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Data Management

M&A growth pushes insurance brokers to address system consolidations. Each acquired agency brings its own Agency Management System (Vertafore AMS360, Applied Epic, etc.) with unique data structures, business rules, and years of accumulated records. Policy data is tied to specific writing companies. Some of them have ‘DNU’ or ‘zzz’ prefixes, many of them marked inactive/not

System consolidation is one of the most complex challenges in mergers and acquisitions. While executives focus on long-term strategic benefits and market opportunities, IT and data teams face the laborious task of merging disparate technological ecosystems. Key takeaways: System consolidation fails by treating data migration as just a technical task. Real data has messy names,

Modern large businesses deal with data scattered across multiple systems. Organizations run complex operations that rely on data. Core systems capture and process transactions, generate reports, manage business processes. Each system generates its own version of master data, which can include customers, insurance policies, products, vendors, intermediaries, employees etc. MDM vendor presentations make it sound

Agile organizations try to put data in the center of their business decisions. Data integrity has evolved from a technical consideration to an immediate operational concern. Organizations see that quality and reliability of data directly impacts the ability to operate efficiently, make informed decisions, and maintain competitive advantage. Naturally, addressing this does not come without

Enterprise Data Management faces a critical choice. Traditional MDM solutions promise complete data centralization but often fail to deliver, creating more problems than they solve. This article explores why centralized MDM approaches fall short and presents an alternative: enhancing existing core systems’ capabilities through focused administrative tools rather than attempting to create new master systems.

Organizations running complex operations face constant pressure to standardize their data. The promise of a Single Source of Truth (SSOT) is compelling: one unified version of all enterprise data, perfect consistency across systems, and clean, standardized reference data that powers all business processes. This vision has driven countless initiatives to implement robust Master Data Management

Growing insurance brokerages struggle with a fundamental challenge: how to efficiently manage the critical reference data that builds the backbone of their Agency Management Systems (Vertafore AMS360, Applied Systems Epic etc.). The common approach leaves agencies with a problem of an underserved business process. It’s entirely based on handling data administration manually with native tools

Data conversion professionals play a crucial role in the insurance industry, particularly during mergers and acquisitions when agencies need to consolidate their Agency Management Systems (AMS). This comprehensive guide explores what the role entails, why it’s essential, and what skills are needed to succeed in this field. From this article you will: find out about

To optimize operations and drive growth, insurance brokers and agencies increasingly rely on analytics to understand the context of their business in the market. How advanced should you analytics be? What if you are set for implementing a homegrown solution? This article explores some important business and technical aspects and considerations for data analytics implementation.

Does digital transformation matter in the age of Artificial Intelligence in insurance? Maybe it’s just a cool buzzword that went obsolete as soon as ML and AI took the spotlight? Actually, today it’s at least twice as important to internalize what digital transformation entails. Through this article, you will: learn what is NOT digital transformation. understand

Data conversion can be a really important piece in how smoothly your operations run and decisions are being made. When a company functions in a sector particularly open to regular data migrations e.g. insurance brokers and carrirers, conversion bottlenecks and inefficiencies can slow the business and hurt down the line. With this article you will:

Data conversion, the overlooked strategic necessity, and the byproduct of variety in the tech landscape. Organizations face the challenge of integrating different systems, each with unique data formats and structures. As businesses grow, merge, or adopt new technologies, the need for data compatibility only increases. With this article you will: learn what data conversion is.

The AI hype train in insurance is huge from US agencies to carriers. You will hear about AI from insurance software vendors, consulting companies, and industry podcasters weekly. It’s an important topic, right? Right, but please, please, do not act on the fear of missing out. This is not another article made to drip-feed you

Data turns into a liability when it’s collected and kept as an afterthought – without direction and care. To benefit from data, organizations need to maintain it and ensure its quality. Data quality dimensions are the structured way of looking at different properties of data assets. Understanding them enables business to systemically approach increasing data

Data management cannot happen without people, practices, and proper tools. Ever-growing data sets require careful monitoring, maintenance, and assessment. You can’t leave everything solely to software and automation – or you open your organization to deterioration of your data assets and potential risks. Specialists are an important safeguard to what happens with your data. Data

Let’s take a quick look at how organizations attempt to use Excel for comparing and matching data, then discuss some of the problems this creates.

