hurdles of traditional data analytics tools is that they are usually tied down to one source of data by default. Businesses, however, might use several sources of data including Microsoft Azure, Google Analytics, OneDrive and SalesForce.
What this means for data scientists is that they have to manually connect these different data sources to the analytics environment, or migrate all the data to the source supported by the analytics platform, which can take a significant amount of time.
Power BI predictive analytics, on the other hand, supports all the different data sources mentioned above and many more, natively, and can load data up from these sources in a shorter time.
The modern, data-reliant business environment calls for advanced analytics methods to help us make better decisions. The analytics tools available, however, can be too complex for us to leverage their series of capabilities to their full potential.
By integrating Power BI into your analytics environment, you can produce hyper-specialised and easy-to-understand insights to drive better decision-making today.
What would your answer be if someone asked you what the most valuable asset to modern organisations is?
Well, the results are out. According to CEOs, data is the most critical and valuable asset to any business, regardless of whether it is a multibillion-dollar tech company or a small family-owned bakery.
Modern businesses are largely dependent on data to make the right decisions. Data-backed decision-making makes modern businesses more efficient than businesses of the past, whose choices relied on nothing more than unscientific instinct and gut feeling.
Any team that doesn’t leverage the power of data analytics, today, is in danger of losing its edge in the market.
Fortunately, there are several tools available for modern businesses to take advantage of the rich reserves of data that are often readily available.
SAS solutions, in particular, help businesses access actionable insights with advanced analytics tools, which are influential in making business decisions that improve organisational performance over time.
SAS solutions are data analytics platforms that use AI and machine learning to collect and analyse vast amounts of data, giving you the insight you need to make critical decisions.
SAS business solutions cater to specific industries, such as education, healthcare, and technology, making it easier to resolve issues and devise innovative solutions that improve revenue and profitability.
Demand and supply are two of the most volatile economic forces that any business has to grapple with. Mishandling these forces can lead to lasting impacts on organisational performance.
Restaurants, for example, have to predict demand on a daily basis to be able to react to changes in demand caused by various factors like seasonality, consumer preferences and unexpected events like the current pandemic.
Unfortunately, more than 50% of restaurants do not handle these shocks in demand efficiently and go under fairly quickly.
With predictive analytics tools like SAS, restaurants can predict potential disruptions in demand and be prepared to handle these disruptions, improving their performance.
Data analytics also help businesses handle disturbances in supply as well.
The global semiconductor shortage, for example, forced many companies across various industries to cut down on expected production output. Certain companies, however, were able to foresee the chip shortage in advance thanks to data analytics tools like SAS and prepare more resiliently.
The majority of modern businesses diversify their returns by investing in various investment opportunities and assets. By diversifying returns, it’s easier to mitigate financial risks, which can lead to bankruptcy if they’re not managed.
Before the advent of data analytics, however, business owners relied on their knowledge of market conditions to make investment decisions. Modern analytics tools, however, help businesses choose the most appropriate investment avenues by providing insights on critical factors like the level of risk and return on investment.
These types of data-backed investment decisions ensure the financial agility of modern businesses, allowing them to improve their financial performance in a very flexible manner.
Most modern organisations offer personalised services to their clients and customers, creating deep, long-lasting relationships.
Did you know, however, that offering personalised services is made easier, efficient and more effective with data analytics?
While it’s true that small businesses offer a certain level of personalisation without the use of data analytics, larger organisations need these insights, through tools like SAS, to create customer profiles, which are critical in creating a personalised customer experience as they contain information like customer preferences.
Using these insights, business teams are able to provide a level of service that is tailored to specific interests and priorities, providing customers and clients with an exceptional buyer experience.
Data analytics has become a buzzword for organisations looking to improve their performance and increase their edge in the market.
With petabytes of data readily available for businesses to use, data analytics tools like SAS are helping organisations improve their performance across the board.
Find out how you can use these solutions and insights to change how you do business today.
The business intelligence applications industry is growing each year with research reports from organisations like Forex Tribune projecting significant growth. There are several reasons for this growth, the proliferation of big data, the expansion of IoT and the need to better understand the collected information are all contributing factors in the growth of BI. However, despite the high value of its contributions, BI can still come with a few improvements. Improvements made thanks to Artificial Intelligence (AI). Hence, I will explain what BI is, how AI improves technology and what it means to organisations.
Business Intelligence – What does it entail?
For those not in the know, business intelligence (BI) refers to systems designed to streamline the collection, processing and analysis of big data. Organisations can make sense of the large volumes of data they collect to sharpen their business processes and make smart decisions. As useful as BI is, there are some shortcomings that hamper the value it contributes to businesses. For example, there is a limit to BI capacity and the sheer volume of data collected is pushing that limit. BI will benefit tremendously from the application of AI because the latter augments the former.
