Data Science in IT: What It Is, Key Techniques, and Practical Applications

If you’re wondering how online stores seem to read your mind and tempt you with irresistible plastic to spend your salary on, it’s not magic—it’s science. Specifically, data science, a concept being applied across countless fields: shopping, weather forecasting, business decisions, politics, military strategy… and also IT management.

That’s why today we’re going to take a deep dive into this discipline, focusing on how to apply it to make managing our technological infrastructure easier.

And we’ll see that its goal is not (only) to make Orwell’s 1984 dystopia a reality—just in an even creepier way than he imagined.

What Is Data Science and What Does It Bring to IT?

The formal and boring definition says it’s the process of retrieving knowledge from data using statistical techniques, machine learning algorithms, and advanced analytics to solve operational problems in the IT field.

In other words, using data to optimize our technology management.

In environments with increasingly complex infrastructures (mixing cloud, on-premise, edge…) and where information grows exponentially, data science allows us to turn metrics, logs and events into actionable knowledge.

This knowledge can allow us to:

• Automate repetitive operations,
• Enable stronger proactive security rather than reactive, against threats.
• Improve decision-making, as it’s based on evidence—not hunches, bias, or the timeless classic: “Because it’s always been done that way.”

Within the context of data science, we find concepts like big data or AI, especially since the exponential growth of data collection capabilities.

Big data refers to how massive that data has become. Meanwhile, AI’s learning and analytical capabilities allow for predictions and the discovery of patterns that are hard—or outright impossible—to detect manually or with traditional analysis.

With well-applied data science, we move from static reports to predictive models that foresee server failures, optimize capacity and resources, or detect sophisticated intrusions.

What’s the Difference Between Data Science, Data Analytics, and Data Engineering?

As it always happens when a field gains popularity, terminology and “experts” multiply. That’s why it’s important to make differences clear between terms that coexist within the broader ecosystem of information management.

Here’s a summary table of what each role implies:

By now, you may be realizing that today’s ability to collect massive amounts of data can help not only governments, Zuckerberg, or Google with surveillance and control—but also improve our daily IT management.

If so, it’s time to start off on the right foot and carry out a data science project in the best possible way.

The Life Cycle of a Data Science Project in IT

When it comes to making projects a reality, it’s better to follow proven paths than reinvent the wheel. In this case, we can apply the OSEMN model (Obtain, Scrub, Explore, Model, Interpret) as the life cycle for data science projects applied to our IT operations.
To do so, we simply follow the steps of the acronym:

Obtain

This step involves gathering raw data, because there is no science without data. The goal is to obtain critical data from every corner of our technological infrastructure.
Obviously, each infrastructure is unique, shaped by our unresolved traumas, so the process will vary. However, these sources are commonly critical:

• Performance metrics (CPU, RAM, bandwidth…)
• System and application logs
• Network events (SNMP traps)
• CMDB (Configuration Management Database) data containing the configuration of assets in our little tech empire

Let’s face it: digging up all this data is tedious when our infrastructure is even slightly complex. Some data we already have, others will require access, and some need to be configured for collection.
That’s why a monitoring tool like Pandora FMS makes this step much easier. Pandora centralizes data collection, simplifies it (e.g., with an integrated CMDB), and greatly reduces time and effort.

Scrub

The data science project will only be as good as the quality of the raw data collected in the previous step. That’s why the next step is cleansing.
This includes:

• Removing noise by separating actual signal from junk (e.g., irrelevant debug logs).
• Correcting anomalous values (e.g., CPU metrics above 100%).

Explore

This is where we start retrieving basic insights from this sea of data, which usually involves:

• Identifying patterns that emerge from the dataset. For example: at what time do servers consistently hit 90%+ RAM usage?
• Visualizing correlations, which helps us understand the causes behind those patterns. For example: is the observed network latency increase aligned with scheduled backups?

Histograms, plotting variable relationships, clustering data to detect trends… Techniques in this step vary, and the skill of the project team makes a big difference.

Model

We didn’t start a data science project out of curiosity, but to help us manage better—especially through prediction to prevent issues and optimize operations, rather than running around putting out fires.
This means training algorithms to forecast scenarios. These can range from complex AI-based models to simple regressions for capacity forecasting.

Interpret

All of the above gives us results that can’t stay in a drawer—they must be turned into action at this stage.
For example, we may need to add on-premise servers to prevent downtime or bottlenecks in employee productivity. Or we might need to scale online hosting capacity during certain times due to the seasonal nature of our business.

Techniques and Models to Automate IT Processes

Steps 3 and 4 of the previous life cycle model are the most practical and interesting because they allow us to apply techniques that improve IT management—which is what we’re ultimately after.
Let’s take a closer look at these techniques with real use case examples of data science in IT.

Classification

Data aggregation and analysis can yield a lot of information—but not all of it will be relevant. Going from blindness to being overwhelmed by alerts is like escaping Alien only to fall into Predator, and will quickly result in information fatigue.
To avoid that, classification is key: prioritizing alerts into Critical, Warning, Informational…
It’s crucial to find out about a possible intrusion and useful to know that something was updated successfully—but not every file copy or every cloud sync.
Pandora FMS helps apply this technique, as it reduces alert fatigue through a customizable system tailored to your infrastructure.

Regression

Regression is used to predict trends, like disk usage. Pandora FMS, through its Predictive Monitoring features, enables data-based capacity planning and helps apply this technique.
Assuming more or less linear behavior, it can tell us how many days remain before a disk fills up, or how many database queries we’ll have in a month.

