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.
Accurate data is an important resource when it comes to doing business in the modern age. Having swift access to this data allows you to stay competitive in the industry.
As a statistical software used for data analytics, SAS is one of the best platforms that can help you improve and evolve your business. With features like innovative analytics, data management tools, and business intelligence software, SAS is a platform that is trusted around the world.
Whether it’s navigating through challenges in the market or deploying new strategies that guarantee optimised results, SAS provides useful insights for faster and better decision-making.
SAS Analytics comes with many tools such as dashboards, predictive analytics, and real-time analytics that let business owners explore and utilise data that is relevant to their businesses.
With the help of SAS, companies can extract useful and accurate data and leverage information to improve their business strategically. SAS also allows you to gain insight into business operations by making it easier to analyse and comprehend big data.
With detailed and accurate data, you can ensure better decision-making and better business performance overall.
With its many, integrated tools, automated analytical functions, and intuitive navigation interface—using SAS for data analysis has become a method of extracting useful information on a swift timeline.
As a platform utilised in business operations, the speed you gain from using SAS for data analysis is not only a result of its accurate functions.
The easy navigation offered to users allows teams to solve complex issues that were too time-consuming or impossible to solve accurately before. Among the experts who use SAS for their business operations, it is also known for providing high-performance tools built for professional analytics.
Combined with its user-friendly interface, these tools make it easier to navigate and operate, fast-tracking the process of gathering useful insights.
The tech-driven analytics functions also perform faster and more accurately than traditional, manual processing. As a result, you will not only be saving time on data gathering but also cutting down on the time you would have spent rectifying errors with more manual processes.
Using the Visual Analytics feature offered by SAS, you can access insights into new data sources. Users can also create visual representations of data that make data analysis faster and easier to understand.
On the other hand, SAS Contextual Analysis lets you identify emerging issues in the industry, buying patterns, and trends in the market in unstructured data without requiring prior knowledge of its contents.
The advanced analytics tools featured in SAS let you not only measure your success, but also identify the threats to your business.
With accurate information provided by prescriptive and descriptive analytics on your side, you can mitigate risks and initiate strategies that can help you overcome potential challenges.
The automated processes introduced by SAS also support the delivery of faster insights. Processing data with the help of technology makes the data extraction and analysis process more efficient and less prone to errors.
The automated text analysis feature can assess textual data that is collected from portals such as social media, customer calls, or comments—data that cannot otherwise be accurately assessed until the development of a manual taxonomy.
This allows you to analyse the data, its characteristics, and the relationships within it faster and utilise this information to uncover relevant patterns.
Every business is trying to get ahead in the industry by using every tool and resource they have at their disposal. This means that you don’t just have to be accurate and efficient with your processes but also faster.
Using SAS for data processing and analysis lets you have it all.
What makes it quicker is not just the speed of delivery, but also the advantage of having accurate information that is easy to comprehend and makes your decision-making processes easier than ever.
Understanding market progression helps companies move forward and expand their business, and data analytics can help organisations understand the market.
For instance, tracking the evolution of consumer behaviour is an excellent way to plan to meet the changing demand—it is never a good idea to venture into the market without an effective strategy.
While there are many data analytics models that help organisations get detailed insights into their operations, forecasting and predictive analytics, in particular, can give you insights into the workings of the market ecosystem and help understand your target audience.
Forecasting is a process that helps you identify future trends and the consumer behaviour patterns that may affect your business at a macro level and design strategies that they can count on when moving ahead in the industry.
Predictive analytics, on the other hand, uses current statistics and gives you an explanation of the possibility of an outcome—you can define each business project or campaign, predict the possible outcomes, and design tailored-campaigns that can guarantee the best results.
In this post, we discuss the differences and practical use cases for both models.
At first glance, forecasting may sound more accurate than predictive analytics as it uses data from the past and the present to estimate future trends.
Predictive analytics, however, is not merely guessing. Instead, it uses advanced analytics algorithms that leverage current and historical data to predict possible outcomes in the future.
Predictive analytics leverages techniques like automated machine learning and artificial intelligence to create specific predictive models that help you identify patterns or possible outcomes of a model.
When you estimate a trend in the market with the forecasting model, you look into past data and base your estimations on them.
You could, for example, forecast your sales margin for seasonal products based on the data from the previous year. You can use this data to determine the quantity you need to supply to the market.
Predictive analytics, on the other hand, helps you identify potential customers for your seasonal product.