In brief, MDM is a process and set of tools for consolidating and managing an organization’s most critical (“master”) data. This data is then cleansed, enriched, and made available to authorized users in a consistent format. And it yields tremendous benefits for companies who implement it properly.

Entity resolution (also known as data matching, data linkage) is the process of identifying and disambiguating real-world entities such as people, places, organizations, and products in unstructured data. For example, teams may be keeping track of customers in different ways, using alternative spellings or abbreviations.

Software tools have been developed to automate the process of data matching. In this article, we’ll take a look at the available types of data matching tools, how they work, and some use cases for each.

The term “heterogeneous data” refers to a body of data that comprises multiple formats and sources. For example, customer data may be scattered across various databases, CRM systems, and spreadsheets. With so many sources involved, it can be difficult to get a complete and accurate picture. In enteprise settings and companies relying heavily on data,

How many data sources are you dealing with? How do they relate to one another, and what specific challenges are making it difficult to get a clear, big-picture view? In this article, you’ll learn how to make sense of KPIs from different data sources.

MDM is the process of creating and maintaining a consistent, accurate, and complete view of an organization’s critical data. It includes identifying, cleansing, and enriching data; governing its use; and synchronizing it across disparate systems.

Inconsistent data can prevent you from gaining a holistic view of what’s going on in your company, causing you to miss connections, trends, opportunities, and threats. In this article, we’ll talk about how to fix that.

Incomplete or inaccurate data has a ripple effect across organizations, leading to decreased productivity, higher churn, and missed opportunities.

MDM is a complex initiative, but one that can offer tremendous benefits to your organization – if it’s done right. Here’s how an MDM consultant can help.

Cross-linking data is the process of linking together data that is stored in different systems. This can be done manually, by entering the relevant information into each system, or automatically, using software that is designed to connect different systems. Either way, the goal is to create a single, cohesive view of your organization’s data –

Having a 360-degree view on who your customers are is the best way to create a solid foundation for all your customer-related processes, so it’s critical that your data is accurate, up-to-date, and easily accessible.

Machine learning offers an alternative approach to data linkage. Instead of using a rigid set of rules, machine learning algorithms can learn from data to identify which records should be linked.

The best way to turn Salesforce into a true asset for data management is to use it as part of a master data management (MDM) framework. MDM is a method of organizing and managing your data so that it is accurate, consistent, and accessible. Did you know that Salesforce has some built-in MDM features, include

There are many benefits to having a canonical record set, such as improved data quality, reduced storage costs, and easier data management. In order to create and manage a canonical record, there are a few things you need to do.

A canonical model (also known as common data model) is a single, common, model that ensures your systems can communicate in a consistent way. It can help you create a definitive source of truth for specific types of data.

For businesses, to make sense of the exponentially growing data, formatting and standardizing it in a consistent way becomes crucial. You can’t achieve holistic, consistent analytics when a data warehouse or data mart doesn’t serve its purpose with data siting there unstandardized, unlinked, and fragmented.

If you’ve taken stock of your organization’s data management practices lately, you may have realized that there is a lot of room for improvement. Data is the lifeblood of every business, yet it’s often treated as an afterthought and handled in a loose, improvised fashion. 

