How to manage time series data for industrial companies

In industrial operations, the effective management of time series data stands as a cornerstone of operational excellence and strategic planning. This specialized form of data, characterized by its sequential order, is pivotal for everything from predictive maintenance to troubleshooting to trend analysis, integrating seamlessly with technologies such as artificial intelligence (AI) and machine learning (ML) to unlock unprecedented levels of insight and efficiency. The importance of time series data analysis in modern industry cannot be overstated, serving not only as the backbone for data-driven decision-making processes but also as a critical component in the advancement of information technology (IT) and operational best practices. The careful handling of these data sets ensures improved data quality, enhanced data security, and the optimization of data storage solutions, thus facilitating more accurate forecasting and effective resource allocation.

The article that follows will delve into the intricacies of understanding time series data and its indispensable role in industrial companies, highlighting the inherent challenges associated with its management. With a focus on tools and technologies, from time series databases to sophisticated data processing techniques aided by the Internet of Industrial Things (IIoT), it aims to guide through the labyrinth of data analysis, security, and storage challenges. Furthermore, it will share best practices for managing industrial time series data, enriched by real-world case studies and success stories that exemplify the transformative potential of meticulous time series data management. From ensuring data quality to leveraging AI and ML for predictive maintenance and trend analysis, the content promises a comprehensive exploration into optimizing the use of time series data in industrial settings.

Understanding time series data

Definition and characteristics of time series data

Time series data is defined as measurements or observations of events as a function of the time at which they occurred. This type of data is distinguished by its sequential order and timestamp, making it a fundamental element for analyzing trends, patterns, and correlations over time ^[7]. The primary characteristic of a time series is that it is indexed or listed in time order, making it distinct from other types of data sets. When plotted on a graph, one axis will always represent time, emphasizing the importance of the temporal sequence in which the data points are collected ^[8][10][4].

Time series data can be structured, often having a predefined data type or fixed length, and is generated by devices with a timestamp associated with each record. This timestamp is crucial for indexing the data, as it allows for computing and analysis based on the time each data point was recorded ^[7]. Additionally, time series data is considered to have a stream-like nature, being continuously collected and flowing into the database. These data streams are independent from each other, contributing to the stability and predictability of the overall traffic in time-series scenarios ^[7].

The analysis of time series data focuses on trends over time rather than values at specific times. This approach can provide valuable insights even if some data points are lost, as the main challenges lie in storing, processing, and analyzing massive data sets ^[7]. Time series data exhibits a high write/read ratio, with data being written constantly to the database but only read occasionally by analytics software to generate reports and run algorithms ^[7]. Moreover, time series data records are almost never updated or deleted, making them append-only, similar to log files. A lifecycle is often defined for the collected data, after which it is deleted to reduce storage costs ^[7].

Examples of time series data

Time series data is pervasive, encompassing everything from business trends to the operating status of industrial equipment. It includes a wide array of applications across various industries, such as weather records, economic indicators, patient health evolution metrics, server metrics, application performance monitoring, network data, sensor data, events, clicks, and many other types of analytics data ^[8][10][4]. This data can be classified into two types: measurements gathered at regular time intervals (metrics) and measurements gathered at irregular time intervals (events) ^[8][10][4].

Furthermore, time series data describes the state of systems and their evolution over time, offering valuable insights for businesses, especially in the industrial sector. Industrial time series data, generated by a vast amount of sensor data during daily operations, holds the key to improving efficiency and reducing costs. With the advent of big data, the previously lost information hidden in industrial time series data is now being uncovered and utilized ^[11].

Understanding time series data and its characteristics is essential for analyzing trends, patterns, and correlations over time, providing powerful insights across a wide range of applications.

The role of time series data in industrial companies

Importance for industrial operations

Time series data analytics is pivotal for understanding changes over time and making accurate predictions about future events, a practice often referred to as time series forecasting due to their intertwined nature ^[16]. In industrial settings, the ability to explore trends and make forecasts is invaluable, with applications ranging from maintenance scheduling to production optimization ^[16]. Most process systems in the manufacturing industry, such as IoT sensors, industrial machines, and control devices, generate time series data. This data is robust, rapidly filling up storage, hence, performance and scalability in its handling are crucial ^[17]. Time series databases (TSDBs) play a significant role in enhancing resource monitoring and optimization, particularly in the energy sector, by tracking energy consumption and production to manage renewable energy and traditional power systems efficiently ^[18]. They also contribute to smart grid optimization, renewable energy monitoring, and predictive maintenance, ensuring uninterrupted operation and consistent quality in manufacturing ^[18].

