As businesses diversify the systems they use to get work done, “data strategy” is a phrase thrown around a lot in the consulting industry. But what does it mean in today’s business context?
While specialized technology exists to accomplish specific tasks, data strategy concepts encourage a systematic approach to building and maintaining data storage standards within a business but across systems.
As such, those responsible for data quality and access are often left accountable for data strategy initiatives — and workers who create and use the data bear no responsibility for its quality, completeness, and security.
Sellers focus on selling, customer service focuses on customer needs, and so on. However, these employees know the data best, as they are the producers and everyday users of the data. They understand more than anyone else in the business how they use the data, what they need from it, and where the data problems arise. Their work is the work that is impacted most when a centralized data strategy is not put in place. And yet, these folks who are closest to the data often don’t have a voice or responsibility in the accuracy, completeness, or consistency of the data (i.e., all of the measurements of data quality).
What creates poor data quality?
When data is duplicated erroneously (e.g., multiple records for the same contact, account, etc.), time can be wasted manually checking the quality of the data with other teams or resources. When data is inaccurate, reporting based on this information can lead to less informed decision-making. Ultimately, the cost is a higher input of time and resources to either maintain data quality or mitigate the impacts of poor data quality — neither of which serves the interest of an organization.
For example, MedTech companies selling or leasing costly medical technology need to keep track of their resources for both financial reporting and to track the physical location of each machine for servicing and parts replacement.
While the question asked is the same (Where are our company’s assets?), the way that each user group defines the asset informs the data necessary to perform their job function. If the data structure and process for reporting do not account for these differences, data can be incorrect and reports can be inaccurate, two outcomes with subsequent impacts on efficiency and financial reporting.
A strong data strategy starts on the ground level
Implementing a comprehensive data strategy can limit negative impacts and ultimately save time and resources that may be better used in other ways. But where do you even begin to understand your existing data strategy and make updates that benefit the organization as a whole?
For large organizations operating out of multiple systems, communication about these inconsistencies can be difficult. It is important to first gain an understanding of the individual contributors’ issues with their data, while also learning about the data landscape as a whole.
According to a survey by Gartner, organizations that actively involve business users in data and analytics initiatives are 2.3 times more likely to achieve significant value from the initiative.
In our example above, this involvement would require an inventory of all user groups who interact with the MedTech asset data (regardless of their purpose), and an understanding of how they use it, where they use it, and where they run into data problems. Additionally, existing data structures that do not provide the necessary level of detail for all users may require adjustments.
One important thing to remember is that data problems are not universally caused by technology, but by people, and as such, creating a systematic data strategy that provides value may be more complex than applying software solutions.
Investing in data strategy is critical to the sustainability of your business
Addressing the inconsistencies in how users think about and use data is vital to creating an approach that:
- Meets the needs of users
- Lives within the guardrails of existing systems
- Stays within budget constraints
In terms of budget, it’s important to note that the benefits almost always outweigh the costs associated with assessing and understanding the data needs of users. It’s estimated that poor data quality costs businesses trillions of dollars each year, from the resources tasked with cleansing, deduplicating, and correcting data, as well as the use of low-quality data in decision-making, and missed opportunities due to unreliable insights.
Having a cohesive data strategy that takes into account the diverse needs of different user groups can significantly improve business outcomes and scalability. When data is more accurate and maintenance processes are put in place, users are able to work more efficiently and no longer have to employ workarounds to get the information they need to do their jobs. This creates better experiences for both employees and customers.
Talk to the data strategists at Atrium
A successful data strategy goes beyond managing data — it transforms it into a powerful asset for decision-making and growth. The data experts at Atrium can help you unlock this potential by creating scalable architectures that deliver high-quality, actionable insights. By aligning your data strategy with your business goals, we’ll empower your team to reduce costs, boost productivity, and drive better outcomes.
Discover how our personalized approach, deep expertise in Salesforce, Tableau, and Snowflake, and commitment to understanding your unique needs can elevate your data strategy. Explore Atrium’s Strategy and Advisory Services and see how we can help you turn your data into your organization’s competitive advantage.
