FINANCE & BANKING

Understanding Structured Vs. Unstructured Data for FP&A and Treasury

Big data. Digital transformation. Agile development. There are lots of words to describe how our personal and professional lives are awash in data and how we manage it. We need to know both what this means, and what it means for us in our role in finance.

This article aims to help you familiarize yourself with key terms of the information age, and the implications for FP&A and treasury.

We need the skills to analyze to stay relevant, and the company that makes smart investments in business analytics will have a key resource

Structured Data

As the name implies, structured data implies a top-down approach and is part of an overall enterprise architecture: “a well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a holistic approach at all times, for the successful development and execution of strategy.”

The data model is the organized structure of the data: what the data is, how it enters a database and it is accessed by users, including potential changes. The data sits in a database and has a set of rules (schema) about how to access the data.

At the root, there are tables of data, much like a single Excel spreadsheet. Just like you can have multiple spreadsheet tabs in a workbook, there can be multiple tables in a database.

Tables can be searched through a query, or an instruction to search the data tables, and this output of this query itself can become a data object. Standard queries can be come reports or views on the data.

This describes the typical relational database, where the data can be queried based on the relationship between the objects described.

Data can be thought of having “dimensions” or key characteristics. Think of a typical graph with variables plotted on the X and Y axes, each of these a dimension of the data. A third dimension would be a Z axis and you can think of a data cube.

Many relational databases today will have seven to 10 dimensions of the data that allow you to “spin the cube” of data.

The data warehouse is data gathered from multiple systems (think of transactional data here), then accessed through a data mart (think of a market where you request and query what you need).

Why It Matters

The amount of data available to analyze is growing exponentially. More data has been created in the past two years than the entire history of the human race.

While structured data is estimated to be only about 20 percent of current data, it is the main source of information that we in finance use and create. Our enterprise resource planning tools are based on structured data—GLs, point of sale, inventory.

Additionally, call center logs, Internet of Things data from equipment sensors, and website data points are all structured data. However, for all the data (structured and unstructured) we create, only 0.5 percent will be analyzed.

The availability of data is in front of us. We need the skills to analyze to stay relevant, and the company that makes smart investments in business analytics will have a key resource. This implies good hiring and training allocations for people, and constantly upgrading the systems that can harness this increasing bounty of information.

As finance professionals, we need to become partial data scientists who can dig through and find the right data. At the same time, separating out the signal from the noise will become the key human skill that will separate us from machine-learning algorithms.

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