How to Structure Your Unstructured Data

In the digital age, data is the lifeblood of a business, and finance is at the center of it. The data you want is usually siloed across multiple systems, making it difficult to aggregate effectively. To track down, it may be necessary to have specific administrative access or IT involvement in some circumstances.

Structure is required in business and finance processes to ensure that the outputs are repeatable and scalable, as well as to provide the necessary knowledge to manage the company. The same can be said for your data. This post will cover the dangers of unstructured data and how to automate structured data to eliminate time-consuming manual tasks that divert resources away from higher-value reporting and analysis.

Structured vs. Unstructured Data

To better comprehend structured data, you must first recognize that data comes in a variety of forms and levels of complexity. It’s a seemingly endless list of transaction details, images, compressed codecs, dates, and so on.

Simply put, structured data is quantitative data that is formatted for reporting and analysis use cases, whereas unstructured data is a form of raw data that is not readily set for consumption. Unstructured data requires further manipulation and classification before it can be used in a specific manner.

Structured data, such as that used in accounting and finance, is often stored in a data warehouse since it can be centralized from numerous sources and utilizes defined formats.

Financial data stored in your general ledger follows a certain categorization, which can be set up during implementation or configured at any point after. It’s ideal to mirror this after your Chart of Accounts (COA), so if you don’t have a detailed one, you’ll want to prioritize organizing that as well. Without fundamental accounting principles in place, you will struggle to fulfill your financial planning and analysis obligations.

For example, you might sell widgets at multiple store locations as well as direct-to-consumer (DTC), and therefore, your accounts will have a department or location classification to keep revenue and other financial records organized.

Unstructured Data Processing and Manual Data Aggregation

Data processing used to be a completely manual process that was not only time-consuming but also prone to costly errors. Problems often develop when you want to compile reports and analyze financial data from your general ledger for planning and forecasting purposes.

Messy data necessitates a rigorous pre-processing procedure, which often falls on the responsibility of accounting and finance professionals. It’s an unavoidable task if you want to prepare accurate financial statements, reporting packages, and other scenario analyses.

When budgeting season comes around, every finance professional’s favorite time of year, the challenges are only enhanced. To develop a detailed budget and targets for the next fiscal year, you need data from disparate systems such as your ERP for financial data, as well as your CRM and HR system for operational data.

Manually aggregating all of your data and combining it in spreadsheets is a time-consuming process that will keep you awake at night and keep you away from your loved ones on Saturdays.

Recognizing this mundane manual process, various FP&A vendors developed integrations and automation tools to provide the structuring for you.

Unstructured Data & Automation Examples

Let’s imagine you need to produce a weekly financial summary report for your management team and investors that includes certain revenue analysis criteria. This is a procedure that accountants and finance professionals are all too familiar with.

To fulfill this request, you begin by logging into your ERP, such as Netsuite, exporting your financial data, and manually importing it into Excel, your go-to resource. Unfortunately, the financial data you export is surely unstructured. So, what does this imply?

The result of importing the data into your spreadsheet is a disorganized tangle of data with items in the wrong columns, empty or partial rows of data, name mistakes, value errors, and so on. Even with a few tricks to help speed things up, manually formatting your financial data for reporting and analysis takes hours every time.

The hours you spend manually processing this data each day deprive your potential to undertake deep analysis that leads to strategic and actionable business insight. This is an instance of how unstructured data can cause financial problems.

Becoming a More Valuable Asset

With the help of FP&A software, repetitive tasks and errors can be eliminated. For example, Datarails FP&A solution automatically consolidates your information from across organizational systems. This includes all your financial and operational data from all your complex Excel files alongside data from any transactional system (ERP, CRM, HRIS, etc.) No matter how complex your data, DataRails is able to bring it all together and create one unified, structured database.

Companies, especially those experiencing rapid growth, must stay agile in order to remain competitive. Using traditional finance procedures like manual data aggregation and processing has a direct impact on the opportunity cost for finance to deliver financial and operational insight that drives strategic conversations by decision-makers and management.

You’ll never be able to construct your pivot tables, reports, or models accurately or without errors if you don’t have good, structured data. Remember that Finance must take the data, make sense of it, visually portray it, and, most importantly, provide an easy-to-understand explanation.

Companies can accelerate their productivity, provide better value, and ultimately operate like a mature finance function by leveraging tools and promptly assessing, pivoting, and implementing change.