Causality cannot be proven from observation data – but novel algorithms can help to understand dependencies in complex data thereby uncovering potential cause and effect relationships. Those algorithms require a holistic view to the object in focus of analysis.

The object might be the "customer", the "machine", the "patient" – whatever your business object is, it is becoming increasingly complex with many data streams attached to it. Xplain Data has developed an approach which goes for far beyond analyzing “rows in tables” – we analyze objects as a whole. Our Object Analytics Engine constitutes a novel Business Intelligence paradigm which allows holistic analysis across all data streams …

… and ultimately is key for development of novel AI algorithms such as our Causal Discovery approach.

Are you a Data Scientist and tired of SQL for analytics? ...then develop next generation intelligent algorithms by operating on entire objects instead of tables, rows and columns
You are an Application Developer and feel hampered by clumsy backends? ...unleash your creativity to build analytical applications with whole objects at your fingertips
As a Business Analyst you have endless data but feel lost in myriads of correlations? ...understand causation beyond correlation based on a holistic view to your business objects

Object Analytics

Relational databases are great. Great for what they are built for – consistently managing complex data. This requires splitting an object, e.g. the “Patient” or the “Customer”, into atomic entities and storing different parts in different tables. Once distributed across many tables, however, an object is hard to analyze “as a whole” – and that is where today’s databases and analytical technologies fall short.

Xplain Data organizes data in a different, “object-centric” way and provides access to objects as a whole. You can define operations on those objects and iterate over them. To really reap from the benefits you can use the Object-Map-Reduce interface to execute an operation massively parallel on millions of stored object instances.

Algorithms previously painful to implement are now easy to apply. Novel algorithms – previously simply unimaginable – are becoming feasible.

Object Analytics will propel the field of Data Analytics and Artificial Intelligence into novel orbits.

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Not just predict – but shape the future: The question behind predictive modelling is: “What is the probability for a future event?” Often, however, the more important question is: “Which actions will change the probability for future events?”. If this applies in your case, then our Causal Discovery approach is what you need.

Missing information leads to flawed conclusions on “causal” effects – and this also holds if you can analyze just parts of a complex object at a time. Xplain Data’s holistic Object Analytics therefore constitutes novel opportunities to uncover potential “cause and effect” relationships – still no proof without experiment – but we can segregate away myriads of meaningless correlations and help you to quickly get to the core.

Knowing causal dependencies means being able to influence a system – a major step towards intelligent systems in real-world environments.


The “Object Explorer” is Xplain’s web-based frontend to view and analyze objects statistically.

This frontend allows you to analyze objects “as a whole” across all available data streams – instead of keyhole views which result from classical DWH approaches and replication of data into “Star schemas” or “OLAP cubes”. No need for experts to coerce data into those constraint analytical schemas, and with that you bring analytics from the ivory tower into your daily business: Interactively follow your train of thoughts from questions to follow-up questions and – supported by predictive models – discover potential “cause and effect” relationships.

Hook this engine on top of a data source (e.g. a relational database) – and an “object-centric” view to your data is quickly built. Different interfaces then allow you to analytically work with data represented as objects:


Xplain.js is our easy to use JavaScript framework. Implement your own analytics application within a few hours. Create complex queries, easy to use analytical tables and powerful charts.


Our REST-based interface enables you to code in whatever language you prefer: R, R-Shiny, Java, PHP, C#, …! Use our generic Web API to connect your application to an Xplain Data backend and query your data.

Xplain.m/r – Object Map Reduce (beta)

Define an operation on an object and execute it massively parallel on millions of stored object instances. With this interface, you can inject algorithms deeply into the core engine of the database. Algorithms come to data instead the data to algorithms: no moving tons of data, no expensively transforming data into constraint formats till they can be sent to and processed by an algorithm.

Our Services

Let us turn your data into an intelligent application

Experience how quickly novel solutions can be built, because...

  • All data is easily accessible in terms of holistic objects. Therefore, no painful data pre-processing or complex SQL queries are necessary.
  • Smart interfaces allow to directly bind analysis results into web-based applications.
  • Custom and advanced algorithm development is much more agile – because without constraints and not prone to run into performance problems.

