By Venki Subramanian, SVP of Product Management at Reltio

Data drives efficiencies, improves customer experience, enables companies to identify and manage risks, and helps everyone from human resources to sales make informed decisions. It is the lifeblood of most organisations today. Sometime during the last few years, however, organisations turned a corner from embracing data to fearing it as the volume spiralled out of control. By 2025, for example, it is estimated that the world will produce 463 exabytes of data daily compared to 3 exabytes a decade ago.

Too much enterprise data is locked up, inaccessible, and tucked away inside monolithic, centralised data lakes, lake houses, and warehouses. Since almost every aspect of a business relies on data to make decisions, accessing high-quality data promptly and consistently is crucial for success. But finding it and putting it to use is often easier said than done.

That’s why many organisations are turning to “distributed data” and creating “data products” to solve these challenges, especially for core data, which is any business’s most valuable data asset. Core data or master data refers to the foundational datasets that are used by most business processes and fall into four major categories – organisations, people (individuals), locations, and products. A data product is a reusable dataset used by analysts or business users for specific needs. Most organisations are undergoing massive digital and cloud transformations. Putting high-quality core data at the centre of these transformations—and treating it as a product can yield a significant return on investment.

The Inefficiency of Monolithic Data Architectures

Customer data is one example of core or master data that firms rely on to generate outstanding customer experiences and accelerate growth by providing better products and services to consumers. However, leveraging core customer data becomes extremely challenging without timely, efficient access. The data is often trapped inside monolithic, centralised data storage systems. This can result in incomplete, inaccurate, or duplicative information. Once hailed as the saviour to the data storage and management challenge, monolithic systems escalate these problems as the volume of data expands and the urgent need for making data-driven decisions rises.

The traditional approaches for addressing data challenges entail extracting the data from the system of records and moving it to different data platforms, such as operational data stores, data lakes, or data warehouses, before generating use case-specific views or data sets. In addition, because of the creation of use case-specific data sets that are subsequently exploited by use case-specific technologies, the overall inefficiency of this process increases.

One inefficiency arises from the complexity of such a landscape, which involves the movement of data from many sources to various data platforms, the creation of use case-specific data sets, and the use of multiple technologies for consumption. Core data for each domain, such as customer, is duplicated and reworked or repackaged for almost every use case instead of producing a consistent representation of the data used across various use cases and consumption models – analytical, operational, and real-time.

There’s also a disconnect between data ownership and the subject matter experts that need it for decision-making. Data stewards and scientists understand how to access data, move it around and create models. But they’re often unfamiliar with the specific use cases in the business. In other words, they’re experts in data modelling, not finance, human resources, sales, product management, or marketing. They’re not domain experts and may not understand the information needed for specific use cases, leading to frustration and data going unused. It’s estimated, for example, that 20% or fewer of data models created by data scientists are deployed.

Distributed Data Architecture – An Elegant Solution to a Messy Problem

The broken promises of monolithic, centralised data storage have led to the emergence of a new approach called “distributed” data architectures, such as data fabric and data mesh. A data mesh can create a pipeline of domain-specific data sets, including core data, and deliver it promptly from its source to consuming systems, subject matter experts, and end users.

These data architectures have arisen as a viable solution for the issues created by inaccessible data locked away in siloed systems or rigid monolithic data architectures of the past. Data fabric decentralises the management and governance of data sets. It follows four core principles – domain ownership of data, treating data as a product and applying product principles to data, enabling a self-serve data infrastructure, and ensuring federated governance. These help data product owners create data products based on the needs of various data consumers and for data consumers to learn what data products are available and how to access and use these. Data quality, observability, and self-service capabilities for discovering data and metadata are built into these data products.

The rise of the concept of data products is helpful for analytics/artificial intelligence, and general business uses. The concept for either case is the same – the dataset can be reused without a major investment in time or resources. It can dramatically reduce the amount of time spent finding and fixing data. Data products can also be updated regularly, keeping them fresh and relevant. Some legacy companies have reported increased revenues or cost savings of over $100 million.

Trusted, Mastered Data as a Product

Data product owners have to create data products for core data to enable its activation for key initiatives and support various consumption models in a self-serve manner. The typical pattern that all these data pipelines enable can be summarised into the following three stages – collect, unify, and activate.

The process starts with identifying the core data sets – data domains like customer or product – and defining a unified data model for these. Then, data product owners need to identify the first-party data sources and the critical third-party data sets used to enrich the data. This data is assembled, unified, enriched, and provided to various consumers via APIs so that the data can be activated for various initiatives. Product principles such as the ability to consume these data products in a self-service manner, customise the base product for various usage scenarios, and deliver regular enhancements to the data are built into such data products.

