The hidden costs of poor data management: Why prevention beats cure
In today’s digital landscape, businesses generate an overwhelming amount of data. As organizations grow linearly, their data outputs tend to increase exponentially, leading to a complex web of information that can be both an asset and a challenge. Much of this data—such as access logs and emails—may be trivial and unimportant. However, buried within this vast ocean of information lies critical data that drives business operations.
Key insights about customer behavior, purchasing patterns, and interactions, as well as vital information on company expenses, can shape strategic decisions across various departments. Every facet of a business—from operations and marketing to finance, sales, and research and development—relies on this information, often for differing reasons and goals.
To harness this valuable data effectively, organizations must engage in the practice of data management. This process involves collecting, combining, organizing, and accessing data to support the needs of teams, individuals, and products. By employing robust data management strategies, businesses can ensure that essential information is readily available and actionable, allowing them to make informed decisions that drive growth and efficiency.
Types of Data Management
Organizations typically adopt one of two approaches to data management: the single-source-of-truth (SSOT) approach and the just-in-time (JIT) approach. Each has its strengths and weaknesses, and understanding these can help businesses choose the right strategy to meet their data needs.
Single-Source-of-Truth Approach
The single-source-of-truth approach is centered around proactively collecting, cleaning, combining, and organizing data into a unified dataset. This means that when teams need information, they can access it quickly and efficiently, reducing the time spent on data retrieval and analysis. With a well-maintained SSOT, businesses can respond to inquiries almost instantaneously, supporting faster decision-making processes.
However, this approach comes with significant costs. Maintaining a single source-of-truth requires a substantial investment in skilled engineers and data analysts who manage integrations, data pipelines, and ongoing data cleaning. While the benefits of quick access and reliable data are appealing, the financial burden can be a barrier for many organizations.
Just-in-Time Approach
In contrast, the just-in-time approach relies on the understanding that data exists somewhere within the organization, even if it is not centralized. When data is needed, analysts are tasked with locating, downloading, and cleaning the necessary information to create a usable dataset. While this method may seem more flexible and less resource-intensive, it comes with its own set of challenges.
One of the primary drawbacks of the just-in-time approach is the time it takes to gather and prepare data. For instance, consider a multi-channel e-commerce company. If the Chief Revenue Officer (CRO) wants to know the latest revenue figures, an analyst must log into multiple platforms—such as Amazon Seller Central, Shopify, and Target Connect—download various CSV files, clean each dataset, and then merge the information to arrive at a comprehensive answer. This process can be time-consuming and cumbersome, which is why most companies tend to rely on monthly or quarterly reports. Consequently, companies may find themselves "flying blind" during the intervals between these reports, making it difficult to detect and respond to changes in real-time.
The second significant issue with the just-in-time approach is the potential for human error. When analysts are manually processing data each month, there are numerous opportunities for mistakes. They may inadvertently forget to remove test data or miscalculate revenue across different channels. Given the complexity of data analysis and the number of steps involved, the likelihood of errors increases. As a result, not only does it take longer to obtain answers, but there’s also a substantial risk that those answers may be incorrect, undermining the integrity of the decision-making process.
It's hard to find a company that relishes the idea of spending countless hours gathering data every time a question arises. In a world where timely decision-making is critical, the inefficiencies of data retrieval can be frustrating. However, many organizations shy away from a single-source-of-truth (SSOT) approach because of the significant costs involved. For a company generating $5 million in revenue annually, spending one-tenth of that budget just to streamline data access seems daunting, if not impossible.
Enter Pliable—a game-changing solution that enables businesses to adopt a single-source-of-truth approach to data management without the hefty investment typically associated with it.
With Pliable, companies can efficiently centralize their data, making it accessible and actionable without the need for a massive team of engineers and data analysts. This innovative platform allows organizations to enjoy the benefits of quick and reliable data retrieval while keeping costs manageable.
By leveraging Pliable, companies can focus on what truly matters: making informed decisions based on real-time insights. Gone are the days of waiting for monthly or quarterly reports; with a single-source-of-truth approach powered by Pliable, businesses can operate with agility and confidence, ready to adapt to changes in the market as they happen.
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