With such insights, you can understand your target audience by evaluating the relationship between demographics and customer preferences and base your marketing and supply strategies on them.
The success of a business relies on understanding the behavioural patterns of its customers, allowing decision makers to tailor strategies according to customer behaviour.
Forecasting is one of the best ways to gain insights into your customer behaviour at a macro level—you can estimate challenges and opportunities in the market and customise your strategies to meet them accordingly.
In other words, forecasting helps you strategise how to navigate the business world, ensure that you avoid potential pitfalls and risk factors, prepare for unavoidable challenges, and optimise your processes for better profits.
Predictive analytics let you understand consumer behaviour at a more micro level.
It provides you with insights into the more human nature of consumer behaviour, helping you understand individual preferences, rank customers effectively, and plan how to deliver a better customer experience to maximise satisfaction.
Forecasting vs predictive analytics: which one is better for your business?
For better company growth, the trick is not figuring out which model is better for your company but identifying how to leverage both for different contexts of each business operation.
Both models, when used intelligently, can provide business leaders with insights that they can leverage for better decision making.
In the highly competitive market ecosystem, using all available techniques—systematically and appropriately—will guarantee better results.
Identity theft is not a new-age problem; it has existed for decades, long before the advent of the internet.
In the past, malicious individuals would sift through piles of discarded mail or look for lost credit cards to steal the identity of someone. They even assumed the identities of the departed to carry out their criminal activities.
Today, the internet has become a hotbed for identity thieves, and with the increasing integration of the World Wide Web into our lives, these pretence-personalities are unlikely to slow down.
According to the Australian Institute of Criminology, the economy loses $2 billion each year due to identity theft. More worryingly, one in four Australians report being a victim of identity theft at some point in their lives.
Identity theft can be devastating for a victim; they may not only lose money but also get in trouble with the law because of the actions of their digital doppelganger.
The good news is that data analytics can help prevent these thefts from happening.
In this post, let’s explore how data analytics can be leveraged to curb identity theft.
Today, businesses process vast amounts of personal financial, and healthcare information of their customers, which are much sought after by identity thieves.
Using this data, criminals can easily bypass the security of traditional data analytics methods, as using these methods to detect and prevent identity theft is difficult at the best of times.
That said, with machine learning, this process becomes much faster and easier. These algorithms get smarter over time as they interpret data, enabling them to detect odd patterns in consumer behaviour.
For example, if a person who usually purchases inexpensive items suddenly starts spending money on lavish goods, that could indicate that they may be a victim of identity theft.
When these strange spending patterns are detected, ML algorithms can flag them, stopping identity thieves before they inflict more damage.
Identity thieves may lurk among regular customers, and risk analytics solutions can help identify potential identity thieves and create a profile for them by analysing the behaviour of thousands of individuals.
These profiles can then be used alongside other customer data to detect any potential identity theft attempts.
Data analytics can prove extremely useful in detecting fraud and preventing identity theft, but several factors may affect how effectively your company can detect identity theft.
Data from identity thefts never reveal similar patterns; identity criminals change their modus operandi all the time, making it difficult to establish relationships between the data and identify unusual patterns.
Analytical data models used by businesses may become outdated, and the insights you get from big data analytics and machine learning may not be perfectly accurate.
To work around this, make sure your analytics algorithms are trained to detect the latest MOs of identity thieves.
Sometimes, the initial attempts at preventing identity theft through data analytics may not bring positive results. When companies do not achieve the identity theft prevention rate they were hoping for, they may forgo the use of data analytics.
Be prepared to put in the effort to effectively police identity theft, even if it does not achieve the desired results in the short run.
Not every company will have data analytics professionals to make sense of the data they collect, and with the increasing demand for data scientists these days, finding an experienced professional is difficult, especially for small businesses.
The data scientist will also need to know about cybersecurity and the data patterns associated with identity theft.
Educate your existing teams by leveraging data analytics training programmes to increase their analytics skills and prevent identity theft.
Identity crimes not only affect individuals, but they can also cause irrecoverable damage to even the largest of companies if not handled properly.
While cybersecurity solutions are available for protection against these crimes, there is no denying that data analytics play a huge role in curbing identity theft.
According to Jobsite indeed.com, in the first quarter of 2020, job postings requiring SAS analytics skills increased by 31%. Another recruitment site reported that SAS Analytics skills are predicted to grow by 4.1% over the next decade.
These statistics show an increasing demand trend for SAS Analytics skills in the job market, which is a direct result of the increased dependence on data for everyday business operations.
