monitoring tool<\/a> like Pandora FMS makes this step much easier<\/strong>. Pandora centralizes data collection, simplifies it (e.g., with an integrated CMDB), and greatly reduces time and effort.<\/p>\nScrub<\/h3>\n
The data science project will only be as good as the quality of the raw data<\/strong> collected in the previous step. That\u2019s why the next step is cleansing.
\nThis includes:<\/p>\n\n- Removing noise<\/strong> by separating actual signal from junk (e.g., irrelevant debug logs).<\/li>\n
- Correcting anomalous values<\/strong> (e.g., CPU metrics above 100%).<\/li>\n<\/ul>\n
Explore<\/h3>\n
This is where we start retrieving basic insights<\/strong> from this sea of data, which usually involves:<\/p>\n\n- Identifying patterns<\/strong> that emerge from the dataset. For example: at what time do servers consistently hit 90%+ RAM usage?<\/li>\n
- Visualizing correlations<\/strong>, which helps us understand the causes behind those patterns. For example: is the observed network latency increase aligned with scheduled backups?<\/li>\n<\/ul>\n
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.<\/p>\n
Model<\/h3>\n
We didn\u2019t start a data science project out of curiosity, but to help us manage better\u2014especially through prediction to prevent issues and optimize operations<\/strong>, rather than running around putting out fires.
\nThis means training algorithms to forecast scenarios<\/strong>. These can range from complex AI-based models to simple regressions for capacity forecasting.<\/p>\nInterpret<\/h3>\n
All of the above gives us results that can\u2019t stay in a drawer\u2014they must be turned into action<\/strong> at this stage.
\nFor 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.<\/p>\nTechniques and Models to Automate IT Processes<\/h2>\n
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\u2014which is what we\u2019re ultimately after.
\nLet\u2019s take a closer look at these techniques with real use case examples<\/strong> of data science in IT.<\/p>\nClassification<\/h3>\n
Data aggregation and analysis can yield a lot of information\u2014but 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<\/strong>.
\nTo avoid that, classification is key: prioritizing alerts into Critical, Warning, Informational\u2026<\/strong>
\nIt\u2019s crucial to find out about a possible intrusion and useful to know that something was updated successfully\u2014but not every file copy or every cloud sync.
\nPandora FMS helps apply this technique, as it reduces alert fatigue through a customizable system<\/strong> tailored to your infrastructure.<\/p>\nRegression<\/h3>\n
Regression is used to predict trends, like disk usage. Pandora FMS, through its Predictive Monitoring features<\/strong>, enables data-based capacity planning and helps apply this technique.
\nAssuming 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.<\/p>\nClustering<\/h3>\n
This method involves grouping elements based on similarities (web servers, critical components like databases\u2026) and allows for group-based monitoring<\/strong> instead of per-element checks, as well as homogeneous management policies<\/strong>.
\nThis brings greater order to chaos, and Pandora FMS supports cluster monitoring<\/strong> and the creation of clusters based on specific needs.
\nCombined with classification, you can define which nodes are critical, set custom alerts for each, and more.<\/p>\nAnomaly Detection<\/h3>\n
This can point to hardware or service failures\u2014or the most terrifying scenario, intrusion<\/strong> by malicious actors.
\nThe key here is to proactively identify suspicious patterns<\/strong> and detect anomalies as soon as they take place.
\nFor that purpose, Pandora FMS has an anomaly detection engine (MADE)<\/strong> that enables automatic detection.
\nFor prescriptive analysis, if the system detects that the database server DB-1701 (yes, the reference is obvious\u2014no 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.
\nAs we can see, this application of data science helps prevent a crash<\/strong> instead of just informing us afterward\u2014along with fifteen angry emails.
\nLoad balancing, container scaling, backup policy adjustment\u2026 Prediction tailored to your infrastructure is one of the holy grails of a successful data science project in IT.
\nBut how do we achieve that prediction?<\/p>\nAI and Machine Learning in IT Operations<\/h3>\n
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.