Data product owners can use this framework to map out key company initiatives, identify the most critical data domains, identify the features (data attributes, relationships, etc.) and the sources of data – first and third party that needs to be assembled – to create a roadmap of data products and align them to business impact and value delivered.

With data coming from potentially hundreds of applications and the constantly evolving requirements of data consumers, poor quality data and slow and rigid architecture can cost companies in many ways, from lost business opportunities to regulatory fines to reputational risk from poor customer experience. That’s why organisations of all sizes and types need a modern, cloud-based master data management approach that can enable the creation of core data as products. A cloud-based MDM can reconcile data from hundreds of first and third-party sources and create a single trusted source of truth for an entire organisation. Treating core data as a product can help businesses drive value by treating it as a strategic asset and unlocking its immense potential to drive business impact.

Jerry Wallis, Head of Industry Strategy, SS&C Blue Prism 

The insurance sector has faced a perfect storm of events these past few years. The covid-19 pandemic accelerated digital transformation around the world – which had the added effect of contributing to increased customer expectations and a surge in competitive pressures.  In addition, many long-established insurers have to maintain legacy systems that support books of insurance that can be years, even decades old – and that cannot easily and cost-effectively be replaced.  Having customer data stored in multiple different systems makes it very difficult for an insurer to build a single 360-degree view of a customer, to serve them better and to sell them more. 

While businesses in various industries have sped up their digital transformations to meet the demands of an online world, the tie to these legacy silos has meant the insurance industry has historically been slow to move into a truly digital way or working.  . The average underwriter, for instance, continues to spend more than 50% of their workday on repetitive tasks.

The sector is under tremendous pressure to process information faster, better, and cheaper to meet the changing needs of today’s customers and secure long-term competitiveness. The adoption of advanced technologies, namely intelligent automation (IA), is helping insurers overcome this challenge by changing how the industry operates across every aspect of the value chain – from product development, underwriting, and policy management, to claims and other processes.

This piece will explore how intelligent automation is launching a new era of improved productivity in the insurance industry.

Digital transformation and IA are imperative to insurers’ future prosperity

The rise of intelligent automation has brought about a new era of possibilities for the insurance industry, with an impressive range of benefits. The introduction of IA and its respective technologies into an insurance firm represents the future of what can become a much more technologically advanced sector. This is particularly important as the industry is under increasing pressure to not only reduce costs but to also maintain, and take steps to improve, customer satisfaction.

Intelligent automation adoption can help resolve this by unifying disparate silos of data, presenting users with a single, digitally capable view of customers, thus giving them the time they need to focus on complex customer cases and the ability to utilize IA to deliver superior, bespoke customer service. IA is a combination of components, including artificial intelligence (AI), robotic process automation (RPA), business process management and other complementary technologies that enable companies to advance workflows and streamline end-to-end processes.   

Digital labour helps workers by automating repetitive and mundane tasks, freeing people from repetitive and time-consuming work. Digital workers connect to legacy or modern applications to automate business processes through a variety of automation techniques.  .

Intelligent document processing allows insurance firms to process vast amounts of data with minimal human intervention at an over 98% rate of accuracy. This replaces laborious and error-prone data entry, which is not only slow but creates an inefficient and costly domino effect when information is input incorrectly. Artificial intelligence components can then use this information to provide valuable insights, predictive analytics and modeling regarding customers and their policies, and suggestions for optimizing processes.

Business process management provides digital oversight, enabling employees to know exactly where in the workflow items are and what needs to be completed to get tasks to completion. Intelligent process mining identifies areas that would benefit from automation, transforming the end-to-end processing of work.

Overall, these and other advanced IA technologies work together to streamline business processes, reduce operational costs, and improve the accuracy and speed of services. Using IA delivers key benefits for insurance firms, which include:

Faster claims processing – IA can automate many of the tasks involved in processing insurance claims. For example, it can read and analyze claims documents (including handwritten documents), determine whether a claim is valid, and calculate the amount of compensation owed. This can help insurers process claims more quickly, reducing the time it takes for customers to receive their payouts.

Improved customer service – The introduction of chatbots powered by natural language processing can answer customer queries and resolve simple issues. This frees up customer service representatives to focus on more complex issues, improving overall service levels. Predictive analytics help workers identify customer needs and preferences to better personalize products and services. Automated notifications can be used to notify customers of policy renewals, claim status updates, and other important information. This can help improve customer satisfaction by keeping them informed in real time.

