Why you should care about your data management (yes, you!)

Brandon Jones 30 min read brandon
You’ve probably heard the phrase “data is king,” but collecting data isn’t enough — what you do with it actually matters far more than how much you have. 

Without a data management strategy to guide your teams, your processes won’t be consistent. Add a few disconnected systems and bad habits to the mix, and that all-important data that’s supposed to inform decisions, big and small, diminishes in value.

If you want to better leverage data to make confident decisions, boost productivity, and scale your business, it’s time to rethink your approach. Data management involves an ongoing commitment to improve data practices. But if your strategy is lacking or nonexistent, where do you begin?

By the end of this article, you’ll understand the importance of data management and how to create a data management strategy that will support your organization’s success.


What is data management?

Before we get into the practice of data management, let’s answer a fundamental question: “What is data management?”

Data management is the process of collecting, storing, processing, and maintaining an organization’s data in a secure, effective manner. Done well, this process provides business functions the information to proactively make informed decisions. It means doing things with confidence.

Your organization’s data management isn’t one team member’s responsibility, though — it concerns everyone. From sales pipelines and marketing campaigns to employee and financial records, data resides in almost every corner of your business. That’s why managing data has to be a team effort, and involves a host of data management activities. The ones you’ll want to consider will depend on the size of your business and what you need from your data.


Source: HubSpot


Why is data management important? 

Good data management is absolutely essential if you want to do great business, especially as you scale. Teams that effectively harness data unlock better performance, improving business and customer outcomes. Nobody wants to pass that up.

Here are a few reasons why data management is important for all involved:

01 Analyze business performance

Though trusting your gut can have advantages, some decisions require data to lead us to insight, then action. High-quality data enables confident decision-making, no matter whether you sit in the C-suite or marketing, sales, or service department. With access to the right information, you can assess current performance and predict future performance. 

This is where business intelligence (BI) platforms come in. They let teams extract insights out of complexity, simplify analysis, and speed up decision-making.

02 Reduce security risks

Following data management best practices will go far in protecting your business from unwanted data losses and data breaches. Data security is most important where data is sensitive, such as health or financial information, and where it needs to comply with HIPAA or GDPR laws, for example. Doing the right thing means you’ll never think twice about data security.

03 Enable sales success

Quality data empowers salespeople to leverage data throughout the sales process. There’s actually a term for this: data-driven sales enablement.

For example, salespeople can refer to lead scoring and determine which prospects are most likely to close. Rather than wasting time and energy trying to convert bad fits, you can conserve resources and provide extra attention to your ideal customers.

Data-driven sales enablement also lets salespeople continuously track sales data, resulting in higher individual and team performance.

04 Deliver targeted marketing

A surefire way to speak directly to your audience is with data-driven marketing. 

Say you’re running a lead-generation campaign. It will be far more targeted and impactful using data than basing decisions on assumptions. With ample data, you can personalize content and target audiences at any stage of the buyer’s journey. That’s a win for potential customers, who benefit from timely, relevant content, and for marketers, who can then refine their strategies.

05 Retain and delight customers

Customers expect consistent and tailored experiences from the businesses they engage. Always. So any data previously shared with you should be used to meet their expectations. For example, customer service teams can deliver faster, personalized advice with historical data housed in one place. 

You can also delight customers with customized offers that grow CLV. Provided your business has data management in order, you should be able to log into your CRM to identify (and engage) the most profitable customers.

06 Scale the company

When teams’ decisions are powered by high-quality data, they work in unison to lift productivity and improve revenue. Over time, a data-informed culture forms, one that makes smart choices and optimal outcomes, leading to bolder growth.


What exactly is high-quality data?

Data can be a valuable strategic resource, but if you focus on quantity over quality, it can also be a detriment. To get on track toward good data management, you will need to know the difference between bad data and high-quality data.

First, business value can be derived from high-quality data for a specific purpose when it is:

  • Accurate. Strive for data accuracy. Error-free, consistent data provides the most reliable information on which to base decisions.
  • Complete. This one’s pretty self-explanatory. If your data doesn’t have any missing pieces, it’s complete!
  • Up-to-date. This attribute goes hand-in-hand with data completeness and accuracy. The more frequently data updates, the more useful it becomes.

Just remember this: not every piece of data will be valuable in every circumstance. It needs to be fit for its intended purpose, so be sure to choose the appropriate dataset(s).

Did you know?

Email marketing databases naturally decay by roughly 22.5% every year! Be sure to clean out your database regularly to preserve data quality.


Conversely, bad data equals poor data quality. It creates inaccurate analytics, resulting in flawed insights, lost productivity, and an average cost to business of $12.9 million annually, says Gartner. One of the tricks in amassing high-quality data involves preventing bad data from the get-go. 

Here are a few red flags to watch out for in your data: 

  • It's fragmented. Data strewn across multiple repositories is problematic because it creates silos and reduces visibility and knowledge sharing.
  • It's outdated. Data must be maintained to maximize its usefulness. You wouldn’t want to base decisions on information that’s no longer accurate. 
  • It’s duplicated. Data duplication is a common occurrence that goes undetected when teams lack a single view of the customer.

