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Information systems and risk management

Information systems and risk management

Jan 9th, 2019

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 and risk management

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.

Welcome to the world of data warehouses, data reservoirs, and data lakes.

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.

Master Data Management

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.

Holistic corporate model requirements

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:

  • perfect understanding of the business operations and organization,
  • high comprehension of the environmental framework, where the organization is operating, and finally
  • steady control of the existing data.

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:

  • High data volumes
  • Many logical dimensions involved and finally
  • Heterogeneous data types, and customized Key Performance Indicators

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.

Where do we stand today?

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:

  • predict and optimize,
  • increase uptime and finally
  • reduce inoperability, servicing and maintenance crews, costs.

However, those objectives do not necessarily cover the risks in the corporate or project context, including those of implementing these new technologies.

Closing remarks and an invitation

Thanks to a Master Data Management (MDM) and ORE you will have at hand a clearer understanding of the systems under your responsibility. Thus:

  • your decision making will become easier and more defensible,
  • costs will be under control and finally,
  • you will reduce the chances of experiencing the stress of coming in over budget, costly delays. Thus you will be more agile, reduce complete project failure (on your watch) likelihood.

ORE fills the gaps in your risk information:

  • enhancing your team’s understanding of uncertainties,
  • evaluating events and the implications of your assumptions,
  • helping you discern between “business as usual” and risks too large to take,
  • performing solid and well balanced quantitative project risk assessments and finally
  • integrating the results of the analysis in a customized project management road-map totally unique to your business.

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.

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Category: Optimum Risk Estimates, Probabilities, Risk analysis, Risk management, Uncategorized

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