Data CleansingData Management

Data Management Best Practices to Improve the Health of Your Data

By June 29, 2022No Comments

Data management helps drive your business by making sure that your data is received, validated, stored, and protected in a standardized way. When you implement effective data management processes, your employees can have confidence that the information they are working with is reliable, accessible, and up to date. In this article, we will provide you with some data management best practices, so you can manage your data effectively, especially when it comes to Salesforce and other CRM data quality. Let’s start by defining the term data management before explaining some best practices and the value for your business.  

What is Data Management?

Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. The goal of data management is to help people and organizations optimize the use of data to make decisions and take actions that maximize the benefit to the organization. Think of data management as the spine that connects all segments of the information lifecycle. It works symbiotically with process management, ensuring that the actions teams take are informed by the cleanest, most current data available — which in today’s world means tracking changes and trends in real-time. 

Typically, data management practices include many different aspects, such as:  

  • Data cleansing: the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. 
  • Data collection: properly integrating all of your sources of data so that your company can derive actionable insights from it.  
  • Data access: ensuring strategies are in place that allow your business to create, update and access data from all collection sources.  
  • Data storage: the data storage strategy you implement needs to leverage various data storage types, such as on-premise storage, private cloud, and many others.  
  • Data availability: this involves making sure that your data is backed up and can be recovered in the event of a disaster, and your business can still operate with minimal disruptions.  
  • Data security and privacy: identifying and implementing needed security and privacy policies must include enforcing those measures on a continual basis.  

Following these practices in a formalized and consistent manner will help ensure that your organization maintains data integrity. Below are our top five suggested best practices for optimal data management.

Data Management Best Practices  

Adhering to data management best practices will allow you to reduce data redundancy, focus on data quality instead of quantity, prioritize data security, and implement monitoring and alerts to improve visibility.  

1) Reduce Duplicate Data

Duplicate data can cause a lot of problems for your employees and the company as a whole. It chokes IT resources and consumes the data quality budget, and can cause you to lose business. Duplicates are also problematic for your employees since they erode confidence in the data. For example, if a sales professional was burned by duplicate data even one single time, they will need to double-check the information in the CRM to make sure it’s correct. A lot of times, they will manually delete duplicate records, which also wastes a lot of time. Setting up a manual or automated process that audits data regularly and removes duplicates can help you better manage your data and reduce the cost of unnecessary storage. This also keeps your data clean and ready for analysis and queries. 

2) Make Data Quality a Priority

High data quality is important to ensure the usability of your data. When companies keep all of the data they generate, it produces a “data swamp” filled with either low-quality or totally unusable data. In addition to storing only current data, make it a practice to continuously validate its accuracy. Old data may be useful for analytical purposes, but before using it, you need to validate that it’s accurate, relevant, and suitable for your analysis.  

3) Focus on Data Security

Building a culture committed to data quality means a commitment to making a secure environment with strong privacy standards. Security matters when you are working to provide secure data for internal communications and strategy or protecting the privacy of a client’s data and information. Building a relationship of trust depends on the reliability of your security. Your management processes must be in place to prove that your networks are secure and that your employees understand the critical nature of data privacy. In today’s digital market, data security has been identified as one of the most significant decision-making factors when companies and consumers are making their buying decisions. One data privacy breach is one too many. Plan accordingly. 

4) Set Up Monitoring and Alerts

Monitoring processes and systems helps you gain visibility into your data repositories. Set up monitoring processes based on metrics that provide specific and actionable insights into important patterns and events affecting your data. The more data you have, the more difficult it becomes to maintain visibility. To extend your reach and control, you can leverage automated data classification systems. Prefer monitoring tools that use behavioral analysis, generating alerts only if behavior deviates from the norm to minimize false positives. 

5) Invest in Data Quality Tools

When considering these best practices together, it is recommended, if not required, that you invest in quality data-management software. Putting all the data you are creating into a manageable working business tool will help you to more readily and accurately find the information you need. Then you can create the right data sets and data-extract scheduling that works for your business needs. Data management software will work with both internal and external data assets and help configure your best governance plan. You can learn more about effective data management tools in our blog “Staying Appy with Data Management Tools on the Salesforce AppExchange.” 

DataGroomr Can Help You Meet Your Data Management Goals

DataGroomr offers robust data cleansing capabilities that can help you increase the quality of your data and help you leverage these best practices. Using reliable software that uses machine learning to cleanse your data will build trust in the quality of your data and can lead to better overall data health. Best of all, machine learning does all of the work for you. There’s nothing to download and no complicated rules to set up. Just sign into Salesforce, connect DataGroomr, and start deduping right away.  

Try DataGroomr for yourself with our free 14-day trial.  

Il'ya Dudkin

About Il'ya Dudkin

Ilya Dudkin is a Business Development Manager at Softwarium. He is a frequent contributor to popular Salesforce outlets such as SalesforceBen, Force Talks, SFDC Panther and many others as well.