10 Common DataOps Challenges Faced by Insurance Companies

We have all been there - that all-familiar situation of having plenty of data in our systems but not knowing how to best extract value from it. In today’s age, that translates to a loss of revenue and operational inefficiency for businesses. With good DataOps practices, companies can make better-informed decisions, improve their operations, understand their customers better, and gain a competitive advantage in the market. That’s not my claim, though - market research backs it up.

The 2020 Global State of Enterprise Analytics study, conducted by MicroStrategy, provided deep insights into the current state of Data Analytics and its impact on businesses worldwide.

A survey of 500 senior business and IT executives across industries in North America, Europe, and Asia revealed:

• Nearly all large businesses (94%) recognize the importance of data and analytics in driving their growth and digital transformation.
• Additionally, most global enterprises (65%) plan to increase their investment in analytics in 2020.
• Brazil and Germany are leading the way in giving frontline employees access to their organization's data and analytics, with 58% of employees in these countries having access.
• Almost half (47%) of enterprises now use cloud-based analytics platforms, representing an increase from 39% in 2018.

These pretty impressive statistics reveal a growing business trend of maximizing the utility of data. What was once a rarity is now becoming a common practice - in this article, we will take a deep dive into the common data problems faced by Insurance companies and how DataOps and Data Analytics tools can solve business problems.

DataOps is Transforming the Insurance Industry

But first, I have another statistic to share. The insurance analytics market is experiencing explosive growth, with the industry's global size reaching $7.91 billion in 2019 and projected to skyrocket to $22.45 billion by 2027 - a remarkable CAGR of 14.2% from 2020 to 2027. That impressive growth indicates how essential data analytics and DataOps have become to the insurance industry’s growing needs.

Insurance analytics has become crucial in managing risk and optimizing the insurance sector's underwriting, pricing, claims, marketing, and reserving processes. It allows insurers to offer better insurance contracts, particularly in health, life, and property or casualty fields.

By leveraging predictive analytics, insurance companies can create reliable reports across multiple product lines and improve customer relationship processes while reducing costs. In short, insurance analytics is transforming the industry by providing powerful insights and driving growth - one graph at a time. However, certain roadblocks still need to be addressed before the insurance sector truly begins to extract value from their data - here are my top ten.

The Top 10 DataOps Challenges in the Insurance Industry

Gathering Meaningful Data

Data collection can be daunting for businesses due to the sheer volume of information available. Often, employees spend significant amounts of time sifting through data to extract meaningful insights, which can prove a waste of resources. Moreover, sorting and analyzing vast amounts of data in real time can result in inaccurate or irrelevant reports.

One solution to this challenge is using DataOps tools to assist in data collection, analysis, and reporting in real-time, leading to better decision-making. This approach also reduces employees' time collecting and analyzing data, thus improving productivity.

In addition to adopting DataOps tools, training employees on effective data utilization is also essential. Training programs or workshops can help employees learn to extract valuable insights from data to inform business decisions.

Dealing with Data from Disparate Sources

Insurance companies collect data from various sources, such as policy applications, claims, and third-party data providers. Additionally, they may purchase data from data brokers and aggregators to supplement their data sources.

Efficient data analysis can be an uphill task for insurers, mainly when data is scattered across the enterprise. Employees might not have the know-how to integrate data from different sources, which can lead to incomplete analysis. Manually combining data from various systems is time-consuming and can limit insights to what is easily viewed.

A centralized data warehouse can solve this challenge by ensuring all information types are available in one location. A comprehensive system provides employees with all necessary data promptly. This frees up time spent accessing multiple sources, enables cross-comparisons, and ensures data completeness.

Making CxOs Understand the Power of Data

Despite the significance that data science and analytics technologies have gained, there is still a need to educate CxO executives on the value of collecting and analyzing the right data. Many decision-makers without technical backgrounds struggle to understand databases beyond basic tools like Excel and CSV. They may find the concepts of data warehousing and data pipelines particularly challenging. This difficulty is not necessarily their fault, as these concepts can be complex and require specialized knowledge to comprehend fully.

According to most experts in the field, educating people about the potential benefits of data remains one of the biggest challenges. Users must ask the right questions so that data can be used to provide valuable insights beyond just counting, reporting, and aggregating numbers.

Convincing traditional insurance companies to shift towards data-driven decision-making processes remains a significant challenge. To overcome this, providing the proper use cases that showcase DataOps' impact on their business is essential. But perhaps the most effective solution is using zero-code DataOps tools since they allow non-technical users to process and obtain insights from data.

Implementing Data Security

On October 6, 2022, Amerigroup Insurance Company announced to the Texas Attorney General’s Office that a data breach had occurred. The breach exposed sensitive information such as names, addresses, Social Security numbers, and health insurance information of specific individuals.

In 2015, the same had happened to Anthem, Inc ., and hackers stole 37.5 million records.

As insurance companies continue to scale rapidly, cloud management has become popular for securing sensitive data. However, the rise of data breaches, including the increasing sophistication of cyberattacks and the expanding attack surface caused by remote work arrangements, has led to concerns about the security of data stored in the cloud.

This has prompted organizations to enact strict measures to protect their central repositories against hackers, leading to additional challenges for data scientists.

Organizations must deploy advanced encryption methods and AI-based security solutions to counteract these threats. Organizations must design systems to comply with all applicable safety regulations and use cutting-edge technology to avoid lengthy security audits.

Challenges in Data Governance

Data governance is crucial to any insurance organization's data strategy, ensuring that data is managed and used correctly. However, data governance can be challenging due to various factors, such as complex regulations like the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the United States.