\nBut that\u2019s not all these models can do. They can further enhance proactive management of capacity, resources, and intelligent ticket assignment\u2026
\nOf course, we can also create and train our own models<\/strong>, whether from scratch or using specialized tools like the ones we\u2019ll explore next.<\/p>\nMost Used Tools in Data Science Applied to IT<\/h2>\n
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\u2014of course\u2014budget.
\nThe main tools to bring data science to life include:<\/p>\n
\n- Python\/R:<\/strong> For developing custom models, and a go-to for almost any scenario.<\/li>\n
- TensorFlow\/PyTorch:<\/strong> Popular libraries for Deep Learning development.<\/li>\n
- Apache Spark:<\/strong> An open-source data processing engine designed for large-scale analytics (you may interact with it using Python via Pyspark).<\/li>\n
- Jupyter Notebooks:<\/strong> Virtually the standard for creating interactive analytical documentation.<\/li>\n
- Cloud Services:<\/strong> Like Amazon Sagemaker, to build, train, and deploy models from the cloud without on-premise setup.<\/li>\n<\/ul>\n
These are not the only tools\u2014Google Colab, Kaggle, Anaconda\u2026 The options are so broad that you could easily procrastinate experimenting with them while your project remains stalled.<\/p>\n
Differences Between Data Science, AI, BI, and Data Analysis in IT<\/h2>\n
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\u2019s why it\u2019s important to clarify common misconceptions.
\nData science uses methods, processes, algorithms, modeling, and more to retrieve insights from data, focusing on interpretation, prediction, and prescription. But data science is not<\/strong>:<\/p>\n\n- Just programming.<\/strong> While programming is used for modeling (and also data collection and cleaning), data science can also involve machine learning and statistics.<\/li>\n
- Just data analysis.<\/strong> That\u2019s one component of data science, but the field goes far beyond it.<\/li>\n
- Just AI.<\/strong> 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.<\/li>\n
- BI (Business Intelligence).<\/strong> Business intelligence uses history and static data to support decisions, although it is evolving to include concepts like BAM (Business Activity Monitoring)<\/a>. Data science goes further by creating models that enhance decision-making.<\/li>\n<\/ul>\n
Here\u2019s a summary table of each concept’s main features:<\/p>\n
\n\n\n| \n Concept<\/strong><\/p>\n<\/td>\n| \n Predictive<\/strong><\/p>\n<\/td>\n| \n Real-time<\/strong><\/p>\n<\/td>\n| \n Automatic action<\/strong><\/p>\n<\/td>\n| \n Technical depth<\/strong><\/p>\n<\/td>\n<\/tr>\n\n| \n Data Science<\/strong><\/p>\n<\/td>\n| \n \u2713<\/p>\n<\/td>\n | \n Sometimes<\/p>\n<\/td>\n | \n Sometimes<\/p>\n<\/td>\n | \n High<\/p>\n<\/td>\n<\/tr>\n | \n| \n AI<\/strong><\/p>\n<\/td>\n| \n \u2713<\/p>\n<\/td>\n | \n Sometimes<\/p>\n<\/td>\n | \n \u2713<\/p>\n<\/td>\n | \n High<\/p>\n<\/td>\n<\/tr>\n | \n| \n BI<\/strong><\/p>\n<\/td>\n| \n \u2717<\/p>\n<\/td>\n | \n \u2717<\/p>\n<\/td>\n | \n \u2717<\/p>\n<\/td>\n | \n Medium<\/p>\n<\/td>\n<\/tr>\n | \n| \n Data Analysis<\/strong><\/p>\n<\/td>\n| \n \u2717<\/p>\n<\/td>\n | \n Sometimes<\/p>\n<\/td>\n | \n \u2717<\/p>\n<\/td>\n | \n Medium-Low<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n How Pandora FMS Enables the Application of Data Science in IT<\/h2>\nSince 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<\/strong>.
