Organizations are well aware of the risks associated with poor data quality and the devastating impact it can have on various business operations. Consequently, considerable time and resources are expended each week to perform data cleansing strategies, such as data standardization, data deduplication, entity resolution, and so forth.
While a reactive approach that identifies and fixes data quality issues may yield results, it is unquestionably unproductive. Companies need a more proactive approach – a framework that continuously looks for data quality issues and ensures that data is kept clean most of the time. For instance, when companies opt for B2B lead generation software, they always make sure that the data is regularly updated to avoid email deliverability issues.
What is Entity Resolution?
Entity resolution means matching different pieces of information to determine which ones belong to the same individual, company, or thing (referred to as an entity).
The process of entity resolution solves one of the biggest data challenges: attaining a single view of all entities across different assets. This refers to having a single record for each customer, product, employee, and other such entities.
This problem often arises when duplicate records of the same entity are stored in the same or across different datasets. There are many reasons why a company’s dataset might end up with duplicate records, such as a lack of unique identifiers, incorrect validation checks, or human errors.
How to Resolve Entities?
The process of resolving entities can be challenging in the absence of uniquely identifying attributes, as it is difficult to determine which information belongs to the same individual. However, we will look at a list of steps that are typically followed to match and resolve entities.
Collect and profile scattered data:
Entity resolution can be performed using data within the same dataset or across datasets. Either way, the first step is to gather and unify all data in one place for identification and merging of entities. Once done, it is essential to run data profiling checks on the collected data to identify potential data cleaning opportunities, enabling the resolution of such errors from the outset.
Perform data cleaning and standardization:
Before matching two records, it is crucial that their fields are in a similar form and format. For example, one record may have a single “Address” field, while another record may have multiple fields storing the address, such as Street Name, Street Number, Area, City, Country, etc.
Data cleaning and standardization techniques must be applied to parse a column, merge multiple columns into one, transform the format or pattern of data fields, fill in missing data, and so on.
Match data to resolve entities:
Now that you have your data together – clean and standardized – it’s time to run data matching algorithms. In the absence of unique identifiers, complex data matching techniques are used because fuzzy matching is required instead of exact matching.
Fuzzy matching techniques output the probability of two fields being related. For example, you may want to know if two customer records belong to the same customer. One record may show the customer’s name as Elizabeth, while the other shows Beth. An exact data matching technique may not be able to catch such discrepancies, but a fuzzy matching technique can.
Merge data to create a single source of truth:
With data matched and match scores computed, you can decide whether to merge two or more records together or discard the matches as false positives. In the end, you are left with a list of reliable, data-rich records where each record is complete and refers to a single entity.
Designing a Comprehensive Framework for Entity Resolution
In the previous section, we looked at a simple way to resolve entities. However, when your organization is constantly generating new data or updating existing data, it becomes more challenging to fix such data issues. In these cases, implementing an end-to-end data quality framework that seamlessly takes your data from analysis to execution and monitoring can be highly beneficial.
Such a framework consists of four phases:
In this stage, you need to assess the current state of your unresolved entities. For resolving customer entities, you may want to know the answers to questions such as: How many datasets contain customer information? How many customers do we have compared to the total number of customer records stored in our customer data platform? These questions will help you gauge the current state and plan what needs to be done to resolve the issue.
During this stage, you need to design two things:
The entity resolution process:
This involves designing the four-step process explained above but tailored to your specific case. You need to select data quality processes that are crucial to resolving your data quality issues. Furthermore, this step will help you identify which attributes to use for data matching, which data matching algorithms to use, and the merge purge rules that will help achieve the single source of truth.
At this stage, you also need to determine how this process will be implemented architecturally. For example, you may want to resolve entities before the record is saved in the database or resolve them afterward by querying data from the database and loading results to a destination source.
This is the stage where the execution happens. You can resolve entities manually or use an entity resolution software. Nowadays, there are vendors that offer self-service data quality tools that can potentially identify and fix duplicates, as well as expose data quality APIs that act as an data quality firewall between the data entry system and the destination database.
Once the execution is in place, it’s time to sit back and monitor the results. This is usually done by creating weekly or monthly reports to ensure that there are no duplicates present. If you do find multiple records for the same entity again in your dataset, you should iterate by going back to the evaluation stage and fixing any loopholes in the process.
Companies that spend a substantial amount of time ensuring the quality of their data assets experience promising growth. They recognize the value of good data and encourage people to maintain good data quality so that it can be used to make the right decisions. Having a central, single source of truth that is widely utilized across all operations is definitely a benefit you don’t want to deprive your business of.
Conclusion: So above is the Designing a Comprehensive Framework for Resolving Entities article. Hopefully with this article you can help you in life, always follow and read our good articles on the website: Megusta.info