Common use cases in industry

1. Maintenance and downtime management
   Analyzing the patterns in maintenance needs and downtime for manufacturing equipment allows manufacturers to schedule maintenance and downtime more effectively. This proactive approach helps in reducing unplanned outages and optimizing equipment availability ^[16].
2. Production optimization
   Patterns in production can be analyzed to identify bottlenecks and inefficiencies. By understanding these patterns, manufacturers can make their processes more efficient, thereby increasing productivity and reducing costs ^[16].
3. Process optimization and equipment health
   Time-series data provides rich insights into process optimization and the health of production equipment. For example, using analytics software like TrendMiner, manufacturers can identify the root cause of common problems and correct them based on data analytics techniques. This includes predicting the most probable cause of catalyst deactivation for predictive maintenance, thereby enabling higher visibility and earlier interventions ^[17].
4. Energy consumption and production
   In the energy sector, time series databases help accurately track energy consumption and production. This makes it easier to manage renewable energy and traditional power systems efficiently, contributing to smart grid optimization and renewable energy monitoring ^[18].
5. Predictive maintenance and process optimization in manufacturing
   Time series databases track equipment conditions and fine-tune production workflows in manufacturing. They spot inefficiencies and forecast machines’ maintenance requirements to ensure uninterrupted operation and consistent quality. This includes using sensor data to predict machinery failures before they occur and scheduling maintenance to prevent downtime ^[18].
6. Logistics and transportation efficiency
   In the transportation and logistics sector, time series databases enhance efficiency and reliability across the supply chain. This includes analyzing shipping and delivery data to optimize routes, improve delivery times, and reduce operational costs, as well as monitoring vehicle health data for predictive maintenance ^[18].

By leveraging time series data, industrial companies can gain invaluable insights into their operations, enabling informed decisions that optimize efficiency, reduce costs, and enhance overall performance. This data not only supports operational excellence but also opens doors to new revenue streams and business models, fostering stronger customer relationships and increasing loyalty ^[14].

Challenges of managing time series data

Data quality and integration

Managing time series data presents significant challenges, particularly in the areas of data quality and integration. Ensuring the quality of time series data is crucial, as it directly impacts the accuracy and reliability of any analysis performed. Data must be complete, consistent, valid, accurate, timely, and relevant to meet specific analytical needs ^[20]. Preprocessing steps such as removing outliers, handling missing values, and ensuring a consistent time interval between observations are essential to enhance the data’s accuracy ^[26]. Furthermore, the integration of data from various sources often introduces complexities due to differing formats and structures, necessitating robust schema evolution strategies and the use of semi-structured formats like JSON ^[24].

Scalability and Storage Issues

As the volume of time series data grows, particularly with the proliferation of IoT devices, scalability and storage become increasingly challenging. The architecture must support the efficient collection, processing, storage, querying, and visualization of vast amounts of data without compromising performance ^[27]. For example, Netflix has addressed these challenges by evolving its time series data storage architecture to handle massive increases in scale, leveraging techniques like compression and chunking to ensure consistent read/write performance and a smaller storage footprint ^[22]. Similarly, scalable cloud infrastructure and real-time ingestion pipelines are critical for accommodating fluctuating data volumes ^[24].

Cost and resource allocation

The management of time series data also involves significant considerations regarding cost and resource allocation. Implementing scalable solutions often requires substantial investment in cloud infrastructure, data processing technologies, and specialized tools like Azure Time Series Insights Gen2 ^[27]. These solutions must not only be effective but also cost-efficient, balancing performance with expenses. Effective data lifecycle management, including defining retention policies and automating data deletion, plays a vital role in controlling costs and managing resource allocation efficiently ^[23].

Tools and technologies for managing time series data

Legacy data historians

Legacy data historians are specialized data storage systems designed specifically for capturing, storing, and retrieving time-series data in industrial and manufacturing settings ^[29]. These systems are built to manage time-stamped data efficiently and are a good fit for applications that require continuous time-series information ^[29]. They offer data compression and aggregation capabilities, which are crucial for efficient data analysis ^[29]. Furthermore, data historians can store large volumes of historical data, essential for trend analysis, anomaly detection, and compliance with industry regulations ^[29]. They are optimized for fast data access, supporting real-time decision-making ^[29]. However, data historians can be expensive to procure and maintain, and they may not support diverse data types, limiting their utility for businesses dealing with multiple data sources ^[29].

