The onset of the pandemic brought in a new era for homeownership, with a noticeable surge in Generation Z buyers – i.e., those born between 1997 and 2012. Studies show that the homeownership rate among 25-year-olds today surpasses that of their parents, as 30% owned homes in 2022, compared with the 27% homeownership rate among Generation X individuals when they were the same age. 

As Gen Z continues to seek homeownership opportunities, the focus on property/casualty insurance becomes increasingly crucial to ensure comprehensive protection for their valuable assets and safeguard against potential risks and unforeseen events. Nearly 40% of Gen Z does not have homeowners’ or renters’ insurance, highlighting the need for innovative and custom solutions tailored to this group. Given the surge in Gen Z homeownership, it is important to understand the profound impact of Gen Z's digital-first approach, preference for personalization, sustainability focus, demand for transparency and emphasis on value and education throughout the insurance industry.

Gen Z's Influence on Insurance Offerings

It’s no secret that technology is part of everything the Gen Z demographic does. Their preference for fast and seamless online experiences influences the insurance landscape, prompting insurers to invest in digital platforms for policy management, claims processing and customer support. While more than seven out of 10 Gen Z consumers expect companies to deliver personalized interactions, the demand for personalized services drives insurers to use data analytics to tailor insurance solutions to individual preferences and risks.

In addition, Gen Z’s focus on environmentally friendly solutions has prompted insurers to introduce sustainable products, such as discounts for eco-friendly homes and vehicles. Their lifestyle preferences are driving demand for non-traditional and cost-effective insurance options such as on-demand or usage-based coverage for specific events or periods. This cost-conscious nature is pushing insurers to focus on transparent communication about policy terms, benefits and competitive pricing.

See also: Strategic Guide to Unlocking 'Gen Zalpha'

The Case for Customization and Transparency in Insurance Solutions

Recognizing the importance of customization in insurance solutions is paramount, especially when catering to the diverse preferences and lifestyles of Gen Z homeowners. Customization ensures effective coverage for emerging risks linked to gig economy work, digital assets or eco-friendly living. It enables more accurate risk assessment and pricing, ensuring policyholders are neither underinsured nor over-insured. Additionally, given Gen Z's technological savviness, insurers have an opportunity to innovate and integrate advanced technologies like AI, IoT and blockchain into their offerings. This generation is more likely to embrace and benefit from digital insurance platforms and tools, providing a promising avenue for the industry to continue evolving and innovating in the insurtech space.

The Gen Z homebuying boom underscores the need for adaptable insurance products. Gen Z's different risk factors, such as environmental concerns or work-from-home implications, emphasize the need for customization. Tech-oriented solutions like smart home discounts or coverage for high-tech appliances resonate with this connected generation. Customized policies can adapt to evolving home ownership dynamics, offering coverage for home offices or green home improvements. Transparency, a value held high by Gen Z, is also enhanced through customization. It provides a clearer understanding of what is covered and what isn't, leading to better-informed decisions. 

Insurers carrying out transparent practices in policy details, pricing and claims processes cultivate robust, trusting relationships. Beyond building trust, transparency is instrumental in enhancing customer engagement and reducing confusion about policy terms and claims processes. Transparency paves the way for a smoother customer journey, fewer disputes and improved customer service experiences. The straightforwardness it brings to customer support boosts the overall experience and effectiveness of the service provided. A transparent relationship encourages honest and constructive feedback, which is invaluable for continuous improvement.

Challenges and Considerations for Tailoring Solutions for Gen Z

As Gen Z homeowners weigh insurance options, it is important to be aware of the challenges and considerations insurers are facing when tailoring solutions to this group. Three significant impacts and challenges arise with Gen Z policyholders: 

• Technological Expectations: Gen Z has high expectations for technology integration in all aspects of their lives. Insurance companies must offer advanced digital interfaces, mobile apps and AI-driven services. Keeping up with these technological demands can be challenging and resource-intensive.
• Data Privacy Concerns: With the use of big data and analytics to customize insurance solutions, there's an increased focus on data privacy and security. Gen Z is particularly conscious about how their personal data is used and protected.
• Educational Gap: Despite being highly informed, Gen Z can misunderstand insurance products and their benefits. Insurers need to invest in educational and engagement strategies to bridge this gap.

​​​​​​​See also: Rethinking Insurance With a Gen Z/Millennial Mindset

Moreover, carriers should be cognizant of changing risk profiles, shifts in marketing and communication preferences, the need for product flexibility and scale, economic factors and the importance of building long-term relationships. 

To stay agile in a rapidly changing landscape, insurers should leverage advancements like data analytics and AI to gain insights into Gen Z's preferences and behaviors, aiding in the development of customized insurance solutions. Modernizing IT infrastructure is crucial to providing digital-first services that align with Gen Z's tech-savvy expectations. Moreover, fostering a culture of innovation is critical for staying ahead of the curve. Creating innovation labs, collaborating with technology startups and engaging with Gen Z through surveys and social media to guide product and service development are all beneficial when developing solutions. Strategic partnerships with insurtech companies can help fill gaps and drive success in transparency and digital initiatives.

Insurtech solutions can significantly enhance an insurance provider’s capabilities, providing seamless digital platforms that integrate into an insurer’s current offerings and cater to Gen Z policyholders. For the claims process, in particular, advanced insurtech solutions are designed to promote transparency for both insurance professionals and consumers, addressing the educational gap and improving comprehension from the initiation of a claim to its resolution. Features like real-time updates and expectation management can keep policyholders well-informed and in control throughout every stage of the claims process. Insurtech platforms also place a high priority on data security, incorporating robust security protocols to safeguard personally identifiable information. Moreover, insurtech solutions cater to policyholders' preferences through various support channels, including phone, email and live chat.

The Gen Z homebuying boom necessitates a paradigm shift in insurance solutions, with customization emerging as a key driver for success. Insurers must adapt, innovate and prioritize transparency to meet the diverse needs and preferences of this influential demographic. This strategic approach aligns with Gen Z’s expectations for individualized services while providing a competitive edge over traditional one-size-fits-all offerings, opening up the potential for increased market penetration. As the industry evolves to cater to the evolving demands of Gen Z, those embracing these changes stand poised for sustained relevance and success in the dynamic insurance landscape

When I started executive and leadership coaching in the 1990s, as an add-on to my leadership workshop training business, it was mostly something new. Clients asked for 1:1 time to discuss what they learned in the workshop and how to best apply it in their situation. Now coaching is a multibillion-dollar global industry, with thousands of coaches working with as many leaders, from brand new supervisors to the most experienced executives.

For all the investments made, we have found there are some critical, basic strategies to follow to ensure you get the maximum ROI from a coaching engagement. 

Who Should Get Coaching?

One of the most important decisions you make, before you even find a coach and launch a coaching engagement, is deciding who should even have a coach.

Providing a coach to the right person, at the right time for the right reasons will go a long way in making sure you realize the often profound benefits from leadership coaching.

The success of any coaching engagement depends mostly on sufficient internal motivation to improve, so the person being coached will invest fully in the process. They must want the outcome. Coaching will almost never lead to any substantial change or real improvement if forced on the leader. Counseling from supervisors or HR experts can be used for those times when a coach is not appropriate.

Your manager can provide access to a coach, and is key in supporting change, but -- in the end -- the person must see benefits for them, not just for the team or organization. 

