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.
Gambling is one of the oldest pastimes in human history, with first accounts of the activity dating back to 500 BC. It is also very popular—statistics show that at least 26% of the world’s population is involved in some form of gambling each year.
The casino industry, which includes all gambling activities, is also one of the fastest-growing industries. Even the pandemic didn’t slow down the growth—the casino industry grew at a faster rate during the last 18 months. In fact, according to Statista, the gambling market reached an all-time high of $227 billion in 2020.
The success and the spectacular growth of the industry are both because casinos always look for new and innovative ways to attract more customers, and keep the existing players hooked to the casino floor.
The industry was quick to identify the potential of internet-based casinos and splurged billions of dollars into creating and marketing digital casinos to hook a new wave of players on board. Now with a wealth of data available from on-site and digital casinos, the house can further optimise its games, security, and marketing campaigns with the power of advanced analytics.
In this post, let’s look at how the casino industry is using advanced analytics to revolutionise how they operate.
Casino fanatics say that the house always wins, but this hasn’t stopped passionate and desperate players from using all means possible to win against the house, and some of these methods—like hand mucking, past posting, and card counting—aren’t legal.
Cheating methods like these have always been a bugbear of the industry; casinos invest millions of dollars into identifying and preventing cheating in their games, but these efforts have not been fail-proof.
With advanced analytics, however, casinos can analyse thousands of players playing online and on-site, and spot anomalies in their playing patterns, winnings, and general behaviour. This can help them implement more robust anti-cheat mechanisms in their games.
Casinos can also educate their on-site floor attendants on using insights produced by analytics algorithms to detect cheating methods.
To keep raking in dollars, casinos need to attract new players to their games, and the best way to do that is by optimising the odds in favour of the gamblers. While optimising their games, however, casinos should be careful not to stack the odds too much in favour of the gamblers. Otherwise, they will lose money on every game.
While game optimisation can be done by studying player behaviour and adjusting game mechanics, it takes a considerable amount of time and human resources to do that.
That’s where analytics software can help.
Advanced analytics tools can collect and analyse vast amounts of data in a fraction of the time and produce actionable insights that help playmakers balance the odds to lure more players without sacrificing profit.
Marketing, for any business, is one of the most critical tools towards success, and the casino industry is no exception. An insightful and attractive marketing campaign reaches its intended audience and gives them information about the latest incentives or the newest games available to them.
Data analytics can help casinos identify their target audience for their online or on-site games and help create effective marketing campaigns by identifying customer preferences and needs, which marketers can then use to tailor the marketing message.
Moreover, online casinos can use data analytics to gather information about user experience, interface intuitiveness, and click-through rates and redesign their website and UI elements to make them more appealing to their target audience.
Gambling is a game of numbers. Both casinos and gamblers stand to lose everything or win a fortune simply by considering their odds carefully.
With the face of the casino industry changing by the day, casinos need to look for more innovative ways to turn the odds in their favour, streamline their operations, attract more players, and stay profitable.
Data analytics can help them do that!
AI and advanced analytics can improve public services. At the moment, public service has problems, like long queues and delays in service provision that could stand to be a bit more efficient. However, despite the inefficiencies, there is also a wealth of opportunity, thanks to data analytics. Most government and non-profit organisations have access to large data bodies that could help improve the rate and quality of public service, provided the data is analysed properly.
This is where AI and advanced analytics can become handy. With proper application, data analysts can help transform public service to make it more efficient and responsive.
At its core, advanced analytics refers to a platform that can breakdown data with granular precision when analysing a diverse dataset. This is invaluable considering that most public service organisations have access to a rich, diverse dataset that could transform service delivery and decision-making.
For example, using advanced analytics, government health organisations can draw parallels between the chances of heart disease occurring with the language used in Twitter posts. Analysis on this level could transform the way organisations make policy decisions, allowing them to be more proactive in their decision-making.
There is also the question of AI. One of the reasons why public service tends to lag behind its private counterparts, in terms of operational efficiency, is because of menial labour tasks. So many menial tasks are still in the hands of employees, which holds up the rate of service delivery.
