Poor data management leads to messy, error-prone data and unreliable insights. Finance teams struggling with these issues wind up left in the dark, and businesses lose their advantage over competitors and are more likely to take actions that will hurt the bottom line.
Companies must maintain a high data management standard to ensure that leadership can adequately guide the company in making sound financial decisions and remain compliant with accounting and data protection regulations.
This blog will introduce you to ten best practices in data management for finance teams so that you can harness the power of data analytics.
Interested in a specific aspect of data management? Click on the best practice below to skip ahead.
- Define your data management strategy and protocols
- Use data to build behavioural models and forecast outcomes
- Be selective in what you’re measuring
- Remember to account for data biases
- Establish access levels based on projects, job roles, and functions
- Back up your data and have a disaster recovery plan
- Prioritize data security
- Create a culture of compliance
- Curate an in-house team of data analysts
- Don’t keep your data in silos
It’s much easier to spot and correct problems early on with a well-structured framework for data management and analysis. To garner better insights, your organization must have clear workflows detailing how to handle data for each of the five steps of data analysis; data definition, collection, cleaning, analysis, and application.
Below are some sample data management workflows covering the five steps of data analysis:
- Definition: You must define why you need data analysis and what questions you’re seeking to answer. This will help to direct the entire process and narrow the scope of your query.
- Collection: You must identify data governance practices and the methodology you want your team to follow for collecting data. Create processes that ensure the right persons have access to the correct data.
- Cleaning: Since your team may be collecting raw data from multiple sources, you’ll need a cleaning process to ensure that the data is organized and error-free before the next step. Your team must follow the same convention for naming and storing data, so it’s a good idea to add a discovery layer and a common query layer.
- Analysis: A thorough data policy, strategy, and dedicated data governance tools are essential to facilitate efficient data analysis.
- Application: The last step of data analysis is for leadership to make informed decisions about implementing the findings.
One of the most valuable applications of data is predictive analytics. This information is vital to creating an action plan for proactively responding to threats and opportunities. Companies can build customer behavioural models with today’s technologies and reliably predict future outcomes.
For example, say you want a good idea of when a customer will pay their due invoice. A robust data management system will enable your team to analyze past payment trends, compare them with other customers, and build a forecast model to determine when that customer will most likely pay their balance. By harnessing these insights, you can build reliable customer aging reports and inform dunning policies to maintain positive customer relationships and reduce involuntary churn.
You’ll gain reliable insights from nothing if you try to track everything. While it’s very tempting to maximize your usage of every available feature and collect every fragment of data, you’ll only burn through your resources and become too distracted to find information to answer your initial query.
After all, good data management is about enforcing the seven standards of reliable data:
- Extracted from a credible source
- Accurate and free from error
- Complete and comprehensive
- Consistent across all systems
- Standardized format
- Collected on time
- Current and relevant
This best practice is similar to the first step of data analysis, but you apply it to your entire data storage system. First, understand which metrics you want to track. Then, identify your search parameters and the variables that impact those metrics. After completing these steps, you can collect the necessary data to build your model.
Beware of data biases! Bias is introduced to data when an error causes certain dataset elements to be over-weighted or overrepresented. Common examples of data biases include:
- Available data not representing the population
- Inaccurate weights distorting data models
- Data not covering all variables required for analysis
If leadership transitions from intuition-based to data-driven decision-making, they need clean data, or they could make a bad decision that harms the bottom line. Data analysts must note the different biases at each data management and analysis stage. Finance teams can even use AI and machine learning to help check data sets and flag potentially biased data to help raise awareness to leadership.
A common problem with poor data management is that team members lack access to necessary data. Leadership may be unaware that certain information already exists because the data is hiding on their servers and make unwise decisions that could have easily been aided by data analytics.
Of course, that doesn’t mean that everyone at the company should always have access to your company’s financial information. The best course of action is to set up data classification protocols that restrict and grant data access based on projects, job roles, and functions. Another strategy is to implement dashboards that track custom company performance metrics and share them at company-wide meetings.
According to Veeam’s 2022 Data Protection Report, the average cost of downtime is $88,000 per hour. While it’s true that number is skewed by larger organizations, that doesn’t mean that it’s cheap inconvenience for small and medium businesses.
Data loss is a distressing, costly event with a plethora of ramifications. Human error, unexpected updates, damage to physical devices like servers, and cybersecurity attacks are all common causes of data breaches and data loss.
The best ways to prevent the more serious impacts of these events are frequently backing up data and having a disaster recovery plan (DRP). A DRP will help to keep business continuity while IT quickly recovers operations, mitigating disruption of product and service delivery.
Protecting your company’s data must be a top priority for all teams. Financial information is confidential, and a breach could result in reputational damage, lost opportunities, and regulatory fines. Strict security protocols are not optional, and any vendors or partners must adhere to the highest data protection standards.
Investing in scalable security tools that support secure sharing and encrypting data flow is essential. Look for SSL encryption, two-factor authentication, advanced firewalls, and automated notifications for new logins. Hosting security awareness training sessions at least once every six months will help your team stay vigilant.
Another great way to protect your data is to build a culture of compliance within your company so finance is always audit-ready. Depending on your industry, you’ll also have to adhere to specific data protection regulations. For example, healthcare organizations must have strong security measures to protect personal information and remain HIPAA compliant.
Check in with your risk and security officers about new technology with autonomous data capabilities that can support compliance. An invaluable data management tool is data discovery, a feature that reviews, identifies, and tracks data chains necessary for multijurisdictional compliance.
However you decide to tackle this issue, no software alone can completely guarantee compliance. Your policies must supplement your technologies to ensure that your team and tools are sustainable and keep pace with rapidly evolving accounting standards.
Spreadsheets quickly lose their appropriateness as the volume and variability of data increase. Real-time reporting and data mining can only truly be achieved with sophisticated business analytics tools and features with AI capabilities.
More importantly, you’ll need a dedicated team of data analysts trained on these latest technologies to enable prompt and accurate insights. Upskilling your existing talent on the finance team will help them become better data managers and result in better forecasting for cash flows, tax liabilities, and revenue growth.
A data silo is an archive controlled by a single entity or is otherwise isolated from the rest of the organization. These repositories crop up in many ways, from files to emails to entire servers, but all share the same trait of hiding potentially vital information.
To gain a full view of your business metrics and understand your financial health at a deep level, your data must be accessible, and your models must include data from different sources. Unstructured, decentralized, and unshared data often cause problems and undermine the rest of the best practices written about in this blog.
If you can only implement one change to your existing data management—abolish the data silos. Whether you need to integrate a legacy system or clean excess raw data, obtaining an accessible and unified data set is worth it.
Further resources for improved data management
If you’re looking for more information about data management or have a specific question in mind, feel free to browse the resources listed below or contact our team. We’d love to hear from you.
- How to improve data management for financial consolidations
- 6 essential financial consolidation software features for your company
- Whitepaper | The finance leader’s playbook for financial transformation
- Case study | DynaLIFE centralizes lease management for 30+ locations
- Booklet | 6 best practices for solving financial consolidation challenges