- LATEST BLOG POST
- echo $post_date ?>
- Improving Sustainability through Reasonable Risk and Crisis Management is the title of our 2007 book. The book has sold through…
- Read More
Information systems and risk management discusses the necessity to create a structured and holistic view on all corporate information. Therefore we look at and how the holistic view should relate to upside and downside risks.
Information systems are the set of analytical applications geared toward preparing corporate or project system’s data for business analyses and support to risk informed decision making.
Downside risk is the product of the probability of occurrence of a hazard by the cost of the undesirable consequences (negative value) resulting from the occurrence of the hazard. At the opposite end of the spectrum, the upside risk is the positive expected value.
Data warehouses are data repositories for structured, filtered data that have already been processed for a specific purpose. As a matter of fact, many good old database-systems can be seen as data warehouses. Data enter once they have been deemed useful and after processing of some kind for a specific purpose.
Data reservoirs and data lakes are “unified containers” sheltering information about a corporation, a project, an organization. What data? Ideally all of them.
Below we will not discuss the risks generated by having all the information “in the same place”, because that discussion is outside the scope of today’s post.
The difference between data reservoirs and data lakes is simple. Reservoirs are bulk containers whereas lakes are there to feed applications, models which build information systems geared toward management, risk management, planning and decision making. Data lake can feed machine learning.
A Master Data Management should be the unifying agent for the meta-data, allowing the integration of applications and models.
For example ORE2_Tailings uses the world-database of tailings dams catastrophic failures, its own predicitve model to prioritize tailings dams portfolios. Ore2_Tailings could be connected with other models within the corporate ERM and IRM, management applications such as Value Chain Optimization, etc.
Ideally the mining client using ORE2_Tailings and the other elements of the information system would have a data lake which include all assets and asset management data, losses, hazards, enterprise resource planning (ERP) data, environmental data, sale force data, process control and automation data.
The Master Data Management (MDM) is what makes it possible for the various applications to swiftly access their respective required information. Application Program Interface (API) is what allows systems or programs to talk to each other and provide requested information.
We thank Massimiliano Cavallo, expert model/app designer for the insight below.
Considering the above, it is clear that shaping a holistic corporate model requires a:
In other words, the requirement is implementing an IT platform, which unifies data-silos. That is after the data cleansing, normalizing and check.
Usually there are three kind of complexities to deal with:
Gartner clusters the above with the name of “Big Data Variety” .
ORE2_Tailings exploits a data repository belonging to the HCR (Hierarchical, Cartesian= Multidimensional, Relational) family. Its design copes very well with the kind of complexity brought in by the Big Data Variety.
Various suppliers are fostering interoperability and data analytics capabilities.
Some support real-time transactions and analytics. They also connect supply chain, customer experience, supplier and workforce management, fleet equipment, operations control and finally maintenance.
They allow for easy data visualization and contribute to the breaking up of silos across equipment providers and their respective softwares.
So, with modern API one can have data coming from control systems, supervision, etc., digested and delivered as needed.
All this is great. Indeed, the field is expanding swiftly and surely, but who deals with the risks?
The purpose of these systems is to:
However, those objectives do not necessarily cover the risks in the corporate or project context, including those of implementing these new technologies.
Thanks to a Master Data Management (MDM) and ORE you will have at hand a clearer understanding of the systems under your responsibility. Thus:
ORE fills the gaps in your risk information:
Finally we have prepared a form that you can download and will help you to understand were you stand. If you send it to us we can help you to prepare corporate or project system’s data for business analyses and support (risk informed) decision making. Download here.