Data consumers don’t bite. Honest.
But they do play increasingly vital roles in modern businesses. And their ability to deliver on behalf of the organization does depend on you keeping them well-fed—with data, that is.
(Cue record-scratch.)
Data consumers are individuals, systems, and applications that access and use data to perform specific tasks or make informed decisions. As data continues to proliferate into more aspects of daily life, this definition becomes increasingly important.
While most of us consume data by default, technical data consumers—those working in data analytics, data science, business intelligence, and the like—rely as much on the quality of data as they do its availability. This necessitates treating data as a product, with the entire organization working together to ensure the highest standards of quality are maintained over the course of the data lifecycle.
This is why it’s imperative for those working in tech-adjacent roles (e.g., managers, executives, decision-makers, and stakeholders) to understand how they can optimize for data consumers within their own organizations. But we’ll circle back to that.
First, let’s lay a foundation by making sure we clarify what separates data producers from data consumers (and what doesn’t) in addition to covering who commonly acts as a data consumer in modern businesses.
Data producers vs. data consumers: As simple as it seems?
It does seem obvious. Data producers produce and/or provide data, while data consumers access and use that data. However, there are nuances to this relationship that are worth understanding. Here are some key factors, common within business environments, that can challenge the seemingly clear distinction between data production and consumption.
Overlapping functions
While often considered separately, a single entity can function as both a data producer and consumer. For example, sales teams often produce data about customer interactions, which counts as a producer role. However, it’s common for these same teams to handle the analysis of this data in order to refine their sales strategies. More generally, feedback loops within a data-savvy organization have this effect as well.
Data transformation
Transformation of data is an instrumental part of data management. In practice, data producers may act as sources of raw data. This data then undergoes a transformation process as it’s ingested into the organization for use downstream. This can blur the line between producers and consumers in practice, as the transformation process necessitates data being consumed from one source before being delivered for use to another.
Variability of data production
Data is produced both actively and passively. An example of active production could be the filling out of a basic web form, a deliberate act of data entry, or data generation. This could be related to customer data or personal data when individuals provide information about themselves.
An example of passive data production is how user interactions are typically logged on a social media platform or a website. These data sources often involve data collection methods that track user behavior. Whether or not data is actively or passively produced can challenge the notion of whether it’s being produced or consumed by a given data system.
Data stewardship and ownership
Granted, data producers generate data. However, they may not have stewardship or ownership over the data produced. At the same time, data stewards who may not produce or consume data directly often play a crucial tertiary role in ensuring data quality, privacy, and security.
Timing and evolution
Producers and consumers of data often interact with data at different times. It’s common for data produced in real-time to be consumed in batches, or vice versa. This asynchronicity can also cloud a binary notion of who’s producing and consuming data over time.
And, over time, the needs of data evolve as business models evolve. Meaning systems traditionally viewed as data consumers, like home appliances, can become data producers (i.e., the Internet of Things).
The basic definitions of data producers and consumers are simple enough. But as business leaders, it’s advantageous to appreciate how fluid these definitions can be in practice.
Who (and what) are data consumers?
With this valuable context in mind, let’s now touch on who—and what—typically functions as a data consumer within an organization. This of course includes individuals and teams, but consumers can also be systems and applications or external entities.
Individuals and teams
- Business analysts and data scientists: Those who analyze data to extract insights, identify trends, and recommend business improvements certainly count as data consumers, as do the experts using advanced statistics, machine learning, and sheer compute to cull insights and predictions from data.
- Sales and marketing teams: The aforementioned sales teams consume data to track sales performance, identify leads, and forecast sales trends. At the same time, marketing teams leverage data to better understand customer behavior, segment markets, measure campaign effectiveness, and, like their counterparts in sales, attempt to forecast trends.
- Finance, accounting, and operations teams: Key players on these teams consume data for budgeting, forecasting, financial reporting, and risk assessment analysis.
- Operations and supply chain teams: These teams consume data to optimize inventory and streamline operations.
- Human resources (HR): Those working in human resources increasingly consume data related to recruitment trends (i.e., supply and demand), workforce analytics, and employee performance.
- Customer support and service teams: High-performing contact centers rely on a constant stream of data to track service requests, evaluate customer feedback, and improve customer satisfaction.
- Product managers: These roles require all available data to monitor product performance, user engagement, and identify areas of improvement.