Today, more than ever, data analytics plays a critical role in organisational decision making across the board. From sourcing to customer service, every business function of a modern organisation relies on insights delivered by data analytics.
The World Economic Forum (WEF) predicts that businesses across the world will collect, process, and store more than 463 exabytes of data each day by 2025, increasing the need for qualified data analytics experts.
That said, the WEF also reports there will be a skills gap in the coming years due to the demand for data analytics far exceeding the supply of required skills.
The summary of it is that you need to invest in educating your teams on analytics skills to stay ahead of the competition. Enrolling your teams in SAS training courses is the best way to go about it.
Although the word data analytics is thrown around a lot these days in business jargon, it is only a blanket term referring to different types of analytics processes like machine learning, artificial intelligence, neural networking, data visualisation and more.
While your teams may be proficient at ingesting numbers into your SAS Analytics platform and optimising it to deliver basic analytics insights, any detailed analytics will require a deep knowledge of the disciplines mentioned above.
Facial recognition, for example, requires knowledge of machine learning and artificial intelligence programming techniques to build the most accurate biometric identification system.
Fortunately, several SAS training courses focus on specific data analytics disciplines. You can choose training courses according to your data analytics requirements and enrol your teams to maximise your ROI on SAS Analytics.
In recent years, SAS developers have made significant progress in making the data analytics platform more intuitive and more accessible to everyday users.
Today, someone with a surface level knowledge of MS Excel can use the platform and produce actionable insights.
The same is not true for SAS administration, however. While it’s an integral part of optimising the SAS Analytics platform, it remains inaccessible to regular users.
To borrow an example from the Linux community, using SAS analytics is like installing Ubuntu, anybody with basic computer knowledge can do it; SAS Administration, meanwhile, is more like installing Arch Linux, you need some computer wizardry to pull it off successfully.
The fact of the matter is that SAS Administration is a job for experts.
Fortunately, SAS courses provide hands-on training and theoretical knowledge on how to become a good SAS Administrator, reducing the dependence on third-party administrators for updates and maintenance.
Today, many organisations don’t have an internal SAS expert and outsource their analytics requirements to third-party providers.
Although third-party providers can augment your efforts with their expertise, hiring an external analytics team can cost your organisation thousands of dollars.
By leveraging SAS training, you can employ an internal analytics team to fulfil your analytics needs, eliminating the cost of hiring outsourced experts.
SAS Analytics skills have become some of the most sought after skills in the job market due to the emergence of data analytics in recent years.
Unfortunately, the demand far exceeds the supply. SAS training courses can help plug this skills gap.
One of the most fascinating things to follow through the course of history is how our ability to communicate has evolved. From the earliest sign languages to modern smartphones, the modes of conversation have seen massive evolution during this period.
The introduction of telecommunication is a pivotal point in the evolution of communication; letters and other written forms gave way to more sophisticated and advanced systems like semaphore lines—a form of optical telegraphs—and electrical telegraphs.
The real impetus for the rise of telecommunication, however, didn’t arrive until the invention of the telephone by Alexander Graham Bell in 1876. It sparked a revolution in the telecommunication industry and has powered subsequent innovations that have given us the world of communication as we know it today.
Now, we enjoy features like instant messaging, live broadcasting, social media, and even video conferencing through the power of telecommunication. This has made the world a global village, giving us more ways to communicate our ideas, thoughts, and feelings with our loved ones.
There are more than 14 billion connected devices in the world, and with this enhanced means of connection and accessibility, we generate more data than ever before. With the power of data analytics, telecommunications providers are utilizing this wealth of data to deliver a better telecommunication service.
In this post, let’s explore the role of data analytics in the telecommunication industry and how it’s driving the industry to new heights.
Despite all the advances in technology, one of the biggest challenges for the telecommunication industry is providing equal service across all geographical locations. There has always been a clear disparity between coverage in rural areas and urban areas.
In developing countries, for example, 17% of the rural population live in areas that have no mobile coverage and a further 19% live in areas with only 2G internet connectivity. Moreover, urban households are 76% likely to have an internet connection while only 38% of the rural population are likely to have the same.
That said, telecommunication companies are making efforts to increase their coverage of rural areas, and data analytics is a critical tool in this process.
With the power of data analytics, telecom providers can identify areas with low or no internet and mobile connectivity and take measures to build telecommunication infrastructure for improved coverage.