Modern open source platforms

In contrast to legacy data historians, modern open-source platforms for time series data management have emerged as flexible and cost-effective alternatives ^[30]. These platforms continue to evolve, adding new value to businesses by embedding significant domain expertise within their systems ^[30]. Open-source time series databases offer higher query performance and faster data retrieval, making them suitable for real-time and analytical use cases ^[34]. They are highly scalable, capable of handling large and growing volumes of time-stamped data ^[34]. Moreover, these databases are adaptable to multiple industries and use cases since they can manage data from diverse sources ^[34]. However, implementing and using these databases may require a learning curve, and migrating historical data from legacy systems can be challenging ^[34].

Comparison between different tools

When comparing legacy data historians with modern open-source platforms, several key differences emerge. Legacy data historians are tailored for specific use cases in industrial and manufacturing sectors, offering features like data compression, aggregation, and fast data access optimized for these environments ^[29]. They are designed to integrate tightly with operations technology (OT) control systems and standards, providing an “all-inclusive” solution for industrial operators ^[28]. On the other hand, modern open-source platforms are characterized by their flexibility, lower upfront costs, and ease of integration with popular data analysis and visualization tools ^[34]. These platforms support data compression to reduce storage costs and are designed to handle large time series datasets without impacting performance ^[34]. While data historians are domain-specific and may lead to vendor lock-in, open-source time series databases offer scalability, versatility, and adaptability to diverse data sources ^[34].

Choosing between legacy data historians and modern open-source platforms depends on specific business needs, including the scale of data management requirements, the diversity of data types, and the desired level of flexibility and scalability.

Best practices for handling industrial time series data

Data collection and preprocessing

1. Real-time data capture: Industrial companies should prioritize capturing real-time data to enable immediate and future analysis. This involves using technologies like Apache Kafka and Amazon Kinesis for effective real-time data streaming, allowing for agile decision-making and enhanced action experiences ^[43].
2. Contextualization of data: Every piece of data collected, especially from sensors, must include a timestamp to provide context. This time-series data becomes crucial for processing and understanding Industry 4.0 IoT data, transforming raw data into actionable insights ^[44].
3. Preprocessing challenges: Time series data often comes with its own set of challenges, including unordered timestamps, missing values, and noise. Addressing these issues through methods like interpolation and denoising is essential for maintaining data quality ^[37][38][39].
4. Data transformation: Converting data into the appropriate date type format and ensuring a constant frequency throughout the dataset are fundamental steps in preprocessing time series data. This helps in referring to values accurately over the time they were recorded ^[38].

Data analysis and visualization

1. Analysis techniques: utilizing a variety of time series analysis models, such as descriptive, explanatory, and forecasting analysis, can help organizations understand trends and systemic patterns over time. ^[40].
2. Visualization tools: Employing visualization tools like InfluxDB and Grafana enables the creation of dashboards and custom graphs. These tools facilitate the easy understanding of time series data by presenting it in formats like line graphs, gauges, and tables ^[41][42].
3. Custom Visualizations: For more specific needs, libraries such as Dygraphs allow for the creation of custom plotters, offering powerful customization for time series charts. This can be particularly useful for visualizing anomaly detection and forecasting ^[42].

Real-time data management

1. Stream processing: Implementing stream processing through time-series databases allows for real-time analysis by continuously querying data as it streams in. This capability is essential for managing large volumes of data and detecting anomalies ^[45].
2. Security measures: Given the use of real-time data, ensuring data security is paramount. Industrial companies must adopt robust security measures to protect sensitive information from exposure, thus preventing financial loss and reputational damage ^[43].
3. Leveraging open source platforms: Transitioning from legacy data historians to open-source time-series platforms like InfluxDB, complemented by Telegraf, can provide a more flexible and cost-effective solution. These platforms offer broad connectivity, enabling easier monitoring and management of distributed systems and networks ^[44].

By adhering to these best practices in data collection and preprocessing, analysis and visualization, and real-time data management, industrial companies can optimize their handling of time series data. This not only enhances operational efficiency but also drives innovation and growth within the Industry 4.0 framework.

