Managed analytics

We provide dedicated resources and expertise for your agile BI needs
ETL - processes

ETL - processes

Data Lake

Data Warehouse and Data Lake


Executive dashboards


Data Science

Find out more

The problem we solve

Advanced data analytics becomes the necessary requirement to successfully compete in many industries for companies of all sizes.But a full blown data analytics team is a prohibitively expensive for most companies with a long wait time (3-12 months) to set up fully.Managed analytics from Qompax offers a smart alternative.


The benefits of our model

Get implemented BI and start getting the results 5 times faster with risks and costs 2 times lower.
  • Time

    Quick Start

    Measure time to value in weeks, not years. With no systems to build or people to hire, managed analytics is quick to launch - typically within 3-6 weeks
  • Team

    Knowledgeable support

    Full team of experts ready to work on the whole cycle without delay
  • connection

    Flexible scalability

    Opportunity to extend range of services or order isolated projects with company’s growth
  • Experts

    Synergy of expertise

    Having analytics outsourced to the experts,, you can actually insource the analytical mindset needed to advance your business
  • partnership

    Long-term partnership

    Analytics needs iteration and experimentation, as well as deep understanding of business
  • chart

    Short time to value

    No need for large upfront costs. Return on investment within 90 days, as projects become self-funded due to the insights gained

BI implementation requires a team, ever changing

BI is a large, diverse, constantly evolving technology stack. 
In order to maximize your return on investment you need an experienced and focused team.

What do you have now
What you receive
1 data storage
7+ data sources
5+ data marts
20+ dashboards
100+ metrics
BI chatbot
Metrics catalogue
  • Post-implementation and after setting up full support
  • Selecting all BI tools that's the best fit for you given data types and sizes, number of users, use case scenarios, capabilities and importance of ML
  • Access to Qx metrics database gained from years of experience
  • Incoming Qx Analytics Consulting
  • Approved implementation roadmap, task-based to track the process
  • Payment amount split into verifiable steps
BI processesBI processes

Examples of components necessary

Database and  data warehouse

SQL ServerPostgresAzure SynapseBigQuerySnowFlakeClickHouseMySQLOracleAmazon RedshiftVertica

Orchestration and transformation


BI visualization

PowerBIMetabaseSupersetre dash

Quick start and shortest time to value

Without us you will need to build a team for meeting all the challenges listed on the right - with us you can promptly start within a few days.
Data compilation. System analysis

1. Reports users technical support and consulting
2. Documentation
3. Accuracy control of the data used for making managerial decisions
4. Communication with reports internal client
5. Mockup of various metrics, cascading them by functional roles

Data connection. ETL engineering

1. Development of data models and rules for calculating analytical indicators
2. Manage both internal and external data sources
3. Data validation and quality maintenance measures
4. Setting up data uploading and transforming algorithms
5. Query performance analysis and optimization
6.Conducting various Ad hoc analytics

Data modeling. Data lake

1. ETL. Data warehouses development and maintenance
2. Data mart. Development of regular and 
ad-hoc data marts for clients within the company (including analysis of demands and implementation options preparation)

Visualization and reports. Dashboards

1. UX and UI design
2. Optimization of performance
3. Corporate brand book guidance
4. Elaborate interactivity and cross reporting
5. Mobile dashboarding

Data analytics. Data science

1. Creating hypotheses that allow to build new scoring models and improve current ones
2. Data processing and preparation to address ML challenges. Development and testing of models to describe and interpret data in Python.
3. Designing a recommendation system for detecting data anomalies.
4. Building replicable and reusable solutions for handling data and models
5. Complete cycle (data collection, feature engineering, model fitting, model management, visualization, model serving) of a models set creation based on transactional and behavioural information
6. Participating in ML - pipelines development and standartisation

Key terms of the contract

First, we start with a starter pack of 60 hours per month. This timeframe prepare one medium sized data source on basic level and start basic analytics. If there is a need to connect and start analyzing a few data sources in parallel, we offer additional packages of 25 hours, which need to be booked in before the month starts, so that we can allocate and plan them for you.

