10 Ways You’re Losing with Static Data

Leadspace Customer Data Platform

Sales and marketing teams rely on a myriad of customer data to do their jobs. Unfortunately, that data often leads to more problems than benefits because of its siloed, unstructured nature. Static data, which refers to data that does not change once it is created, has several inherent problems and limitations. Acquiring a variety of static data from multiple vendors means that we get to see several aspects of our buyers at specific moments in time, but we still need to manually piece it all together and continuously amend it in order to paint a picture of our buyers that we can actually use to target them effectively.

To get the most out of your data, it needs to be unified, accurate and up-to-date. Odds are, your customer data consists of numerous disparate datasets that you purchase from a variety of static data vendors. Did you unify it? Did you de-dupe it? How do you keep it up-to-date? Is all of that data synchronized across your CRM and Marketing Automation systems?

  • Procuring all these static signals from numerous vendors gets expensive – quick.
  • Blending it all together is brutally cumbersome, time-consuming and error-prone.
  • Keeping it up-to-date is tedious and often neglected.

Let’s explore 10 ways that you’re losing with static data – and how you can change that.

1. Lack of Timeliness

Static data can become outdated quickly if the information it represents changes frequently. As time progresses, the data may no longer reflect the current situation, making it less useful for decision-making. When your data is static, you should expect it to become stale and irrelevant over time.

2. Limited Flexibility

Static data often has a fixed structure, which can be limiting when new data types or formats need to be incorporated. It may be challenging to scale static data systems when the volume of data grows. It’s inherently fixed structure and inability to be supported at scale give you less flexibility in amending it with additional disparate data.

3. Increased Maintenance

Keeping static data up-to-date requires manual intervention, which can be labor-intensive and prone to errors. Ensuring data accuracy and consistency over time is difficult without automated updating mechanisms. Maintaining your data’s integrity through manual updates is incredibly tedious, time consuming and error-prone.

4. Inefficiency in Dynamic Environments

In environments where data changes frequently (e.g., financial markets, e-commerce), static data is inadequate for real-time analytics and decision-making. Organizations relying on static data might miss out on opportunities or fail to respond promptly to threats. Its static nature leads to delayed reactions in situations (routing, follow up, personalized content, etc) where time is of the essence.

5. Redundancy and Storage Issues

Without dynamic updating, static datasets might contain redundant information, leading to inefficiencies in storage. Keeping multiple versions of static data can increase storage costs and complexity. Duplicate records can cause complications internally and lead to a higher cost of storing less operationalizable data.

6. Incompatibility with Data Management Systems

Many modern applications and systems require real-time data for operations such as predictive analytics, AI, and machine learning. Integrating static data with systems that operate on real-time data streams can be complex and problematic. Modern systems often demand real-time information in order to function effectively, which tend to deliver less value when faced with integrations that are not seamless.

7. Lack of Insights

Static data is limited in providing insights into trends over time unless regularly updated snapshots are maintained. It is less effective for predictive analytics, which rely on up-to-date information to make accurate predictions. Both historical analysis and predictive analytics depend on timestamps to calculate accurate insights in relationship to time, making your static data fit for effective analysis processes.

8. Security and Privacy Concerns

Static data containing sensitive information might be more exposed to security risks if not regularly updated or managed properly. Maintaining static data without proper updates can lead to compliance issues, especially with regulations that require current and accurate information. Sensitive information and compliance issues are less of a hurdle when your data can be easily accessed and amended automatically, which is not an option within the realm of static data.

9. Increased Data Procurement Costs

Static data requires constant manual updates. We don’t know with certainty at what point our data becomes outdated, meaning we must purchase entire datasets again and amend our systems to maintain the data’s accuracy. Beyond being a cumbersome process, it becomes increasingly expensive to achieve up-to-date data across your systems as you need to purchase that same data over and over. Manually updating your data regularly also reveals an increasing opportunity cost for that time and effort spent.

10. Limited Visibility into Hierarchies

When we manually unify disparate datasets, we don’t have an underlying Identity Resolution framework to automatically associate data with people, company and buying team profiles. Without attaching identifiers to information and mapping it to correct profiles, and being able to mathematically associate profiles to each other, we can’t understand the hierarchies that exist across our Total Addressable Market (TAM). With static data, you can’t easily achieve that valuable hierarchical understanding.

To mitigate these problems, organizations often employ dynamic data systems that allow for continuous updates, real-time processing and integration with various data sources. Having a single, comprehensive view of your customer data in one place makes it significantly easier to effectively target the right people at the right time with personalized campaigns. Sales and marketers must automate the process of resolving identities and mapping their buyer data to the correct account, while keeping it all up-to-date. 

A Customer Data Platform (CDP) can empower sales and marketing teams to seamlessly blend all of their siloed customer data into a single source of truth – developing buyer profiles for people, companies (even divisions with hierarchies) and accounts. Even better, with certain CDP solutions, those unified profiles are automatically updated in real-time as source data changes and new data is ingested. The framework behind the scenes that makes it all possible is known as Identity Resolution. To learn more about how you can scale personalized outreach with dynamic data that leverages Leadspace’s best-in-class (according to Forrester) Identity Resolution framework check out the webinar, Identity Resolution Explained. Bring your static data to life with Leadspace’s dynamic B2B buyer data.