What Is Data Freshness? Definition, Benefits, and How to Ensure It

Data processing and collection are never-ending tasks because of the various data quality metrics you must maintain for it to be useful. Data freshness is one of the most crucial aspects for efficient decision-making processes, but whether you should sacrifice other data quality aspects for freshness varies.

It might be impossible or impractical to maintain the most up-to-date data pipelines. A common advice is to aim for appropriately fresh data for your projects, but what exactly it entails can only be understood by applying the data freshness in practice.

What Is Data Freshness?

Data freshness refers to how well data reflects what is happening “right now” or at least very recently. So, we can define data freshness as the degree to which information is up-to-date, measured by the elapsed time between when an event occurs and its record on a system.

In practice, it’s often used as a binary term – we either have fresh data or not. That’s because systems already include use case expectations and data processing limitations that can vary greatly. What’s fresh data for quarterly reports might be stale for a real-time stock investment data pipeline.

Other data quality metrics can also be more or less relevant for specific tasks. They overlap in some respects, but should not be confused with data freshness.

• Data timeliness measures whether data is available when needed for a specific task. In fraud prevention, for example, data timeliness is more important to data quality as fresh data isn’t always required, but when it is, it’s needed right now.
• Data accuracy measures the extent to which data accurately describe a real-world entity. For specific information, such as a user’s birthdate, data freshness is less relevant than accuracy, as the data won’t or is unlikely to change over time.
• Data completeness is about the coverage of all the needed data points. A newly sent invoice can meet all the data freshness requirements, but if it lacks required fields, it will still be incomplete.

Why Data Freshness Matters in Real-Time Decision Making

Stale data is outdated and has not been renewed within an appropriate timeframe, rendering it inaccurate in representing the current situation. The negative effects of stale data are the most obvious in cases of real-time decision making.

If your sales data pipeline lags behind by days or weeks, it can result in misleading reports, which lead to bad leadership decisions that cause you to lose money. All other business aspects, from adjusting supply chains to making investments, can be similarly protected if you maintain data freshness.

It all comes down to the competitive edge fresh data brings. Recent data means quick decisions, first mover advantages, and early possibilities to avoid unnecessary risks. Ensuring data freshness is more difficult than it seems, as you first must decide how fresh your data needs to be.

How Fresh Does Data Need to Be?

Data freshness is more of a strategic term than purely a technical one. So data freshness policies are more often decided by leadership roles than by data scientists. A good start is to look into data freshness expectations in your industry and try to stay at least a bit ahead of the competition.

Data Freshness vs Latency vs Recency: What’s the Difference?

Once you decide on the data freshness requirements for your data pipeline, it must be correctly conveyed to data engineers. A few distinctions are essential here: they can be used to measure data freshness and are related to it, but they aren’t entirely the same.

• Data latency reflects the gap between collecting and processing data for the user.
• Data refresh rate captures how often data changes or new data is introduced from the source.

Data latency and refresh rate are frequently used to measure data freshness as they represent two necessary but insufficient parts of fresh data. If data is only delivered quickly, but isn’t updated regularly or vice versa, it won’t be fresh.

While data freshness consists of both, in some cases, one is more important than the other. In financial trading, healthcare monitoring, or fraud detection, low latency is more important for data quality, as users may need it at specific times.

On the other hand, in marketing analytics, inventory updates, or customer profiling, relying more on data refresh rate as a data freshness metric might be more critical. It’s essential to define clearly which freshness dimension is more important before the data generation project begins.

How to Check and Ensure Data Freshness

Data quality assurance is a topic of its own, but the general consensus is that you can increase data freshness by consistent monitoring efforts. It involves regularly checking the age of your data assets to detect delays or other anomalies and promptly addressing them.

It all starts with correctly setting up your data warehouse so you can ensure data observability through metadata, logs, alerting, and other means. Various tools make this process easier, ranging from simple command-line tools to large-scale enterprise data management systems.

• Dbt projects is an open-source command-line tool to test, manage, and document information inside a data warehouse that can be used for free on local or CI/CD pipelines.
• Elementary Data automates freshness monitoring within dbt projects using a machine learning model to detect anomalies and delayed updates.
• Databricks on AWS includes built-in data observability and freshness monitoring capabilities within your data pipeline running on their cloud services.
• IBM Databand can provide data lineage reports and monitor data quality in real-time using machine learning models for freshness, schema changes, and other anomalies.

Besides using correct tools, data observability is impacted by human factors as well. It’s important to establish data ownership and maintain accountability for the freshness monitoring of specific data sets. Often, the most actionable strategy is to start with data management roles and then move on to technical solutions.

Conclusion

Increasing data freshness is not a one-time task, as it requires ongoing monitoring, quick reaction, and continuous data processing. Understanding what data freshness is and how it impacts your business decision-making will help to streamline your data engineering and collection efforts.

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