9 Dos and Don’ts of Data Management for Fast-Growing Finance Teams

Poor data management practices and their consequences

Poor data management practices such as not storing data securely, using raw data for analysis without cleaning it, and collecting inaccurate data can all negatively impact your business and its operations.

Here’re some grave consequences of managing data poorly.

• Wrong insights

  : Wrong data insights can lead to lost revenue and opportunities in many ways. For example, sales processes fail to convert because the underlying client data is inadequate or inaccurate. Poor data can mean incorrect business growth forecasts, cash flow expectations, and tax projections for finance teams. Wrong insights lead to wrong business decisions that slow down your growth.

• Security issues:

  Security breaches tarnish your company’s reputation, leading to erosion in your client base and market share. Almost half of all businesses have reported at least one data breach or data breach attempt at some point. Security of sensitive data is critical. Poor security has many consequences, including putting confidential business and customer data in the wrong hands.

• Regulatory compliance issues:

  Regulatory non-compliance involving data security and privacy issues results in steep penalties for the erring business. Data regulations such as GDPR are stringent about how you store and share customer data. Regulatory fines cost you millions of dollars and result in a lot of negative publicity. Ensure that you store and share data in a secured manner to avoid penalties.

In the following sections, we’ll look at some data management best practices that organizations can follow.

Data Management Best Practices: The Dos

Data management best practices can help your employees handle data correctly and in a secure manner. This section looks at nine dos and don’ts of data management to ensure better business results.

Do: Define a data management workflow

Having a structured approach to data management and analysis will help you maneuver even complex problems easily. Here we list three data management workflows that you can follow for better insights.

• The first step in data analysis is data collection and cleaning. You must define data collection processes and identify data governance practices that you want your team to follow. Your team may be collecting raw data from multiple sources but you need a cleanup process structure to organize it, remove outliers, and make it ready for analysis.
• You should also have a clear data approval process. Set workflows for data approvals to ensure that the right persons are using the right data for the right analysis. Also, have a method for naming and storing data.
• Define event data workflows. Have a well-structured data policy and data strategy. Use dedicated governance tools such as customer data platform (CDP) to help organize and store your data.

Do: Use data to predict outcomes

One reason why data is so valuable is because it can be used to predict future outcomes. Do you want to know when a customer would pay their due invoice? Look at past payment trends and build a model with various influencing variables such as previous payment dates, invoice amounts, etc. to predict when the customer is likely to settle the invoice.

The right data when applied to robust forecast models can help you predict even complex business scenarios. Today, predictive technologies are more accessible and sophisticated than ever. Using tools like predictive analytics software, you can accurately anticipate business outcomes and proactively respond to problems and opportunities.

Do: Make data accessible within your business but with appropriate controls

Your managers may often say that they never knew a particular data existed when in reality it was up there on their servers waiting to be found. One of the most common causes for the lack of uptake of data analytics is that the people who can put it to best use lack access to it.

Keep proper records to ensure that your employees know what data is available and what isn’t. You can also build dashboards that can keep track of custom company metrics and share them at company-wide meetings.

At the same time, also use data classification methods to restrict data access based on jobs roles and functions. You wouldn’t want your junior market research analyst to know about undisclosed contract value deals.

Do: Have data backups and a disaster recovery plan

Data loss is both costly and distressing. Data is invaluable to run your business and any loss can lead to downtime and missed opportunities. Accidental deletion of data, data theft by hackers, and damage to physical devices such as servers all lead to loss of business data.

Backing up data and having a disaster recovery plan is critical to keep your business functioning smoothly even in the event of such data breaches.

Talk with your data server vendors or cloud providers to implement the required data recovery levels and data synchronization points. Have well-defined business continuity plans that you can refer to in case of contingencies.

Do: Build an in-house team of data analysts

As the volume, variety, and velocity of data increases, you can no longer rely on spreadsheets for data analysis. Sophisticated business analytics tools and features with AI capabilities are needed to derive real-time data insights. You also need a dedicated set of data analysts trained on the latest technologies to enable faster generation of insights.

Your finance team also needs data experts to accurately predict cash flows, revenue growth, and tax liabilities. Invest in upskilling your existing talent to help them become better data managers. Also, look at infusing new data science talent in your team to strengthen your data analytics capabilities.

Data Management Best Practices: The Don’ts

Don’t: Try to measure everything

A data management plan that tries to track everything will only pull you in multiple directions without giving the desired results.

In today’s digital landscape, we are bombarded with all types of data from different sources—consumer behavior, market trends, purchase data, invoice data, etc. But trying to collect every piece of data will only lead you to burn your resources without getting the needed insights.

You must first understand the metrics that you want to track. Then, identify the parameters that affect it and collect the necessary data to build your model. For example, if your objective is to track customer payment patterns, you don’t need data around your production capabilities for it. Instead, you should look for data around customers’ previous payment schedules, the items they purchase, their preferred modes of payments, and the economic conditions of the clients as well as the general market.

Don’t: Neglect data bias

The ‘numbers don’t lie’ maxim has been the strongest argument for data-driven decision-making over intuition-based decision-making. But what happens when biases start to creep into your data? Yes, data bias is true!

Bias in data creeps in when:

• Available data is not representative of the population
• Data does not cover all variables required for analysis
•  Erroneous weights distort data models

Data bias can result in bad decisions that affect the bottom line. Data analysts should keep track of the different biases that happen at each stage of the data management and analysis process. AI tools and algorithms trained to flag potentially biased data can help CFOs and business leaders be more conscious of data bias.

Don’t: Take data security lightly

Keeping your company’s data safe should be a priority for all teams. Financial information such as budgets and investment plans are confidential information that if breached prematurely can lead to loss of competitive advantage, reputational damage, and regulatory fines.

You must ensure that any vendors or partners you work with, adhere to the highest standards of data protection. Implement data security tools that encrypt sensitive data and support secure sharing

Also, implement standard security measures for everyday access to data, such as requiring employees to log into company apps with two-factor authentication. Use data classification systems to restrict access to only the right set of people. Run security awareness training sessions for your employees periodically, at least once every six months.

Don’t: Keep your data in silos

To get a 360-degree perspective on business metrics, your models need data from different sources. For example, to build a customer score model, you may need credit risk scores, customer service history data, client location details, etc. While some of this data may be owned by your finance teams, other data points may be owned by your sales and marketing teams.

Build holistic data models by ensuring data access to different teams in a secure fashion with appropriate controls.

Next Steps

The key objective of data management is to get the maximum value out of the data you collect. Establishing strong data management practices is a long-term, continuous effort.

Practicing the dos and don’ts that we discussed above can help you overcome your data management challenges. Implement data management best practices throughout your organization and use the right tech tools to support data management, data sharing, and governance.

Autonomous solutions, such HighRadius cloud-based AR solutions, help you to build a tight ecosystem that uses data from multiple sources and connects seamlessly to drive all concerned operations. To watch how our products help streamline AR data collection and use, schedule a demo today.