Four common data management mistakes

Invalid data, missing fields, disorganized systems — they all trace back to teams’ data management habits. While practices like storing too much data might seem harmless, poor data management tends to go unnoticed until it’s completely unmanageable.

Here are four of the most common mistakes businesses make with their data management:

01 Not having a data management strategy

Knowing how to “do” data management starts with knowing what not to do first. But without a strategy in play, the chances of your team knowing what’s best are slim. How would quality decision-making happen? Unfortunately, it wouldn’t — at least not accurately.

02 Data exists in departmental silos

Siloed data stays out of mind. Most likely, team members will be unaware information exists by virtue of it living outside their business function and/or within a platform they don’t know about. This reduces data accessibility and visibility, prevents knowledge transfer, hampers collaboration, and creates friction between teams. Data is useless in isolation and its business value increases significantly once integrated.

03 Lack of data governance

Data governance defines the people, processes, and tools used to manage and protect an organization’s data. Think of it as one of the many essential ingredients of good data management. Businesses tend to go wrong by not defining the “who” and “how.”

Without data governance, no one is accountable for data-related decisions and risk mitigation. Which brings us to the biggest mistake of all...

04 Inattention to data security

Data privacy and security remains a hotly debated topic for enterprises and consumers alike. Take, for example, the recent string of data breaches in Australia. Customers tend not to forget the fallout, even after the dust settles (Cambridge Analytica, anyone?).

So it goes without saying that proactively protecting enterprise data in all forms is absolutely crucial. Don’t be complacent in your data security. The reputational cost of data misuse or a data breach is not a risk worth taking.

Were any of these mistakes relatable? Read on to find out how to create a data management strategy that everyone can stick to. 


How to create a data management strategy

Your data management strategy should set guide rails for storing, maintaining, and integrating data. You will also want to consider the business’s potential future needs.

What is a data management strategy?

HubSpot describes data management strategy as “a comprehensive and holistic vision of how a business handles data.” It integrates the collection, storage, maintenance, usage, and cleaning of data across applications and teams. This way, you have high-quality data to leverage.

Enterprise data strategy takes this concept one step further by aligning data management with business objectives.

Becoming a data-informed enterprise takes time, though a solid data management strategy will get you there sooner. Even if you have one documented, it never hurts to revisit it. Keep reading for guidance on how to create a data management strategy of your own.

01 Assess your data maturity level

When we talk about “data maturity,” we mean how well a business uses its data (more on that here). An assessment of your business’s level of data maturity will reveal your current problems and what might need to change.

You might like to ask yourself and other business functions questions like:

  • What types of data exist in our business?
  • Who currently interfaces with and manages those data?
  • How are we using qualitative and quantitative data, and why?
  • How do teams typically monitor and measure their versions of success?
  • What problems do we repeatedly encounter when analyzing data?

Once you start looking for problems — and we mean really looking — you might discover more than you anticipated. Often, there are a number of reasons why things aren’t working.

If you realize your data or related practices aren’t up to scratch, don’t sweat. It’s fixable! Take a breath and move onto Step 2.

02 Form the data architecture

Next, consider your data needs per your objectives. We recommend balancing two data strategy frameworks as you create the new architecture: offense and defense.

Data offense involves optimizing your analytics to enhance decision-making. On the flipside, data defense emphasizes protecting data security and remaining compliant. Both have value in your data architecture.  

Here are a few questions to help you plan it out:

  • Where will your data be stored?
  • How should it be organized?
  • How should it support decision-making?
  • How will it be kept secure?

Knowing the answers will help you build a plan that’s bespoke to your business, including the systems you might need. Top-tier tools like HubSpot Operations Hub allow you to transform the complex nature of data management into something much more manageable. In an ideal world, that’s how data management should be. 

HS_Data Quality_OpHub Blog Image
Source: HubSpot

03 Understand data interactions and assign ownership 

Every business function has different data needs and means of managing it. Case in point: both sales and service benefit from contact records in the CRM, yet the reasons for using them are entirely different.

Consulting every business function at Step 1 will give you a clear picture of their needs and habits. Only then can you assign data ownership to keep team members engaged and accountable.

04 Create data governance

Lastly, establish a concrete set of policies that inform how data will be collected and stored to maintain quality. While documentation is crucial, we recommend putting your energy into coaching teams to adopt the new standards. If you demonstrate the benefits and practices as they relate to their roles, they’ll get on board. Then, it’s up to everyone to do good data management.  

Before you embark on formulating a data management strategy, understand that it isn’t a once-and-done event. You’ll need to regularly review the strategy to shake out any blockers or bottlenecks and replace them with practices that improve business outcomes.


Ready to wrangle your data?

Impactful decision-making calls on everyone at all levels to have good data management practices. You just need to lead them with a strategy that’s clear, comprehensive, and, above all, empowering. Because having data work for your business (not against it) means the difference between so-so performance and serious growth.

If the thought of tackling your data management feels intimidating, let Salted Stone take care of it. We can identify and execute the best strategy to meet your business objectives.