The importance of data quality to Railz's Accounting Data-as-a-Service™ API
Data quality is an important goal for Railz and our accounting and financial data-as-a-service solution. Often, it becomes the end rather than the means to our analytics. It was only a few months ago that I had extensive discussions with the rest of the data team and our CTO for the calibration of certain analytics that would validate the accuracy of the Banking Transactions data that we offer using our Accounting Transactions data. Sharing insights that we draw from our effort to ensure data quality with our clients is key to continuing to build a service that provides high quality data. Below, we’ll explore why having high quality data is important for your organization using our service and for our services as well.
What is data quality and why does data quality matter to the Railz Accounting Data-as-a-Service™ API?
Data quality refers to when data is transferred or transmitted and it retains its meaning, integrity, and granularity, which indicates you can trust and use your data. Data quality is one of the most important aspects that underlies any data-as-a-service provider, as it is the equivalent of product quality for a manufacturer. At Railz, data quality defines the way we conduct business as an accounting data-as-a-service provider. However, data quality is important for reasons that go far beyond the realm of their provider’s prestige. Data that is drawn from a widely represented population and from different sources and historical periods are a very powerful weapon in the hands of model developers and business leaders.
How data quality is important in allowing financial institutions to effectively use their data
Over the last decade the amount of data created, stored and consumed has increased expeditiously - 90% of the world’s data was created in the last two years and it’s predicted to double in size every two years thereafter. Along with data creation, new tools and methods to analyze data have emerged due to accessibility to computational power that was previously unavailable. An internal example is our Railz Normalization Engine, which uses machine learning (ML) to standardize financial data across various accounting and financial service data providers.
Following the need for big data collection and analysis, financial institutions and other businesses are choosing analytical approaches that require large volumes of data, such as Artificial Intelligence (AI) or ML. All these changes have raised the significance of the size of a dataset that is used to produce analytics.
Large volumes of data do not necessarily guarantee robust models or suitable analytics and insights unless the data used is also of high quality - meaning that there is certainty in the data’s applicability, quality, and granularity.
The essential characteristics of data quality and how Railz strives to achieve them:
- Completeness: It refers to the extent to which the dataset includes all the expected records/observations for the desired variables. In Railz, ensuring data completeness of accounting and financial data requires a bit more than common data science techniques. Our subject matter experts are constantly checking and verifying the datasets to interpret potential gaps and ensure that our data are always telling the full story. In addition, automated checks are constructed to ensure the completeness of our data where accounting intuition allows it (such as the check that Assets should always equal Equity plus Liabilities in a complete balance sheet).
- Representativeness: It concerns the variety of the population from which the data is collected. High representativeness is desired in most cases but there are exceptions where the best way to derive insights is from specifically targeted populations. At Railz we examine each dataset and piece of analytics separately to ensure that it uses the range of data that will maximize its representativeness and therefore optimize its potential use from our clients. The same philosophy is implemented in our Extract Load and Transform pipelines.
- Validity and Accuracy: While these two characteristics are different they are oftenly ensured through the same set of procedures. The first term refers to the ability to be able to verify the data through other sources while the second refers to how representative of the real world the data is. Often invalid data can be inaccurate one as well. In Railz we give high importance to both aspects as can be demonstrated by the use case mentioned below (link to the use case about banking reconciliation).
- Time relevance: Refers to the period where the data were selected and how this period is relevant for the analysis time frame. Usually contemporary data are the most reliable to draw conclusions, however when constructing a historical sample of data, it is important to include different time periods to ensure data representativeness from a time perspective. At Railz we update and expand our data sources on at least a daily basis to make sure we provide the most up-to-date information to our customers and the widest possible range of historical data to them.
- Consistency: This characteristic refers to the absence of difference between data that come from the same population and source. Data consistency is highly important when the end-user is trying to make any inference from the data that involves its interpretations as a group including the development of predictive analytics. Railz deploys several sophisticated methods such as data normalization to ensure the consistency of our data regardless of the differences between the sources.
How our financial institutions and financial technology companies are using Railz’s data quality capabilities
At Railz, we know that our financial institutions and financial technology (fintech) companies will use their small- and medium-sized businesses’ (SMBs) data to calibrate models. These models will drive important decisions from our financial services companies and these decisions will, in turn, impact their SMBs. We are passionate about cleaning and optimizing the quality of each variable that we provide as the overall quality of the data has business-impacting decisions around it. Data quality is such an important focus internally that it becomes the end goal rather than the means to our analytics.
Quality data means robust analytics and service offerings for your financial institution to support your commercial customers
We will continue to build robust models based on high quality data for our financial institutions and fintech companies, building on top of our accounting and financial data API. Doing so enables your financial company to provide the best possible services, products, insights and analytics, and offerings to your commercial customers. We can only build the largest financial data network by providing access to high quality data - and that’s our main mission at Railz.
Chrysafis Tsoukalas is a Senior Quantitative Developer at Railz. He has experience as a quantitative consultant specializing in Credit Risk and Market Risk models. In his free time he enjoys reading literature, analyzing chess positions and watching the latest games from European soccer leagues.