If you’re looking for a way to streamline your business operations, reduce costs, and improve employee satisfaction, operational master data management (MDM) may be the solution you need. This system helps organizations keep their critical data consistent and accurate across multiple applications and databases, providing a centralized view of important information that can be accessed

Are you preparing to migrate your organization’s data to a new storage solution? Maybe you are replatforming, integrating systems, or trying to create a data mart? Data migration can be a daunting task, but it doesn’t have to be a nightmare. With careful planning and following best practices, you can ensure a smooth transition. Essential

Large-scale data migration can be a daunting task, but with careful planning and execution, it can be made easier. In this article, we offer ten tips for a smooth migration, including thorough planning, automation, testing, backups, monitoring, downtime planning, data integrity verification, data cleanup, future-proofing, and documentation. By following these tips, you can make your

Organizations rely heavily on efficient data management systems to drive decision-making, streamline operations, and enhance customer experience. As technology evolves and business needs change, companies often find themselves in need of migrating data between systems, consolidating databases, or replatforming. While data migration is a common practice, it presents inherent risks that can jeopardize data integrity,

Wealth of available data and its exponential growth brings the challenge of extracting actionable insights. Data cleaning and normalization are crucial steps in the process of ensuring that the data is accurate, consistent, reliable, and ready for analysis.. Ready to unlock valuable data insights? Learn how to identify and correct inaccuracies and inconsistencies in your

Are you tired of dealing with messy, inconsistent data that makes it hard to gain insights and make informed decisions? Data normalization software may be the solution you need. This software can help you standardize your data, correct inaccuracies, and add missing information, all while integrating data from multiple sources.

If you’re looking to take control of your data management practices, you may have heard of both master data management (MDM) and enterprise resource planning (ERP) as solutions. But which one is right for your business? In this article, we explore the similarities and differences between ERP and MDM and provide guidance on choosing the

Data is the lifeblood of 21st-century organizations, and data governance and master data management (MDM) are two strategies used to manage data. You may be confused by how interchangeable these terms are used. However, they actually refer to very different concepts. In this article, we’ll take a closer look at the differences between data governance

In today’s fast-paced and dynamic business world, managing employee data effectively is more critical than ever. Employee Master Data Management (MDM) can help organizations maintain a centralized, accurate view of employee data, avoiding inefficiencies, errors, and delays. In this article, we explore the benefits of implementing employee MDM, such as improved data quality, increased efficiency,

Optimizing Your ERP’s Master Data For Better Performance is essential for any organization that wants to stay competitive in today’s fast-paced business environment. This process involves cleansing, standardizing, and enriching your master data to ensure that it is accurate and reliable. By doing so, you can improve your ERP system’s performance, save time and money

How to Optimize Location Master Data Management is a comprehensive guide to managing location data, from identifying and cleansing to enriching and maintaining data over time. Organizations face unique challenges when it comes to location data, such as inaccurate or incomplete data, and data spread across multiple systems. The article provides valuable insights on how

Managing data can be a messy and tedious task. Organizations are increasingly exploring machine learning (ML) technology to power their data quality improvement efforts. ML can help companies get more from their data by reducing manual work and enabling better decisions.

In this article, the importance of customer master data management for a successful business is discussed. The article provides ten best practices for managing customer data effectively. These include defining data needs, identifying data sources, assessing data quality, establishing data governance, implementing data quality programs, creating a single view of the customer, implementing data security

Learn about the benefits of automating master data management (MDM) using a software solution that is tailored to your organization’s needs. The article discusses two types of MDM automation – process automation and data management automation – and how they can be used together to create a fully automated MDM system.

Learn about the advantages and challenges of implementing a master data management strategy in healthcare, including improved data quality, more efficient coordination of care, enhanced privacy and security, and cost savings.

Are you looking for an efficient way to manage your most critical data? Master Data Management implementation is the answer. With its goal of creating a single, authoritative source for all key data elements, it can help organizations make sound decisions and reduce costs.

Master Data Management (MDM) is crucial for organizations that want to ensure data accuracy and consistency across all their systems and applications. However, with so many MDM frameworks available in the market, selecting the right one for your organization’s specific needs can be overwhelming. This article will provide you with a comprehensive guide to choosing