How AI boosts business intelligence applications
AI expands BI functionality
Artificial intelligence boosts the functionality of business intelligence applications. With AI, business intelligence is better placed to breakdown large volumes of big data into granular insights, allowing organisations to better understand the value of smaller components into a larger picture.
Then, there is the issue of real-time insights, given that in their current iteration, BI can process and visualise big data, but cannot predict trends or generate real-time insights. However, AI incorporates the latest technology, like machine learning to deliver real-time insights about trends that will take place in the future. Thus, expanding the functionality of business intelligence applications and improving its value to organisations.
Close the gap
AI-enabled BI allows businesses to develop critical insights into data not examined before. Business intelligence applications powered by AI can examine fresh data and identify any trends relevant to the organisation. AI also allows BI to utilise the latest technology, like predictive analytics, machine learning and natural language processing to expand insights presented. Organisations are no longer satisfied with a visual dashboard of big data trends, they need tools that can close the gap between visual representation and actionable insights. AI-powered BI can help close this gap.
Simplifying a complex process
Surveying big data is often a complex process even with business intelligence applications. Professional data analysts have to survey 100s of charts and dashboards to get the insights needed. However, AI technology can simplify the process. The simplification happens because AI-based technologies, like NLP and machine learning, are closing the gap between machine and human communication. AI technologies allow machines to better understand human language and vice versa, making it easier for data analysts to find connections and insight. AI-powered BI allows organisations to tackle a much larger variety of data, including structured and unstructured data to get more detailed, comprehensive insights.
Solve problems related to talent storage
Business intelligence presents data findings on a visual dashboard, however, with data coming in from multiple sources, it becomes harder for dashboards to present the data in an easy to read format. However, with AI, the information can be defined at scale, making it easier to gain actionable insights. There is also the issue of talent. As of 2019, there is a severe shortage of data analysts. The right processing software can help alleviate some of the problems from a talent shortage by performing some of the functions normally delegated to a data analyst.
AI is the future of business intelligence applications
Business intelligence applications have been a tremendous asset to organisations, allowing them to better understand big data. However, times change and the needs of organisations evolve. They not only need intelligence software that can process and present data into visual findings, but they also require software that can predict trends, anticipate actionable insights in real-time and process a variety of data. This is where AI comes in since artificial intelligence allows organisations to breakdown big data into granular levels, so it is easier for organisations to make smarter decisions.
In-memory processing represents the next step in data analytics and BI. With companies processing terabytes of data, it is important to invest in technology that can process large data sets, quickly. Sadly, traditional disk-based processing is no longer up to the task. While processing large data sets is manageable, the process is slow, inefficient and it prevents companies from taking full advantage of data analytics. Fortunately, there is an alternative in the form of in-memory processing – an evolving technology that is rapidly gaining traction.
What is in-memory processing?
In-memory processing is the processing of data using RAM or flash memory. It is an emerging technology that is replacing disk-based processing because it is better suited to the demands of BI and data analytics. Memory-based processing is said to increase data access speeds by 10,000 to 1,000,000 times over disk-based processing, making it better suited to the demands of analytics.
How is it different from database processing?
Disk-based processing refers to relational database management systems structured by query languages like SQL. Programmers must first load the data from the disk (usually done with several tablets and structures) before processing the data for business needs. It slows down the rate of data processing because loading large volumes of data from a disk creates bottlenecks and hampers performance. By contrast, in-memory processing loads data onto a RAM or flash memory minimising bottlenecks and increasing the rate data is processed.
Relational databases based on SQL are optimised for row-based databases, while in-memory relies on column-centric databases. Row-based databases arrange data in a single row, where different variables – for example, first name 1, last name 1, first name 2 and last name 2.
However, in-memory processing relies on column-centric databases or columnar storage. With this method of data storage, similar variables are grouped in the same category, for example, first name 1, first name 2, last name 1, last name 2. Row-based storage is best suited for transactional processing but is unsuitable for the demands of BI, which requires only partial processing but deeper, more complex calculations.
By contrast, column-centric storge is more suited to the demands of BI, making in-memory processing more suitable for BI and analytics.
What are the advantages of in-memory processing?
The biggest advantage of in-memory processing is speed. Working from RAM or flash memory removes many of the bottlenecks found in disk-based processing. Thus, businesses are able to analyse large datasets in real-time, which generates better insights from data analytics. Along with better processing speed comes higher storage capacity and better transfer speed. These advantages are possible because data can be stored in in-memory databases, while several processing units (computers) work together to deliver different clusters of data.
