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Analytical data vs operational data – how are they different?

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Intro

Making the right business decisions is impossible without high-quality data. However, depending on your current challenges there are different data types you can turn to, i.e., analytical and operational data. That being said, the case here isn’t about choosing one over the other; both are necessary to address different types of needs.

That’s what we’ll cover in this piece. We’ll also give you a relatable data sharing example to show you how these two data types differ.

Analytical data vs operational data – key differences

Understanding differences between operational data and analytics data is essential if you want to make sure that you use each data type most effectively. Let’s discuss how they differ below.

Analytical data

Analytical data is a collection of data that is leveraged to support organizations in their long term decision-making processes. It is mainly built of historical information, i.e., the data isn’t used to monitor real-time processes. As the name indicates, this data type is used for analytics purposes, and is stored mainly in so-called ‘read-only’ databases.

Analytical data is used by businesses to help them make choices backed up by verifiable, statistically meaningful information. The goal is to get as many data sources as possible, and use ML/AI-powered algorithms to generate optimization recommendations and to support business intelligence activities (specifically, analysis).

Analytical data is used by businesses to help them make choices backed up by verifiable information
Source: Unsplash

Perhaps most important of all, what matters in analytical data is statistics, not specific cases. The amount of data you have is essential here, as it allows you to minimize statistical error and draw useful conclusions (especially, if you’re dealing with low-quality data).

Analytical data is typically stored in data lakes or data warehouses. That being said, one of the sources for analytical data can be operational data, generated during the implementation of certain business processes. We mention this type of data next.

Operational data (also colloquially called real-time data)

Operational data becomes available as soon as it’s created and acquired, which is why some also call it real-time data. While analytical data includes data on past performance, operational data is always current. It supports real-time processes, so it has to continuously reflect the current state of objects which are used in these processes.

Since they always show the present state of things (such as the position each object is in), it’s absolutely key that the data used is up-to-date. Using analytical data to support real-time processes isn’t sufficient as they operate based on single (specific) objects, and not on statistical data. This type of data and the results of the analysis carried out based on it can be used to design these processes and for their optimization.   

To get a brief overview of how operational data and analytical data differ take a look at the table below. 

Operational dataAnalytical data
Core: Single object matters
Purpose: Real-time processes
Facilitates: Operations
Ownership: on an object level
Sharing: Real-time
Quality: Single piece of data’s quality is important
Solution: Trusted Twin
Core: Statistical object matters
Purpose: Non-real-time analysis
Facilitates: Optimizations
Ownership: on a data set level
Sharing: Non-real-time
Quality: Statistical data set’s quality is important
Solution: Warehouse / Data lake
Real-time data, known more commonly as operational data
Source: Unsplash

A predictive maintenance process as a data sharing example

Now that we’ve discussed the key differences between analytical and operational data, let’s look at a data sharing example and give you a sense of how both of these types could be used in practice. For this particular scenario, we’ll focus on predictive maintenance, which is a process that requires both real-time and historical data.

Let’s imagine that a refrigeration equipment management company cooperates with an external maintenance service. The joint goal for both of these companies is to predict and avoid device failures.

The first company gathers historical data from hundreds of refrigeration appliances. This data can either come directly from the device supplier and/or manufacturer or be stored by them in a platform like Trusted Twin.

Based on this statistical data, the company commissions data analyses with the use of AI / ML. The purpose of these studies is to identify typical failure patterns, i.e., those that would serve as tell-tale signs of an upcoming error.

Next, the company compares real-time, operational data from specific devices with failure patterns. If the probability of a specific failure exceeds a predetermined threshold, a verification/repair request is automatically generated to the cooperating maintenance company. They are provided with the operational data at the disposal of the company, but they may also collect additional operational data themselves, as they deem needed.

Both of these companies begin exchanging operational data that relate to each device repair. That includes data from IoT sensors as well as documents and reports related to service operations. This allows both of them to report compliance with service level agreements (SLA) – both those in their own contracts, as well as those signed with their end customers. 

In the scenario above, we’re dealing with the following:

Analytical data:

  • Analytical data from monitored devices makes it possible to identify failure patterns based on statistics. The higher the volume of this data type, the better. The same goes for historical data – the further into the past the data reaches, the easier it might be to nail down the original culprit for each failure type. As you can see, in this particular case, associating with each specific device (i.e., ownership) isn’t important.
  • Running an analysis of data relating to the conducted service processes allows you to optimize costs and response times of the external, cooperating maintenance company. Analytical data also makes it possible to properly estimate the costs of executing contractual agreements for the end customer.

Real-time, operational data

  • Operational data which comes from a specific device is compared in real-time with common failure patterns. The faster this data becomes available (in real-time), the quicker the malfunction risk will be identified. Using a single platform to cooperate with multiple partners (in this case service companies) allows to quickly pass on alerts or warnings and initiate appropriate actions. Data integration and sharing enable quick access to additional diagnostic data which is collected by subcontractors (service firms).
  • Operational data on past repairs allows to instill trust into the process and minimize the risk of making mistakes. Sharing of data such as reports on a single data sharing platform diminishes the probability of data loss or delay in data sharing. All updates to the shared process state conducted in cooperation with multiple partners are available in real-time for everyone involved, and with trust.
Data sharing example for leveraging both real-time data and analytical data
Source: Unsplash

The importance of using the right data sharing software

The data sharing example discussed above clearly shows how important sharing operational data between partners is. It is necessary to use a reliable data-sharing method, whenever there are processes involving multiple partners simultaneously. To further back up this claim, imagine a scenario where one partner relies on data from another partner to make ad-hoc business decisions and it turns out that the data is either outdated or inaccessible. This would put the whole decision-making process in jeopardy. 

Data security is also a concern when it comes to shared processes, where any interference might have serious consequences. This isn’t the case when it comes to analytical data as most analytical processes rely on historical data making the data-sharing layer not as critical. The analytical data processing itself can take place on a separate, encapsulated infrastructure. 

Trusted Twin provides the perfect operational data sharing layer. We understand the importance of keeping your data safe and the infrastructure reliable. Trusted Twin is the best solution for creating real-time data-based processes. 

Operational data & analytical data – final remarks

As we’ve shown in this article, analytical data stored in data warehouses and data lakes has a different purpose and character than operation data. For this reason, the challenges related to storing and sharing are also different for each of these data types. 

Hence, it’s good to understand the differences, as only then will you be able to make the most of each data and choose the right infrastructure and software. If you’re looking for the right data sharing software for your operational data, then Trusted Twin might just be the right choice. It acts as a data-sharing layer, allowing easy operational data exchange with partners while maintaining full control and security of shared data.
Learn more about how to build a secure and reliable data sharing solution fast and cost-effective.

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Krzysztof Malicki
Krzysztof Malicki

Business and IT executive with a solid IT system development and design background combined with broad experience in business development and IT sector M&As. Direct experience and long-term relations in the telecommunications market in Poland. Formerly Chief Inventor and Designer of the largest Polish VoIP platform.

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