For multi-site manufacturers and oil and gas operators # Connect your industrial data in minutes. Connect OT, lab and ERP sources to your chosen destination, without building custom pipelines. Configure each source in 5 to 7.5 minutes once access is in place. Every published number is deterministic and traces to its source tags and definition version. No language model calculates it. Book a technical demoSee how it works 01 **Your data sources** OT, lab and business systems 02 **Connected in minutes** 5 to 7.5 min to configure each source* 03 **Your chosen destination** Your platform or a live dashboard Connect your sources **OT systems** Historians, control systems and industrial protocols PI **AspenTech** Historian 800xA **Lab and production systems** LIMS and MES LIMS MES **Business data** ERP and files ERP Files Plant connectivity: outbound only · TLS 443 · no inbound ports **Aditum Data** No-code source configuration - Extract 01 - Merge 02 - Filter 03 - Transform 04 - Map 05 - Enrich 06 OT time series joined with batch, recipe, grade and lab Versioned · audit log · one-step rollback Your data platform Databricks Snowflake InfluxDB PostgreSQL Deliver connected data to the platform you already use. Live dashboards **Steam per tonne** Chemicals · Comparison Site A Site B Site C Or explore your data in a live dashboard. *Configuration time per source, once the extractor is installed and access is approved. Managed by Aditum Data in Frankfurt (EU), or in your own cloud tenant. Aditum Data Wintershall Dea Press release > “Digitalization is a strategic priority for Wintershall DEA, and we already capture significant value from the use of data and analytics. Working with Aditum Data allows us to onboard more assets faster and at a lower cost …” **Ulrich Lorang**VP of Data Governance, Data Science and Data Hub, Wintershall Dea [Read the press release](https://aditumdata.com/media) Wintershall Dea offshore production platform Core capabilities ## Everything between the plant and a comparable KPI. It is not another database. Historians keep the raw time series, a lakehouse, where you have one, keeps storage and compute, and analytics tools keep the analysis. Aditum Data is the governed path between them. 01 **Connect** From the plant to a clean pipeline 01 ### Industrial-native connectors Connectors for the protocols and systems already in your plants. OPC UA Modbus TCP MQTT Historians MES · LIMS · ERP 5 to 7.5 min per new source 02 ### Edge extractors A small Windows or Linux service at each site, owned by your team. Outbound only No inbound ports Buffers & replays Typically installed in a day 03 ### No-code pipelines Merge, filter, transform and map with explicit rules, previewed on sample data before they run. No-code SQL Python, if you want it Versioned · one-step rollback 02 **Govern** One basis, rules you can read 04 ### One KPI definition Define a KPI once. Each site maps its own tags and records any approved exception. Units & basis Operating states Site exceptions Same number at every site 05 ### Quality travels with the data Quality flags stay with every value. Trips, start-ups and frozen sensors are excluded by explicit rules. Late values Frozen sensors Trips & start-ups Every exclusion explained 06 ### AI-assisted, never AI-calculated AI proposes mappings and dashboards. You preview and approve. It never calculates a published number. Real tags only You approve Can be switched off No AI in published numbers 03 **Deliver** To your people and your platform 07 ### Live dashboards Anyone authorised asks in plain language and gets a live dashboard. A KPI A comparison A monitor Under a minute · secure link 08 ### Your platform, your format Prepared data lands where you choose, in open formats you own. Databricks Snowflake BigQuery PostgreSQL InfluxDB AVEVA PI No proprietary format 09 ### Secure by design Designed to the zones-and-conduits principles of ISA/IEC 62443. Managed in Frankfurt (EU). Company SSO Encrypted credentials JSON export No inbound port into the plant From the OT team ## Onboarding an asset, then owning the pipelines. Jasem Elayeb is OT Architect and Product Owner for Cognite Data Fusion at Wintershall Dea. He describes how the team shortened new-asset onboarding and kept pipeline ownership in house. > “We have reduced all this time from one and a half months to five minutes. You tell them, right after the meeting: your data is there.” Jasem Elayeb, OT Architect and Product Owner, Cognite Data Fusion, Wintershall Dea [Request the white paper](https://aditumdata.com/white-paper)Book a technical demo **Jasem Elayeb**OT Architect and Product Owner, Cognite Data Fusion · Wintershall Dea How it works ## From historian to dashboard in under ten minutes. “Which of our units uses the least steam per tonne of product, measured on the same basis?” This is the path that answers it, once the extractor is installed and access is approved. Example for 01 **At each