If, there are any hours left in your packages, we will transfer them into the next month.

If you do not request any work tasks, we do not charge any monthly subscriptions.

What does it cost?

When comparing to the fulltime analytics team, we cost considerably less. Plus we offer flexibility in costs, which cannot be reached by the FTE (full time employees). Analytics is not a constant, by definition. It is always iterative and volatile in terms of workloads on different roles. We match this volatility with out managed analytics model.

When do you need a dedicated in-house team?

Usually it’s appropriate and cost effective for digital product companies, where data needs to be analyzed hourly as that is the  rate of change that must be effectively captured. When, you need output of advanced analytics on daily or weekly basis, then managed analytics is always a smart alternative. More so, even beforementioned product companies can gain an advantage over their competitors by augmenting their analytics team with a managed analytics to cover for peak times.

We might still want to add an in-house analysts. How would this work?

After implementation and integration, which takes from 4 to 7 months, we can switch to support mode or continue looking for insights and new opportunities for analysis. In any case, the analytical infrastructure created by us will allow you to hire less technically demanding specialists with greater flair in analytics. Hence, you are closing the gap in analytical technology through us and in analytics mindset in-house.

Price formation

Starter pack​
Extra pack
Price formation timeline

Hours balance outstanding at the month-end is transferred to the next month.​ As we are using different team experts in order to complete your tasks, you pay one comprehensive and fixed amount. In order to better allocate resources for you, it is best to book packs at the beginning of the month. Before the month start, they can also be increased, reduced or freezed for up to 3 months. Flexibility for our clients is one of the key advantages of our model.

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Your QompaX team members track their time by each task. This helps us to stay focused, while providing unmatched transparency to our clients even when comparing to FTEs. And yes, being the BI specialists, we collect and analyze this data daily and give you the same access to this analytics through a client portal.

Client portal

  • Client portal with all the analytics you need

    Key business users often need to have one access point to view a collection of dashboards and reports. For your convinience we developed a private portal, where your  BI reports are embeeded. This portal can be hosted on your domain or ours. We can also create such portal for your clients with their filtered data.
  • Telegram chat bot

    Let’s face it, reporting must compete for your attention with social media and messages. And we recognize that. Hence, we developed a chat bot that sends screenshots of mobile friendly dashboards on a daily basis. These dashboards have the most important information to start your day delivered in a clear visual format. 
#analytics as a habit.

Analytical frameworks

Accumulated experience allows us to find common patterns and identify most effective methods to unlock the full potential of your data.

Ready play books of KPIs by industries

Often companies start their BI journey by collecting KPIs from each team by function. This can be a lengthly process. However, with us, clients can jump  start with the KPIs already aggregated and tested to work through our experience of specializing in certain industries, such as FMCG, healthcare and ecommerce.

irregularitiesdata enrichmentSpecific indicatorsGroupingFactor analysis

Data Science

Threshold detection

Threshold detection

This allows to detect anomalies in data and pinpoint the exact causes. It might be just a data glitch or a growing operational problem that needs addressing.
Trends and predictions

Trends and predictions

Trends and forecasting allow to gain the maximum value to decision making from your data.
Anomalies and Outliers

Anomalies and Outliers

Looking closer in a distribution of a dataset, data science methods can identify data point too far from the mean. Explaining them usually leads to understanding of the nuances of your operation.


Correlation is a powerful tool to see the relatinship between two variables. This then can serve in forecasting or what-if analysis.
Changes in Trends

Changes in Trends

As trends change we can become prepared for the new normal, if we can spot it beforehand.

Our clients

Our managed analytics model is a right choice for almost any business

  • FMCG companies

  • Healthcare

  • Startups

  • Private equity funds

Юсуповская больница
Yusupov Hospital
Operating officer
Reports are developed in two clicks, not two days ...
A distinct competitive advantage
Head of PE fund
It’s an ideal report, everything is clear and everything is immediately visible!
Be True
Be True
Shareholder of Be True
A very necessary thing, otherwise is no longer possible!

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