The Oxford Dictionary defines “principle” as “A fundamental truth or proposition that serves as the foundation for a system of belief or behavior or for a chain of reasoning.”

Principles are the prerequisite for growing a well-behaved software system, too. The most famous principles in the computing history are probably the “IBM System/360 Principles of Operation” (POO). Those principles have been the foundation of a whole industry and created the software world we are living in.

At Trufa,  we strongly believe in principles as well. Those principles have and do determine our daily decisions. We have even gone so far to abstract those principles into a set of 12 rules in order to share them with our customers, partners and employees.

Rules Explanation
True Numbers All numbers are calculated rather than approximated. No bias due to aggregates or cleansing.
All Process Details No relevant information gets lost due to process gaps or omissions.
No Semantic Gap The same language throughout the whole world of data.
Transcending Silos All business functions look at the same source of truth.
Forward-looking Simulating the future leveraging true relationships insights.
Intuitive Usage Deciders understand their discipline without intermediaries (e.g. data scientists).
Instant Live No data preparation (especially modeling) beyond access to raw data.
“Viral” Implementation All potential users can study the data without further ado.
Collective Intelligence Cloud-centricism enables collaborative decision making.
Ready-to-run Apps No IT (ERP/BI) projects in order to install, configure and customize the software.
Always Up-to-Date Friction-less and latency-less data provisioning.
Simple Technology Robust and reliable due to significantly shortened technology stack.

These 12 rules are built on Edsger Dijkstra’s insight that, “Simplicity is prerequisite for reliability.” A bit more color might be warranted.

  1. True Numbers
    True in this context means not altered by leaving out details due to aggregations. So far aggregations have been the norm because that was the only way to achieve decent performance. The unfortunate thing with aggregations is the fact that you are making assumptions about how your business runs by specifying the dimensions of the cubes. Those assumptions don’t have to be true necessarily as we have seen in many of our customer situations.
  2. All Process Details
    In order to understand and govern your business, you have built models of your processes. Models are by nature generalizations with conscious omissions and simplifications. Now for the first time you can reconstruct your executed process reality in such a manner that it can be analyzed in sincerity.
  3. No Semantic Gap
    Separating the transactional from the analytical world has done us a great disservice. Because more often than not, the business intelligence world has invented its own language which cannot be traced back to the underlying transactional systems anymore. We have seen customer situations where actions derived from BI could not be applied to the ERP system because master data have become out of sync.
  4. Transcending Silos
    Though financial figures are determined by logistics, the respective stakeholders are hardly able to talk with each other. This is because money and goods flows are being looked at separately to a very large degree. Just ensuring that people understand each other can overcome stalemate situations, as we have seen with our customers.
  5. Forward-looking
    Reporting is required for statutory purposes. For all other purposes it is a crutch because you want to understand what happens next or how you can influence what happens next.
  6. Intuitive Usage
    This rule goes without saying, though it is very hard to achieve.
  7. Instant Live
    ERP is notorious for very long implementation times. Also, migrating from one generation of an EREP system to the next generation of an ERP system is a years-long effort. People cannot afford this time with modern analytical solutions.
  8. “Viral” Implementation
    ERP is notorious for multiple so-called waves of rollouts. This is no more tolerable in today’s world of constant change.
  9. Collective Intelligence
    Decision making is by nature a collaborative effort. Hence it must be built in in modern analytical software.
  10. Ready-to-run Apps
    Lengthy customizations are a sin of the past. Modern analytical solution cannot afford these delays anymore.
  11. Always Up-to-Date
    Experimental exploration of your data requires you to copy hundreds of millions of data records and keep them in sync. This requires pretty sophisticated delta synchronization algorithms.
  12. Simple Technology
    Over the last 30 years, business intelligence has created a monster of technology which is so hard to master that it hardly works satisfactorily. We know of customers who have implemented their business intelligence environment over many years, only to end up with magnitudes higher number of reports than users.

Looking forward to any comments you might have.