Why does business need this?
Any corporate process ultimately ends with reporting: the manager looks at KPIs, the financial director closes the period, the operations unit controls the SLA, production analyzes deviations, IT is responsible for the quality of service, and the owner wants to see a unified picture of the business. If data is torn between 1C, SAP, CRM, Service Desk, Excel, portals and external systems, the company loses control.
Our task is not to put up another beautiful dashboard, but to assemble a trusted decision-making loop. We link accounting systems, integrations, DWH, marts, cubes, data quality rules, BI tools, access roles and AI scripts so that the numbers can be trusted and explained.
From ERP to management reporting
An ERP project cannot be considered complete until the business has received clear reporting. Implementation of 1C:ERP, 1C:UH, SAP, SAP → 1C migration, custom development, Service Desk, portal or industry product should answer a simple question: what decisions can now be made faster, more accurately and more calmly.
ERP without reporting does not work for management
The set up process is important, but the business sees the result through plan-facts, budgets, sales, purchases, balances, receivables, SLA, production and operational indicators.
Reporting without data quality becomes a controversy
If directories, statuses, periods, currencies, contracts and responsibility centers diverge, the dashboard only accelerates the spread of errors.
DWH connects history and new systems
Storage and showcases help preserve SAP history, connect 1C, combine CRM, Service Desk, portals and external sources without overloading accounting systems.
AI-copilot requires a verified data layer
AI can explain deviations, prepare comments and answer management questions only when it sees the source, formula, period, access rights and restrictions of the data.
Solution providers and platforms
We work not with one BI tool, but with a technological map for the customer’s task: in some places it is more correct to develop SAP BI, in others to build reports in 1C, in others to launch PIX BI as a Russian self-service platform, in others to use Yandex DataLens, Qlik, Grafana or open-source BI. The choice depends on the architecture, regulators, placement, cost of ownership, launch speed, self-service requirements and further AI layer.
SAP
SAP BI, BusinessObjects, Analytics Cloud, Datasphere
PIX BI
self-service BI, ETL, AI assistant, Russian platform
Yandex DataLens
dashboards, datasets, visualization, cloud/on-prem outline
Qlik
Qlik Sense, QlikView, associative analytics
1C
SKD, reports 1C, 1C:ERP, 1C:UH, 1C:ZUP
Grafana
operational dashboards, time series, monitoring, IoT
Apache Superset
open-source BI, charts, SQL Lab, dashboards
Metabase
open-source BI, data questions, dashboards
Logos are provided as identifiers of technology suppliers and copyright holders. All trademarks belong to their respective owners; specific versions, licenses and delivery conditions are confirmed before the project.
What we can implement
Regulatory and management reporting
Financial, operational, commercial, HR, IT, manufacturing and industry reports for CEO, CFO, COO, CISO, CIO and process owners.
Data cubes and analytical models
We design multidimensional models, indicators, dimensions, hierarchies, aggregation rules, reconciliations and access rights for SAP BI, 1C, Qlik, PIX BI and DWH.
Data warehouses and data marts
We build DWH, data marts, integration layer, ETL/ELT, quality control, change history, indicator catalogs and a layer for BI/AI.
Self-service BI for business
We give business users a controlled way to assemble their views and panels without the chaos of formulas, access rights and sources.
Dashboards and Grafana
We collect panels for SLA, infrastructure, service desk, telemetry, time series, R&D, industrial operation and monitoring.
Embedded analytics and portals
We embed reports into personal accounts, service portals, corporate products, 1C-Bitrix, web interfaces and internal systems.
BI Migrations and Logic Preservation
We help you transfer reports from Power BI, Qlik, SAP BI or Excel contours to PIX BI, DataLens, open-source BI, 1C or DWH without losing the meaning of the indicators.
AI-copilot on top of reporting
We add a layer of data questions, explanations of deviations, draft management comments, method searches, and data quality tips.