Motivations vary:

• Foundational Coaching – the person is new to management or leadership and needs the basics of moving from individual team member to leader.
• Transitional – the person is now leading an innovation effort, growth initiative or other large-scale change.
• Executive/Middle Manager – the leader is now taking on corporate or organizational/global scale responsibilities, or managing other managers.
• Senior Executive – the leader is taking on a C-suite role leading the company or key division or function.
• Stakeholder Centered Coaching – The leader has received input from stakeholders on a need to change, which the leader would like to address.

See also: Insurance Needs More Women in Leadership

When Is Coaching Most Effective?

In the process of coaching, the individual, with the support of their coach, will need to set specific goals for gaining and applying new skills and capabilities. They will need “real life cases” where they can try out new skills and ways of thinking. If a person is not facing any growing scope of work, or new role, or new goals or challenges, coaching will be like talking about skiing but never going to the slopes! 

21st century coaching is expected to be more than academic learning but should be immediately applicable for better impact on business results. 

Coaching can be very significant if provided at the right time:

• Promotion Preparation – This leader has recently not been selected for a role they wanted. Coaching can be used to prepare the leader and improve their internal brand, so they are both qualified for the next opportunity, and have good relationships with the decision-makers.
• After Promotion – Coaching can be provided as the leader takes on a new or expanded role, so the leader gets off to the right start and uses their first 100 days as wisely as possible.
• Closing the Gaps – This can be used for a very effective leader with a good track record who has a few areas that are holding them back and that will keep them stuck in their current role if those gaps are not closed.
• Reinvention and Transformation – Sometimes in a rich and successful career one must take on roles that are not within the leader’s current knowledge or capability -- maybe leading a new region of the world, or a new function within the company that the leader is not accustomed to or expert at. Coaching will help apply their strengths to maximum benefit in the new situation.

Why Do Coaching?

Ultimately, the purpose of coaching is to accelerate the self-development of the leader, so they can achieve new outcomes for themselves, their teams and their organizations.

This vision is reached more quickly with a coach, with more flow and success for the leader – in whatever ways are most important to them. Most coaching is not in any way corrective or a means to solve a performance problem or issue. Sometimes a coach can be helpful in those situations, but most coaching is to provide support to great leaders who have yet to fulfill their full potential. Most coaching is not focused exclusively on financial goals, though increased compensation due to higher performance may be part of the outcome, depending on the role.

We sometimes call the people being coaching “HiPos,” which is short for “high potentials,” who are offered coaching because it will help them grow and be even better, sooner. They are individuals management has decided are worth the investment of a coach, with the expectation this will pay off for the company in the form of higher performance and new achievements.

See also: The Evolution of Leadership Intelligence

How We Do Coaching Now

In the new 2024 world of coaching, a few key processes must be in place. In 30 years, I have learned we do not need very much oversight or bureaucracy, but just the right amount of process discipline can go a long way.

The problem is it can be hard to make sure the coaching process is working and is worth the cost. How do we know what is going on in the coaching sessions if they are confidential? How can we trust the time is productive and helping the leader and the business? Even the best-meaning coach and the leader can easily get distracted by the topic of the moment, having nice conversations but in the end not making a sustainable impact on the leader’s performance.

For each coaching engagement, the key initial deliverable must be some form of written goals for the engagement, which should include:

1. what skills will we work on,
2. what business impact do we expect to make using these skills,
3. how will we measure ROI (what indicators will we track) and
4. which stakeholders do we plan to check with to evaluate results?

Initially, the leader and their coach will draft the goals, based on all the input they have, including employee surveys, 360 degree feedback, performance appraisals, assessment tools and any other input they have received. 

Once that draft is complete, the leader, the coach and the leader’s manager should meet to agree on those goals. Are they best for the leader? Will they affect the team? How can the manager support the leader to achieve these goals? How will we know if it’s working?

This approach will ensure from the start that the coaching investment has a payoff in the end and will make later evaluation easy.  

Later, the goals sheet will also support celebration of accomplishments in a closing meeting, again with the leader, the coach and the leader’s manager, when we discuss the changes the leader has made and the positive impact they have had.

Give the Gift, but Give Wisely

Getting a coach can be the biggest gift you can give yourself or an employee. The right coach can support true transformations – I know it can be done. 

Do not hesitate to use coaching, but be sure you follow the key practices of who, when, why and how, to be sure you get it right.

A major challenge in all sectors is resolving the intricacies involved in consolidating independent data clusters into a modernized, unified architecture. 

Imagine being able to access data all in one place, any time and to have the power to fulfill endless business needs. This should become the goal in the age of digital transformation. The rise of cloud-based technology, big data and machine learning tools has made this increasingly possible. 

Integrated data platforms can open a plethora of opportunities for intelligence, innovation and modernization. With data easily accessible in real time, businesses can make more informed decisions, predict customer behavior, manage risks, optimize operations and eventually realize their business vision. 

Typically, with the right data management solutions and data analysis tools, businesses can access and analyze all their data from a single platform. This not only improves efficiency and accelerates the decision-making process but enables businesses to unlock new insights, identify growth opportunities and drive innovation to stay ahead of competitors. 

The primary aim is to provide a unified and comprehensive view of data to all groups of people within a company such as managers, analysts, marketing teams and business decision makers. 

Let's look at the key factors that are essential in planning for building an enterprise platform involving the process of defining, integrating, storing, and managing all data related to business operations and transactions. 

Define Objectives 

Clearly define the goals of the data platform. It could be to improve decision making, streamline business operations, increase customer satisfaction, manage applications or predict trends. 

Identify Data Sources and Create a Data Dictionary 

Record all places from where the company collects data. It could be customer databases, data documents, web analytics, revenue tracking, social media activity, competitors' performance data, market data and more. Designate each data set by its function in the overall business process. This data dictionary will serve as a foundation to build the data model and lay out the logic for data relationships. 

See also: 6 Steps for Cultivating a Data Culture

Prepare a Data Model 

Design dataset relationships to view communication connections between different data sets. The data model will outline the integrated view of the organization's data. Data modeling provides well- defined technique to organize data to meet business goals, effectively becoming a road map to strategize and guide an organization's IT infrastructure. 

Data modeling tools are used to design and develop complex database structures and ensure that data objects, relationships and their rules are accurately represented. Here are some commonly used data modeling tools: 

• ER/Studio: Embarcadero’s ER/Studio provides robust logical and physical modeling and allows for forward and reverse engineering. 
• Sparx Systems Enterprise Architect: This tool provides well-rounded support for data modeling, including UML, BPMN, SysML and many other designs. 
• IBM InfoSphere Data Architect: IBM's tool allows for visual design, management and analysis of relational database schemas, with a specific focus on IBM DB2.
• Oracle SQL Developer Data Modeler: This is a free tool that provides forward and reverse engineering capabilities for Oracle SQL and is also integrated with Oracle's SQL Developer. 
• Toad Data Modeler: It supports a wide range of databases, has a user-friendly interface and provides functionalities for both logical and physical modeling. 
• PowerDesigner (SAP): It supports model-driven architecture for data modeling and can handle metadata management. 
• Microsoft Visio: While not strictly a database modeling tool, Visio includes templates and shapes for building entity-relationship diagrams. 
• CA ERwin Data Modeler: ERwin enables users to visualize complex database structures, build and maintain databases and work collaboratively on data models. 
• MySQL Workbench: This free tool from MySQL includes database design and modeling along with other development features. 
• Navicat Data Modeler: Navicat supports multiple databases, offers functionalities for both logical and physical modeling and provides reverse/forward engineering capabilities. 

Build a Data Integration Framework 

Develop a process to automatically gather and ingest data from multiple sources and formats into one consolidated and cohesive view. This can be achieved through ETL (extract, transform and load) processes. There are many ways to build data ingestion frameworks based on business model architecture. However, data ingestion itself can be done in two ways, batch or streaming. 

