Data Analytics

What Are Time Series Snapshots?

Matt Potts
Lead Solution Engineer, Own Company
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In the world of data analysis and forecasting, time series data plays a crucial role. It refers to a sequence of data points collected and recorded over a period of time. One important concept related to time series data is time series snapshots. In this post, we will explore what time series snapshots are and how they can be useful.

Understanding Time Series Snapshots

Time series snapshots are essentially a collection of data points taken at specific intervals or timestamps. These snapshots provide a snapshot of the state of the data at a particular point in time, such as daily. The frequency of the snapshots can vary depending on the application and the specific requirements of the analysis.

Time Travel

Many business systems (CRMs, ERPs etc) typically only contain the current version/state of the data (i.e. the current version of the truth). For example, a CRM system will show the current sales value (actual sales + pipeline). The system may not show you what the sales value was yesterday (or at any point of time in the past). In other words you cannot time travel back in time to see what the data looked like in the past or see how the data has changed and evolved or trended over that time. That’s what time series snapshots allow us to do, time travel.

A simple example can be seen in the diagram below. The sales pipeline value is a sum of all of the future open opportunities in the CRM system. The CRM system only shows the current/today’s pipeline value so a daily snapshot needs to be stored elsewhere for time series analytics.

Typically time series snapshots are stored in specific databases (such as data warehouses and data lakes that are separate to the CRM and ERP systems where the data originated).

Benefits of Time Series Snapshots

Firstly, they enable us to observe and analyze the evolution of data over time. By comparing snapshots taken at different intervals, we can identify trends, patterns, and changes in the data. This information can be invaluable for making informed decisions and predictions.

Secondly, time series snapshots allow us to perform historical analysis. By looking at past snapshots, we can gain insights into the behavior of the data over time. This can help in identifying seasonality, cyclic patterns, and other recurring trends that may exist in the data. Historical analysis is also particularly useful in forecasting future values.

Lastly, time series snapshots facilitate the detection of anomalies or outliers in the data. By examining the snapshots, we can identify data points that deviate significantly from the expected pattern. These outliers may indicate unusual events or errors in the data collection process. Detecting and understanding these anomalies can help in investigating and resolving any issues that may arise.

Time series snapshots provide a valuable tool for analyzing and understanding the evolution of data over time, perform historical analysis, and detect anomalies in the data. By leveraging the power of time series snapshots, we can gain valuable insights and make informed decisions.

Learn how Own Discover can help your organization activate historical SaaS data and amplify your business.

Matt Potts
Lead Solution Engineer, Own Company

Matt Potts is a technical architect within the Solution Engineering team at Own, partnering with customers to help them evaluate the benefits the Own Data Platform can bring to their business. Matt has experience across multiple technology and data domains such as business analytics, data warehousing, integration, CRM and ERP.

Data Analytics
Data Analytics
Data Analytics

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