Companies rely on data to build business intelligence, improve their processes, and generate more revenue. However, the opportunities data brings come with a risk.
When data isn’t properly managed, it can deteriorate, become unwieldable, or even get lost, hurting the business it was originally supposed to support.
Proper data management is therefore crucial to business success.
CRM data management is the practice of collecting, verifying, storing, and using customer relationship management (CRM) data to support business operations and reach business goals. Good CRM data management happens in a secure and efficient way, allowing companies to get the most from their data while remaining cost-effective and operating according to all data regulations.
A well-thought-out strategy is required to encompass the many different data management functions, such as:
These functions or activities take place across different tools and platforms, which together form a data management system.
It’s important to note that data management encompasses the management of all kinds of data, not just CRM data. It also oversees employee records, reference data, research data, reports, payroll data, and other kinds of internal data and externally-sourced information.
As such, data management affects everyone who interacts with the data your company stores.
Data has become a core asset in many companies. Companies use data to steer marketing campaigns, improve customer service, optimize workflows, and make high-level business decisions.
This extensive use of (and even dependence on) data means proper data management is crucial. Without it, it’s just a matter of time before data quality issues arise and lead to problems at all levels of your organization.
Inconsistent, incomplete, or incorrect data can lead to:
And if you don’t have a proper data management process set up, it won’t be long before all that data you’re gathering becomes hard to handle and starts eating up more of your resources instead of helping you perform better.
In short, without proper data management, you risk losing time, money, and even your brand’s reputation.
On top of that, companies have a legal obligation to keep their data secure and comply with privacy regulations such as GDPR and the California Consumer Privacy Act. Data management is crucial to remain in compliance with these regulations.
The importance of proper data management goes beyond problem prevention. A strong data management strategy brings a lot of benefits with it.
By keeping your data high quality and well organized, it becomes much easier to use for everyone in your organization. People can draw on the data that’s relevant to their tasks, run analyses, and suggest improvements without needing to rely on data specialists to interpret it all.
All of those customer and prospect records carry with them big responsibilities. Keep them safe, and you’ll maintain the trust of those who work with and buy from you. But if anything goes wrong, that security breach, loss of data, or privacy violation will make a dent in your reputation.
Consistent CRM data management ensures data privacy and security are constant priorities, safeguarding your reputation in the process.
Gathering large sets of data is one thing, but using them efficiently is another. A goal-oriented CRM data management strategy can give you a competitive edge by providing you with insights that allow you to improve your internal processes, as well as your marketing and sales efforts.
When data flows are properly documented and data is streamlined to provide single sources of reliable information across tools, it becomes much easier to create repeat processes and avoid running the same types of analysis over and over again. (Not to mention the resources you save by avoiding data duplication.)
Being such a large field, there are different types of data management that each come with their own set of responsibilities.
Data governance refers to the set of principles, procedures, and policies that determine how a company manages its data in every stage of the data lifecycle, from the moment the data is gathered to where and how it is stored, made accessible, used, moved, and (potentially) deleted.
Every company has its own data governance framework based on the company’s business goals and data needs. Bigger companies may have dedicated data governors who oversee data stewards, data security teams, and other data quality and management professionals, as well as anyone else who plays a role in safeguarding data quality and security.
A data steward is someone who implements and enforces data governance policies across the company. They don’t set the rules, but make sure guidelines are being followed and best practices are being implemented.
Oftentimes, data stewards are also responsible for ensuring data quality. But in larger companies, there may be dedicated data quality managers helping with this task.
Where data governance sets the rules for data management within a company, data architecture focuses on providing the technological infrastructure that allows data to be managed according to the set-out principles.
Data security concerns all processes that ensure a company’s data is protected, such as:
Data security teams don’t just oversee the actual data but ensure the hardware and software on which that data lives is secure.
Data preparation consists of profiling, validating, cleaning, and transforming raw data so that it becomes usable for analysis and as a source for business intelligence. It often also includes combining data from different sources.
Data modeling documents the relationships between different data and how data moves through a company, both within specific tools and across organizational systems.
Master data management focuses on ensuring a company is always working with a single, verified, current, and reliable piece of information. This means pulling data from all of your different data sources and presenting it as one usable, trustworthy source. It also involves making sure the data is represented in the same qualitative way across different systems and tools.
Data warehouse management determines how a company’s data is stored physically and/or in the cloud. It provides, oversees, and handles the infrastructure in which raw data is gathered and organized.
Big data management refers to how a company gathers and stores huge amounts of raw and structured data so that teams across the company can use this data to improve their processes and make data-driven decisions.
Data management will look different from company to company depending on the business’ goals and data needs. However, there are a few best practices you’ll want to keep in mind regardless of the policies and procedures you decide to implement.
As with most big undertakings in your business, your data management practices should support your business goals. This means you need to determine which data you need in order to perform efficiently and effectively, as well as how that data is best stored and used.
Gathering too much data may put a strain on your resources and draw the focus away from things that will really help you move the needle. Gathering too little data can make you miss out on opportunities as you develop more detailed procedures and workflows.
No matter what your business goals are, keeping your data secure should always be a priority. It’s important to actively work on this and not just take action when a breach occurs. Create a data security maintenance plan that allows your team to spot security risks and solve them preventatively.
Data security isn’t just the responsibility of your data management team. Everyone in the company who handles data needs to be equipped with knowledge of data security best practices. Ideally, this happens through ongoing training and communication around the latest threats, such as phishing techniques and ransomware attacks.
To safeguard your data quality, you need a clear definition of what high-quality data looks like within your company. Once you set these standards, develop a plan to continuously monitor them. Check for accuracy and possible data corruption so you can fix errors as soon as they turn up.
Here are a few data quality metrics you can include:
In this regard, it’s important that data management best practices are known throughout the company so everyone can do their part to keep the data as clean as possible. You can also define tool-specific data management guidelines, such as CRM data management best practices, as long as they don’t conflict with your overarching data governance policies.
In the case of an emergency, detailed backup and recovery procedures can help you get your data systems up and running quickly. They also ensure you can go right back to working with the high-quality data you’ve worked so hard to gather and maintain.
To best prepare your team, define what can go wrong in advance and map out how you’ll handle each scenario. Outline a step-by-step approach and use flowcharts to make it visually clear what needs to happen (and when) so everybody knows what to do when the pressure is on.
It’s also a good idea to log how all of your data management tools work and what the workflows between them look like. This makes it easier to troubleshoot in a targeted way and run recovery procedures only where needed.
When it comes to storing your data securely, the 3-2-1 methodology is commonly used and recommended. It says to store three copies of your data using two different methods of storage, with one of them being off-premises. This system ensures you’ll always have a copy of your data left in case a storage method or location is lost or destroyed.
The whole point of properly managing your data and keeping it safe is so it can be of use to your company. This means it needs to be easily accessible and wieldable for users. A certain database design might work well for your IT team, but it may be confusing to other users.
When developing your data management practices, always keep the end users in mind and ensure a smooth user experience for them. You may not always get this right from the get-go, which is why it’s important to gather feedback and make adaptations as needed.
In the end, your data can only work for you as much as your team members can work with the data.
Following data management best practices is easiest when you use data management software built to support these best practices. The right tool for you will depend on your company’s goals and data needs, but here are a few things you’ll always want a data management tool to have:
Data management has become an unavoidable aspect of running a business in today’s data-driven world. However, it’s not easy to get it right.
Because it encompasses so many different ways of handling data, data management can easily become messy and impact your data quality in a negative way.
That’s why we created our guide The Dirt on Data Quality. It gives you step-by-step instructions to create an effective data management strategy so you can ensure your data works for you, not against you.