Data ingestion tools help in the process of importing, transferring, loading and processing data for later use or storage in a database. Here are some popular data ingestion tools: 

• Apache NiFi: It's an open-source tool for automating and managing data flows between different systems. It allows data routing, transformation, and system mediation logic. 
• Fluentd: This is an open-source data collector that unifies data collection and consumption. 
• Logstash: This is part of the ELK Stack (Elasticsearch, Logstash, Kibana), which is primarily used for log and event data. It provides real-time insights from the ingested data. 
• AWS Kinesis: This is a cloud-based service from Amazon that's used to process large streams of data in real time.
• Google Pub/Sub: This is a scalable event ingestion service provided by Google that allows real-time analytics from ingested events. 
• Apache Flume: This is a distributed, reliable and highly available service for efficiently collecting, aggregating and moving large amounts of log data to a centralized data store. 
• StreamSets: It's a platform designed for building, executing, operating and protecting enterprise data flow pipelines. 
• Sqoop: This is a tool designed to transfer data between Hadoop and relational databases.
• Informatica PowerCenter: It's well known for its ETL capabilities. PowerCenter also supports data ingestion tasks. 

These tools play a crucial role in managing the challenges of volume, velocity, variety and veracity of big data. The choice of the tool depends on the specific requirement and the technology stack implemented in your organization.

Decide How and Where the Data Will Be Stored 

The right answer could be on-premises, cloud or a hybrid solution depending on company size, budget and business needs. A variety of digital technologies can be used depending on data usage from analytical systems to transactional processing. The data storage mechanism used should be robust, scalable, cost-effective, efficient and reliable, with fault tolerance. The right storage solution should offer capabilities to enable automation and digitalization, apply machine learning models and be dev-ops compliant. 

These are some methods pertaining to data storage platforms: 

• Databases: This is perhaps one of the most common forms of data storage. Databases like MySQL, Oracle, PostgreSQL and SQL Server help in storing structured data. 
• Data Warehouses: Data warehouses such as BigQuery, Snowflake, Amazon Redshift and Teradata help store data drawn from transactional systems, relational databases and other sources. 
• Data Lakes: They store both structured and unstructured data at any scale. Examples include Amazon S3, Microsoft Azure Data Lake Storage and Google Cloud Storage. 
• Cloud Storage: Services like Google Cloud Storage, Amazon S3 and Microsoft Azure Storage provide a scalable environment to store data. 
• Distributed Storage: Distributed storage systems like Apache Hadoop, Cassandra and MongoDB store data across multiple nodes to ensure redundancy and speed and to lower the risk of data loss. 
• On-Premises Storage: This includes traditional data storage like hard drives and SSDs. 
• Hybrid Storage: It uses a combination of on-premises and cloud-based storage to create a balanced infrastructure that optimizes cost, speed, security and availability. 
• Object Storage: It offers infinite scalability at a lower cost. Examples include Amazon S3, Google Object Storage Cloud Storage and Azure Blob Storage. 

Each of these platforms is suited to different types of data and use cases; the right platform for an enterprise depends on their specific needs. 

See also: Data-Driven Transformation

Design Data Transformation Rules 

Data transformation tools are software or services that convert, clean and standardize data into a format that can be used for data analytics and reporting. The different types of data transformation involve data cleansing, Ddata deduplication, application of business rules, master data management, validation and data quality checks. 

Here are a few commonly used data transformation tools: 

• ETL Tools (Extract, Transform, Load): These tools, like Informatica PowerCenter, Microsoft SQL Server Integration Services (SSIS) and IBM InfoSphere, help extract data from various sources, transform the data into an appropriate format or structure and load it into a final target, commonly a data warehouse.
• ELT Tools (Extract, Load, Transform): These are similar to ETL, but the transformation is done after loading data into the target system. Examples include Google's BigQuery, Amazon's Redshift and Snowflake. 
• Data Cleaning Tools: OpenRefine, Google Cloud's Dataprep and Trifacta Wrangler are examples of this type of tool that help clean up messy data, finding inconsistencies and making the data more usable. 
• Data Pipeline Tools: Tools like Apache Beam, Fivetran, Stitch and Airflow allow you to build data pipelines that can extract, transform and load data in real-time or batch modes. 
• Scripting Languages: Python, especially with pandas library, and R are often used for data transformation because of their flexibility and the extensive amount of libraries they offer for data manipulation. 

Selection of the appropriate tool depends on the specific requirements, like the type and volume of data, the complexity of transformations, the target system and the required performance. 

Build Data Security Mechanisms 

Data Security: Deploy methods to ensure the security of data both during transit and while at rest in the database. This includes robust access management system, sensitive data encryption, and frequent security audits. Modern data security tools leverage advanced technologies, like artificial intelligence, machine learning and automation, to provide robust security measures for data protection. Some of these tools are: 

• Cloud-Native Security Platforms: Tools like Prisma Cloud by Palo Alto Networks, Google's Chronical and IBM Cloud Pak for Security provide comprehensive security for multi-cloud and hybrid cloud environments. 
• AI- and ML-powered Security Solutions: Tools like Darktrace and Cylance use artificial intelligence and machine learning to predict, detect and respond to threats in real time. 
• Security Orchestration, Automation and Response (SOAR) Tools: Solutions like IBM Resilient, Splunk Phantom and Swimlane enhance the efficiency of security operations by automating tasks and orchestrating responses to incidents. 
• Endpoint Detection and Response (EDR) Solutions: Tools like CrowdStrike Falcon, SentinelOne and Carbon Black provide real-time monitoring and protection for endpoint systems from various cyber threats. 
• Zero Trust Network Security Tools: Solutions like Zscaler, Akamai's Zero Trust and Cloudflare Access help enforce the zero trust model, which assumes no user or system is trusted by default, whether inside or outside the network. 
• Data Loss Prevention (DLP) Tools: Modern DLP tools such as Symantec DLP, Forcepoint DLP and McAfee DLP provide advanced features such as fingerprinting data, machine learning-based analytics and integration with cloud and other IT services. 
• Blockchain-Based Data Security: Blockchain technology can improve data security due to its decentralized, transparent and immutable characteristics. For example, Guardtime uses blockchain to ensure the integrity of data. 
• Advanced Threat Protection (ATP) Solutions: Tools like Microsoft ATP and Symantec ATP offer comprehensive, coordinated protection against sophisticated threats across endpoints, networks and email.

Data security is a continuing process and requires not only the use of the latest tools but also a commitment to best security practices, regular audits and continuous staff education. 

Data Visualization 

Incorporate a dashboard that makes data easily digestible and visible to stakeholders. This tool will provide insights and metrics that will help in making business decisions. Here are a few commonly used data visualization tools: 

• Tableau: This is a powerful tool to create interactive, real-time dashboards and access data sets from multiple sources. It offers robust reporting and sharing capabilities. 
• Power BI: This tool by Microsoft allows users to create interactive reports and dashboards using a simple drag-and-drop interface. It also offers the ability to embed reports in other applications. QlikView: QlikView supports a variety of analytics and business intelligence functions, facilitating the creation of sophisticated reports and dashboards. 
• Looker: Looker is a data platform that makes it easy to create, deploy and iterate on data visualizations and to share these across an organization. 
• D3.js: This is a JavaScript library that allows users to create sophisticated data visualizations for web applications. 
• SAS Visual Analytics: It provides interactive reporting and dashboards, and self-service data discovery, and is a part of the SAS Business Intelligence Suite. 
• Google Charts: This is a straightforward tool for creating a variety of charts and graphs that can be used on websites. 
• Datawrapper: This is an online tool popular among journalists for creating simple charts or maps quickly. 

