NextTables Cloud
On every data platform we build there is a set of tables the business owns and the architecture has no place for: mappings, classifications, corrections, the reference values that decide what a report actually says. NextTables Cloud is where those tables live.
Business teams maintain them in a familiar grid, every entry is validated before it is written, and the records stay on your platform. We design it, build it and run it with you.
NextTables started here
NextTables was built inside NextLytics. It began as an answer to a problem our consultants kept meeting on SAP Business Warehouse projects, it grew into a product in its own right, and since September 2025 it has been an independent company, NextTables GmbH. The first systems it ever ran in were our customers' SAP landscapes.

The gap you hit on every data project
You know the pattern, because we run into it too. Business-maintained data surfaces during modelling, and again in UAT, with only a spreadsheet behind it. Every dashboard we deliver has a handful of these tables somewhere underneath it.
What happens today
A mapping changes. Someone asks around for the current version, maintains it in Excel, and hands the file over to be loaded. It fails in the pipeline run, the data engineering team has to pick it up, and by the time it lands the dashboard has already been wrong twice.
What that costs on a project
Spreadsheet maintenance gets rejected in UAT. Custom write-back development eats into project margin. Scope creep and post-go-live support pile up. And the next project starts the same problem again from scratch. It is the requirement that arrives late, carries no budget line, and lands on whoever is closest to it.
Where the common workarounds run out
By the time the requirement has a name, the options on the table are usually these three. We have costed all of them, and delivered most of them.
SharePoint Lists into SAP Datasphere
Quick to stand up, and it sits outside the platform. Value help, validation and row-level access have to be rebuilt around it or given up, and it strains the moment modelling and UAT push real volume through it.
A custom app on SAP BTP
Fits the use case exactly, and carries a high build cost per use case. Validation, value help and row-level security get rebuilt every single time, and each app then needs an owner and a maintenance budget for as long as it lives.
SAC Planning
Commercially heavy and built for planning, so it brings a large setup effort to something as ordinary as keeping a mapping table current, and it puts a licence question in front of a data-quality problem.
Three things a maintenance layer has to be at once
Get one of these wrong and the other two stop mattering. Business teams abandon a tool they find slow, and IT blocks a tool it cannot account for.
Built for business teams
Familiar, guided, ready to use. Grid-based editing, filtering, sorting and import, in business terms rather than technical ones, so the people who own the data are productive on day one. Built-in value help and instant validation feedback mean a user finds the right value and knows immediately whether the entry was accepted. The team that owns the data owns the app, from idea to live.
Governed
Validated and authorized, at the platform boundary. Every entry is checked against your rules and your master data before it reaches the platform, and errors surface on the spot so the business user fixes them there and then. Access is role-based with row-level rules, scoped to folders, and those row rules can reuse an access table you already maintain on your platform. Your business data stays on your platform throughout. One security perimeter, the one you already run.
Powering AI
Business-maintained context for AI. The reference mappings, parameters and classification tables that models and agents need in order to answer correctly are maintained by the business, on the same platform the models read from. An AI answer is only ever as good as the classification table sitting behind it, and that table belongs to the business rather than to a data engineer with a ticket.
What your team can do
Work in a grid they already understand
Filter, sort, copy and paste, bulk update. The grid behaves the way a spreadsheet behaves, on data that lives in your platform rather than on somebody's desktop. Column setup, data types, required fields and locks are configuration, so a new table is a conversation rather than a build.
- The people who own the data maintain it.
- Column rules set as configuration, not code.

Guided entry, with search against your master data
Value help reads the master data tables the app is connected to, so people select a validated value instead of typing a code from memory. Feedback is immediate: a user knows on leaving the field whether the entry stands.

Import a file, checked as it lands
Bring larger changes in from Excel or CSV and every row is checked against the same rules the grid applies, before anything is written. Invalid rows come back with the reason, so the person who owns the data fixes them rather than a data engineer guessing at intent.
- Bulk changes at spreadsheet speed.
- Every row validated before it reaches your platform.
Build the entry form as configuration
A drag-and-drop form builder puts single-record entry masks together from the fields of a connected table, with validation rules and low-code logic attached where they are needed. A department gets the entry screen it asked for, and it stays configuration rather than a build.
- Single-record entry, without a development project.
- Validation rules attached where they are needed.


