Between spelling variations, misinterpretations, lack of standardization, and cultural differences, there are endless obstacles businesses must circumvent to ensure their data is accurate, trusted, and relevant.
Even if you bring the data together through data replication, migrations, or other integrations, without proper data cleansing and harmonization, unified data might give you more headaches than you bargained for. Creating a single view of your data goes well beyond simply unifying your data types across disparate sources.
Standardized naming conventions, normalization, validation, and so much more, are all integral aspects of matching customer profiles, unstructured data, P&L, and inventory, in a seamless, trustworthy manner. An integral part of data quality and master data management, data harmonization is meant to optimize the value and accessibility of data across the business.
Data harmonization is the process of bringing together data from varying file formats, naming conventions, and systems, and transforming it into one cohesive data set. It’s an integral part of maintaining data quality and goes well beyond the overly simplified “lift and shift” approach.
Harmonization of data means having a single source of data. Make no mistake - this doesn’t necessarily mean that this enormous amount of data is stored in a single data lake. Disparate data sources can be stored in different locations, schemas, and technologies and still be harmonized via a set of standardized APIs or services that change, move, and clean data along the way. Data integration and replication, for example, uses Change Data Capture to update data with disrupting the journey from source to target destination, so both systems maintain accurate data in real-time.
For a true and trusted 360-view of your customers, accounts, third-parties, internal resources, you name it, you need to harmonize your data.
Whether your undergoing a merger or acquisition, digital transformation to the cloud, or simply trying to keep up with your customers, enterprises need to unify data across historical CRM data while consolidating and minimizing data storage and systems.
Data harmonization makes it possible for users to confidently transform data for business intelligence without IT involvement. Modern data unification solutions have been shown to free up tech resources by over 67% and decrease the amount of time spent on repetitive data quality tasks.
Some of the benefits to accurately matched, harmonized data are:
Combining data across multiple sources, systems, and regions is challenging for any company. Throw in the scale and speed at which we now ingest new data, and it can seem downright impossible to maintain “100% accuracy.”
Rarely is there a company that exists today whose audience, customers, partners, or employees aren’t spread far and wide across various regions and cultures. Wide variations in naming conventions, languages, regulations, and addressing present a complex challenge when attempting to unify data across global systems. Just with contact information alone, transliteration must take into account regional and cultural differences in how names and addresses are formatted, entered, and organized – while maintaining a high level of accuracy that anyone, from any region, in any language, can trust.
With the increasing volume of records being entered and re-entered by businesses across systems, companies need a fail-proof way to ensure that data is accurately and correctly matched. Without proper data harmonization, enterprises will be unable to obtain (and maintain) a centralized data governance strategy. Differing insights and values will be generated for the same standard of data from different sources. Without a clear indication of which is the single, trusted, and most relevant source, teams may be trying to access multiple sources of data to tackle the same issue.
Clearly, raw, unharmonized data isn’t suitable for marketing analysis. It can ruin even your best efforts to maintain quality data and cause confusion among analysts. Raw data often contains irrelevant data clusters, incorrect values, and duplicates. That’s why it’s crucial to understand how data harmonization works to standardize your data in the right way.
To drive real value from your data, it's important to understand the integral operations that take place during the data harmonization process. Let’s take a look at some of the most integral processes to accurate data harmonization:
Data compiled across various data warehouses, data lakes, cloud environments and platforms are hardly ever organized in same structure or schema. Data standardization ensures that all data is sorted with the same formats and labels, before it’s been integrated, migrated, or ingested by other target systems. These clearly defined attributes and definitions are the basis of your metadata and the foundation for a comprehensive data catalog. By improving access to your most relevant information, standardizing your data is the pathway to 99.9% accurate data.
Normalization is such an integral part of data quality that enterprise solutions place this at the forefront of their harmonization and contact matching processes. Using AI/ML to perform human-like analysis (at a superhuman scale) of each individual data element, complex tables and concatenated fields are broken down and scored. This advanced data normalization technique measures the trustworthiness of specific attributes before applying algorithms to deduplicate and unify data.
Deduplication is a data harmonization technique that refines datasets and helps users get only the data they need by eliminating duplicate and irrelevant information. Sometimes referred to as data filtering, proprietary and phonetic algorithms to seek out similar records and identify groups for further comparison and scoring. Deduplication shouldn’t have to depend upon a single datapoint being accurate, consistent, or even present, but instead leverage insights gleaned from the normalization and standardization process. This method allows data quality solutions to perform real-time lookups across billions of records throughout the business in minutes.
To avoid data discrepancies and inaccuracies, automated rules and AI-powered algorithms are used to verify data integrity and notify users when data issues are encountered. Notification can be automatically triggered when an undefined data field or potentially mis-matched record needs further review.
Data from various sources in various formats need to be harmonized to obtain structured data, but that doesn’t mean manually monitoring a single data lake. Cleansing and wrangling data is an integral part of maintaining a clean database, but a full data harmonization strategy is vital to real-time accuracy. Without preventative measures to check dirty data at the door, even freshly cleaned data quickly decays at about 30% per year.
To manage the scale and complexity that global enterprises now face, data quality solutions like Syniti Data Matching feature automated, AI-driven processes to reduce data prep and wrangling. Automated standardization and normalization remove the manual pre-processing tasks typically required before moving and integrating data. And new, cloud-based UIs enable business users to easily create and share customized workflows without a background in SQL scripting. These new advances in data quality software takes the stress out of harmonizing data and puts the control back in the hands of the user, optimizing the value of the data you already own.
Learn more about how various algorithms and solutions tackle name matching and why. Download The Complexity of Name Matching, a free resource from Syniti for a deeper dive.