These tools cater to different needs and vary in their complexity, required skill level, cost and versatility. 

Real-Time Data Processing 

We are living in an era where real-time data streaming is becoming extremely important. Almost every consumer-based application requires real time data updates. We need companies to make this transformative change and adopt real-time techniques. Real-time analytics will make sure the latest information is available for consumers for accurate, timely decisions. 

Real-time data processing tools are designed to process or analyze data as soon as it enters the system or database. These are often used in streaming applications where immediate insights are crucial. Here are some commonly used real-time data processing tools: 

• Apache Storm: Known for its real-time processing capabilities, it enables users to smoothly process unbounded streams of data.
• Apache Flink: This open-source stream processing framework is mainly designed for real-time data analytics, batch processing and machine learning. 
• Spark Streaming: As part of the Apache Spark platform, it delivers high throughput for processing real-time data streams. 
• Kafka Streams: Developed by LinkedIn, Kafka Streams is a client library for building applications and microservices that process streamed data in real time. 
• Google Cloud Dataflow: It's a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform, performing both batch and real-time data processing tasks. 
• Amazon Kinesis: Provided by Amazon Web Services (AWS), Kinesis is capable of processing massive amounts of data in real time and can be used for real-time analytics, log and event data collection, and more. 
• Apache Samza: Developed by LinkedIn and incubated by Apache, Samza is designed to handle real-time data feeds at scale. 
• Azure Stream Analytics: A real-time event data streaming service from Microsoft Azure that includes out-of-the-box integration with Event Hubs, IoT Hub and Blob Storage. 
• Pulsar: Apache Pulsar combines high-performance streaming with flexible queuing in a unified messaging model. 

Each of these tools has its own strengths, and the choice among them depends on factors like the volume and velocity of data, the nature of the insights required, the existing technology stack, and the required reliability and fault tolerance. 

See also: The True Cost of Big (Bad) Data

Here is how a modern data stack looks in an integrated modernized data platform: 

Other important factors to consider include:

User Training: Provide usage manuals or training to the employees on how to use the data platform and understand the information presented. 

Maintenance and Upgrading: Ensure regular performance tuning, keep up with the changing business goals and meet new project requirements. Implement feedback loops for constant iteration on the design and function of the data platform. 

Compliance: Make sure all the data collection, storage and usage comply with the applicable legal and regulatory requirements. 

In conclusion, moving toward a unified architecture for managing data is crucial for businesses in the age of digital transformation. This process involves defining clear objectives, identifying data sources, creating a data mode and managing the ingestion, storage, scalability, transformation, security and visualization of the data. Numerous tools are available to support each of these steps, each suited to different types of data and use cases. The right selection depends on the company's specific needs. This integration not only fosters efficiency and accelerates decision-making but also opens opportunities for innovation, helping companies to stay competitive and realize their business visions. 

An African proverb says, "It takes a village to raise a child." Similarly, it takes a team of data scientists, data architects, programmers, systems analysts and end-users from various business divisions for a successful implementation of the data platform.

The customer journey begins the moment a consumer first visits an agency’s website and grows as touchpoints are made throughout the relationship. Customer success is the process of using these touchpoints to keep customers happy and engaged.

Today’s hard market is causing insurance premiums to rise, leading more and more customers to shop around for new policies. This is why it's more important than ever for agencies to focus on the role customer success plays in retention.

Sounds easy enough, but where to start?

See also: The Key to Preventing Insurance Agent Burnout

Use the Right Tools

The easiest way to make an insured happy is to provide the best coverage for their needs. Agents typically need to manually submit quotes individually through each carrier’s portal, which is a time-consuming and tedious process. It limits the number of quotes an agent can provide their client, which means the client may not get the best possible coverage option.

The good news is that there are solutions out there to help! Agencies should consider adopting technologies that streamline the quoting process. Single-entry quoting solutions, for example, allow agents to enter a client’s information once and receive quotes from multiple carriers. Agents can even quote multiple lines of business at the same time. Expanding distribution channels in this manner is an easy way to give clients better coverage options and set them up for success. As a bonus, agents save a significant amount of time, freeing them for higher-value tasks that further contribute to enhancing their customers’ experience.

Embedded insurance solutions are another valuable tool agencies can employ in their customer success efforts. In today’s increasingly digital world, consumers expect an Amazon-like experience -- from access to products and services at the best price to meeting customers where they are -- which is online and at whatever time is convenient for them. Unfortunately, agents cannot be available 24 hours a day. This is where embedded quoting tools come into play. Embedding quoting tools into an agency’s website allows buyers to get multiple, real-time quotes wherever and whenever is most convenient for them. There’s no need to delay getting quotes if the client’s and agent’s schedules don’t align; plus, the clients have time to weigh options before speaking with their agent.

Foster Relationships

Now that the agency has streamlined its quoting process to find the best possible coverage for the client, and the client is happy, what comes next in the customer success process? Fostering and maintaining a positive relationship.

It’s important to maintain an open line of communication with clients. Agents are in a unique position to serve as trusted advisers as clients navigate new risks and claims. Checking in regularly gives agents a chance to answer questions, assess new risks and provide strategic advice when needed. It also allows the agent and client to develop a comfortable rapport, making the client more likely to stay with the agency.

Integrating an organized CRM system can make this easier. Throughout the customer lifecycle, agents can log calls with the customer and leave notes. This allows the agent to refer back to past conversations. They can also set reminders to reach out to the customers at various touchpoints to ensure no customer gets left behind. These little actions pay off in a big way by strengthening relationships with clients.

See also: Retaining the Millennial Insurance Agent

Increase Retention

Customer success efforts look different at every company, but the goal remains the same: develop strong relationships and help customers be successful and satisfied with both the service and the company as a whole.

Many things go into making a customer happy, but providing the best service possible and having strong relationships are the best ways to increase retention and contract renewals for the agency.

The world of insurance is being shaken up by technology startups targeting major emerging markets such as Latin America, Africa, and the Middle East. These insurtech ventures are using mobile apps, digital platforms and data analytics to provide insurance to millions of underserved people in developing economies.

After taking root in the U.S. over the past two decades, insurtech is now rapidly expanding across emerging markets that have large, underinsured populations, rising middle classes and widespread smartphone usage. Powered by mobile technology, insurtechs are delivering customized insurance solutions tailored to local needs and customs.

Insurance is often overlooked as a driver of economic growth in emerging markets, but it's a key behind-the-scenes factor. Technology is providing a path to bring affordable insurance as a financial safety net to the masses.

Insurtech's Latin America Surge

Latin America has emerged as a hot insurtech market, fueled by a growing middle class, low insurance penetration rates and high digital adoption. The region's insurance market hit $174 billion in 2022 and is expanding at an 11% annual clip, outpacing global growth.

Latin American insurers have higher expense ratios than their European counterparts, pointing to massive opportunities for insurtechs to support and enable the current market players, while expanding product portfolios, digitalizing customer interactions, creating new business models and contributing to closing inequality gaps.

Brazil has taken the lead, accounting for nearly half of Latin American insurtech investment. Its insurance regulator is implementing an "open insurance" system that advocates sharing data across industries to create innovative cross-sector products and services at lower costs.

One insurtech cashing in is Olé Life, a digital life insurer offering instant approval for up to $1 million in coverage across 30 countries. The Miami-based startup uses artificial intelligence and decades of underwriting data to rapidly assess applicants via mobile apps and web platforms.

