Reducing data costs without jeopardizing growth in insurance
Data is key for business intelligence, but as the volume of data expands exponentially, the price tag for acquiring, storing, and processing data can stunt growth.
Many organizations are unaware of how much they are spending on data. According to McKinsey, a midsize institution with $5 billion in operating costs spends more than $250 million on data across third-party data sourcing, architecture, governance, and consumption.
Data-related costs represent 5 to 10 percent of total IT budgets and can account for up to 30 percent of an insurer’s overall costs.
[.emph]of overall costs are related to data collection, management, storage, privacy, compliance and cleanup[.emph]
But while reducing data spend may sound like a good idea in the short term, it can actually have long-term consequences for businesses. As organizations cut back on data, they are also cutting back on critical insights that could help them weather the current crisis and emerge stronger in the future.
As we are entering the hard market, with the global downturn projected to be one of the worst on record, it is more important than ever for insurers to be mindful of data costs and explore ways to reduce them without sacrificing growth.
With most companies in resiliency mode, how can they ramp up data efforts while managing data costs?
Review your data governance strategy
A good data governance strategy will help you optimize your data usage and spend. It will also help you keep track of your data sources and ensure that the data you are using is of good quality.
One way to tackle this challenge is by examining your organization's overall strategy for data use. Many companies have a "data-first" mentality, prioritizing collecting and analyzing data above all else. While this can be a valuable approach in some cases, it can also lead to wasted effort and resources if not managed properly.
A more cost-effective approach is to use data to support critical business goals. For example, if your goal is to improve customer retention, you would collect and analyze data specifically related to customer behavior and churn. This approach can help you avoid costly data collection and analysis efforts that don't directly support your business goals.
Once you've identified your organization's specific data needs, you can work on reducing costs in three key areas: data acquisition, data processing, and data storage.
Reducing data acquisition costs
Optimize usage of third-party data sources
Many organizations rely on third-party data sources, such as market research firms, to supplement their internal data. While this can be a valuable way to get additional insights, it can also be expensive.
To reduce costs in this area, start by evaluating which data sources are most important to your business goals. Optimizing usage and eliminating unused and underutilized feeds, defining clearer permissions around data access, and allowing your users to reuse proprietary data to be reused for longer periods can help reduce data acquisition costs.
Use internal data more effectively
In many cases, organizations have access to valuable data that they're not using effectively. This internal data can be a valuable resource for insights, but it's often underutilized.
To get more value from your internal data, start by identifying which data sets are most important to your business goals. Then, work with your team to find ways to use this data more effectively. For example, if you have customer data that you're not using, you might be able to use it to create targeted marketing campaigns.
In addition to using your internal data more effectively, you can also save money by storing it more efficiently. For example, if you're storing data in a relational database, you might be able to switch to a cheaper NoSQL database. Or, if you're storing data in the cloud, you might be able to use a cheaper storage option.
Digitize customer data intake
Many core processes in insurance require customer data, such as applications for coverage and claims. One way to reduce costs associated with these processes is to digitize customer data intake.
There are a number of ways to do this, such as using digital forms, chatbots, and automated data capture. This can help you avoid the need to manually enter data, which can be time-consuming and expensive.
There are several software platforms that can be used to digitize customer data intake. These platforms allow customers to submit their data and sign documents electronically, which can save time and money.
There are many other software platforms available, so be sure to explore the different options and see if they offer features that can support your organization's goals.
Reduce data processing costs
Examine your data processing workflow
Your organization's data processing workflow will likely include several steps, such as data cleaning, transformation, and analysis. Each of these steps can add to the overall cost of processing data.
To reduce costs in this area, start by examining each step in your workflow and identifying ways to streamline or automate the process. For example, if you're manually cleaning data, you might be able to use a data cleansing tool to automate the process. Or, if you're doing a lot of manual data transformation, you might be able to use a tool like Trifacta Wrangler to automate some of the work.
In addition to examining your workflow, you should also look for ways to reduce the amount of data you're processing. One way to do this is by identifying and removing unused or irrelevant data. For example, if you're collecting data that you never use, or that is no longer relevant, you can save money by stopping the collection of that data.
Finally, you can also save money by choosing less expensive data processing options. For example, if you're using a cloud-based data processing platform, you might be able to switch to a cheaper option that still meets your needs.
Use data more efficiently
In many cases, the way data is used can have a significant impact on costs. For example, if data is accessed frequently, it can add to the costs of storage and bandwidth.
To use data more efficiently, start by identifying which data sets are accessed most often. Then, work with your team to find ways to minimize access to these data sets. For example, you might be able to cache data locally or compress it to reduce the amount of data that needs to be transferred.
Reducing data storage costs
Data storage is one of the most important, but often overlooked, aspects of data management. Organizations can quickly rack up storage costs by keeping too much data, storing data in inefficient formats, or using expensive storage options.
To reduce data storage costs, start by evaluating how much data you're using. Many organizations keep large amounts of data they never use, which is a waste of money.
In addition to reducing access to data sets, you can also save money by deleting data that is no longer needed. For example, if you have data that is more than a year old, you might be able to delete it without impacting your business.
Finally, you should also consider the cost of different storage options. For example, storing data in the cloud can be more expensive than storing it on-premises. As such, you should evaluate whether the benefits of cloud storage justify the cost.
The bottom line
Data is becoming increasingly important to organizations, but so are data costs. To reduce data costs without jeopardizing growth, organizations should focus on streamlining data collection, processing, and usage. By doing so, they can save money while still leveraging data to drive growth.