Mastering Predictive Analytics: Powering Engines for Continual Insight

By Jessica Wachtel / Developer, Real Time Analytics
Feb 26, 2024

Predictive analytics are a powerful tool, enabling organizations to make informed data-driven decisions. These tools are far-reaching and can deliver impactful results, either in the long term, like supply chain management and overall equipment effectiveness, or in the short term, like anomaly detection. Let’s take a look at what predictive analytics are and how to power predictive analytics engines for continued, meaningful insight into your data and operations.

What are predictive analytics?

Predictive analytics use statistical models and machine learning algorithms to identify trends based on historical and real-time data. Businesses can design custom statistical models and machine learning algorithms or use templates. Depending on your business needs, you can use predictive analytics to determine when to replace inventory on certain stock items, analyze usage trends on factory equipment to optimize their lifespan, and detect anomalies in network traffic.

The key to harnessing the power of predictive analytics is real-time and historical data—data powers forecasting tools. Without consistent, reliable data collection, storage, and retention, resulting predictive forecasts will be inaccurate.

Time series data in predictive analytics

Time series data—a collection of observations stored as data points in a database—is vital when it comes to building predictive analytic models. If we want to see how this shakes out in a practical setting, imagine a factory that equips all its machinery with sensors. Data collected from these sensors tells the story of each machine: how its operations affect machine health and impact productivity. Examples of time series data include temperature, pressure, and heat measurements. In this scenario, real-time data reveals how the machine currently works, and historical data uncovers how it worked in the past. Without reliable time series data collection, the model is incomplete, like a book with missing pages. You might know the arc, but you’ll never know the whole story.

Time series database

A time series database, like InfluxDB, is optimized for storing and managing time series data. Time series databases efficiently handle the ingestion, processing, and querying of timestamped data. Expert management of vast amounts of sequenced data makes time series databases the leading option for predictive analysis.

Data lake

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Data lakes are cost-effective and flexible solutions for storing raw data. While time series databases are the optimal choice for real-time time series data, data lakes offer a cost-effective long-term data storage option. Native data lake queries are slower out of the box, but developers can optimize their systems for faster querying. Data lakes are primarily designed for storing raw data for future analytics needs—precisely the data required for predictive analytics models.

Better together

Time series databases and data lakes both serve a purpose in predictive analytics. Time series databases are optimized for collecting the high-velocity, time-stamped data generated by data sources. This is the data needed to build and train predictive analytic models. Data lakes provide a centralized repository for storing the massive amounts of structured and unstructured data the models need to create a complete historical record. Combining these technologies allows organizations to leverage the benefits of both data collection methods to generate deeper insights.

Real-time analysis with long-term support

A time series database can perform tasks such as alerting, monitoring, and anomaly detection. Keeping a record of these events by offloading the data to a data lake for long-term storage helps predict similar future events.

Data integration

Data lakes are centralized repositories for diverse data types. By integrating data from various sources into the data lake, organizations can enrich their time series data with additional contextual information, such as weather data and economic indicators. The enriched data can provide valuable insights and improve the accuracy of predictive analytics models.

Flexible data storage

Using a time series database and data lake together allows organizations to capture time series data directly from operational technology, sensors, and devices and store that data long-term at a low cost. Rather than simply evicting data from a time series database once a dataset reaches its retention policy, sending it to a data lake is a low-cost alternative. Historical data and real-time data are both vital to predictive analytic engines. Using a data lake or a data warehouse as a long-term data storage solution is one way to enable predictive maintenance and keep high-fidelity data without breaking the bank.

Predictive analytics solutions

Time series data is vital for successful predictive analytics forecasting. Time series databases are excellent at collecting and storing time series data in the short term. Data lakes provide a scalable, efficient, and centralized platform for long-term data storage. By leveraging a time series database and data lake in concert, businesses gain the necessary storage infrastructure for effective predictive analytics, machine learning, and statistical models.

Test drive the leading time series database, InfluxDB, to start your predictive analytics journey.