However, thanks to AI, these menial tasks can now be conducted by machines at a faster rate. This will help improve the rate of service in public organisations because employees no longer have to waste a significant portion of their time performing trivial tasks. While the AI handles more monotonous tasks, public servants can do more advanced work at the same time, improving productivity and service delivery
Furthermore, there is also the degree of accuracy to consider. It is easy for government employees to make mistakes, considering their workload. Advanced analytics and AI can help improve accuracy by a significant margin. Better accuracy means fewer reworks required, which means more productivity (which means fewer headaches for all involved!). Through proper use of advanced analytics and AI, we have the opportunity to improve the quality and efficiency of public service.
While there is no denying the benefits of AI and advanced analytics in public services, it does entail a degree of forethought and preparation.
This is because advanced analytics and AI is not as simple as installing software and running it. It needs to be installed and administered correctly by the right team. Even after systems are installed and working, there needs to be a workforce skilled enough to take advantage of the data analytics systems.
Organisations need a force of IT personnel that can extract data from the analytics platforms and generate useful insights from it. Even beyond the core analytics personnel, managers (or anyone in the middle management level) need to be skilled enough to identify opportunities presented by data analytics platforms.
Furthermore, public servants will need to adapt to working with machine-assisted, decision-making algorithms, which not only leads to a technical shift in how the organisation works but also a cultural one. This is because everyone’s role within the organisation will shift, along with their skillset. In some cases, there might even be a question of whether they need to build in-house analytics expertise or work with a consultant, which could have further implications, in terms of contractual obligations.
To improve public service, we need to explore all potential avenues, including the use of advanced analytics and AI. Advanced data analytics can help organisations move more efficiently than before. Furthermore, AI can help augment the rate of productivity because of several factors, like the ability to automate several functions and even eliminate human error from certain processes.
Given all these factors, advanced analytics and AI can help organisations by a significant margin. However, to make full use of these analytics platforms requires a complete shift in the way the organisation works, from both a technical and cultural perspective.
In 2013, estimates suggested that there were about 153 exabytes of healthcare data generated that year. (For reference one exabyte is one quintillion bytes!) However, projections for 2020 indicate that there could be as much as 2,314 exabytes of new data generated. Given the vast volume of data, advanced analytics is needed to process it and generate value.
Advanced analytics is breathing life into pharma company strategies. The tools can integrate and process millions of records on structured and unstructured data, including physician notes, clinical trial data, medical transcripts, claims, patient records, and even social media posts. The deep and often unique insights generated allow pharma companies to invest wisely into new drugs and develop more precise and differentiated sales force strategies in distribution, market opportunities per therapy area and innovative value generation.
To delve deeper into the true plus points of entwining advanced analytics into pharma companies operations, let us explore the many benefits on offer.
Advanced analytics can accelerate the drug discovery process. With patents for red carpet drugs nearing expiration and the cost of bringing a new drug to the market pushing $5 billion, accelerating drug discovery and development can be beneficial down the line. The ability to intelligently search vast datasets of patents, scientific publications, and clinical trial data should accelerate the discovery of new drugs by enabling researchers to examine previous test results.
Applying predictive analytics to the search parameters should help them hone in on relevant information and also get insight into avenues that can yield the best results.
According to a Chief Data Officer, advanced analytics can deliver at least 10 per cent net impact from a “top-and-bottom-line perspective”. The pharma company’s pricing strategy and messaging can all be refined based on past transactions, behaviour, future trends and patent needs of different players in the buyer ecosystem. Most pharma companies are already using predictive and prescriptive modelling (advanced analytics) to forecast either revenue or customer lifetime value.
This, in turn, will enhance the relevance and impact of pharma commercial strategies and improve revenue generation.
Through the power of advanced analytics and the plethora of software available, pharma companies can leverage the benefits of customer segmentation and targeting algorithms to identify physicians serving patients that meet the company’s relevant patient profile. Analytics can also identify physicians’ preference for specific channels and communication frequency. With significantly reduced physician access, these inputs can help design interactions that are tailored, on-point and convenient – improving physician satisfaction and loyalty toward the brand (increasing demand and reach).