Not all mobile or internet users are alike; different people use their devices based on their needs. One of the common trends, however, has been the waning popularity of traditional calls as a means of communication, as video conferencing, instant messaging, and other audio-related communication modes have become popular, especially among the younger generation.
With data analytics, telecom providers can identify usage patterns of users, which can then be used to create hyper-personalized mobile and internet plans.
Reliance Communication, a major mobile service provider, for example, used data analytics to recognize the shift in the market; more and more customers came from rural areas and introduced Reliance Jio, which offers better internet and mobile connectivity at low prices, to attract rural customers.
US mobile carriers like Verizon, T-Mobile, and AT&T also offer specialized mobile and internet plans that cater to specific customer needs and preferences.
The case in point here is that data analytics allows mobile and internet service providers to identify niche customer segments and provide specialized services that meet their requirements.
The telecommunications industry has come a long way since its beginnings in the late 19th century. Today with billions of connected devices, it has helped reinvent how we communicate with our friends and family.
Faced with modern challenges, the industry is transforming itself again, and data analytics is at the forefront of this transformation.
In the truest sense of the word, manufacturing is the engine of the world economy. In fact, the manufacturing industry is one of the most valuable industries to any economy.
For example, the GDP of the two largest manufacturing hubs in the world, China and the USA, had contributions of $4 trillion and $2.3 trillion respectively from the manufacturing industry.
Without manufacturing, we would not be able to access the products and services we love and use. From the cars we drive to the phones we communicate with, we depend on the manufacturing industry to deliver our beloved products to us.
Ever since the industrial revolution transformed how we approach manufacturing, the business world has been looking for new ways to streamline the process even more; leading companies are outsourcing their manufacturing processes to more efficient offshore plants, and manufacturers are automating their production processes to reduce human errors and improve quality.
With the help of these advancements, leading manufacturers across the globe can produce thousands of products with tighter tolerances and minimal human intervention.
The most critical advancement in the manufacturing industry in recent years, however, has been the integration of data analytics into the manufacturing process, which has given manufacturers enhanced production capabilities.
In this post, let’s explore the benefits of data analytics in manufacturing and how it’s improving product quality across all industries.
Traditionally, introducing a product design to the market involved a lot of trial and error. Most often than not, the first iterations of the product design had an underwhelming welcome among consumers due to the less than ideal ergonomics and design.
Today, however, with the power of data analytics, manufacturers can test their designs for efficiency and ergonomics without ever making a physical prototype.
Modern data analytics tools utilise machine learning and artificial intelligence to create computerised product designs and help manufacturers put them through their paces.
In automobile manufacturing, for example, even wind tunnel testing has been moved to systems powered by data analytics.
We need to talk about the rise of automated manufacturing when considering the benefits of data analytics in manufacturing.
Today, in many industries, the manufacturing process involves minimal human interaction—everything from delivering raw materials to quality control of the finished products is executed through advanced algorithms powered by data analytics.
The tight integration of data analytics also allows manufacturers to eliminate issues due to human error. A recent study found that 23% of unplanned idle time in the manufacturing process is due to human error.
With automated manufacturing systems, manufacturers can minimise unplanned downtime by optimising the analytics algorithms to detect potential anomalies in the production process or equipment and conduct proactive maintenance.
Deciding how much to produce is perhaps one of the most critical decisions across the entire production process. Overestimating the demand can lead to overproduction, costing the manufacturer millions of dollars in the process.
Underestimating market demand, on the other hand, can tarnish the reputation of the company due to the untimely delivery of products and services.
Therefore, manufacturers need to estimate their demand accurately to maximise their profit.
Data modelling and predictive analytics use historical demand data and simulate future market conditions to produce accurate demand forecasts for the future, helping manufacturers meet the market demand without wasting their resources.
Today, many industries across the globe are going through the fourth industrial revolution—driven by the power of data analytics and digital infrastructure—and the manufacturing industry is no exception.
The benefits of data analytics in manufacturing enable modern manufacturers to enhance capabilities to deliver more quality and efficiency in the production process.
Fashion has always been one of the fastest-moving industries in the world.
With its capricious trends and the tempestuous changes in consumer values, brands have to be on their toes to find what works, what doesn’t, and change their collections in line with that at break-neck speed.
Especially with the disruption caused by COVID-19, most fashion houses and retailers found themselves on the back foot. With overnight halts across international supply chains and most consumers confined to their homes, the industry needed to take a step back and re-evaluate the direction it was headed in.
In this environment, where trends come and go at dizzying speeds, brands need to be able to stay relevant without the trial and error. While data analytics have been a part of how fashion companies operate, their use is absolutely critical in the new normal of fashion marketing.