These regulations require organizations to collect, store, and use data in compliance with specific rules and procedures, making data governance even more critical.

Furthermore, data governance involves many stakeholders, including IT, legal, and business teams, making it a complex and time-consuming process. Ensuring everyone understands the data governance policies and procedures can be a significant challenge, especially in larger organizations with multiple departments and locations.

To overcome these challenges, organizations must have a clear data governance framework that aligns with regulations and involves clear policies and procedures for data collection, storage, and use, as well as the tools and technologies to support them.

Balancing DataOps Costs and Benefits

According to industry experts, building and implementing a bespoke enterprise-grade DataOps solution for insurers can cost at least $200,000. Such a cost can be a substantial financial burden for small and medium-sized enterprises, making it challenging to adopt DataOps.

Smaller players can opt for software-as-a-service (SaaS) based DataOps products, which offer a lower upfront cost. However, companies can pay $20,000 annually even with SaaS-based solutions, quickly adding up over time.

DataOps costs depend on several factors, including the quality and amount of data a company has, its specific analytics needs, the choice of analytics tools, customization efforts, and organizational agility. With the sheer volume of data, businesses generate and collect, storing and processing it can be a significant cost factor.

To reduce the cost of DataOps, companies need to define clear data objectives and develop a data management strategy. Starting with a few primary use cases and gradually scaling their analytics efforts is a good approach.

Dearth of Talent

Data scientists, engineers, and analysts play a crucial role in the success of any enterprise's DataOps team. Their responsibilities include managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution across the organization.

These professionals are instrumental in finding trends in data sets and developing algorithms to transform raw data into actionable insights that benefit the enterprise.

The growing need for DataOps and artificial intelligence has created a high demand for data science talent across various industries, including insurance. Unfortunately, the supply of data scientists has not been able to keep up with the rapidly increasing demand, leading to a shortage of qualified candidates.

Amidst the thousands of layoffs in the tech sector announced by major companies such as Google, Microsoft, Amazon, and Facebook in the past six months, it's surprising that the demand for data engineers remains high. The reason is that data engineering is a distinct skill set that takes time and resources to acquire. LinkedIn reports 5800 new job openings for data engineers, while only about 1000 are available for Android developers. This demand-supply gap is expected to worsen before it improves, underscoring the critical need for investment in data science talent recruitment and training programs.

Identifying Key Metrics and KPIs

To gain insight into fundamental KPIs such as Return on Equity and Loss Adjustment Expense, BI tools are not required. Instead, new KPIs that generate value must be developed.

However, data engineers cannot achieve this on their own. While they can develop algorithms, they require direction on what is needed. Proper collaboration between different stakeholders is essential to achieve synergy in KPI creation.

Data scientists face challenges in accurately measuring the performance of their machine-learning models. The metrics used may not serve the ultimate purpose of implementing data science.

Insurance companies need to focus on measuring the effectiveness of their DataOps solutions in terms of their ROI. This includes taking into account the cost of implementation, the accuracy of the insights provided, and the impact of these insights on key performance indicators (KPIs) such as revenue growth, customer satisfaction, and retention rates.

Effective Data Visualization

Data visualization represents data and information in a graphical or pictorial format to communicate complex information quickly. It helps to identify trends, patterns, and insights that are difficult to discern from raw data.

One of the most common issues is the lack of consistency in data visualization. Different teams or individuals may interpret the same data in different ways and represent it differently, leading to confusion and miscommunication. This can be addressed by developing standard guidelines and templates for data visualization within the organization.

Data accuracy is also a critical issue in data visualization. Inaccurate or incomplete data can lead to incorrect conclusions and decisions. Data validation and cleaning processes can ensure that the data used for visualization is accurate and complete.

Effective Scaling

Scaling DataOps can be a tough challenge for many organizations as the volume and complexity of data they collect and analyze continue to grow. One of the significant challenges of scaling DataOps is the ability to manage and integrate data from disparate sources promptly and efficiently. DataOps becomes more complex as organisations rise, making it difficult to create reports that provide actionable insights.

To effectively scale DataOps, organizations need to invest in a system that can grow with them. This requires a system that can accommodate the growing volume and complexity of data while still providing insights that can be acted upon. One key factor in scaling DataOps is to define clear goals and use cases for the data. Organizations can focus their efforts and ensure they get the most value from their data by identifying the specific business problems that DataOps can solve.

Another essential consideration when scaling DataOps is building a strong data governance framework. This involves developing processes and policies for data collection, storage, and analysis to ensure data accuracy, consistency, and security. Investing in data visualization tools and training employees in data analysis can help organizations scale their DataOps efforts.

DataOps Is Solving Data Problems

The old saying that progress is never without pitfalls holds true for using data in business. While DataOps has the potential to transform businesses, enabling them to make data-driven decisions is not always smooth sailing.

The exponential growth of data presents an ever-increasing challenge for businesses. In my previous article , I mentioned how the volume of data is increasing at a staggering rate every year.? According to Statista, data creation will soar to 181 zettabytes by 2025. The sheer volume of data can make storing, managing, and analyzing it a daunting task, making it difficult for businesses to make informed decisions and gain a competitive edge.

This was one of the key factors that influenced my decision to work with Kanerika in creating our native DataOps tool, FLIP. Having worked with insurance companies, I realized that building an easy-to-use data tool was the only effective way of solving data problems within companies. While all of us may not have the luxury of possessing an entire development team dedicated to DataOps, now we have another choice - zero-code DataOps tools that give the power of data analysis directly to business owners.

I can only imagine what the future holds for insurance and data, but it clearly will not be writing about data problems faced by companies -? DataOps tools are already solving most of them.

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