Clustering

This method involves grouping elements based on similarities (web servers, critical components like databases…) and allows for group-based monitoring instead of per-element checks, as well as homogeneous management policies.
This brings greater order to chaos, and Pandora FMS supports cluster monitoring and the creation of clusters based on specific needs.
Combined with classification, you can define which nodes are critical, set custom alerts for each, and more.

Anomaly Detection

This can point to hardware or service failures—or the most terrifying scenario, intrusion by malicious actors.
The key here is to proactively identify suspicious patterns and detect anomalies as soon as they take place.
For that purpose, Pandora FMS has an anomaly detection engine (MADE) that enables automatic detection.
For prescriptive analysis, if the system detects that the database server DB-1701 (yes, the reference is obvious—no points for catching it) is at 98% capacity, it might warn that if the trend continues and nothing is done, it will fail in 15 minutes.
As we can see, this application of data science helps prevent a crash instead of just informing us afterward—along with fifteen angry emails.
Load balancing, container scaling, backup policy adjustment… Prediction tailored to your infrastructure is one of the holy grails of a successful data science project in IT.
But how do we achieve that prediction?

AI and Machine Learning in IT Operations

In the case of Pandora FMS, the final goal of the MADE anomaly detection engine is to train and use AI models for early detection.
But that’s not all these models can do. They can further enhance proactive management of capacity, resources, and intelligent ticket assignment…
Of course, we can also create and train our own models, whether from scratch or using specialized tools like the ones we’ll explore next.

Most Used Tools in Data Science Applied to IT

When it comes to modeling within our project (step 4 of the life cycle), turning that data into actions and tools that support IT management, we have many options depending on our knowledge, time, and—of course—budget.
The main tools to bring data science to life include:

• Python/R: For developing custom models, and a go-to for almost any scenario.
• TensorFlow/PyTorch: Popular libraries for Deep Learning development.
• Apache Spark: An open-source data processing engine designed for large-scale analytics (you may interact with it using Python via Pyspark).
• Jupyter Notebooks: Virtually the standard for creating interactive analytical documentation.
• Cloud Services: Like Amazon Sagemaker, to build, train, and deploy models from the cloud without on-premise setup.

These are not the only tools—Google Colab, Kaggle, Anaconda… The options are so broad that you could easily procrastinate experimenting with them while your project remains stalled.

Differences Between Data Science, AI, BI, and Data Analysis in IT

Data science has become very trendy lately, but as with all trends, people tend to stretch the concept or rebrand anything with marketing creativity. That’s why it’s important to clarify common misconceptions.
Data science uses methods, processes, algorithms, modeling, and more to retrieve insights from data, focusing on interpretation, prediction, and prescription. But data science is not:

• Just programming. While programming is used for modeling (and also data collection and cleaning), data science can also involve machine learning and statistics.
• Just data analysis. That’s one component of data science, but the field goes far beyond it.
• Just AI. AI is a tool used in data science, especially for prediction and prescription, but other methods like regression or statistical analysis may also be applied.
• BI (Business Intelligence). Business intelligence uses history and static data to support decisions, although it is evolving to include concepts like BAM (Business Activity Monitoring). Data science goes further by creating models that enhance decision-making.

Here’s a summary table of each concept’s main features:

How Pandora FMS Enables the Application of Data Science in IT

Since data science involves collecting, filtering, analyzing data, modeling, and deploying solutions, a global monitoring tool like Pandora FMS helps carry the heaviest load in such a project.
We’ve already seen some examples while discussing the implementation steps of a project, but they are not the only ones.

• For instance, it makes data collection easier through unified retrieval via agents, APIs, etc. This makes the initial project stages faster and simpler.
• It can create custom alert systems when applying techniques like classification or clustering.
• Application of AI for anomaly detection with its MADE engine.
• Advanced visualization of key elements derived from the data science project, thanks to its customizable dashboards.

This science requires data, and advanced monitoring tools like Pandora FMS are experts at collecting, correlating, displaying, and classifying them.

Towards Data-Driven IT Management

In many cases, IT infrastructure management is based on the skill, intuition, or habits of its administrators. Or it is built around the capabilities of the available tools instead of optimal processes.
Today, with the rise of big data, data-driven management has been implemented in most fields because, being based on the specific evidence of each organization, optimization is greater.
We must avoid the paradox where technology, which made data-driven management possible, does not apply it to itself — fulfilling the saying: “The shoemaker’s son always goes barefoot.”
A good data science project will bring tangible benefits, such as:

• Reduction of MTTR (Mean Time To Repair): With predictive models and customized alerts.
• Cost savings: Through optimization of cloud/on-premise resources, reduced failures and associated expenses, etc.
• Enhanced security: Enabling mitigation of feared zero-day attacks, for instance, via anomaly detection.

As for the future of data science, everyone has their predictions. But those who really know also know those predictions are worth about as much as a six-euro bill.
That said, there are some emerging trends in data science, such as:

• AIOps: Or Artificial Intelligence for IT Operations, aiming to integrate it alongside machine learning to enhance and automate management.
• Hybrid models: Combining expert rules, deep learning, and other techniques to get the best of all worlds while mitigating their weaknesses.
• Total automation: Covering everything from issue detection to resolution without human intervention.

The future is unwritten—especially in this field—although many rush to sell it before it exists. But the reality is that, in the present, data science should be at the core of modern IT infrastructure management.
Implementing it doesn’t require a massive project. Even the most modest architectures can retrieve a huge value from their data and turn it into competitive advantages.
After all, smart competitors are already doing it.