This is especially the case given the shift in consumer values and behaviour; it’s no longer enough for businesses to design clothes that are in style—brand values and messaging also need to hit the right notes.
Here’s how data analytics is making all of that possible for the industry.
Following an unprecedented stint indoors, this year, many high fashion houses presented collections that addressed the demand for both casual glamour and overstated opulence people are craving after a year indoors.
This is just one example.
As the seasons change and styles come and go (and come back again), predictive analytics can tell companies which products are likely to be a hit, and how and when they should be released.
Especially when it comes to marketing campaigns, promotions and other outreach programmes, fashion companies need to get their timing right.
By looking at the patterns and insights data analytics present us with, it’s easier to understand how consumers respond to certain products or collections. At a time when there’s both a resource scarcity and a greater onus to embrace more sustainable and ethical practices, the right data can help companies operate more resourcefully and create pieces that resonate with only the most relevant needs.
What’s more, these insights can also inform marketers about how to market the launches of new items and even promote and sell older silhouettes.
Another very useful data analytics tool fashion companies should not miss out on is sentiment analysis.
Also known as emotion AI, these insights relate to the affective mood and other forms of subjective information that gives brands insights into how people feel.
When applied in the context of fashion, brands can get almost immediate feedback via social media about how their collections or pieces resonate with their audiences.
These insights, in turn, can add significant value to the marketing messaging and approach companies take to market their products.
By understanding how consumers are speaking about products and the emotions they associate with each brand, it’s easier to create messaging that resonates with what people are talking about, thinking, and feeling.
Especially in today’s context where marketing campaigns can’t be overly sales-oriented, must be conscientious, and align with community values, fashion companies risk reputational losses if they’re not in touch with how people feel about what they do.
The pandemic and its resulting effect on consumer behaviour and preferences have changed everything businesses thought they knew about the audiences they interacted with. For the fashion industry, this is truer than ever.
One way data analytics is helping companies navigate this uncertainty is by creating up-to-date customer profiles using data collected from various touchpoints.
From social media engagements, previous interactions, and even website visits, fashion brands can take a look at how their consumers are behaving, what their interests are, products they’re interested in and other information, including personal information like age and gender identity.
These insights can then be compiled to create more insightful consumer profiles that inform marketing strategies and brand directions fashion houses and other mass manufacturers go down.
Today, these interests are likely to include sustainability, social justice, ethical sourcing and other best practices.
In a world where individualism and the expression of our identities are more celebrated than ever, fashion plays a profound role.
With consumer habits, preferences, and even identities changing rapidly and in response to the mass upheavals we’re seeing in society, staying ahead of these shifts in demand and values is pivotal to the success of fashion companies.
At a time when companies like Shein and other fast fashion brands are being called out for their practices and values, understanding your consumers and connecting with them is the key to marketing success in the new normal.
The world is still in recovery mode amid the evolving global health crisis. Interest rates are lower than ever to encourage loans and investments, however, this has also resulted in minimal returns for paid-in premiums.
The insurance sector is seeing an unprecedented decline in product penetration growth—especially in mature markets. The latest statistics have revealed that the fee structure for financial advisors presents a conflict of interest that can harm consumers, especially in Europe, the US, and Australia.
This has industry analysts and experts hunting for new solutions. The only viable solution seems to be adopting a more customer-centric approach to insurance.
The path to achieving that involves big data. Big data can provide the insurance industry with plentiful, unprocessed, raw information, and data analytics platforms can process it into the insightful data that is needed to incite change.
Data shows that customers who are loyal to their insurers cost less to serve, stay longer, buy more, and recommend their insurance provider to family and friends. The survey, however, also reveals that insurance companies find it hard to build customer loyalty.
The same set of data processed through different filters allows us to understand that the main reason behind this is the lack of interaction between insurance providers and their customers.
Data analytics provide insurance companies with the insights they need to build all-inclusive policies. This means that customers can get tailor-made policies that fit all their requirements instead of purchasing multiple plans from the same provider.
Data analytics can also help insurance companies optimise their communication channels to have better interactions with their clients.
Knowing the different demographics that form your customer base and noting their preferred mode of communication, whether it is through a website, in-person, phone call, or video chat can help build relatability.
In short, interaction is the pillar of building customer loyalty and data analytics is its facilitator.
Fraud is one of the biggest issues faced by the insurance industry. Statistics suggest that at least one in ten of every claim filed is fraudulent. Considering the number of policyholders these companies serve, this can reach a staggering number.