Life insurance protection gaps totaling $162 billion remain in Latin America, underscoring the opportunities.

See also: Insurtech Startups Are Doing It Again!

Africa's Daunting but Promising Market

Africa represents the most underinsured region globally. Despite a $70 billion market, 97% of Africans lack insurance coverage. Technology can help bridge this vast protection gap.

South Africa accounts for 65% of African premiums, with life insurance representing 80% of that market. Microinsurance for low-income populations is an area of focus for insurtechs across the continent.

Companies such as Pula Advisors are using digital platforms to sell affordable crop, life and health policies tailored to smallholder farmers and informal workers. In just a few years, Pula has amassed over 15 million microinsurance customers.

Cultural barriers such as lack of awareness, religious objections and distrust of conventional insurance hinder adoption, however. And regulatory hurdles, uneven mobile penetration and lack of underwriting data pose challenges.

In the realms of healthcare and insurance, insiders in the industry suggest that many individuals still prefer to rely on God as the ultimate — or only — physician or allocate their financial resources to address other needs first. The tendency among Africans is to neglect their health until it becomes an emergency.

Middle East's Islamic Insurance Drive

With the lowest global insurance penetration, at under 1%, the Middle East is a tough nut to crack for insurtechs. But respecting Islamic financial principles, insurtechs are gaining ground with innovative "Takaful" insurance policies based on concepts like mutual protection and risk-sharing.

The global Takaful insurance market hit $31.7 billion in 2022 and is projected to reach $126.8 billion by 2032, with Saudi Arabia the largest market. Takaful policies, while open to all faiths, align with cultural values across the Muslim world. 

The United Arab Emirates and Saudi Arabia have emerged as Middle Eastern insurtech hubs, nurturing startups through regulation, sandboxes and partnerships with conventional insurers. UAE's Sukoon Insurance recently partnered with startup WAX to launch the country's first insurance product for digital collectibles.

See also: The Next Wave of Insurtechs

Hurdles and Necessity

While insurtech companies face challenges navigating complex regulations, underdeveloped insurance ecosystems and a lack of underwriting data in emerging markets, the primary objective remains clear: providing affordable insurance and risk protection to billions of underserved individuals.

Insurance plays a pivotal role in financial stability and inclusion, yet its penetration in emerging economies has been hindered by accessibility barriers. Insurtechs have the potential to overcome these obstacles through technology, customized products and innovative business models.

As incomes rise and demographic shifts occur, emerging markets present the largest growth opportunity for the insurance industry in this century. Insurtech holds tremendous potential to enhance financial resilience and inclusion. By harnessing the combined power of technology and local market knowledge, start-ups can create new avenues to protect the underserved.

As insurtech ecosystems mature in emerging markets, their impact is expected to extend beyond insurance alone. Analysts predict that they will expand access to financial services, contribute to economic development and improve society's ability to manage risks.

This article is adapted from a longer version on LinkedIn.

Matthew Grant has been building and advising companies on the innovative use of technology, data and analytics for over 30 years. With a degree in mechanical engineering, Matthew has been involved in the entire cycle of risk management, insurance and capital management. He was responsible for launching catastrophe modeling into Europe in 1992 and was on the executive team of Risk Management Solutions (RMS) for eight years. Over his career at RMS from 1996 to 2016, his roles included running global sales and marketing, leading the product team and developing emerging market solutions, including catastrophe bonds and parametric products. Matthew joined InsTech in 2016 and is the CEO and co-owner. InsTech currently works with over 170 companies in insurance and technology that are supporting innovation, and the InsTech team provides advice, events, content and introductions. InsTech engaged with over 100,000 people around the world in 2023. Matthew hosts the weekly InsTech podcasts, writes regularly on the topics and themes of relevance to insurance and risk management, and parametric insurance is a major theme at this time. Matthew speaks frequently at InsTech events and other industry conferences. Matthew is an angel investor in U.K.-based technology companies supporting insurance and financial services.

Paul Carroll

When insurtech became a trend, people initially talked widespread disruption. “Amazon is going to come in and kill all the insurers”—that sort of thing. People seem to have moved past that, and we’re now in Insurtech 2.0 or even 3.0. To start us off, how would you broadly characterize the sweep of insurtech over the last 10 years or so?

Matthew Grant

It has occurred to me that innovation is what you do when nothing else works.

People can't do thinking incrementally. And unless you've got some big external force driving change, it's almost impossible to come along and say, Here's a new idea.

We're not going to change the world if people don't have a compelling need to change.

I am careful about using the term “insurtech” because it’s imprecise, and it can exclude companies that have been around for more than a decade. Many well-established technology companies are still innovating and successfully releasing new products.

Paul Carroll

Where do we go with funding from here? Everything was up, up, up. And then for the last year and a half or more, funding has trailed off. Do you see the big funding days ever coming back? Or do you see us moving into a new phase in terms of how people fund innovation?

Matthew Grant

If you look at the unicorns, what drove the crazy valuations was this rush to IPOs [initial public offerings], a long way from any conventional earnings multiples. What we’re seeing now is much more of a return to a traditional EBITDA model. We are also seeing more backing by private equity that needs to make short-term returns to fund its acquisition debt.

Things are slightly different in the U.S. and Europe, because Europe is always a little bit more traditional. The survivors among the start-ups are the companies that typically were started by people in the industry, which are taking a more old-fashioned approach. They get customers, generate revenue, start to make a profit and THEN go out and look for investment. At this later stage, they can bring a decent investment, and they use that to grow the business.

But it’s very difficult now to get investment based on a valuation of the multiples of earnings, or even revenue, that were common a couple of years ago. VCs [venture capitalists] and PE [private equity] still have to deploy their funds somewhere, so it’s not drying up totally. But high interest rates complicate things, and we’re coming back to a greater level of scrutiny.

Paul Carroll

Yeah, I've seen this before, in particular in the internet days. People get excited by the possibilities of growth and convince themselves that trees can grow to the sky. Then reality sets in, and investors start to demand profits and cash flow.

Matthew Grant

I also think there’s a key difference between insurtech and the rest of fintech. In the banking world, we make decisions much more frequently than we do in insurance. In banking, someone might make a decision a few times a day. If you’re a trader, actions happen in milliseconds. But insurance contracts come once a year. So you can live with a lot more of a clunky process.

When you look at the thousands of insurtech companies that we’ve started up, most of them address problems that are No. 11 on a board’s list of top 10 priorities. The insurtechs have nice ideas, and you can see how they’d make some money, but is a company really going to make the effort to bring in the insurtech’s product or service if there isn’t a burning problem for them that demands change now?

Paul Carroll

Back in the early internet days, I, and others, wrote favorably about what we called the arms suppliers. Lots of companies tried to rewrite the rules of commerce, and they generally failed. But a company like Sun, which sold lots and lots of servers to the companies trying to rewrite the rules of commerce, thrived. Do you see that sort of trend with insurtechs?

Matthew Grant

If you look at the technology adoption curve, there's only ever going to be a small number of insurance companies that are willing to work with new technology and new companies and be comfortable with the pain of onboarding. The industry is still working through that stage of “early adopters” before we move to the “early majority.”

What is really interesting that’s happening here in the U.K.—I think more than in the U.S.—is that companies that started up eight to 10 years ago have proved themselves to some insurance companies and are being trusted to do more across their client company to make other processes smoother. And start-ups now don’t just collaborate with the insurer, they collaborate with each other, too. You get this collaboration of the willing, where they cross-fertilize each other, and you then get a multiplier effect of benefit to all

Some established companies like Guidewire and RMS are starting to open up their environments to anyone they think is strong enough technically to connect in.

