Most analysts estimate that for a given project well over half of the time is spent on collecting, transforming, and cleaning data in preparation for analysis. This task is generally regarded as one of the least appetizing portions of the data analysis process and yet it is the most crucial, as trustworthy analyses are borne out of clean, reliable data. Gathering and preparing data for analysis can be either enhanced or hindered based on the data management practices in place at a firm. When data are readily available, clearly defined, and well documented it will lead to faster and higher-quality insights. As the size and variability of data grows, however, so too does the challenge of storing and managing it. Like many firms, RiskSpan manages a multitude of large, complex datasets with varying degrees of similarity and connectedness. To streamline the analysis process and improve the quantity and quality of our insights, we have made our datasets, their attributes, and relationships transparent and quickly accessible using graph database technology. Graph databases differ significantly from traditional relational databases because data are not stored in tables. Instead, data are stored in either a node or a relationship (also called an edge), which is a connection between two nodes. The image below contains a grey node labeled as a dataset and a blue node labeled as a column. The line connecting these two nodes is a relationship which, in this instance, signifies that the dataset contains the column. There are many advantages to this data structure including decreased redundancy. Rather than storing the same “Column1” in multiple tables for each dataset that contain it (as you would in a relational database), you can simply create more relationships between the datasets demonstrated below: With this flexible structure it is possible to create complex schema that remain visually intuitive. In the image below the same grey (dataset) -contains-> blue (column) format is displayed for a large collection of datasets and columns. Even at such a high level, the relationships between datasets and columns reveal patterns about the data. Here are three quick observations: In the top right corner there is a dataset with many unique columns. There are two datasets that share many columns between them and have limited connectivity to the other datasets. Many ubiquitous columns have been pulled to the center of the star pattern via the relationships to the multiple datasets on the outer rim. In addition to containing labels, nodes can store data as key-value pairs. The image below displays the column “orig_upb” from dataset “FNMA_LLP”, which is one of Fannie Mae’s public datasets that is available on RiskSpan’s Edge Platform. Hovering over the column node displays some information about it, including the name of the field in the RiskSpan Edge platform, its column type, format, and data type. Relationships can also store data in the same key-value format. This is an incredibly useful property which, for the database in this example, can be used to store information specific to a dataset and its relationship to a column. One of the ways in which RiskSpan has utilized this capability is to hold information pertinent to data normalization in the relationships. To make our datasets easier to analyze and combine, we have normalized the formats and values of columns found in multiple datasets. For example, the field “loan_channel” has been mapped from many unique inputs across datasets to a set of standardized values. In the images below, the relationships between two datasets and loan_channel are highlighted. The relationship key-value pairs contain a list of “mapped_values” identifying the initial values from the raw data that have been transformed. The dataset on the left contains the list: [“BROKER”, “CORRESPONDENT”, “RETAIL”] While the dataset on the right contains: [“R”, “B”, “C”, “T”, “9”] We can easily merge these lists with a node containing a map of all the recognized enumerations for the field. This central repository of truth allows us to deploy easy and robust changes to the ETL processes for all datasets. It also allows analysts to easily query information related to data availability, formats, and values. In addition to queries specific to a column, this structure allows an analyst to answer questions about data availability across datasets with ease. Normally, comparing pdf data dictionaries, excel worksheets, or database tables can be a painstaking process. Using the graph database, however, a simple query can return the intersection of three datasets as shown below. The resulting graph is easy to analyze and use to define the steps required to obtain and manipulate the data. In addition to these benefits for analysts and end users, utilizing graph database technology for data management comes with benefits from a data governance perspective. Within the realm of data stewardship, ownership and accountability of datasets can be assigned and managed within a graph database like the one in this blog. The ability to store any attribute in a node and create any desired relationship makes it simple to add nodes representing data owners and curators connected to their respective datasets. The ease and transparency with which any data related information can be stored makes graph databases very attractive. Graph databases can also support a nearly infinite number of nodes and relationships while also remaining fast. While every technology has a learning curve, the intuitive nature of graphs combined with their flexibility makes them an intriguing and viable option for data management.