In today's data-driven world, getting value from your information is more crucial than ever. Your data can reveal valuable insights, improve operations, drive innovation, and help you outpace your competition. Oblytech's Data Analytics services help your organization turn raw data into strategic assets that fuel growth and success.
Every interaction, transaction, and process within your organization generates valuable data. However, this data is only as valuable as the insights you can extract from it. At Oblytech, we specialize in turning your raw data into actionable intelligence that fuels strategic decision-making.
Our data analytics services cover the entire data lifecycle, from collection and storage to analysis and visualization:
We’ll work with you to define your data goals, identify key metrics, and develop a comprehensive data strategy that aligns with your overall business objectives.
Our skilled engineers will design and build sound data pipelines that streamline data collection, processing, and storage, ensuring data accuracy, reliability, and accessibility.
We’ll create a centralized data repository (data warehouse) that consolidates information from various sources. Then, we’ll develop powerful business intelligence tools that enable you to analyze your data, generate reports, and create insightful dashboards.
We use cloud computing to process and analyze large volumes of data, which can surface patterns that are hard to find with traditional methods. Our big data solutions help you identify trends, predict outcomes, and uncover hidden opportunities.
We translate complex data into intuitive visualizations, charts, and dashboards that help your team understand and act on insights in real time.
We apply advanced techniques like machine learning, predictive modeling, and artificial intelligence to uncover deeper insights and predict future trends, which can help you plan ahead.
Data analytics projects tend to begin with a complaint rather than a specification. Finance cannot agree on last quarter’s revenue because two systems count orders differently. A sales director wants to see pipeline by region every morning and receives a spreadsheet built by hand. An operations team has collected sensor or transaction data for years and has never had a way to ask it a question. Each complaint points to a different first investment, and buying the wrong one is an expensive way to learn that the real gap was somewhere else.
This hub helps you sort those complaints into routes. It covers strategy and consulting, data engineering and pipelines, warehousing with business intelligence, and big data with cloud analytics. The routes are not a ladder that every organization must climb in order. A business with clean data and a clear question may need only a reporting layer. A business with fragmented sources may need pipelines before anything else is worth building. The sections below explain how to tell which situation applies.
The routes differ in the kind of question they answer and the kind of output they leave behind. The table is a decision aid. Each row names the question a buyer usually has at the start and the route that answers it most directly.
| Starting question | Route to read first | What it leaves behind |
|---|---|---|
| Which measures should we track, and who is accountable for them? | Data strategy and consulting | Data goals, key metrics and a roadmap for acting on them |
| Data arrives late, in the wrong shape, or from sources that do not agree | Data engineering and pipeline development | Automated pipelines with data cleaning, error handling and monitoring |
| Leaders need one trusted set of reports across several sources | Data warehousing and business intelligence | A consolidated store, a data model and dashboards that people use |
| Volumes or variety exceed what the current store handles comfortably | Big data and cloud analytics | Cloud-based processing and analysis of large data volumes |
Two rows in that table are often confused. Strategy answers what the business should measure and why. Pipelines answer how the data reaches the place where measurement happens. A buyer who skips strategy tends to build pipelines for measures nobody reads. A buyer who skips pipelines tends to produce strategy documents that cannot be tested against real numbers.
A well-sequenced program usually moves through the routes in a particular order, although the order varies by organization. Strategy comes first when the business cannot yet agree on its measures. Pipelines come next when the sources are unreliable. Warehousing and reporting follow once the data arrives on time and in a consistent form. Cloud-scale processing is added last, and only when the volume or the variety of sources genuinely requires it. Organizations that start with the cloud platform often find that the warehouse design and the pipelines have to be rebuilt to match the business definitions that were never agreed.
Each route also leaves a handover that the next one depends on. The strategy produces a list of measures with definitions. The pipeline work produces tested transformations that implement those definitions. The warehouse produces a model that the dashboards read. If the definitions drift between stages, the final report will look polished and be wrong, and the fault will be hard to trace back.
Analytics work stalls in predictable places. The most common is a data source that nobody owns and that changes without notice, such as a field renamed by a vendor’s update. The second is a dashboard built before the measure was agreed, which then has to be rebuilt once two directors disagree on the definition. The third is a pipeline that works in a test extract and fails on the full history because of a volume or encoding issue nobody checked. Each of these is avoidable with an early definition workshop, a named source owner and a test against real historical data before the first release.
Advanced analytics, including machine learning and predictive modeling, depends on the same foundations. A model trained on inconsistent records will look accurate in testing and fail in use. For that reason predictive work generally belongs after a reliable data set exists, not before it. The AI data readiness page sets out checks on data sources, quality and access that come before building on AI.