Paul Carroll

This has been great. Any final thoughts?

Matthew Grant

If you look at Lloyd’s, which is intended to be the insurer of last resort, there are a few isolated cases of people doing interesting things with unusual risks, but there’s not this wholesale adoption of new ideas. It's more about following the business where rates have gone up. If rates are up 10% for reinsurance, they will want to go and write more reinsurance. Rates are going up for cyber, so write more cyber.

I think the insurtech vision dried out because there wasn't a big enough appetite on the receiving end, and change is hard.. Parametric is an example of a good idea of changing how insurance is offered, and of a solution where traditional insurance isn’t available. There are an increasing number of companies in this space, but only a few have been very successful.

Whether that’s a warning or not, we can't just blame the disruptors for being overly enthusiastic. From a commercial point of view, the insurance market has to overcome a lot of friction to do things differently.

Paul Carroll

Thanks, Matthew. I always feel smarter after we talk.

KEY TAKEAWAYS:

--The four main challenges agencies face in getting new technology adopted are: lack of incentive and motivation; too little time and capacity; a feeling by agents that they are unready; and not enough emotional and informational support.

--Three steps can overcome the obstacles: Identify a champion, define the value and measure the results.

----------

With more insurance agencies ready to lean into this digital transformation era, brokers need to embrace new technology to succeed.  

However, change is hard: So hard that the same framework for modeling the 5 Stages of Grief are used by modern change management professionals to facilitate organizational change. And the path to accepting change is represented perfectly by the Kübler-Ross change curve. This model sets out the different stages of change: shock, denial, frustration, depression, experimentation, decision and integration. While the stages aren't linear, they set out how any significant and disruptive alteration will always rock the boat—but doesn't need to be the end of the world. 

These recognized stages also mirror the four main challenges I’ve experienced as an insurtech owner and change advocate. Change management will become a critical capability for insurance agencies, as resisting technological innovation can cost companies millions, so let's look at the four main challenges to technology adoption and see how agencies can overcome them. 

The four challenges

1. Lack of incentive and motivation 

It's unlikely that brokers will feel motivated if the benefits of technological changes aren't personally relevant to them. If the brokers don’t internalize how new technology will simplify their day, like reducing manual processing or making it easier to delight their clients—helping them achieve their bonuses—brokers may struggle to see the transition's value. 

The fear of the unknown can also be a powerful demotivator; brokers will likely hesitate to embrace new technologies if they're uncertain about how these tools will affect their daily tasks, client relationships and overall job satisfaction.

If brokers hit a rough patch in technology adoption, they’ll likely want to throw the towel in—who can blame them?

2. Reduced time and capacity 

Like everything, all good things take time. However, the multifaceted nature of a broker's role often involves balancing priorities simultaneously, so it's common for brokers to need help making time for it. 

Furthermore, integrating a new technology can disrupt established workflows and operational processes. In the beginning, brokers will be adding another system to an already complex workflow; their understanding of the desired outcome will keep the business running as usual, but doing it in a new way can be initially unsettling. This type of change and uncertainty often leaves little time for in-depth exploration and integration of a new technology, leading to a discomfort and a perpetual cycle of deferred adoption.

Put simply, brokers need to allocate time to learn how to use the new technology or plan time for it because they already have a day job, which is their priority. Learning something would need to fit into the gaps in their schedule unless there is explicit time planned for it.  

See also: The Next Generation of Talent

3. Feeling unready and not prepared 

The speed at which technology advances can leave brokers feeling like they’re in a constant game of catch-up. New tools, software and platforms continually emerge, creating a dynamic landscape that demands adaptation. It's no wonder this can make brokers feel unprepared. 

Although some insurance executives may disagree, before you throw brokers in at the deep end, you need to teach them how to swim. Brokers may not have received adequate training on the latest digital tools, analytics platforms or AI applications, leaving them ill-equipped to navigate the evolving technological terrain. 

4. Lack of emotional and informational support 

In a nutshell, brokers need somewhere to ask questions (or vent) and get feedback when they're asked to adapt to technological change.

There exists a palpable fear among some brokers that embracing technology might make certain aspects of their roles redundant, evoking concerns about job security. In addition, when a solution automates a multi-step process that a broker could previously validate along the way, there is an inevitable sense of uncertainty about the quality of the output. Without emotional support from leadership and colleagues, this fear can escalate, leading to decreased morale and productivity. 

So, as you can imagine, when brokers don't know what to expect, the natural reaction is to push back against the change.

Three steps to aid technology adoption

As Albert Einstein wisely said, "The measure of intelligence is the ability to change." In other words, only forward-thinking agencies will succeed in this competitive market.  

But adopting a transparent change management system might be all you need to overcome the four hurdles listed above, and it can be as simple as following these three steps.

1. Identify a champion

Every story needs a hero, and that's no different when it comes to change. Insurance agencies need to find someone internally to be accountable for the rollout. This person understands and cares about the problem and wants to advocate for the proposed technology adoption. 

Agencies need to let everyone know who is leading the charge. But they also need to provide incentives for their champion: Determine what they want, and give it to them if they do well.  

Gartner found that when employees primarily own implementation planning, the success of the change increases by 24%. So identifying a primary candidate who can coordinate with their colleagues and departments, ensuring everyone is heard and catered to, can massively increase the change adoption's chances of success. 

By simply giving the technology change a human face, this can help those who need extra support, as they know the person spearheading the charge and can request assistance.

See also: Why Brokers Should Embrace AI

2. Define the value

From the start, it needs to be clear: What benefit does the technology give each individual broker? What advantage does it provide the company? And how will it help the organization’s bigger picture? 

To answer these questions, management must decide what metrics to use to determine if the technology delivers value. 

Defining the value can also help companies decide if they want to roll out their technology change across all departments or if phasing it in one by one is more suitable. Clarifying this can help overcome the hurdles of motivation and readiness, as brokers clearly understand how this new technology will benefit them and how it will be measured.

3. Measure the results

It's essential to start with the end in mind and ensure you have an established method to measure the expected value. One essential metric to measure is employee adoption, as knowing this helps predict the change's potential longevity. 

Whether it’s policy processing time, renewal rate, employee adoption or agent productivity, by measuring key metrics regularly, brokers can keep their change management plan on track. It is necessary to check in often with the champion and employees to ensure that measurements are correct and that the technology adoption is successful and aiding brokers in their daily lives.  

Wrapping up

Nobody ever said change was easy, but that doesn't mean it needs to be a traumatic uphill battle. By recognizing broker pain points and using the three steps to overcome them when implementing the tech-related change, brokers can feel understood and heard while accepting and appreciating how the new tech is here to serve them.

KEY TAKEAWAY:

--There are four key elements to consider when evaluating and predicting how climate change affects life and health insurance: types of insurance product, demographics of insureds, their location and their ability to adapt.

----------

2023 was a turbulent year for us in the insurance industry in many aspects. Risks continued to manifest worldwide, especially those from the geopolitical, economic and natural catastrophe landscape. On many fronts, this trend is expected to continue in 2024, especially for risks related to climate change.

In the U.S., observers point to effects from extreme heat, drought, flooding and catastrophic wildfires striking throughout the nation, widely and deeply affecting lives and livelihoods.

Life and health insurers have a naturally long-term perspective, and their interests are aligned with those of the people they protect, with a common interest in everyone living a long and healthy life. It is from this viewpoint that they need to understand the relevance of climate change to their insureds’ mortality and morbidity.