Advanced analytics can deliver unique insights into a specific drug’s effectiveness. This insight allows them to identify drugs that do not trigger chronic illness medication, allowing pharma companies to market the drug to the relevant specialists.
Furthermore, patients will respond differently to treatment for different reasons. Combing through the data with advanced analytics allows drug companies to spot trends and patterns that allow them to come up with more targeted medication for patients that share common features. This allows pharma companies to curate and effectively market the relevant drugs in the right state and to the right demographic.
Clinical trials are costly and time-consuming to run, pharmaceutical companies want to ensure they have the right mix of patients for a given trial. Advanced analytics can assist in identifying the appropriate patients to participate in a trial through remote patient monitoring, reviewing previous clinical trial events and analysing demographic/historical data to identify potential side effects before they become a reality.
Global management consultancy firm, McKinsey, says that patients’ big data could also help pharmaceutical companies take into account more factors, such as genetic information to help companies identify niche patient populations, streamline procedures and reduce the cost of trialing new medication.
Back in the days of yore, there was no plausible alternate reality where the large amount of data churned out by the healthcare industry could be collected and analysed in real-time. With the advent of advanced analytics in healthcare, we see technology pushing the envelope on what we can truly achieve in this space.
Advanced analytics platforms help pharma companies leverage insights from machine learning, AI, advanced forecasting and optimisation to gain a competitive edge and secure success, bringing tremendous benefits to the pharma industry and all those who collaborate with it.
Curious to know how advanced analytics can benefit other industries? Visit our website for more information!
One of the most exciting aspects of data analytics is its constantly evolving nature. What started off as analytics systems that could only analyse data to describe the current situation has evolved into advanced data platforms that can process petabytes of data to predict future trends. The proliferation of advanced analytics is one of the more exciting developments in this industry.
But, what is advanced analytics? What is its business value? That is what we will be explaining in this article.
As the name implies, advanced analytics can do far more than what the standard analytics software can do. Advanced data analytics refers to systems that go beyond the capabilities of standard BI and analytics. These systems enable data analysts to go deeper into datasets using machine learning, pattern matching, sentiment analysis and cluster analysis, just to name a few techniques, which cannot be done on earlier versions of the software.
Advanced analytics functions are different from conventional analytics systems. It incorporates classic approaches to analysing data with newer, more advanced machine-driven techniques, like deep learning. Data mining is a key differentiator that separates standard analytics and BI from more advanced solutions like machine learning, neural networks and data visualisations. These advanced methods find the patterns and correlations in big datasets, setting the groundwork for deeper analysis.
Advanced analytics holds a huge advantage over standard data analysis software because it leads to better, more reliable answers. These data analytics platforms can mine data at a deeper level than what standard BI can manage. While some solutions, like self-service BI, hold a lot of value for certain business functions, they cannot compare to the level of analysis provided by advanced analytics. These analytics systems feature quality-tested algorithms that are always updated to analyse data to reflect current and future trends. Capabilities that allow data analytics software to create a more accurate picture than before.
In addition to a deeper, more comprehensive level of analysis, there are also other benefits. Some of these benefits include creating superior data models and simplifying data preparation.
Furthermore, the right analytics software simplifies analysis. When using advanced analytics platforms, like SAS advanced analytics, data experts don’t need several software platforms to complete different types of analysis, like categorical data analysis and psychometric analysis.
As you can imagine, this makes it much easier for any data analysts to use the system.
There is no denying that advanced analytics offers several technical advantages, but how can businesses translate these technical benefits into systems that generate value?
Corporations in different industries can use advanced analytics to conduct more sophisticated levels of analysis. For example, in marketing, instead of examining what is popular with customers at the moment, businesses can take it one step further to decipher how consumer preferences evolve, which can be used to refine marketing campaigns. Another example is manufacturing, where businesses can use analytics to create self-maintenance systems that reduce wear and tear. This is possible because traditional BI systems analyse data to examine historical and current trends, but advanced analytics examine data to predict future trends.