Big data comes in two formats, structured and structured. In the past, businesses have struggled to store unstructured data like images and videos in conventional databases. However, with in-memory data processing, this is no longer an issue because it is easier to store both structured and unstructured data. Thus, it is easier to get richer and deeper insights from data analytics.
Are there any drawbacks?
Despite the obvious benefits of in-memory data processing, we would be remiss not to mention some of the drawbacks of this method.
The main disadvantage of in-memory is its reliance on computer systems. If something were to happen to a computer, especially to the RAM or flash memory, then data is compromised. Hence, information is not as secure in-memory compared to on disk. The other disadvantage is cost – memory-based systems are incredibly expensive compared to their disk-based counterparts.
As such, the technology is only feasible for large corporations with massive data warehouses. However, I suspect that the price structures will change in the long-run because of technological advancements.
In-memory processing allows companies to process big data at a faster rate, and deliver insights in real-time, something that is not possible with disk-based processing. The new technology uses RAM or flash memory to process data, significantly increasing processing speeds and removing bottlenecks found in older data processing methods. The faster results are possible due to new, and innovative technologies like columnar storage.
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Machine learning (ML) refers to a software application that learns processes from past data much like how a human would learn. ML integrates into and augments business intelligence (BI), an expansive software that collects and analyses business data to deliver better insight into company operations. BI is vast, comprising several applications like predictive analytics, data mining and performance management systems. Hence, integrating ML improves the function and capability of BI.
Machine learning expands BI capabilities
Reduces damage and injury
Certain industries like the oil industry depend on several factors, like worker safety and weather for continuing operations. If a worker gets injured, or a machine fails, it disrupts productivity. BI utilises analytics and predictive modelling to monitor machine operations in real time. Machine learning learns from past data to discover the cause of worker injuries and critical failures in machines. Using the data, ML then “predicts” the chances of a worker getting injured or a machine reaching a critical level before it happens.
Thus, with BI and ML, oil companies not only collect and analyse data but also take pre-emptive action to prevent a disaster before it happens. Therefore, BI can increase productivity, reduce worker injury rates and improve the lifespan of equipment using ML. It’s not just the oil industry that will benefit – any industry where workers are at risk reduce the chances of injury through ML and BI.
Machine learning receives a lot of attention because it boosts productivity significantly. BI collects and analyses data from several processes, but ML can streamline and automate several processes. The automation process takes place through intelligent automation, where systems can survey thousands of operations in a single day and flag exceptions. Human agents examine the flagged cases.
As a result of this, companies make better use of their human capital. Instead of having human agents examine thousands of processes, they only look at the most critical cases, which are beyond automated systems. A process using both automation and human intuition is useful in specific instances like fraud detection.
ML can also streamline processes like customer service, risk management, business capabilities management and more. The combined appeal of automation and streamlining means ML can boost productivity by a significant amount.
Boost sales and marketing
Businesses are using BI to gain deeper insights into customer purchasing habits. With machine learning, companies will know audience reactions to new products or marketing campaigns. BI collects information on customers from different sources like browser searches, purchases and much more. ML leverages this information, analyses the trends and predicts customer reactions.
Companies use technology to discover how their audience will take to new products or campaigns before either launch. With this capability, businesses increase their chances of success while also sidestepping any problems that damage the brand.
Businesses are now working in a knowledge economy, which means research is important for success. BI and machine learning tools can improve research processes through a BI-search platform. Search platforms based on BI and ML are more responsive to consumers, providing suggestions that are change based on the questions asked. The search platform responds to the needs of the user and not the other way around. Thus, users can get more concise answers in less time with these new search platforms.
Forecasting has evolved over the years from excel sheets to predictive modelling but will evolve even further with BI and machine learning. Forecasting is a huge part of improving productivity from predicting sales to optimising supply chains. Machine learning improves the process by taking terabytes of data and using it to predict trends. In the future, forecasting will be so sophisticated that algorithms will answer specific questions rather than generate models.
Find real-time anomalies
BI systems enable businesses to find anomalies in real-time, but ML builds and improves on this system. Fine-tuning of this system is crucial because it allows firms to sharpen specific processes like fraud detection. Finding real-time anomalies opens up several opportunities for businesses not seen previously. One possibility is the option is to see someone browsing your website in real-time instead of just knowing about the people who bought from you. It will give you better insight into what you have been doing wrong and reveal the best way to increase the conversion rate.
Machine learning can improve the functionality of BI because the software collects and analyse terabytes of data to predict future trends. Anticipating what will happen before it happens is one of the best investments a business can make and it can only be obtained through machine learning.
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