The quickest way to get there is our proven POC process...

  • We hook our Object Analytics Engine on top of your data, and jointly start exploring your business objects with the Object Explorer. You will enjoy a novel experience analyzing e.g. your “customers” across multiple data streams.
  • Thereby our Causal Discovery approach helps you to get a better understanding of potential “cause and effect” relationships which drive your business goals.
  • Insights gained will be the basis to define an application which turns the “one shot analysis” into a repeatable analytical workflow. A draft implementation of such an application might be part of the POC.

Our Mission

The buzz about Artificial Intelligence is ubiquitous, but there is no talk about causality... How can a system act intelligent to achieve a goal without a notion of cause and effect? We help domain experts to understand complex data in terms of potential cause and effect relationships.

Our Vision

Statistical and machine learning algorithms need to cast data into constraint analytical schemas, typically a flat table... While real world data is much more complex than that. We imagine algorithms that will process any complex “objects” as they are, and live in a real world instead of an artificially prepared analytics environment.

...soon – with no need for experts to pre-structure data – intelligent algorithms will be digging autonomously through complex and constantly changing data environments. They will detect likely “cause and effect” relationships, and – based on that knowledge – take best actions or assist experts to achieve desired outcome.

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“Each new idea passes through three stages. First, people will ridicule it. Second, it is violently opposed. Finally, it will be considered self-evident.” - Schopenhauer (1788 - 1860)

About the Company

Xplain Data is a 100% privately-owned and self-funded start-up company. In 2015, we have set off as a small team to develop some groundbreaking innovations in the context of Big Data and Artificial Intelligence. The “Object Analytics” paradigm emerged for that – a novel concept how to analytically work with entire objects – and based on that our unique approach for “Causal Discovery”.

Innovation requires entrepreneurship – and an entrepreneurial cooperation model with early adopter customers. We are looking for visionary customers and partners who want to bring leading edge intelligence into their portfolio. We offer novel ways of cooperation such as our co-innovation model, which – instead of pay for service – shares risk and reward.

If you want to have a fresh view on analytics and if you want to get some new ideas on how to combine the strength of established companies with that of a small, agile start-up, please feel free to contact us.

Dr. Michael Haft

has a PhD in Theoretical Physics and Neuroinformatics and more than 20 years' experience in developing analytics technologies at major companies like Siemens, Accenture and SAP. Before founding Xplain Data he worked as Chief Architect at SAP with a focus on Big Data Analytics. Earlier in his career, he co-founded a startup where he was responsible for the entire product lifecycle of analytics innovations. Michael gathered broad and unique knowledge spanning from database and BI-technologies to Statistics, Mathematics and Machine Learning. From numerous projects he knows how to apply those technologies in a business context.

Peide Wang

has a Diploma in Mathematical Sciences and joined us from SAP where he last worked on predictive maintenance as a development architect. Throughout 13 years of business application development experience he gained extensive knowledge of different technologies and platforms, ranging from R/3 modules (ABAP) and database algorithms (C++) to modern web applications (Java/Javascript). At Xplain Data he designs and implements delightful user interfaces for our customers and brings his vast algorithm expertise to the table.

Dr. Hanjo Täubig
(Senior Data Scientist)

holds a diploma in computer science and got a PhD for a thesis on structure searching in protein databases. He worked as a substitute professor of theoretical computer science at the Chair for Efficient Algorithms. For several years, Hanjo taught at TU Munich on fundamentals of algorithms and data structures. He also gave master level lectures on computational biology and advanced network and graph algorithms. Hanjo is an expert in algorithms and data structures. In particular, he is a specialist for bioinformatics and graph/network-related problems.

Dr. Christian Koncilia

holds a PhD in applied computer science for developing algorithms to deal with structural changes in Data Warehouses. He worked for more than 25 years in the Life Science and Health Care industry, working on projects for different hospitals, insurance companies and pharmaceutical companies. Christian has strong hands on experience with different Business Intelligence tools, database management systems and many programming languages and frameworks. During the last decade he focused on the development of different web applications.

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