The supply chain can be easily automated to improve operational efficiency. While some decisions will always be in the hands of humans, advanced analytics can be trusted to make decisions autonomously without any human intervention. Certain parts of the production process, like inventory checking, can be handled by systems developed using advanced analytics platforms. Other functions like monitoring, data gathering and forecasting can be executed using platforms built from advanced analytics. Due to their architecture, this system can function with greater efficiency than their human counterparts, giving organisations the chance to make significant gains in productivity and efficiency.
As data analytics becomes more prominent, we begin to embark on an exciting era where advanced analytics powered by machine learning and AI becomes the norm. When that happens, we are going to see organisations make significant gains in productivity and efficiency. This is because analytics systems can do so much more than before, they provide deeper insights into data that allow organisations to be more proactive in their operations, making it easier to cut costs, save time and create exciting new technologies.
Advanced analytics is changing the investment industry. We associate the multibillion-dollar industry with formal suits, complex rules and the 2008 recession. However, data analytics is changing the way the industry does its business because of its capacity to process billions of transactions in real-time. The sheer capability of big data opens new avenues for companies specialising in financial investment. In this blog post, I will explain what analytics does for the investment industry and what it means for financial investments.
What advanced analytics allows investors to do?
Advanced analytics brings several benefits to the investment industry.
Improve the research process
The research process is faster and more comprehensive than before thanks to advanced analytics, like NLP and machine learning. Analytics software analyses big data and flags points of concerns for asset managers to look at. The software can analyse large volumes of complex structured data in minutes. For example, analytics can analyse public filings to identify changes in sentiments and processing petabytes of data in short time. Furthermore, analytics opens up new sources of research that would not have been possible. For example, asset managers can study social media data to optimise investment and trading decisions. Data analytics software allows for a better, more comprehensive research process.
Debiasing investment decision
Bias has long been a problem in the investment industry because key decisions are made by humans who, despite their best efforts, rely on intuition over logic. However, advanced analytics promises to improve the decision-making process by eliminating bias. Analytics can collect and process data to reveal insights on a granular level while investors get an incredible, in-depth insight into how a company works.
Analytics can stitch together data from sources to discover trading patterns and a company’s history to discover their prospects for the future. With data providing detailed insight into company performance on a microscopic level, investors no longer have to rely on their intuition or other emotional cues that lead to biased investment decisions.
The financial sector finds itself under pressure to tighten risk management procedures due to regulation, which means more time and resources dedicated to trading surveillance and risk management. Advanced analytics plays a huge role in this function because of its ability to scan communications for conduct breaches and build datasets from internal and external sources – making it easier to uncover instances of misconduct.
Asset managers using advanced analytics software found that they spend about 55 to 85 per cent less time on monitoring trades, while simultaneously improving risk management. The improvement occurs because machine learning algorithms were better at detecting risk than an experienced expert.
Optimise personalisation services
Investors and asset managers use advanced analytics to provide the right service to the right customer on the right channel. Previously, asset managers would look at the size of the client to determine how their services should be provided. Data analytics allows asset managers to fine-tune their service strategy to cater to the needs of their client and the specialities of their asset managers. The result is an increase in sales (because of better customer service), while also freeing up sales force capacity.
Automating time-consuming tasks
Transferring information, devising solutions to policy breaches and other operations are incredibly time-consuming. However, advanced analytics solutions in the form of NLP allow investors to automate many tasks that would otherwise consume a lot of time. Asset managers can now upload hundreds of documents to repositories using NLP to significantly improve the efficiency of core operations within the industry.
Customer Lifetime Value Prediction
The value of an organisation’s relationship with a customer is becoming more important than ever before. However, calculating this lifetime value is a complicated task because there are so many sources to draw upon. Demographics, use of diverse banking services and cost of acquisition are just some of the variables to consider. This is where advanced analytics programs like Generalised Linear Mode (GLM), regression trees (CART) and stepwise regression come into play. These analytics models allow asset managers to assess the value of a customer’s relationship over time.