Embed it in the dashboard where the problem shows up
Data maintenance apps embed into any BI dashboard, SAP Analytics Cloud, Power BI, Tableau or standalone. The update is validated, written to the platform, and visible in the dashboard, so maintenance happens where the decision is being made.
- The correction happens where it was noticed.
- Validated on the way in, either way.
One login, the one they already have
Users sign in through your enterprise single sign-on with the credentials they already carry, on Microsoft Entra ID or Google Workspace. Identity stays with your identity provider, and access stays with the roles you set.
Your business data stays on your platform
This is the first question your platform owner asks, and it is worth answering before anything else.
NextTables connects through each platform's native interface and writes directly to your tables, so the platform you already run stays the single source of truth. Business data passes through during a read or a write and remains on your platform afterwards. What lives inside NextTables is configuration metadata: the apps, the table and column setup, the validation rules and the role assignments.
Three consequences follow, and they are the ones that matter in an architecture review. One security perimeter rather than two. One source of truth, with the maintenance layer sitting alongside it rather than in front of it. And because the records were only ever written in place, stepping away from NextTables is an access change rather than a migration project.
Which platforms it runs on
SAP Business Data Cloud
SAP Datasphere
Databricks
PostgreSQL
Where this fits in what we do
The maintenance gap is not one service line's problem. It turns up in all three of ours, and it is the same gap every time.
SAP Data Warehouse
SAP Datasphere · SAP BW/4HANA · SAP Business Data Cloud
The mapping and reference tables that a model depends on, maintained by the business rather than raised as a ticket against the warehouse team. On SAP Business Warehouse landscapes the on-premise SAP BW edition does the same job inside the SAP system, so a landscape mid-modernization is covered on both sides.
Data Science and Engineering
Databricks
The parameters, thresholds and classification tables that a pipeline or a model reads at run time, owned by the people who actually know what the values should be. Fewer hard-coded lookups in notebooks, and fewer tickets that are really just a value change.
Dashboarding and BI
SAP Analytics Cloud · Power BI · Tableau
A maintenance app embedded in the dashboard itself, so a wrong classification is corrected in the report where somebody noticed it, and the correction is validated on the way in.
How we engage, in four steps
Assess
Find the business data being maintained outside the platform, and work out which of it is worth an app.
Pilot
Stand up one governed data app on a real table with real users, often as a paid proof of concept.
Roll out
Extend across domains and platforms on the pattern the pilot proved.
Enable
Train the business teams who will own the apps, and hand over the configuration so they can add the next table themselves.
Why work with us on NextTables Cloud
NextTables was built inside our own practice, and although it is an independent company today, our consultants wrote it. On a tool whose whole value is configuration, that difference is practical rather than sentimental: it shows up in how quickly the first app is right.
What we bring to a NextTables Cloud project
- Platform connection and tenant setup, on SAP Business Data Cloud, SAP Datasphere, Databricks or PostgreSQL.
- The first data apps, built with the business team that will own them rather than handed to them finished.
- Validation rule design, which is where a maintenance app either earns trust or loses it in the first week.
- Role and row-level access design, mapped onto the access tables and the identity provider you already maintain.
- Enablement, so your teams add the next table themselves.
And the judgement about which tables deserve an app at all, which is the part that decides whether anybody uses it six months later.
More than 20 NextTables customers
Across 30-plus releases and counting. Product details, pricing and the knowledge base are at nexttables.com
Questions your platform owner asks first
On your enterprise data platform, in your own tables. NextTables connects through the platform's native interface and writes directly to them. Business data passes through during a read or a write and stays on your platform afterwards. Only configuration metadata, meaning the apps, the table and column setup, the validation rules and the role assignments, lives inside NextTables.
They sign in through your enterprise single sign-on with the credentials they already have, on Microsoft Entra ID or Google Workspace. NextTables holds the connection to the platform, so a business user needs one login, and the roles you have set decide what that login can reach. Adoption stays light, which is usually what decides whether a maintenance app survives.
Identity stays with your enterprise single sign-on. Roles are assigned at folder level with read, write, create and delete rights, and they inherit down to the data sources connected inside each folder. Row-level access is driven by a control table saying which user may reach which rows. Maintain that table in NextTables, or point at an access-control table you already keep on the platform, so your existing policy is enforced automatically.
It runs as a managed SaaS app, so upgrades and infrastructure are maintained by NextTables. Your team configures apps, validation rules and roles through the interface, in business terms. The platform you already run stays the single source of truth, with your tables, governance and security perimeter intact. NextTables sits alongside as the maintenance layer, and we do the configuration work with you.
Rules live in NextTables as configuration. Each app validates entries against the master data tables it is connected to, so a value is checked against your source of truth before anything is written. Every rule runs at the point of entry, ahead of the write, so a user sees and fixes an invalid entry on the spot and only validated data reaches your tables.
The form is the easy part. What takes the time is validation against your master data, access decided by role and by row, entry that guides a business user to the correct value, and a write that lands straight in the platform. A low-code tool asks you to build all of that per app and then own it. Here it is configuration, and the same pattern carries to the next table.
By promotion transport, a mechanism NextTables provides for exactly this. You build and test an app in one environment and promote the configuration to the next, so the app that goes live is the app that was tested. It follows the promotion pattern your platform team already uses elsewhere, and we set those environments up as part of the project.
SAP Business Data Cloud, SAP Datasphere, Databricks and PostgreSQL. Connection is through each platform's native interface, with writes going directly into your tables. Validation can run against master data on one platform while the write lands on another, which is useful when reference data and reporting tables sit in different places. We connect the tenant to your platform as part of the setup.
Your records were written in place on your own platform the whole time, so they stay there in your tables. Stepping away is an access change rather than a migration project, and the configuration metadata can be exported before you switch it off. Access is withdrawn, the apps stop, and the tables carry on being read by everything else that uses them.
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