How Data Profiling Can Help Uncover Hidden Details in Your Financial Information
- Zara Ziad, Product Marketing Analyst at Data Ladder
- 07.03.2022 01:00 pm #Data #finance
Businesses – especially financial and insurance companies – invest a lot of resources in gathering data from a wide variety of sources. They also strategize and implement processes that utilize the collected data for making decisions, calculating risks, and forecasting profits.
But 24 percent of insurers say that they are ‘not very confident’ about the data they use to assess and price risk. This lack of confidence in data that is used across all business operations can cause a lot of damage to an organization.
This article will help you to understand what bad data means, how it affects a business’s financial operations, and which technique can help identify such issues. So, let’s get started.
Data quality
If data can be confidently used for any intended purpose, then it is known to be of quality. Anything that falls below this expectation introduces a lack of confidence in data usage and adoption across the organization. This generally happens when a dataset has:
Missing information,
Incomplete data fields,
Invalid data formats and patterns,
Duplicates records relating to the same entity,
Disparate sources containing inconsistent data entries, and so on.
Impact of poor data on finances
Operating an organization with bad or inconsistent datasets can cause a lot of issues. Sometimes, business leaders are not even aware that failed outcomes are a result of poor data quality. If you are facing issues listed below, there’s a high chance they are a sign of bad data quality.
Increased financial fraud: The presence of duplicate entity records and no easy way to identify data matches can lead to increased identity theft and suspicious transactions.
Missed business opportunities: If inaccurate or incomplete datasets are used, there is a high chance that you missed out on new market opportunities, potential customer acquisitions, as well as possible competitive advantages that you could have gained in the industry.
Failed regulatory compliance: Lack of data aggregation capabilities and risk reporting practices can result in failing to meet regulatory compliances and standards, such as BCBS 239.
Lost revenue: Poor quality information can cause you to make uninformed decisions associated with prices and risks, and can amount to huge revenue losses.
Reputational damage: Incurred losses in revenue, missed opportunities, and failed compliance are primary reasons that impact a brand’s reputation in the industry, causing your potential customers to sign deals with other competitive, reputational brands.
Data profiling: The first step in the adoption of a data culture
The impact of poor data quality is not limited to the issues mentioned above. But whatever the impact is, the solution starts with the ability to understand your data better. A lack of confidence in data arises when you are unable to assess the current state of your data – is it clean? Is it well-prepared? Is it ready to be used for any intended purpose?
This is where data profiling can play a key role.
Data profiling can help uncover the hidden details in your financial information. It runs multiple algorithms that help to:
Analyze and assess statistical as well as qualitative details of a dataset.
Detect anomalies – data values that show abnormality as compared to rest of the values in that column.
Understand metadata, including the definition of an attribute, as well as the acceptable data type, size, and domain.
The result of these algorithms is a detailed data profile report that gives insights into the contents and structure of a dataset.
The primary contents of this report and how they help in making decisions are mentioned below in the table.
Reports | What does it include? | How it helps? |
Range analysis | The range of values a data column covers. | Helps to identify any anomalies that may be present in a column. |
Null analysis | The percentage of null or empty values in a column. | Helps to identify incomplete information, so that the values are updated before used for crucial operations. |
Uniqueness analysis | Whether a column value occurs once or multiple times. | Helps to identify unique records in the dataset. For example, a column like Social Security Number should contain all unique values, and multiples can indicate potential duplicates. |
Mean analysis | The average value for numeric or timestamp columns. | Helps to calculate average stats – for example, average price, average sales, etc. |
Median analysis | The middle value of an ordered column list. | Helps to detect any anomalies that may be present in the column. |
Size analysis | The maximum size a column covers. | Helps to identify data column requirements, as well as the presence of anomalies – for example, a Phone Number field having a size of 50 raises concerns. |
Data type analysis | The data type of a column, such as string, number, float, alphanumeric, etc. | Helps to identify data column requirements, and whether the right data types are used. Incorrect data types increase the probability of errors. |
Pattern analysis | The format and pattern that a data column follows. | Helps to identify incorrectly formatted fields, and uncover possible standardization opportunities. |
Domain analysis | The space out of which the data column values are derived. | Helps to identify anomalies or incorrect values, for example, the column City should contain values from a list of possible cities. |
Using data profiling to understand your financial data
The few metrics mentioned above are not all a data profile report can contain. Different organizations include various stats in a data profile report – something that helps them to understand their data better. Given a detailed data profile report, you can now better understand the current state of your data, and assess what needs to be fixed before it can be used efficiently.
Some organizations use manual methods of calculating these metrics while others employ self-service data profiling tools that can generate a complete, 360-view of your data in a matter of seconds.
Assessing the suitability of data, and its conformance to the definition of quality is a crucial need of every financial institution. And data profiling can act as the first step in the identification and resolution of critical data quality errors.