Better risk assessment – By analyzing large amounts of data, the AI features of intelligent automation can identify patterns and make predictions about future events as well as customers. This can help insurers to price policies more accurately and avoid underwriting risks that may otherwise be too high.

More efficient underwriting – By automating tasks involved in underwriting policies, insurers can improve efficiencies and productivity. For example, IA can analyze customer data to determine their risk profile, check for policy compliance, and generate policy documents. BPM ensures the underwriting process moves along to completion efficiently. This efficiency reduces the time and costs involved in underwriting policies, allowing insurers to process more policies in less time.

Enhanced fraud detection – By analyzing large amounts of data, intelligent automation’s AI capabilities can identify patterns and anomalies that may indicate fraudulent behavior. This can help insurers detect and prevent fraud before it occurs, reducing the amount of money lost to fraud.

Insurers can build thriving workforces in the age of IA                                     

Another benefit of introducing IA is that insurers can develop and improve their workers’ skillsets to match the needs of an increasingly digitalized world. Insurers can also recruit new talent that is interested in learning about advanced technology. Team members that were once spending their days completing repetitive, time-consuming tasks, can be trained in the latest IA technologies to establish them as customer-focused underwriters.

Improvements in efficiency, skillsets, and recruitment help insurers build stronger workforces.

Intelligent automation is for all sector players

Don’t make the mistake of assuming the benefits of IA are confined to the big-league, multinational, insurance players. Intelligent automation is for all in the industry– from agencies and specialty insurers to regional insurers and – yes – multinationals.

For small and mid-sized players, intelligent automation presents an opportunity to overcome staffing and scale challenges to effectively compete in the marketplace and, thus, optimize revenue. Not only does IA improve efficiencies but it can also help insurers innovate and develop new products and services more quickly and effectively, allowing them to stay ahead of the competition and meet the evolving needs of their customers. But to be successful, businesses need to make digital transformation a strategic priority. For those that do, they will prove their ability to adapt to a rapidly evolving market and ensure their future growth.

“The rise and introduction of intelligent automation has brought about a new era of possibilities for the insurance industry. The gradual adoption of IA technologies into an insurance firm represents the future of what can become a much more technologically advanced sector. The adoption of AI and IA technologies is help insurers overcome this challenge by changing how the industry operates across every aspect of the value chain – from product development, underwriting and policy management, to claims and other processes.

“IA is a combination of components, including artificial intelligence (AI), robotic process automation (RPA), business process management and other complementary technologies that enable companies to advance workflows and streamline end-to-end processes. Digital labour helps workers by automating repetitive and mundane tasks, freeing people from repetitive and time-consuming work. Digital workers connect to legacy or modern applications to automate business processes through a variety of automation techniques.  For instance, intelligent document processing, improved customer service via chatbots, better risk assessment with AI features of automation identifying patterns and predictions, enhanced fraud detection and efficient underwriting. AI and IA will redefine processes within the insurance industry, but also help insurers innovate and develop new products and services quickly and effectively, putting them ahead of competitors and allowing them to and meet the evolving needs of their customers.”

Jen Brown, Senior Director, EMEA at GoTo

Burnout and stress continue to be prevalent in the workplace, yet few industries are feeling the burn quite as intensely as IT. You just have to look at the figures to prove that new pressures and pain points just keep adding to the workloads of IT leaders. New global 2023 research targeting Chief Information Security Officers (CISOs) found burnout and stress to be the most significant personal risk CISOs face in their role today, for the second consecutive year. Not only that, but the number reporting burnout and stress jumped up notably year on year. A recent GoTo study found that 65% of businesses say their IT team workloads have increased over the last year, with 92% reporting that reducing this burden is a critical consideration when choosing their IT software. 

What’s behind the burnout? 

Recent years have meant that IT Professionals have had to make hard work and agility their mission critical. As the need for tech in business keeps ramping up, IT teams have doubled down like never before to lay all the right foundations for business success and survival. The old protocols are out while ushered in are hybrid and work from anywhere teams and rapid digital transformation with more devices and infinite data to secure far beyond the secure perimeters of the office.  

The new working world has arrived with a bang and IT Professionals have never been as critical to or as synonymous with its success. But with that shift comes the white-hot heat of demanding hours and smaller teams as businesses do battle against cost of living and talent shortage challenges. IT pros have been working at pace to protect their teams at all costs but with sky high reports of elevated burnout, it’s clear these levels of stress are unworkable in the long term. The only end result will be driving good talent out of the industry completely. 