Not always. A small number of clean sources can feed a dashboard directly. A warehouse becomes necessary when several sources must be combined consistently, when history must be kept, or when many people need the same trusted figures.
It depends on volume, the frequency of queries and how much the platform is used. Costs are easier to control when storage and compute are sized to actual use and when someone reviews them monthly.
Yes, and it is often the right first step. The output should be a set of decisions, measures and a sequenced plan that the business can test. A strategy that only describes a future state is less useful than one that names the first build.
Check completeness, consistency and history for the fields the model would use, and confirm that the definitions match what the business believes they mean. A short readiness review usually answers that before any model is trained.
The fastest route to a sensible decision is to name the question a leader is trying to answer and the source that currently fails to answer it. That pairing usually points to one of the four routes. Discuss your data analytics requirement with our team, and the conversation can start with the decision and the sources rather than with a platform.
Data analytics answers four kinds of question: what happened, why it happened, what is likely to happen next, and what to do about it. Each kind rests on the one before it. Reporting built on agreed definitions comes first, and predictive work pays off only once that base can be trusted.
The table maps each kind of question to the groundwork it needs and the route on this page that supplies it.
| Kind of analytics | The question it answers | What it needs first | Route that supplies it |
|---|---|---|---|
| Descriptive | What happened last week, last quarter, by region? | One agreed set of measures and loads that arrive on time | Data warehousing and business intelligence |
| Diagnostic | Why did the figure move? | Detail at transaction level with keys that match across sources | Data engineering and pipeline development |
| Predictive | What is likely to happen next? | Enough history to show a pattern and records of the outcomes | Big data and cloud analytics |
| Prescriptive | What should we do about it? | A named owner who can act on the output | Data strategy and consulting |
The practical test for any proposal is to ask which row it really sits in. Work described as predictive that cannot name its history or its outcome records is usually reporting with a new label. If the answer is the first row, the right first spend is the data warehousing and business intelligence service. If two systems disagree on the same number, start with data engineering and pipeline development.
Predictive work has a further condition that is easy to miss. The records must describe the outcome you want to forecast, not a proxy for it, and someone must be able to check a forecast against what actually happened. The AI data readiness page lists the checks. Where nobody can say which decision the output should change, the data strategy and consulting service is the place to settle that first.
Business intelligence is the reporting layer: agreed measures, a consolidated store and dashboards that people check routinely. Data analytics is the wider practice, which includes business intelligence plus investigating causes and building forecasts.
A warehouse stores cleaned data in a modeled shape for reporting. A lake keeps raw data in its original form for later processing. Many organizations use both, with the warehouse serving reports and the lake feeding exploration.
A business owner for the definitions and an engineering owner for the build. If only engineering owns it, definitions drift. If only the business owns it, nobody can change it safely.
Data analytics examines data a business already holds to answer a defined question and support a decision. Data science and machine learning go a step further, building models that find patterns or make predictions nobody specified in advance.
The practical difference is the output. An analyst hands over a figure, a chart or an explained finding that a person reads and acts on. A machine learning team hands over a model that runs repeatedly and produces scores or classifications inside another process. The first needs agreed definitions and clean data. The second needs those plus enough history with known outcomes, a way to test against fresh cases, and someone who watches it after release.
| Discipline | Typical output | Question it answers | Main risk |
|---|---|---|---|
| Data analytics | Reports, dashboards, explained findings | What is happening, and why? | Teams reading different definitions |
| Data science | Experiments and prototypes | Is there a pattern worth acting on? | Findings that never reach a decision |
| Machine learning engineering | A model running inside a process | Can this judgement be made automatically? | Accuracy declining unnoticed |
A simple decision rule helps. If a person will read the result before anything happens, you need analytics. If the same judgement is made many times a day and the current rule of thumb handles it badly, a model may be worth considering. If you cannot tell which applies, start with analytics, because it shows whether the pattern exists and most of what is built for it can be reused if a model follows.
The most common mistake is hiring modelling skills to fix a reporting problem. The team then spends months reconciling source extracts, which is engineering and analytics work, and the model never reaches production. The reverse also happens: a business with plenty of clean history and a repeated decision keeps producing dashboards when a model would remove the manual step.
Once a candidate decision looks suitable, the AI use case discovery page explains how to test whether it is worth modelling, and the machine learning development page covers what building and running a model involves. To judge afterwards whether either effort paid off, read about measuring AI value in terms leadership trusts.
Every data and analytics service page on this site.
Guides from the Oblytech ServiceNow library that explain the practice behind this page.