site** Your OT network PI **AspenTech** Historian 800xA **Siemens** PCS 7 · WinCC Proficy **Kepware** OPC UA **OPC UA** Modbus MQTT Joined with MES LIMS ERP Files Batch, recipe, grade and lab results enrich the OT time series **Edge extractor** Windows or Linux · buffers and replays Outbound TLS 443 outbound only 02 **Aditum Data** 5 to 7.5 min to configure **One engineering basis** Managed in Frankfurt (EU) - **Extract** each source - **Merge** batch · lab - **Filter** trips · frozen - **Transform** units · basis - **Map** tags to KPI - **Enrich** asset · state Versioned · audit log · one-step rollback routed 03 **Your people and platform** Behind company sign-in #### Live dashboards Site A Site B Site C A KPI, a comparison or a monitor, in under a minute #### Your platform Databricks Snowflake InfluxDB PostgreSQL Open, native formats you own **5 to 7.5 min** to configure a source **once** to define a KPI ** **Jan Kaluza**OT Architect, Data Hub and Governance · Wintershall Dea Customer use case ## Wintershall Dea: six weeks to five minutes, per asset. > “We created working use cases in five minutes. It really was five minutes.” Jan Kaluza, OT Architect, Data Hub and Governance, Wintershall Dea Book a technical demoTalk to an industrial data architect Proven at Wintershall Dea **6 weeks to 5 min** to a working pipeline, per asset **2 people** ran every pipeline, across every asset **Tens of millions** of data points per day **Kept in place** the existing industrial data platform Define once, use at every site ## One KPI definition. Every site mapped to it. One shared definition · version 1.3 · owner: group energy lead Example ### Specific steam consumption, t steam per t product Boundary steam delivered across the unit boundary Basis saleable product, dry basis States running ≥ 30 min; start-up, shutdown, cleaning excluded Quality bad quality, frozen values and gaps excluded Site A FI-2104.PV · t/h Mass flow; product from the batch system, dry basis No exception Site B U30_STM_HDR_FLOW · m³/h Volumetric meter; product from orders, wet basis Exception: converted to mass; wet to dry via lab moisture Site C FT-7710 · kg/s Header shared with a second unit Exception: steam allocated by metered fraction One comparable figure per site Tags and exceptions are illustrative. Running in production ## Six failure modes, handled explicitly. These occur in every plant network. Left unnoticed, each one quietly changes a number that someone will use to make a decision. This is how Aditum Data handles each of them today. #### Site network interruption The edge connector buffers data in memory and on disk and replays it in order on reconnect. A very long outage can exceed the disk buffer, and a stale source opens an incident. #### Source tag renamed In scheduled pipelines, the run reports the missing tag with its likely new name, and the other sites keep writing. A tag that stops reporting opens an incident. The mapping is re-pointed once, and the change is versioned and recorded in the audit log. #### Late-arriving or corrected value Any stored pipeline version can be rerun for a past period, so the period is recalculated with the corrected value. #### Frozen sensor or bad-quality reading Named rules exclude frozen values, bad-quality readings, shutdowns and gaps from KPI and dashboard calculations, and each result states which rules it applied. #### Edge connector stops sending It is shown offline three minutes after its last heartbeat, and an alert opens within ten minutes, by email or in Teams, Slack or a webhook. Owners get one email per root cause, not one per symptom. A connector that sends heartbeats but no data shows as “Heartbeats only”, never as healthy. #### Mapping change with an error Every pipeline change is saved as a version, with one-step rollback, and recorded in the audit log. KPI definitions are versioned too. How to compare ## Build it, buy a platform, or add a layer? Whichever approach you choose, the same questions show whether it will hold across a network. Ask them of any vendor, including us. Question to ask any vendor Aditum Data's answer **01** Speed How long does adding one source, or one KPI at one more site, take, and was that measured or estimated? 