Compared with a decade ago, more insurers are taking this subject into account and have new knowledge and tools to help transform gained insights into strategic decisions.

See also: Making Life Settlements More Transparent

Major routes of impact 

We have identified the following four relevant consequences of climate change on human health, which are expected to have significant impacts on the life and health insurance industry:  

1. Extreme temperatures. Unusually high and low temperatures, exacerbated by climate change, disrupt ecosystems and challenge the bodies of humans and livestock. Extreme temperatures, for example, increase the risk of cardiovascular, respiratory and cerebrovascular diseases and worsen chronic conditions such as diabetes and kidney diseases. Whether the expected gains from fewer cold-related deaths will balance the losses from more heat-related fatalities and medical conditions depends on the geographic location under consideration. Adaptation will be the largest variable determining the long-term impact.
2. Air pollution. Climate change — along with industrial activity, transportation and traffic — causes air pollution. Wildfires and dust storms expose people to additional loads of particulate matter, exacerbating asthma and chronic obstructive pulmonary disease. Increased pollen exposure driven by climate change also triggers allergic reactions and lung diseases. Likewise, indoor air quality is a major health issue and is influenced, for instance, by heavy rainfall and floods.
3. Infectious diseases. Climate change leads to shifts in ecological conditions that, in turn, trigger changes in the spatiotemporal (location and time) distribution of pathogens, parasites and diseases. Many of those are vector-borne, that is, carried by intermediate hosts such as ticks or mosquitos. Among the transmitted diseases are Lyme disease, malaria, Zika virus fever and dengue fever, which all surged in recent years. The story is complicated, as overall warming is only one influence among many. Others include rainfall patterns, urbanization and human mobility, as well as increased interactions between humans and wildlife.
4. Mental health. Climate change not only affects human bodies but also affects our minds. Coping with the consequences of changes in our environment takes its toll: Studies have shown that hotter temperatures are associated with mood disorders, substance use disorders, anxiety stress disorders, schizophrenia and self-harm. Living through a natural catastrophe or extreme weather event can cause post-traumatic stress disorder. In addition, poor mental health can harm the immune system. Although the magnitude of the impact of climate change-related mental health conditions is yet to be determined, it could become a major concern for both mortality and morbidity if not properly addressed. 

See also: How Millennials Revolutionized Life Insurance

Relevance of climate change for life and health insurance 

The impact of climate change on people’s lives is highly complex and depends heavily on certain factors. The following are four key elements to consider when evaluating and predicting how climate change affects life and health insurance: 

1. Types of insurance products. The degree to which climate change affects insurers’ life and health insurance business depends on their product portfolio and associated risks. Health insurance, which carries morbidity risk, for example, may be exposed to more direct and short-term impacts compared with life insurance. But life insurance, particularly permanent life, could face long-term and possibly larger mortality risks. Annuity, critical illness or long-term care products also have their own types of risk exposure.
2. Demographics of insureds. Demographic and sociodemographic elements, including age, occupation, income and education, heavily influence people’s vulnerability and resilience to climate change risks. Studies find that those in lower socioeconomic groups, as well as children and older people, tend to be overexposed to the impacts of climate change. Regarding the insured population, medical underwriting, during which some applicants have to be turned down due to preexisting health conditions, acts as a filter that leads to an insured sub-population with better average health compared with the general population.
3. Geographical consideration. Insureds’ geographical factors naturally influence their climate risk exposure, such as extreme temperatures, humidity, air quality, catastrophic events and ecological conditions. The fatality rate caused by many diseases is also influenced by the quality and availability of access to local healthcare, which is linked to people’s places of residence.
4. Adaptation. The long-term trajectory of response to climate change’s impacts on biometric factors is highly dependent on the speed, size and quality of adaptation. It has been shown, for instance, that the death toll of heat waves is not driven by absolute temperatures but by relative increases against what people are used to. Even small changes in behavior, such as checking for ticks after a walk or draining stagnant water pools, and preventive measures such as ensuring hydration and rest during peak exposures, can move the needle a lot when implemented broadly and consistently.   

Outlook

Insurers can gain significant benefits from understanding climate change and its most relevant aspects and from partnering with stakeholders, including the medical community, researchers, reinsurers and climate experts. The aim is to better understand the impact of climate change on the mortality and morbidity of the insured population and, ultimately, on the insurers’ business. The common goal will always be to support individuals in living long and healthy lives, now and in the future

For more in-depth information, see the multi-part article, “The relevance of climate change for life insurance,” published by SCOR.

According to the Centers for Disease Control and Prevention (CDC), here are the numbers of annual deaths in the U.S. from leading causes: 

• Heart disease: 695,547 
• Cancer: 605,213
• Accidents (unintentional injuries): 224,935
• Stroke (cerebrovascular diseases): 162,890
• Chronic lower respiratory diseases: 142,342
• Alzheimer’s disease: 119,399 
• Diabetes: 103,294 
• Chronic liver disease and cirrhosis: 56,585
• Nephritis, nephrotic syndrome and nephrosis: 54,358 

While we cannot prevent death itself, we can certainly make strides in reducing the mortality rate for many of these leading causes of death by harnessing the power of data science. The intersection of data science and public health is catalyzing transformative advancements, reshaping the healthcare landscape.

Key developments include improvements in disease prevention, health surveillance and delivery models. Data science's multifaceted impact on public health ranges from evolving methodologies and integration of diverse data sources to improved decision-making processes. Data science can highlight areas for intervention at a population level — such as identifying regions with high prevalence of a disease or areas lacking in healthcare resources. This can lead to more targeted prevention campaigns and healthcare strategies. 

The advent of machine learning, artificial intelligence and other data science methodologies have revolutionized public health by enabling analysis of complex, large-scale data sets. These techniques extract meaningful insights from various sources ranging from electronic health records, genomics and wearables to social media, enhancing our ability to predict diseases, detect them early and provide personalized medicine.

Predictive models, clustering algorithms and natural language processing have accelerated research and facilitated precise, tailored public health interventions. Applying machine learning algorithms to patient profiles can help make predictions about patients' health trends, automate routine tasks and even provide diagnosis based on the profile inputs, enhancing the overall interaction and engagement with patients. While it’s unlikely we will fully eradicate all these causes of death, leveraging data science can help us make significant strides in understanding, preventing, and effectively treating these diseases, ultimately improving quality of life and extending longevity. 

See also: Maximizing AI's Impact in Group Insurance

Benefits 

Patient Engagement: Data science tools can monitor a patient’s health in real time, allowing healthcare providers to engage patients by providing feedback and health tips. This helps improve health outcomes and patient satisfaction. 

Improved Communication: Patient interaction can be significantly improved with email reminders for appointments or regular check-ups, notifications for medication schedules and detailed explanation of their treatment plans. 

Automation of Routine Tasks: AI can automate routine administrative tasks such as data entry, appointment scheduling and billing, which frees healthcare professionals to focus on patient care. This results in cost savings in terms of labor hours and reduces human error. 

Predictive Analytics: AI can analyze patient data to predict health trends, prevent disease onset and determine the most effective treatments. This not only improves patient outcomes but can also decrease costs associated with unnecessary treatments or late interventions. 

Improved Diagnostics: AI can be used to analyze images and diagnostics tests more quickly and accurately than humans can. This speeds up the diagnostic process, decreases the need for repeat tests and can get treatments started earlier, all of which lead to cost savings. 

Patient Monitoring: AI systems can monitor patient conditions in real time, reducing the need for hospital admissions or lengthy hospital stays and minimizing the risk of readmissions, creating considerable savings. 

