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Data Management

Creating a complete data ecosystem

Data Management

Creating a complete data ecosystem


of enterprises say data and analytics

are important to their business growth

and digital transformation


of P&C insurers say that predictive analytics

has a positive effect on rate accuracy

With machine learning and artificial intelligence becoming increasingly powerful, data management has become a key focus in the insurance industry.

Data available to insurance companies lack consistency and transparency as they are often spread in siloed IT architectures among a multitude of systems. Insurers look for solutions to make better use of their data bases. Successful data management helps to increase efficiency in marketing and sales, to improve client service and to better support underwriting decisions.

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Data strategy

Prepare your data lakes to support digital initiatives, process efficiency and regulatory compliance.

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Data quality

Implement Data Quality solutions to improve and ensure the quality of your data.

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Single source of truth

Gain a complete and reliable view of your customer through adopting a Master Data Management solution

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Optimize data flow

Ensure efficient data flows between transactional systems and data bases by implementing data integration and ETL solutions.

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Data architecture

Develop data models and lifecycles to ensure that your company has the right data in the right place

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Artificial intelligence

Prepare for emerging technologies and help data science teams to gain the biggest insight from the data of your company

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Data migration

Ensure a seamless transition of complex data sets to new applications

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Data security

Design processes to secure your data and to provide compliancy with GDPR and other data privacy regulations

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“Insurers take serious steps to integrate the use of machine learning and artificial intelligence into their businesses.”

Mateusz Stasiak

Mateusz Stasiak

Head of Data

Sollers Consulting

Our solution DeTool is helping insurers to align data warehouses with core systems.

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“Our solution DeTool is helping insurers to align data warehouses with core systems.”

sollers team

Sollers experts support data management for financial services

Based on the advice of Sollers Consulting’s data management team numerous insurers have set up strategies for master data management. Sollers specialists focus on data quality and data integration to prepare insurers for the use of machine learning and artificial intelligence. Their expertise in data management has helped insurers to prepare for the cloud and to improve their data analytics capabilities.

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Mateusz Stasiak
Mateusz Stasiak
Head of Data

Technology & Market Insights

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Frequently asked questions about Data Management

Data should be enabler to achieve business goals. The first step is always to understand what business requirements and business strategy are and how data management can support that. Insurance companies realize the importance of data governance and data management.


Many of them have already introduced Chief Data Officer role and appointed Data Offices. Insurance companies put strong emphasis on establishing reliable data governance framework that determines data ownership, data stewardship and allow business users to find data they are looking for.

Let’s start with the most obvious case, that frequently is the first one that we discuss with our customers about data. The customer insights – by properly collecting data from multiple sources and integrating it to create a single customer view, the new set of possibilities opens up. Your salespeople know what are the next best actions or next best products that they can offer to particular customers. It increases sales success ratio and improves overall sales result.

  • lack of data ownership;
  • very siloed domains that do not exchange information with others;
  • multiple versions of the same dashboard/spreadsheet/data showing different results.

Implement multi-factor authentication, encrypt data, perform regular security assessments, prepare data backups regularly, harness data masking software, use anonymization whenever possible.

Everything depends on the size of the insurer. The method that works is to implement MDM master data management system that will consolidate data from various sources and enforce data quality standard. But in the case of smaller insurers, the cost of MDM solution can be too high. In that case it is also possible to deploy data matching algorithms to identify and merge duplicate records. Also, it’s good to set up data quality checks and clean the database to ensure consistency and accuracy. Establish clear data governance policies and train staff to adhere to standardized data entry procedures.

Now it’s simplier than ever. There are vast options available by major cloud providers. Utilize scalable database technologies like NoSQL (e.g., MongoDB, Cassandra) for high-volume, unstructured data, and distribute SQL databases (e.g., Google Spanner, Amazon Aurora) for structured data.

Start by thoroughly assessing your current systems and data quality, setting clear integration goals and key performance indicators. Develop an integration plan that includes choosing the right tools, mapping data flows, and establishing data standards and governance. Roll out the integration in phases, beginning with pilot testing and gradually extending it, while continuously monitoring and making necessary adjustments. Provide training and support to ensure that all stakeholders understand the new processes and can access help when needed.