\nWe’ve already seen some examples while discussing the implementation steps of a project, but they are not the only ones.<\/p>\n\n- For instance, it makes data collection easier<\/strong> through unified retrieval via agents, APIs, etc. This makes the initial project stages faster and simpler.<\/li>\n
- It can create custom alert systems<\/strong> when applying techniques like classification or clustering.<\/li>\n
- Application of AI for anomaly detection<\/strong> with its MADE engine.<\/li>\n
- Advanced visualization<\/strong> of key elements derived from the data science project, thanks to its customizable dashboards.<\/li>\n<\/ul>\n
This science requires data, and advanced monitoring tools like Pandora FMS are experts at collecting, correlating, displaying, and classifying them.<\/p>\n Towards Data-Driven IT Management<\/h2>\nIn 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.
\nToday, 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<\/strong>.
\nWe must avoid the paradox where technology, which made data-driven management possible, does not apply it to itself \u2014 fulfilling the saying: “The shoemaker\u2019s son always goes barefoot.”
\nA good data science project will bring tangible benefits<\/strong>, such as:<\/p>\n\n- Reduction of MTTR (Mean Time To Repair):<\/strong> With predictive models and customized alerts.<\/li>\n
- Cost savings:<\/strong> Through optimization of cloud\/on-premise resources, reduced failures and associated expenses, etc.<\/li>\n
- Enhanced security:<\/a><\/strong> Enabling mitigation of feared zero-day attacks, for instance, via anomaly detection.<\/li>\n<\/ul>\n
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.
\nThat said, there are some emerging trends in data science<\/strong>, such as:<\/p>\n\n- AIOps:<\/strong> Or Artificial Intelligence for IT Operations, aiming to integrate it alongside machine learning to enhance and automate management.<\/li>\n
- Hybrid models:<\/strong> Combining expert rules, deep learning, and other techniques to get the best of all worlds while mitigating their weaknesses.<\/li>\n
- Total automation:<\/strong> Covering everything from issue detection to resolution without human intervention.<\/li>\n<\/ul>\n
The future is unwritten\u2014especially in this field\u2014although 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<\/strong>.
\nImplementing 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.
\nAfter all, smart competitors are already doing it.[\/et_pb_text][et_pb_button button_url=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF9saW5rX3VybF9wYWdlIiwic2V0dGluZ3MiOnsicG9zdF9pZCI6IjM2MjI3MCJ9fQ==@” button_text=”\u2190 Back to IT Topics” button_alignment=”left” _builder_version=”4.22.0″ _dynamic_attributes=”button_url” _module_preset=”default” custom_button=”on” button_text_size=”1em” button_text_color=”#0C312F” button_bg_color=”#FFFFFF” button_bg_color_gradient_direction=”90deg” button_bg_color_gradient_stops=”#82B92E 0%|#3CB92E 100%” button_bg_color_gradient_start=”#82B92E” button_bg_color_gradient_end=”#3CB92E” button_border_width=”1px” button_border_color=”#eaeaea” button_border_radius=”100px” button_use_icon=”off” z_index=”0″ custom_margin=”60px||0px||false|false” custom_padding=”10px|50px|10px|50px|true|true” custom_padding_tablet=”” custom_padding_phone=”10px|20px|10px|20px|true|true” custom_padding_last_edited=”on|phone” custom_css_main_element=”right:0!important;||font-family:%22Pandora-Bold%22!important;” global_module=”367749″ locked=”off” global_colors_info=”{}” button_bg_color__hover_enabled=”on|desktop” button_bg_color_gradient_start__hover=”#eaeaea” button_bg_color_gradient_end__hover=”#f4f4f4″ button_bg_color__hover=”#eaeaea” button_bg_enable_color__hover=”on” button_bg_use_color_gradient__hover=”on” button_bg_color_gradient_stops__hover=”#eaeaea 0%|#f4f4f4 100%”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”on|desktop” admin_label=”Final CTA” _builder_version=”4.22.0″ _module_preset=”default” background_color=”#161327″ use_background_color_gradient=”on” background_color_gradient_stops=”rgba(22,19,39,0.5) 17%|rgba(22,19,39,0.5) 