The investment industry is changing
The financial industry has become more competitive and more stringent in its regulation than ever before. To overcome these challenges and enjoy benefits such as time-saving and cost-cutting, the industry has turned to advanced analytics. There is a lot of potential for data analytics to transform the industry into something that is more transparent, easier to grasp and more efficient to operate.
Of course, this is just the beginning – as data analytics becomes more advanced, the possibility of what it can do also expands leading to even more possibilities in the future.
With dwindling resources and rising costs of energy, organisations are looking for ways to manage their power usage. The phenomenon is known as smart energy management, and it refers to technology that enables organisations to use power more efficiently. Big data and advanced analytics play a huge role in intelligent energy management. In this blog, we are going to explain the connection between analytics and energy management.
What is advanced analytics?
Advanced analytics is an umbrella term for high-level methods and tools that focus on projecting future trends. It consists of predictive data analytics, data mining and big data. This type of analytics is used to predict future trends and take meaningful action. It’s a stark contrast to other forms of analytics, like exploratory analytics, which is more about finding connections in data sets.
You will find those advanced analytics used in several different industries like healthcare, marketing and of course, smart energy management.
What is smart energy management?
Smart energy management refers to the phenomenon of tracking and managing power usage intelligently. The purpose is to discover new and clever ways to reduce the cost of energy by making it more efficient while eliminating the wastage of resources.
What’s the connection between advanced analytics and smarter energy management?
With the rise in utility costs and changing technologies, energy providers are turning to analytics to make better use of resources.
Reduced cost in upgrading infrastructure
Advanced analytics can reduce the cost of upgrading infrastructure. One of the issues utility providers face is upgrading infrastructure to take advantage of the latest technological developments. Upgrading the power grid is a multi-billion dollar project. There are several reasons for the high cost of upgrading, like expanding the local network and associated infrastructure.
The redundancies to manage peak, troughs and protection from blackouts and power surges. However, advanced analytics can reduce the cost of upgrading infrastructure by making more efficient use of current assets. With advanced analytics and big data, the system will be more interconnected.
Moreover, operators will have an easier time surveying the demand and supply of power. A better understanding of power usage leads to savings and more efficient use of power, thus, reducing the pressure to upgrade and expand infrastructure.
Efficient use of current assets
We have seen an explosion of new smart products, like smart metering and DERs. These new products, along with advanced analytics, allow power grids to capture thousands of data points that were not possible some time ago. We have seen an explosion of data-collecting assets like sensors, placed throughout an electric grid to capture data. The data collected provides decision makers with invaluable information.
The data provides better insight into the demand and supply of power in real time, the amount of power used at peak or downtime and more. The data also gives decisionmakers better insight into the demand for energy. Therefore, it’s possible to make more efficient use of energy by cutting costs while still maintaining the needs of the public. As a result of this, there is less pressure on current resources and less pressure to expand.
One significant problem for the utility industry is maintenance. It’s difficult to predict when systems need maintenance, but the fallout from negligence is high – think blackouts. However, with advanced analytics, it’s now possible to engage in ‘predictive maintenance’. A method of maintenance that prolongs the lifespan of the infrastructure and improves the efficiency of the equipment.
Advanced analytics provides real-time information via sensors on equipment. For example, under conventional circumstances, technicians would read a remote gauge once a month. But if there is a serious condition not displayed on the gauge, then the technician will not be able to know the severity of the situation. However, with analytics, technicians have access to and can read full-time data for immediate identification of trouble. The option to identify any issues in real-time improves maintenance processes significantly.
Advanced analytics leads to smarter energy management by making the most out of current energy use. What’s the result of all this? More efficient use of energy infrastructure, better maintenance, and lower costs associated with upgrading infrastructure. Even better, advanced analytics paves the way for more energy innovations, like decarbonisation and the proliferation of renewable energy. Without analytics, it’s impossible to improve maintenance and efficiency.
Data analytics doesn’t just improve energy management but also public transport, aviation and so much more. Find out more on our blog.