Given 99% of Fortune 1000 companies invest in data initiatives, it’s a solid bet that your brand already utilizes at least one analytics platform. However, just as no two brands are the same, not all analytics are created equal. In today’s blog, we’ll comb over the details of one of the most versatile types of data analytics: time-series.

What differentiates time-series analytics from other analytics is how data points are tracked and analyzed. Rather than recording data points intermittently or randomly, time-series analytics track data points at specific, consistent intervals over a fixed period. 

In short, time-series analytics go further than simply presenting the relationship between data points; they tell you why.

What is time-series data, and can you harness it? 

First, it’s important to understand what time-series data is. Time-series, or time-stamped data, is 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.

Time-series data can give brands nuanced and granular insights into their users and product—whether they’re tracking multi-device IoT data or customer journeys on their site. But, in order to truly unlock the power of time-series data, brands should turn to platforms that can tap into that power: a time-series analytics platform.

The question isn’t whether your brand should leverage time-series data, but whether your data platform is capable of processing it. Given how sophisticated the UI and visualizations many traditional data platforms generate, it’s deceptively easy to believe your data platform is optimized for your data. Down the line, however, once your analysts are buried in query requests, you may realize just how many valuable insights could have been lost in the lurch.

Challenges without time-series analytics

Not sure if you have time-series data? One real-world example of time-series data is the U.S. stock market. While open, stock price changes are tracked and updated every minute—with each price change or data activity time-stamped. This activity is then visualized in line charts and enables traders and stockholders to closely track market activity as it happened, happens, and will happen.

But, as mentioned above, not every platform has the capacity to truly leverage time-series data for impactful analytics. Here are some of the challenges your brand may face without a way to perform analytics on time-series data:

• Stale insights: Though it’s not impossible to glean time-variable insights with typical data platforms via queries, not only is this a time-consuming, labor-intensive process, but it still might be enough to discover the most valuable, granular insights only possible when isolating variables with time-series analytics.

• Limited scaling: Time-series data accumulates quickly, and not all analytics are not designed to scale. Even if you do manage to scale your data—again, a tedious, lengthy process—many behavioral analytics platforms and BI tools are incapable of handling such a massive data quantity. Platforms that are built for time-series data enable us to append information quickly, allowing us to measure, analyze, and forecast change at scale.

• Lack of predictive analytics: Traditional analytics may present the relations between data points, but time-series analytics shows you the reason why. These underlying patterns behind the data can be used to optimize business decisions based on predictive analytics. Brands that fail to capitalize on the power of predictive analytics may soon realize they’re outclassed by the competition—an Allied Market Research study found predictive analytics to be one of the fastest-growing tech industries, estimated to be $35.45 billion by 2027.

• Skewed data sets: Time-series analytics parses the forest from the trees, enabling brands to easily pick out and remove outliers from the data set. Without time-series analytics, brands may have difficulty parsing outliers from standard data points, resulting in skewed data sets and inaccurate insights.

Benefits of time-series analytics

To make the most of time-series data, your brand needs to turn that data into insights. Some of the benefits of well-suited time-series analytics include:

• Predictive analytics: Time-series data analytics are closely associated with predictive analytics—and for good reason. From optimizing inventory levels in retail to informing to predicting customer behavior for more effective marketing campaigns, predictive analytics give data-driven brands the confidence to make dynamic business decisions.

• Fully scalable: Typical data analytics platforms arrange relational data points in rows and columns. While this organization is adequate for simple queries, the addition of variables such as time will slow query discovery to a crawl. Time-series analytics platforms avoid this by organizing data by the linear progression of time, allowing them to scale automatically.

• Accurate insights: By removing outliers and filtering out the noise, time-series analytics allows for a comprehensive view of data over time. Additionally, brands can receive even more accurate insights by isolating variables, such as specific time periods.

• Enhanced efficiency: In addition to scalable data ingestion, efficient data organization also enhances the quality of analysis. For example, improved fast data compression, improved performance, and faster queries. Not only will these save your brand time and money, but optimized performance will also save your brand on energy and storage costs as well.

• Faster insights-to-action: When navigating dynamic market conditions, real-time analytics is key to making confident, data-driven decisions. When real-time analytics are combined with a times-series platform, brands are empowered to find the answers they need—fast.

6 ways to business can use time-series analytics

1. Identify user behavior patterns

 ExpertVoice, an advice platform for industry experts, wanted to contextualize key KPIs such as conversion rate by better understanding their users, but lengthy query requests meant they were waiting weeks for insights they needed yesterday. By leveraging time-series analytics, ExpertVoice teams were able to glean valuable insights into user behavior, such as the time between widget download and widget usage.

1. 2. Predictive analysis for consumer behavior
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3. As the competition among streaming services heats up, providing an effortless user experience is key to reducing customer churn. By leveraging time-series data with predictive analytics, streamers can increase user time-in-app by recommending similar titles users may enjoy.

1. 3. Collect and analyze IoT data
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3. For brands that manage billions of daily IoT events, collecting, organizing, and analyzing that data can be a logistical nightmare. Fortunately, one such music subscription service was able to accomplish just that with Scuba’s time-series analytics. The company was then able to easily identify and mitigate user pain points, eventually optimizing the customer journey so well that users could go from signing up to listening to curated playlists within five minutes.

1. 4. Monitoring sensor data
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3. In critical infrastructure industries like telecommunications, monitoring weather sensor data is essential to ensure systems are running smoothly. In doing so, brands can send proactive alerts to customers if something is not operating correctly. 

1. 5. Track assets
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3. Ride-sharing apps, like Uber, primarily benefit from analyzing time-series data. Uber leverages times-series data by constantly recording its fleets’ activity and location. Once analyzed, this data is used to inform decisions across the entire organization, from directing drivers to high-traffic routes, planning routes, and optimizing pricing.

1. 6. Enhance data security
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3. A recent IBM study found 83% of organizations had one or more data breaches in 2022, 43% of which were cloud-based. To ensure data security, Asana’s security and engineering teams leveraged Scuba’s analytics to track error logins over time and identify security vulnerabilities.

Unlocking data’s full potential with Scuba

Are you using your data to its full potential? Unless your brand is leveraging the power of time-series analytics, key insights may be buried in a sea of data.

Scuba’s customer intelligence platform is here to help. 

Our fully scalable data ingestion means no insight is too big—or too granular—to uncover. By leveraging the power of time-series data, we’ve reduced time-to-insights from weeks to minutes. Scuba’s intuitive UI, visualizations, and no-code queries mean anybody—regardless of data literacy—can harness the full potential of your data.

Interested in learning more about how times series analytics can help your business? Request a demo today or talk to a Scuba expert.