- Decision makers and stakeholders: In data-driven organizations, senior leaders and managers rely on aggregated data and insights to make more productive, strategic decisions.
Systems and applications
- Business intelligence tools: BI tools like Tableau, QlikView, and Power BI consume data in order to create visual representations and dashboards that analysts (also data consumers, see above) use in their work.
- Customer relationship management systems: Alternatively, popular CRMs (think Salesforce and Hubspot) also consume data. Some do produce data visualizations as well, but this functionality is secondary to their primary goal of helping manage customer data and interactions.
- Enterprise resource planning systems: ERPs are comprehensive software solutions built to consume massive quantities of data in order to integrate various business processes and functions into a single system, one that aids in data management, process automation, reporting, analytics, and more.
- Automated reporting systems: Complexity is not a prerequisite for being a data consumer, as straightforward reporting systems like NinjaOne and Alteryx consume data only as needed to aid in data preparation and report generation.
- Data warehouses and data lakes: Typically thought of as data sources, centralized data repositories engineered to store structured and unstructured data must, by design, consume data in order to serve their purpose.
- Machine learning models: The algorithms driving artificial intelligence (AI) consume troves of data in order to make business-related predictions or automate decision-making processes.
- Application programming interfaces: And, while APIs do not technically consume data to serve their purpose, they do get an honorable mention here since they create the pathways different systems and applications use to communicate, consume, and share data together.
External entities
- Partners and affiliates: Business partners like suppliers, vendors, marketing agencies, and consulting firms often need to consume organizational data to measure joint campaign performance or align strategies.
- Regulatory bodies: In highly regulated industries like finance and healthcare, organizations may need to share data with regulatory authorities to ensure business operations and data management are in compliance.
- Your valued customers: Last but not least, digitally sophisticated customers are voracious when it comes to data consumption in the world of B2C. And, in B2B scenarios, customers are also increasingly consuming data reports and analytics as they grow both more available and accessible to less technical audiences.
15 best practices to optimize your org for data consumers
In business, sadly, not all individuals get to enjoy the luxury of thinking about data management and information technology all day, every day. For those who cannot, all the information we’ve shared at this point was provided to foster an understanding and appreciation for the nuanced and varied roles consumers play in the data lifecycle.
But, often, these are the same people who have the influence and pull needed to empower a truly data-driven organization. For this reason, the following list of 15 succinct best practices is curated for those stakeholders and decision-makers who want to help set the table, so to speak.
Let’s dig in:
1. Engage with your data consumers
One of the best things a stakeholder can do is simple: Regularly communicate with data users in the organization. Take the time to understand the distinct requirements and challenges they face in their role.
Pro tip: Make constant communication easy by scheduling regular, brief check-ins with those you consider to be key data users. Quick, consistent 15-minute meetings are far more useful than longer meetings set up and attended “once in a while.”
2. Treat your data as a product
Restaurants live and die based on the quality of the ingredients they source. The needs and importance of your data consumers are just one of many reasons to make sure your data is of the highest possible quality, ensuring it is reliable, timely, and consistent.
Pro tip: The volume of data flow through even a relatively small organization is often more than can be managed manually. Work with your data leaders to implement automated data quality checks. Also, make sure that any alerts these systems use when they detect anomalies are set up to go to the right people, at the right time.
3. Adopt a strong data governance framework
“Data governance” can sound intense. But at its heart, governance is really just a formalized approach to defining and maintaining clear roles for data ownership and stewardship.
Pro tip: Robust governance is a must in today’s data-driven business world. Do your part by making sure the team or data stewards in charge are given the support and bandwidth required to succeed in their role (especially if one of those stewards will be you).
4. Prioritize data security and compliance
It’s a lot harder to deal with a burglar when they’re already inside your house. Ensure that encryption, access controls, and regular audits are all in place to protect data on behalf of your consumers. It’s important to ensure everyone is up to date with regularly updated data privacy regulations specific to your organization and business model.
Pro tip: In partnership with your data leaders, look to leverage cloud-based solutions that include built-in security features. Doing so reduces the burdens of managing security completely in-house.
5. Provide accessible data tools
Champion tools and platforms for your data consumers that have earned a reputation for being highly intuitive to use. Powerful solutions that are also user-friendly encourage wide adoption, enhanced productivity, reduced training costs, and increased data accuracy.