A new style of leadership  

Today, we need a total rethink and reset on how we turn the tide on the worrying burnout trend. Business demands are evolving and so too must the role of IT leaders. Once upon a time IT leaders were a siloed part of most businesses, beavering away without complaint in the background. Today they are an indispensable bridge between the C-suite and an entire organisation’s security posture, working to drive value and buy-in to IT management and security in ways that resonate with everyone at every level across a workforce. 

As the world becomes increasingly built on software, today the transformational IT leader must map out a future where consolidation and smarter more streamlined ways of working are all the name of the game. This approach must bring together the very best in both technology and people strategies. It’s essential groundwork but consolidating tech, automating workflows and embedding streamlining into the approach are all essential parts of the process. This will allow IT leaders to make smart choices that empowers teams. Today the transformational IT leader is more than ever a people manager who is charged primarily with helping workforces change their behaviours to support the working world of today. Here’s what a transformational IT leader needs to have in their roadmap right now:  

Success through streamlining  

Consolidation among SMBs has become increasingly popular as decision makers start to acknowledge its many benefits. The main advantages being increased productivity, lower costs, and ease of management.  

The recent report on IT Priorities sees 83% of businesses considering consolidation of communication and IT management and support tools an important initiative for 2023. Crucially, this consolidation helps to alleviate the burden on IT, a key goal for 92% of the report’s respondents. It can do this by providing greater oversight and control for less money whilst increasing employee productivity – a golden triangle of outcomes amid the current economic headwinds.  

Consolidation of tools is even more valuable when IT teams are provided with a comprehensive view of operations. Unifying status updates, performance insights, and more information in a single dashboard to control and monitor processes, through a remote monitoring tool, can dramatically improve workflows and enable quick resolutions without overburdening IT teams.  

Automation: paving the way for increased efficiency 

For businesses that do not have dedicated support staff to handle administrative, customer service, or other time-consuming tasks, IT automation tools can be the difference between growth or stagnation. Automating tedious tasks frees up time for teams to focus on projects that require detailed human attention and move the business forward, allowing companies to allocate resources more effectively. It can also serve as a morale boosting tactic, helping employees to tick more off their never-ending to-do list by giving them valuable time back to focus on more fulfilling tasks.  

Furthermore, automating certain business practices helps to alleviate the stress put on individuals and avoid bottlenecks at the same time. Simplifying tasks and responsibilities means that teams aren’t left in limbo if colleagues are out sick or away from their desk – knowledge and workloads can easily be shared and managed without direct management.  

For all these reasons, built-in automation features are considered absolutely critical when choosing new business solutions. Moreover, integrations with new generative AI technologies like ChatGPT are introducing even more valuable automation capabilities across applications like customer engagement, generating and running programming scripts, and more. AI chatbots can also aid in IT ticket deflection and resolution which would otherwise need to be opened and worked on by support staff. All of this means that AI tools are increasingly handling even complex tasks with minimal time and resources required from human team members.  

Collaboration and pooling resources for greater impact 

The final piece of the puzzle is complete when companies can make the most of the resources already available to them. By eliminating the limitations of a traditional in-office mindset, businesses can combine resources by region and empower transformational IT teams to offer support from anywhere, anytime.  

A company with multiple offices around the country can still effectively operate with one shared IT team to look after different regions. Additionally, implementing unified problem management processes across teams and employee locations can ensure faster resolution times when incidents do occur, and significantly reduce the potential for subsequent disruptions. Sharing resources and practices in this way can save significant costs, reduce downtimes, and improve efficiency. And with 50% of businesses still using hybrid workplace models, IT management needs to reflect this flexibility. 

This is why features such as unattended access and multi-session handling are now considered essential. A remote access tool can not only minimise operational downtime and ensure continuity, but also save on travel expenses and office costs – allowing IT teams to support customers and colleagues from anywhere in the world. Additionally, when companies no longer need to worry about providing support in close physical proximity to their employees, this also means that companies can recruit and source the top talent for the job they need, regardless of their location. 

The road ahead 

In times of uncertainty, budgets are squeezed, and workloads are stretched to capacity. Ultimately, the keys to success during such times are streamlining technology and prioritising the people that make up a workforce. By looking for ways to consolidate their technology stack, automating menial tasks where possible, and pooling resources, companies can reinvest money into employees and customers instead. Let’s lean into these approaches so we beat burnout and help put people first in today’s workplace.