How to choose a tool
| Tool | When especially appropriate | What does the customer get? |
|---|---|---|
| SAP | SAP BusinessObjects BI, SAP Analytics Cloud, SAP Datasphere, SAP BW/4HANA, SAP HANA, SAP BW, SAP BEx/Query Designer, enterprise cubes and regulatory reporting. | we build reports, cubes, showcases and migration reconciliations around the SAP landscape; we preserve management indicators during the transition from SAP → 1C and design a historical BI archive. |
| PIX BI | PIX BI, PIX ETL, PIX Meta, JS Chart, PIX BI AI Assistant, mailings, commenting, mobile and web access, migration from Power BI and Qlik. | As a partner BI environment, we use PIX for self-service analytics, management dashboards, KPI, service desk, financial analytics, DWH showcases and BI import substitution. |
| Yandex DataLens | Yandex DataLens, datasets, charts, dashboards, connectors to sources, publishing and collaboration with reports. | We quickly assemble management panels, KPI showcases, operational analytics and reporting prototypes where launch speed and a clear interface for business are important. |
| Qlik | Qlik Sense, QlikView, Qlik Cloud Analytics, associative engine, data integration, embedded analytics, self-service dashboards. | We support existing Qlik reporting, develop models and showcases, prepare migrations to Russian or open-source BI platforms without losing the business logic of the reports. |
| 1C | Standard and management reports 1C, SKD, reports 1C:ERP, 1C:UH, 1C:ZUP, regulated reporting, 1C integration with BI/DWH. | We do reporting in 1C itself, carry out heavy analytics in DWH/BI, connect 1C with cubes, storefronts, portals, AI-copilot and management scenarios of the holding. |
| Grafana | Grafana dashboards, alerting, data sources, time series, logs, metrics, traces, industrial/IoT dashboards, observability panels. | we use Grafana for technical and industrial indicators: monitoring SLA, infrastructure, telemetry, time series, R&D data, operation and NOC/SOC panels. |
| Apache Superset | Apache Superset, charts, dashboards, SQL Lab, semantic datasets, connection to modern analytical databases and lakehouse/DWH stack. | suitable as an open-source BI layer for custom environments where flexibility, placement control, integration with DWH and lack of dependence on one commercial vendor are important. |
| Metabase | Metabase, data questions, dashboards, models, embedded analytics, alerts, permissions, open-source and commercial editions. | We use it for fast analytical loops, internal teams, product analytics, easy business access to data and embedded dashboards in corporate products. |
Data Loop Architecture
We design Data/BI/DWH as an industrial loop, not a set of reporting files. Data owners, integration, lineage, quality, access rights, update frequency, load on sources, historicity and a clear operating model are important.
Sources
1C, SAP, CRM, Service Desk, portals, files, API, industrial systems, external data, logs, telemetry and historical archives.
Integration
ETL/ELT, APIs, buses, exchanges, schedules, incremental loading, error control, repeatability and logging.
DWH / Data Lake
Storage, historicity, raw and cleansed layers, change management, SAP/legacy archive and preparation for analytical workloads.
Showcases and cubes
Indicators, dimensions, aggregation rules, master data, reconciliations, access roles, semantic layer and metrics catalog.
Reports and Dashboards
SAP BI, PIX BI, DataLens, Qlik, 1C, Grafana, Superset, Metabase, mailings, comments, embedded analytics and mobile access.
AI-copilot
Questioning the data, explaining deviations, preparing comments, searching by methods, controlling sources and human verification.
information security and Federal Law No. 152-FZ
Access control, masking, anonymization, logs, secure test/dev data, personal data protection and regulatory requirements.
Operation
SLA, download monitoring, data quality, storefront releases, user support, performance development and change management.
SAP BI, 1C and migrations SAP → 1C
RESTART has strong historical expertise in SAP and ERP projects, so we always connect Data/BI/DWH with accounting logic. When moving from SAP → 1C, you cannot simply transfer reference books and documents: you need to preserve management reports, familiar sections, calculation methods, reconciliations, historical data, archives and user trust.
SAP BI and historical cubes
We analyze existing reports, cubes, BW/BEx/BusinessObjects logic, indicators and sources to understand what to transfer, what to archive, and what to rebuild in the new BI/DWH architecture.
1C reporting and access control system
We make reports in 1C where it is correct for the accounting process, and carry out heavy analytics in DWH/BI where 1C should not become an analytical combine.
Migration reconciliations
We build control reports and showcases for comparing SAP, 1C, archives and intermediate data: balances, turnover, contracts, articles, counterparties, periods and statuses.
Management continuity
The business should continue to see the usual indicators after migration: plan-actual, P&L, cash-flow, receivables, purchases, sales, SLA, assets and industry KPIs.
PIX BI as a partner practice
PIX BI is important for RESTART as a Russian self-service class BI platform. According to the PIX partner presentation, the platform covers online analysis, mailings, commenting, work on different devices, big data, simple and complex calculations, as well as migration scenarios with Power BI and Qlik. The PIX product ecosystem also includes ETL, PIX Meta, JS Chart and PIX BI AI Assistant.