5 to 7.5 minutes per source once access is in place. At Wintershall Dea, a working pipeline per asset went from six weeks to five minutes . In a fixed-scope test, configuration time per source and per site is measured on your own sites. **02** Calculations Where are calculations run? Is any published number produced by a language model? In standard numerical libraries, never by a language model . Every result references its source tags and definition version . **03** Exceptions Show us a site exception, such as a wet basis or a shared header, defined, approved and traceable. Each site maps its own tags to one shared definition and records any approved exception : wet to dry via lab moisture, or steam allocated by metered fraction. **04** Corrections What happens to a late, corrected or frozen value? Any stored pipeline version can be rerun for a past period , so the period is recalculated with the corrected value. Named rules exclude frozen values, bad-quality readings, shutdowns and gaps, and each result states which rules it applied. **05** Access Can people outside the data team get an answer themselves, and how long does it take? Yes. An authorised user asks in plain language and has a live dashboard in under a minute , at a secure link behind company sign-in . **06** Exit On exit, what format are our data and logic in, and where do they already live? Your data already lives in your own destination, in open, native formats . Definitions and mappings are stored as JSON and can be retrieved through the API. **07** Fit Does this replace what we already run, or feed it? It feeds it . Aditum Data does not replace your historian, lakehouse or analytics tools. Engineers keep their analysis tools, now fed with comparable data from every site. Aditum Data removes the plumbing, not the people. Engineers keep their analysis tools and data teams keep their platform. Both receive prepared, comparable data in minutes rather than weeks. Technical evaluation ## See one of your KPIs prepared the way your sites actually work. In 45 minutes with your OT and analytics teams we take one KPI across three sites, show the mappings and transformation steps on an anonymised sample, and agree the acceptance criteria before any plant access. - **Inspect** the pipeline, not a slide deck - **Compare** how the same definition maps at each site - **Decide** with criteria your engineers wrote Book a technical demo FAQ ## Questions from OT and data teams **Does it replace our historian or data platform?** No. Aditum reads your historians and other systems read-only and delivers prepared data into the platforms you already run, such as Snowflake, Databricks, Azure Data Lake or PostgreSQL. It can also do the whole job itself, from source to live dashboards, P&IDs and 3D models. What is different is how it handles many sites: each KPI is defined once and every site is mapped to it, so a new site is a mapping, not a new project, and every site's figure is calculated the same way. That gives you speed (a new source in minutes), ease (set up without code), traceability (each number can be traced to its source) and reach (results land in your own tools or on dashboards in Aditum). **We already do this in Python and Databricks. Why change?** Keep what works. The time goes into the repetition: per source, per site, and again every time a tag is renamed. Aditum takes over that repeated work without code and delivers into Databricks or Snowflake, and bespoke steps can still run as sandboxed Python. **Why do pilots stall at one site?** Because the logic is tied to one site's tag names, units and meters, so it gets rebuilt for the next. In Aditum, the KPI is defined once, with an owner and a version, and the next site maps its own tags to it, with any exceptions approved and recorded. **What do we install, and what does IT have to open?** One small read-only Windows or Linux service per source, under its own low-privilege account, opening outbound HTTPS on port 443 only. There is no inbound port, and nothing is written back to the plant. It is designed along the zones-and-conduits principles of ISA/IEC 62443. **What does the AI see, and does it calculate anything?** By default it sees only metadata, such as table structure, column statistics, tag names and units. It sees data rows only if you opt in, and never document text. AI drafts pipelines and dashboards for your approval, the platform calculates every number, and your admin can switch AI off or keep all requests on your own Bedrock or Azure contract. **Where is our data hosted?** In the EU, with the application in Frankfurt and the database in the Netherlands. The platform can also run in your own Azure as a project; AWS and Google Cloud are on request. Edge data can stay on site in local mode, but then it doesn't reach Aditum's dashboards. **How do we know a number is right?** Saved pipelines run without AI, so the same input always gives the same output. You can check any dashboard figure against your own SQL reference, and the result is recorded. Each KPI result also states which exclusion rules it applied. **What does it take to get started?** A 45-minute working session to choose one KPI and three sites. Share a few anonymised tags, and we build the demonstration on your own data. Contact [sales@aditumdata.com](mailto:sales@aditumdata.com). Get in touch ## Discuss architecture with an industrial data specialist. Source compatibility, security boundaries, deployment and multi-site operation. Or request a 45-minute technical session to select one KPI and three sites and draft the acceptance criteria. Sales [sales@aditumdata.com](mailto:sales@aditumdata.com)