100%” background_color_gradient_overlays_image=”on” background_image=”https:\/\/pandorafms.com\/wp-content\/uploads\/2024\/01\/img-banner-it-topics-resumen-de-producto.webp” background_position=”top_center” z_index=”1″ max_width=”1080px” max_width_tablet=”98%” max_width_phone=”98%” max_width_last_edited=”on|tablet” module_alignment=”center” custom_margin=”80px||80px||true|false” custom_padding=”40px|20px|160px|20px|false|true” custom_padding_tablet=”40px|0px|120px|0px|false|true” custom_padding_phone=”40px|0px|120px|0px|false|true” scroll_scaling=”40|55|85|100|100%|120%|100%” motion_trigger_start=”top” background_last_edited=”off|desktop” border_radii=”off|20px|20px|20px|20px” border_color_all=”#ffffff” box_shadow_style=”preset1″ box_shadow_vertical=”0px” box_shadow_blur=”80px” box_shadow_color=”#506da0″ global_module=”367407″ global_colors_info=”{}”][et_pb_row use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.22.0″ _module_preset=”default” max_width=”550px” module_alignment=”center” custom_margin=”0px||0px||true|false” custom_padding=”0px|0px|0px|0px|true|true” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.22.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.22.0″ _module_preset=”default” header_2_font_size=”2em” text_orientation=”center” module_alignment=”left” custom_margin=”0px||20px||false|false” custom_padding=”0px||0px||true|false” global_colors_info=”{}”]<\/p>\nCan one tool have global visibility?<\/p>\n [\/et_pb_text][et_pb_button button_url=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF9saW5rX3VybF9wYWdlIiwic2V0dGluZ3MiOnsicG9zdF9pZCI6IjMzMDcyMSJ9fQ==@” button_text=”Find out!” button_alignment=”center” button_alignment_tablet=”center” button_alignment_phone=”center” button_alignment_last_edited=”on|phone” _builder_version=”4.22.0″ _dynamic_attributes=”button_url” _module_preset=”default” custom_button=”on” button_text_size=”1em” button_text_color=”#ffffff” button_bg_use_color_gradient=”on” button_bg_color_gradient_direction=”90deg” button_bg_color_gradient_stops=”#82B92E 0%|#3CB92E 100%” button_bg_color_gradient_start=”#82B92E” button_bg_color_gradient_end=”#3CB92E” button_border_width=”0px” button_border_radius=”100px” button_use_icon=”off” z_index=”0″ custom_margin=”40px||0px||false|false” custom_padding=”10px|40px|10px|40px|true|true” custom_padding_tablet=”” custom_padding_phone=”15px|15px|15px|15px|true|true” custom_padding_last_edited=”on|phone” custom_css_main_element=”right:0!important;||font-family:%22Pandora-Bold%22!important;” locked=”off” global_colors_info=”{}” button_bg_color__hover_enabled=”on|hover” button_bg_color_gradient_start__hover=”#05201F” button_bg_color_gradient_end__hover=”#05201F” button_bg_color_gradient_stops__hover=”#181818 0%|#181818 58%|#181818 100%” button_bg_use_color_gradient__hover=”on”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Sections What Is Data Science and What Does It Bring to IT? The Life Cycle of a Data Science Project in IT Techniques and Models to Automate IT Processes Most Used Tools in Data Science Applied to IT Differences Between Data Science, AI, BI, and Data Analytics in IT How Pandora FMS Enables the Application […]<\/p>\n","protected":false},"author":12,"featured_media":402897,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","_joinchat":[],"footnotes":""},"categories":[7768,3505],"tags":[],"class_list":["post-402878","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-automation","category-it-topics"],"_links":{"self":[{"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/posts\/402878","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/comments?post=402878"}],"version-history":[{"count":6,"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/posts\/402878\/revisions"}],"predecessor-version":[{"id":404612,"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/posts\/402878\/revisions\/404612"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/media\/402897"}],"wp:attachment":[{"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/media?parent=402878"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/categories?post=402878"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pandorafms.com\/en\/wp-json\/wp\/v2\/tags?post=402878"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}} | | | | | | | | | |