Pro tip: Don’t sleep on the power of data visualization in business. BI tools in particular tend to offer dashboard capabilities that allow for quick insights without the deep dives.
6. Educate and train staff
Data literacy should no longer be relegated to specific departments. It’s not a chore—it’s a responsibility. The health and quality of data is determined by all of its consumers, both direct and indirect. Promote this mission-critical knowledge through regular training sessions and workshops, in addition to up-to-date, accessible resources.
Pro tip: On-demand online training modules can drastically simplify an ongoing data literacy initiative. They serve as a means to centralize information and resources, in addition to minimizing the need for lengthy (read: costly) training sessions.
7. Design for scalability and adaptability
Help ensure data systems in your organization are built to scale with the needs of your business and your data consumers.
Pro tip: Explore how cloud solutions can also support your ability to scale with respect to data. As these solutions grow more ubiquitous, it becomes easier to find one that can grow in sync with your business, without the need for frequent upgrades.
8. Encourage cross-functional collaboration
One data-related area that firmly falls within the onus of leadership is whether or not collaborative environments are actively fostered within the organization. Optimizing for data consumption requires departments proactively working to share data and insights, and minimize data silos.
Pro tip: Think of inter-office communication platforms like Slack or Teams as collaboration platforms. Encourage quick, specific chats as a way of keeping your data consumers out of lengthy meetings.
9. Streamline data collection processes
To ensure efficiency and reduce redundancy, normalize the ongoing automation and optimization of data collection.
Pro tip: Automating everything isn’t the goal. Look at where a little bit of automation will have the biggest impact. Sometimes, something as simple as utilizing APIs to pull data without manual intervention can net significant benefits.
10. Iterate based on feedback
There should be no set-it-and-forget-it with organizational data management. Data processes need to be continuously refined based on the feedback you get from data consumers. More than just understanding their needs, you can play a vital role in keeping those needs met.
Pro tip: Business is complicated, but soliciting valuable feedback doesn’t have to be. Look to use simple feedback tools or surveys. Think about how you can reduce barriers so feedback relating to data usage is easy to give and receive.
11. Stay in the know regarding data innovations
Like data literacy, many people within an organization should be investing time to review emerging data-related trends through the lens of their role, experience, and skill sets. The sparks that lead to early adoption can come from anywhere. And the competitive advantages of early adoption can often prove invaluable.
Pro tip: Subscribe to a few industry newsletters and data-related Substacks. Just as regularly reading the trades is a must in Hollywood, use a few moments of your morning to review who’s doing what with data in your industry.
12. Clarify data use cases
Use cases are an excellent way to make sure specific scenarios or objectives your data consumers face align with the business’s overall goals.
Pro tip: It never hurts to base consumer use cases on clear business objectives. Keeping a focus on goals enables you to help streamline data processes and reduce unnecessary work.
13. Leverage external data resources
Consider formal partnerships or collaborations with external data sources that can enhance and complement internal data. Market trends, demographic information, and industry benchmarks can often enrich internal sales data. Analyzing data from external sources can lead to new market opportunities, potential risks, and emerging trends. And external data can be used to benchmark organizational performance against industry standards, or the competition.
Pro tip: Outsourcing some data needs can save time and improve data quality. But always endeavor to partner with the most reputable and trustworthy data providers.
14. Document your data processes
Documentation is a critical part of maintaining high data quality. Your organization should maintain an accessible repository of all data guidelines, standards, and workflows, ensuring consistency and clarity in data practices.
Pro tip: To keep documentation from becoming daunting, leverage documentation tools with simple yet robust templates. Over time, these templates reduce the effort needed to update and maintain your documentation.
15. Conduct regular data audits
Data access, usage, and quality all need to be reviewed periodically. By making data audits consistent, you’ll help identify areas of improvement while ensuring compliance.
Pro tip: Don’t expect your peers to party when audit time rolls around. But do work to schedule audits during less busy periods in the business. This consideration, along with keeping up a regular cadence with the audits themselves, can prevent unexpected issues and reduce stress.
Organizations are brimming with data-hungry professionals—make sure the data you source is always up to code
We’re still a bit in the Wild West when it comes to managing data at scale, especially with the recent rise of large language models. However, there are valuable third parties that can lend a hand through the use of data contracts. Gable.ai is transforming how data contracts work, and the data consumers in your organization deserve to benefit from what we’re cooking up in our own kitchen.
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