What is Time-Series Data?

As companies increase their reliance on analytics to stay competitive, so does their reliance on data. However, before companies can leverage their data into insights, it must first be ingested into an analytics platform. There are multiple types and methodologies of data ingestion, but one of the most efficient types is time-series data.

Read on to learn more about what time-series data is and how your company can leverage it for positive business outcomes.

Looking at data through a time-stamp

Time-series data, or time-stamped data, is defined as a collection of observations of a single subject at different time intervals. Essentially, time-series data is data that has a specific time tied to it. What makes this data distinct from other types is in the name–the relevance of time as an axis. These time intervals are measured in two ways:

• Metric: Measurements gathered at regular time intervals, for example, weather temperature readings.

• Event: Measurements gathered at irregular time intervals, such as log data between software applications and operating systems.

Everyday examples of time-series data

The importance of time-series data cannot be overstated. Nearly every facet of modern life relies on time-series data, including:

• Climate forecasts: Regularly recorded climate data is useful for more than just predicting rain. Global air and shipping routes, electrical grid strain, and even wildlife migratory patterns are greatly impacted by climate; and climate models informed by times-series data help predict, track, monitor, and anticipate events.

• Healthcare: Time-series data is utilized by a slew of medical technologies, including electrocardiograms (EKGs), more widely known as heart monitors. EKGs record the electrical currents passing through the heart, and when recorded over a linear graph, make it easier for both doctors and patients to detect potentially life-threatening anomalies.

• Finance: Time-series data analysis is fundamental to how financial institutions predict interest rates, foreign currency risks, and stock market volatility, among other market conditions. Without time-series data, the market would be even more uncertain.

How companies gain an edge with time-series data

Information is power. Companies armed with time-series data analysis can glean actionable insights for a competitive edge and better understand their users and customer journeys. Here are a few ways time-series data is beneficial:

• Pattern identification: The most common way companies leverage time-series analysis is through pattern identification. For example, time-series data analysis can inform struggling retailers exactly when sales began trending downward, empowering them to make dynamic decisions that could turn sales around in real-time. 

• Opportunities to clean up data: Not all data is created equal. Pesky outliers can seriously skew your results, so businesses use time-series data to quickly identify these data points and remove them from the set–or investigate where they’re coming from and why

• Forecasting future trends: Although time-series data cannot predict the future, it can do the next best thing–forecast future trends by examining past behavior and extending those patterns into the future.

Dive into data with Scuba

 Does your enterprise have massive quantities of time-series data? Consider leveraging it with Scuba’s real-time analytics. Our continuous intelligence platform ingests, reads, and produces actionable insights from your time-series data–all in real-time.

With Scuba’s no no-code queries, customized dashboards, and dynamic visualizations, we’ve democratized data analysis for everyone in your company.