Drug Discovery: AI can speed the process of drug development and clinical trials, which are typically costly and time-consuming. By being able to analyze vast amounts of data quickly, AI can identify potential treatments faster and cheaper. 

Precision Medicine: AI can help create treatment plans customized to each patient's specific needs based on their genetics, lifestyle and other factors. This results in more effective care and can prevent the use of expensive, unnecessary treatments. 

Supply Chain Management: In healthcare facilities, AI can predict the need for resources such as hospital beds, medical equipment or even staffing. This allows for better usage and planning, reducing waste and associated costs. 

Telemedicine: AI-driven telemedicine platforms can decrease unnecessary hospital visits by providing virtual consults for non-emergency cases or regular follow-ups. 

See also: Why Becoming Data-Driven Is Crucial

Data Integration: 

Data science has greatly improved our ability to integrate diverse data sources, leading to more comprehensive insights into health determinants and outcomes. 

The use of data science in public health also favors the integration of traditionally siloed data sources ranging from clinical data to socio-economic and behavioral data. 

By combining clinical, environmental, socio economic and behavioral data, researchers can take a holistic approach to public health. As a result, professionals can identify socio-demographic disparities, spot environmental risk factors faster and evaluate intervention effectiveness more accurately. Through real-time monitoring and analytics, health practitioners can respond promptly to health threats and implement evidence-based interventions more effectively. 

Despite these advantages, data science's use in public health does present ethical considerations and challenges. Safeguarding privacy, ensuring data security and addressing algorithmic bias needs careful consideration to ensure equitable distribution of data science benefits across diverse populations. Establishing strong ethical frameworks, using data responsibly and communicating transparently are important for fostering public trust and upholding ethical principles. 

Data science plays a crucial role in shaping public health policies and strategies. Decision support systems powered by data science aid in evidence-based policy formulation, optimized resource allocation and streamlined healthcare delivery. Examination of case studies where data-driven insights informed successful public health interventions highlight the effectiveness of this approach. Integrating data science into public health governance opens avenues for innovative approaches to tackle challenging health issues. 

Collaboration and interdisciplinary partnerships emerge as pivotal themes, with the need for synergies among data scientists, epidemiologists, healthcare professionals, policymakers and communities being emphasized. Successful collaborations have led to the development of robust data infrastructures and empowered communities through participatory approaches. Democratic access to data science tools and knowledge is key to building a resilient public health ecosystem. 

In conclusion, it's clear that data science tools are critical in public health, revolutionizing research methodologies, enhancing decision-making processes and informing policy formulation. This article serves as a road map for researchers, practitioners and policymakers to harness the full potential of data science for global health betterment.

In recent years, retail companies have increasingly moved their applications and infrastructure to the cloud to take advantage of its scalability, flexibility and cost-effectiveness. However, this shift to the cloud has also introduced new security challenges, particularly in the realm of application security. Attackers are constantly looking for ways to exploit vulnerabilities in retail applications to gain access to sensitive data or disrupt business operations. To mitigate these risks, retailers need to adopt a comprehensive security posture management approach that covers both cloud security posture management (CSPM) and application security posture management (ASPM).

While CSPM solutions focus on monitoring and securing the cloud infrastructure itself, it's the ASPM solutions that secure the retail applications running on that infrastructure. ASPM is a holistic approach to application security that involves continuous discovery and monitoring, assessment, business logic exploitation and remediation of applications and their vulnerabilities across the entire software development lifecycle. It helps organizations identify and prioritize security issues and provides guidance and tools to help them mitigate and remediate vulnerabilities -- protecting firms from unauthorized data access; interception; manipulation; regulatory violations (including the payment card industry data security standard, or PCI DSS, and the General Data Protection Regulation, or GDPR); fraud; and disruption of services.

By integrating ASPM into their security posture management strategy, retail organizations can discover APIs in use they may not have known about, identify vulnerabilities in their applications, prioritize remediation efforts and ultimately reduce their overall security risk. Furthermore, by filling coverage gaps in CSPM, ASPM can help retail companies save money by avoiding costly security breaches, financial losses, compliance issues, reputation damage and downtime.

See also: Tackling the Surge in Cyber Premiums

To leverage ASPM to save costs and fill coverage gaps found in CSPM, follow these best practices:

1. Discover and prioritize critical applications - One of the biggest challenges for CSPM is discovering and determining which applications and services are most critical to the organization. ASPM can help by discovering all APIs in use, mapping those APIs to specific web and mobile applications, providing visibility into the security posture of all applications and identifying which ones have the most sensitive data. This information can help retailers prioritize their security efforts and allocate resources more effectively.

By focusing on the most critical APIs and applications first, organizations can save costs and reduce their overall risk exposure, particularly because they deal with so much sensitive customer information -- including financial transactions, addresses, purchase history and account details. They can also ensure that their security efforts are aligned with their business goals and objectives.

2. Automate security testing and compliance checks - Another way that ASPM can save costs and fill coverage gaps is by automating security testing and compliance checks. With the increasing complexity of cloud environments, manual testing and compliance checks can be time-consuming and error-prone. Automating these processes can help retail firms identify vulnerabilities and non-compliant configurations more quickly and accurately, helping to protect their reputation and consumers' private data, and build trust with customers.

By automating security testing and compliance checks, organizations can save costs on manual testing and reduce the risk of human error. They can also ensure that their security efforts eliminate regressions as features are added to cloud-native applications in today's dynamic environments.

3. Integrate security into the development process - ASPM can also help retail companies fill coverage gaps by integrating security into the software development process. By incorporating security scans into this process, retailers can ensure that security is built into the application from the ground up. This can help reduce the number of vulnerabilities that need to be remediated later.

4. Monitor application behavior in real-time - Another key aspect of ASPM is monitoring application behavior in real time. This involves using runtime tools that can detect and alert on suspicious activity, such as unauthorized access attempts or data exfiltration. By monitoring application behavior in real time, retail organizations can quickly detect and respond to security incidents, minimizing the potential impact on the business. Anomaly detection based on machine learning has become more mainstream, addressing these types of API and application-centric attacks in recent years.

5. Use automation to streamline remediation efforts - Remediating vulnerabilities can be a time-consuming and resource-intensive process. However, by using automation tools to streamline the process, retailers can reduce the time and effort required to fix vulnerabilities in application code, infrastructure-as-code (IaC), and cloud services. For example, some ASPM solutions can automatically provide Terraform and CloudFormation scripts to auto-remediate application- and API-layer exploits by hardening runtime production configurations. By using these tools to automate the remediation process, organizations can save time and reduce their overall security risk.

See also: Why Becoming Data-Driven Is Crucial

Integrate ASPM with CSPM

To get the most out of their security posture management efforts, retail companies should integrate ASPM with CSPM. By doing so, they can fill coverage gaps in CSPM -- including API discovery and vulnerability checks -- to identify and address vulnerabilities in their applications that cannot be detected by CSPM alone. This integration can also help organizations save costs by avoiding security breaches, compliance issues and fines and downtime caused by application vulnerabilities. Unlike CSPM, ASPM enables organizations to continuously monitor the security posture of applications and services so they can identify areas for improvement and remediate vulnerabilities and reduce risks.

Overall, ASPM is a powerful tool. By discovering all APIs, identifying and prioritizing critical applications, prioritizing remediation efforts, automating security testing and compliance checks, integrating security into the development process, using risk-based prioritization and monitoring for continuous improvement and auto-remediation, retail companies can reduce their overall risk exposure and ensure that their applications and data are secure.