For the client, this provides a practical route for import substitution and development of analytics: a pilot on a limited showcase, then management panels, KPI, financial analytics, service desk, DWH and self-service for business users without turning each report into a separate IT project.
Open-source and promising BI stack
Open-source BI is appropriate where the customer needs control over placement, flexibility, integration with their own DWH, lack of dependence on one commercial vendor, and the ability to integrate analytics into their product. In such scenarios, we consider Apache Superset, Metabase and Grafana as different classes of tools: analytical dashboards, data queries, embedded analytics, operational monitoring and time series.
The promising data stack is evolving towards lakehouse architectures, semantic layer, data catalog, data quality, real-time/near-real-time downloads, time series analytics, embedded BI, natural language questions and AI-copilot. But choosing a technology should start with business issues, quality of sources, and accountability model, not a fancy platform name.
AI-copilot on top of reporting
Once reports and showcases are trusted, AI-copilot can be built on top of them. It helps the manager or analyst ask: why the margin has changed, where the receivables are overdue, which departments have fallen outside the SLA, what affected the cash flow, what data is incomplete and what comment to prepare for the management report.
AI-copilot does not replace financial and managerial responsibility. We design it with source checking, rights restrictions, logs, method specification, human review for critical findings, and links to the corporate knowledge base.
Data, security and Federal Law No. 152-FZ
A data project almost always involves sensitive data: personal data, trade secrets, financial indicators, salaries, contracts, customer bases, production data and access information. Therefore, BI/DWH cannot be built separately from information security.
Access rights
We separate roles at the level of sources, DWH, storefronts, BI, embedded dashboards and AI-copilot so that the user sees only valid data.
Masking and depersonalization
For test/dev, analytics, AI and external teams, we design masking, tokenization or anonymization of data where necessary.
Logs and responsibility
We record who looked at the data, who changed the model, who approved the indicator, when the download took place and where the quality error occurred.
Federal Law No. 152-FZ and regulation
We take into account the requirements for personal data, ISPD, CII, GIS, internal policies and restrictions on data placement.
What does the client get?
| Result | Practical value |
|---|---|
| Map of sources and indicators | It is clear where the data comes from, who the owner is, what restrictions there are, where there are duplicates, what indicators are controversial and what reports are critical for the business. |
| Target BI/DWH architecture | The customer sees what remains in 1C/SAP, what is transferred to DWH, what BI tools are used, how access, updates and operation are built. |
| Work reports, cubes and display cases | Financial, operational, commercial, manufacturing, HR, IT and service dashboards with proven formulas and clear acceptance. |
| Reduced manual reporting | Fewer Excel assemblies, fewer disputes about versions of the truth, fewer manual reconciliations, faster period closure and management package preparation. |
| Foundation for AI | AI-copilot, Enterprise RAG and analytical assistants receive prepared sources, rights, methodologies, context and journals. |
| Development plan | Roadmap for storefronts, sources, data quality, migrations, AI scenarios, information security, team and operations. |
First practical step
It’s better to start with a data assessment: collect a map of systems and reports, identify the owners of indicators, find the most painful manual reports, assess the quality of data, the load on 1C/SAP, information security limitations and the target audience of the reporting. After this, you can choose the right route: quick BI pilot, DWH showcase, reporting migration, SAP BI/Qlik/Power BI audit, development of 1C reporting or AI-copilot on top of already verified data.
Typically, the first pilot should be done on one management scenario: plan-actual, cash-flow, accounts receivable, sales, purchasing, service desk, SLA, telemetry, production or industry KPI. This way, businesses quickly see the benefits, and architecture doesn’t turn into an endless project.
Frequently asked questions
Is it possible to leave some reports in 1C?
Yes. We don't automatically put everything into BI. If the report is related to an accounting action and is convenient for the user in 1C, it can remain in 1C. DWH/BI includes heavy analytics, consolidation, history, cross-system indicators and management panels.
Do I need to build a large DWH right away?
No. It often makes more sense to start with one storefront and a set of critical metrics, and then expand the outline as trust, users, and clear economic impact emerge.
Is it possible to make AI-copilot without BI?
Technically possible, but the value will be lower. A good AI-copilot requires prepared sources, access rights, techniques, history, data quality and a clear way to verify the answer.
Let's discuss your environment
Describe the task, current systems, constraints, and expected results. We will offer a practical first step: diagnostics, pilot, audit, roadmap or project team.
