One important factor to a lot of data analysis is the impact time has on it. That’s where time series analysis comes into play. As with many common types of data analysis, it can be difficult to understand how time series analysis works without either deep theoretical knowledge or real-life examples. That’s why we compiled this list of real Tableau customers who have used our time series analysis tools to make a demonstrative difference in their companies.
What is time series analysis?
Time series analysis is a type of data analysis that takes an in-depth look at time series data, which is data that changes over time or for which time is considered a variable in the results. Time isn’t just a measurement included in the data—it’s the primary axis on which the data sits.
The main difference between time series analysis and other forms of analysis is that the data is collected over regular intervals of time. This helps the analysis identify systemic patterns in the data that help form trends, cycles, or seasonal variances. When there is a consistent time frame of historical data, time series forecasting can be used to predict likely future data. Because time is an integral variable in data for many industries, a variety of applications for time series analysis exist. Explore a few time series analysis examples below.
Tableau customer time series analysis examples
1. Exelon uses data analysis for data-driven audits
Problem: Traditional audits were time-consuming and did not add much value.
Solution: Exelon needed to conduct regular audits of financial processes, and traditional audits take time. Audits used to involve interviewing the counselors or administrators who own the processes and see the records at the time of the audit, but Exelon wanted to do more. The company used Tableau to look at an entire year of data and uncovered trends they may have missed.
Stock prices are also reported over time and involve time series analysis. Trading algorithms that work autonomously also utilize time series analysis, collecting data on the ever-changing market to react to minute financial changes and trade appropriately.
In other areas of finance, time series analysis can be found in trend analysis, seasonality, budget analysis, interest rates, sales forecasting, and financial markets. Since finances are so regularly recorded, it makes it a suitable subject for analysis over time. Beware that time series analysis of financial data can include so many variations that complex models are required. A model that is too complex can lead to either lack of fit or overfitting, which does not distinguish between noise errors and true relationships, resulting in skewed analysis.
2. Stamford Health uses data analytics for patient care and reducing costs
Company: Stamford Health
Problem: Inefficient use of resources and inflated costs of care and operations over time.
Solution: Stamford Health used data analytics to identify opportunities to improve patient care and reduce costs for patients and the system. The company used the historical length of patient stays, treatments, and conditions data to chart when patients received certain treatments and how that affected patient outcomes. Using these combined data sources and data analysis, Stamford Health identified better times to administer medication and reduced the average length of stay. This reduced both patients and hospital costs.
Healthcare professionals have been making great strides with data, both through patient care and technological advancements. While informatics improves patient care and patient information, and the Internet of Medical Things automates and augments patient data — time series analysis is found in chronic disease research. With time series analysis, chronic diseases, defined as diseases that last a year or more and require ongoing medical attention, can be tracked over time, as time is a major component of these diseases. In the same vein, time series analysis plays a crucial role in epidemic-scale research.
For everyday healthcare, time series analysis is used to monitor a patient’s heart rate through an electrocardiogram (EKG) or even brain waves through an electroencephalogram (EEG). The devices record electrical signals coming from the brain or heart over a period of time, identifying abnormal activity that indicates a medical issue.
3. Texas Rangers use data to identify sales opportunities
Company: Texas Rangers
Problem: Data analysis was not fast enough to make decisions days before game day.
Solution: The Texas Rangers front-office team combined all their data sources so they quickly had a 360-degree view of the data. The sales and marketing teams used time series analysis dashboards (and other data analytics strategies) to quickly identify opportunities, especially related to forecasting against seasonal trends.
In one practical example, the sales team looked at up-to-date dashboards and realized that their projected sales for an upcoming game were lower than normal. About a week before the game, the marketing team strategized on how they could increase ticket sales. They developed a marketing strategy four days before game day, but they had time to create a promotional Father’s Day ticket offer to increase sales.
4. Bronto Skylift uses more accurate forecasting to save costs
Company: Bronto Skylift
Problem: Operations, manufacturing, and sales forecasting were inaccurate and time-consuming.
Solution: Using better data and faster analysis, Bronto Skylift cut analysis time from one day to one hour. Using time series analysis and forecasting modeling, the company can forecast supply chain and processes in its manufacturing department and forecast seasonal trends. Before investing in data analytics, the data was stale and siloed. Now, these forecasts are much more accurate, reducing costs in inventory, supply chain, labor, and capital equipment.
5. Tinuiti centralizes data sources to effectively scale marketing analytics
Problem: Evolving marketing technologies made it difficult to quickly analyze information to present to their media clients.
Solution: Tinuiti adopted Tableau to centralize over 100 data sources. The Tableau platform allows Tinuiti to quickly pull data from any one of a complex mix of data channels and create hyper-accurate, custom dashboards for the clients. This helps staff easily make sense of channel-level data and reduces their average time spent on data reporting by 60%.
Not only did Tableau help Tinuiti streamline their reporting, it also allowed them to develop new kinds of reports for their clients. They utilized time series analysis combined with media forecasting to create “what if” analyses for their clients. This helped to answer questions like “what would happen if we invested here, instead of there?” and “If we invested more money, what return would we see?” This allows their clients to make the most informed decisions possible when deciding to invest with Tinuiti.
6. MYTOYS Group reduces IT costs with self-service analytics
Company: MYTOYS Group
Problem: Business decisions were based on static reports manually compiled by the team, which ate up time and resources.
Solution: MYTOYS gave all their department staff access to up-to-date data in Tableau, empowering them to dig into it while working. The addition of these dashboards, used instead of the static reports, have decreased IT costs 20% by enabling the staff to work on other projects instead.
MYTOYS also uses Tableau to accurately forecast retail and IT trends using time series analysis. The retail reports units sold and ordered, prices, and revenue by time of year, so they can base their product orders based on this information. For IT, the team built an availability report that tracks all open tickets, measuring severity and processing time. They use time series analysis to accurately estimate how much time tickets will take to resolve, leading to better resource allocation and planning.
Begin your own application of time series analysis
Because time is an essential variable in so many data models, time series analysis has a range of applications—only a few of which are listed above. The Tableau platform provides comprehensive time series analysis with the built-in date and time functions that allow you to drag and drop to analyze time trends and easily perform time comparisons, like year-over-year growth and moving averages.
Begin your own application of time series analysis with easy-to-use visualization software to easily identify trends, find outliers, and compare data over time. Check out more time series resources and customer stories to help you get started.