Case studies and success stories

Successful implementations in the industry

1. Financial Risk Monitoring System: Robinhood, a pioneer in commission-free investing, developed a real-time risk monitoring system using InfluxDB and Faust. This system combines time series anomaly detection with real-time stream processing to effectively manage financial risks. By setting alerts based on deviations from the norm, Robinhood could detect anomalies in complex, trending time series data, enhancing their operational security and efficiency ^[49].
   • Benefit: Robinhood’s implementation shows the importance of adaptability in anomaly detection systems. By moving beyond threshold-based alerting to a model considering standard deviations, Robinhood could better manage complex time series data, highlighting the need for systems that can evolve with data trends ^[49].
2. Forecasting Advanced ML Platform: Infosys utilized its NIA Advanced ML Platform to tackle time-series forecasting challenges. This end-to-end data science platform automated machine learning tasks, enabling efficient bank account balance and product sales forecasting. The platform’s success demonstrates the power of ML in surpassing traditional statistical algorithms for time-series analysis ^[50].
   • The benefit: the use of Infosys NIA Advanced ML Platform illustrates machine learning’s superiority over traditional statistical methods in forecasting. This case demonstrates the benefits of ML in handling complex time-series data, offering more accurate predictions and insights
3. Prediction Engine: Intelliarts helped a company in the electronic interconnect industry develop a machine learning prediction engine. This engine improved demand prediction and stock availability, leading to better resource management and cost reduction. The case underscores the impact of ML on enhancing operational efficiency in manufacturing ^[46].
   • Benefit: Intelliarts’ collaboration with the electronic interconnect industry service provider reveals machine learning’s potential to transform manufacturing. By accurately predicting demand, the company could better manage resources, showcasing ML’s role in optimizing production and reducing waste
4. Inventory Management: Walmart uses time series analysis for demand forecasting and inventory management. Analyzing historical sales data allows Walmart to optimize its supply chain, reduce stockouts, and improve customer satisfaction ^[46].
   • Benefit: Walmart’s application of time series analysis for inventory management demonstrates the technique’s effectiveness in optimizing supply chains. By forecasting demand accurately, Walmart could maintain optimal stock levels, enhancing customer satisfaction and operational efficiency
5. Electricity Demand Forecasting: National Grid applies time series analysis to forecast electricity demand, ensuring grid stability and efficient energy distribution. This case highlights the importance of accurate forecasting in preventing blackouts and maintaining reliable energy supply ^[46].
   • Benefit: National Grid’s use of time series analysis for electricity demand forecasting emphasizes the critical role of accurate predictions in energy distribution. This case shows how forecasting can contribute to grid stability and the reliable supply of electricity, essential for modern societies

These case studies illustrate the transformative power of time series analysis and machine learning across various industries. From financial risk management to supply chain optimization and energy distribution, the successful implementations and lessons learned highlight the importance of embracing these technologies to stay competitive and efficient.

What we learned

Throughout this exploration, we’ve journeyed through the complex yet critical world of managing time series data within industrial settings, underscoring its paramount importance for predictive maintenance, trend analysis, and strategic decision-making. We’ve delved into the inherent challenges, best practices, and transformative potential of this data, emphasizing how it serves not just as the backbone for data-driven processes but also as a catalyst for operational excellence and innovation. By leveraging technologies such as AI and machine learning in conjunction with sophisticated data processing techniques, companies can unlock vast efficiencies, propel forward in their respective industries, and harness the full power of Industrial Internet of Things (IIoT).

The essence of our discussion elucidates the broader implications of efficiently managed time series data, demonstrating its significance in enhancing competitiveness, optimizing operations, and fostering sustainable growth. As industries continue to evolve amidst rapid technological advancements, the agility in managing such data becomes increasingly crucial. Therefore, it’s imperative for businesses to continuously refine their approaches, embrace the most suited tools and technologies, and consider the insights gathered from successful implementations to remain resilient and proactive in an ever-changing industrial landscape. In doing so, they not only maximize their operational efficiency but also pave the way for pioneering innovations that can redefine their market positioning and contribute meaningfully to their long-term success.

FAQs

1. What is the method for managing time series data?
Time series data management involves plotting data with a time axis, analyzing the relationship between past, present, and future data points. Techniques such as regression and auto-correlation are commonly used for this analysis.

2. What are the recommended practices for storing time series data?
To effectively store time series data, it is advised to optimize insert operations, batch document writes, maintain consistent field order in documents, increase client numbers, enhance compression, exclude empty objects and arrays from documents, round numeric data to a minimal number of decimal places, and improve query performance.

3. How should time series data be organized?
Time series data is organized sequentially, with each observation arranged in chronological order according to timestamps or time intervals. This structure emphasizes the temporal dimension, facilitating the examination of trends, seasonality, and time-dependent relationships.

4. Which database is suitable for time series data?
The top databases for managing time series data include InfluxDB, Prometheus, Kdb, TimescaleDB, and Graphite. Each of these databases is designed to handle the unique requirements of time series data efficiently.

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