The result of this is an increased premium for the rest of your client base. With data analytics, however, it is possible to find these cases, resolve them, and prosecute the culprits swiftly before it causes widespread effects.
Big data solutions such as social network analysis and telemetrics can be used to achieve this.
Leveraging these solutions pays off because keeping your premiums low is a guaranteed way to keep customers satisfied and loyal.
Data analytics can also be used to speed up settlement cases. The main reason lawsuits and claims take a long time to settle is because of the large amount of analysis that needs to be done.
Data analytics allow firms to check the claim, analyse it, and access the customer’s claim history instantaneously. This can increase the speed with which a firm can give customers their payouts.
The rise of online aggregators or comparison sites, to use the more popular term, has led to an even greater decline in the interactions between customers and service providers.
Technology has, however, allowed many companies to redefine their image and service as insurance providers in order to build meaningful relationships with their clients and, in turn, develop loyalty.
These changes and innovations would not have been possible without the insights gleaned from data analytics and the advances made in data modelling techniques.
Advances that include:
With the information gathered from social media, mobile data browsing history, and purchasing history, companies in the insurance industry can gain a more personal understanding of their clients.
Equipped with this information, they can find swift solutions to process and service issues, providing the customer with an easier and more wholesome experience.
When coupled with the ability to build insurance plans and policies tailor-made for individual customers, we can confidently say that big data and data analytics together can help increase customer satisfaction and build loyalty.
The Coronavirus has dealt a heavy blow to the global economy. No industry, however, has suffered as much as either aviation or tourism.
Tourism makes up to 10% of the world’s GDP and creates 1 in every 10 jobs. It’s the livelihood of many people around the world, and developing nations are especially reliant on the revenue it generates.
It’s not hard to see, then, how travel bans, quarantines and social distancing can be major roadblocks to travel experiences and how people continue making a living.
It is yet to be seen how our spending ability and habits have been affected in the long run by the pandemic and we can’t say for certain how it will end—either we cure it completely or learn to live with it.
Regardless, we can expect a post-vaccination boom in travel especially with the introduction of initiatives like vaccine passports. In the new normal of travel and tourism, how do tools like data analytics help us forge a new way forward?
In today’s global community, data is an undeniable currency. On the subject of tourism, what data would be valuable to the industry and how can it be used?
On a macro level, the GDP generated by the industry and which sectors contributed to it, and to what degree this took place, is invaluable.
Governments, tourism boards and travel agencies can use this data to identify key areas they need to invest in and where they need to improve their infrastructure and services.
It can also help them identify what’s being done right so critical investments are only funnelled into the right areas.
More important still is understanding the preferences and habits of various demographics. By knowing a visitor’s country of origin, how long they stayed, how much they spent, and which places they visited, tourism companies can create and offer tailor-made experiences.
While this is being done to some extent at the present, we can expect COVID-19 safety considerations to be factored into travel packages and experiences in the new normal.
By understanding each class of travellers, companies can develop targeted marketing campaigns, identify which countries to focus on, and allocate their marketing budgets accordingly.
Big data also allows governments and companies to understand where and how they need to improve so they can offer tourists safer and more unique experiences. It can also measure success and failure and in the long run, make greater strides in sustainable tourism.
When the world settles into some semblance of normalcy, it’s highly likely that most people won’t have the same habits and preferences as they did before. While we can expect an increase in people wanting to travel, it’s reasonable to anticipate that their requirements and priorities will change.
Travellers might, for instance, opt not to travel to developing countries given the relative lack of medical facilities and emergency services. It is also quite possible that for years to come, tourists will seek destinations closer to home.
Popular destinations in Asia, Africa and South America may find themselves in a predicament where they need to improve critical infrastructures such as transportation, sanitation, healthcare and other public facilities to convince tourists that these locations are popular, safe, and rewarding destinations to sojourn to.
Data analytics can also help us identify key areas that need to be improved or services that need to be introduced.
It can also give policymakers and the spate of travel and tourism companies insight into emerging trends and help the industry prepare and respond better to a post-COVID-19 world.
Given the potential of big data and the range of analytics insights we have access to today, we can expect the tourism industry to be back on its feet sooner rather than later.
The lean efficiency that is the benchmark of the industry is only possible with the insights offered by data analytics, however. It’s even safe to say that data analytics and data modelling techniques are the cornerstone of the industry’s recovery and retooling in the new normal.