How Bouygues manages an Empire-Sized Footprint

Bouygues is into telecoms / media, and building and road construction. It also knows it has to watch its energy footprint closely. Owning 47% of energy giant Alstom keeps it constantly in the media spotlight. Shall we find out more about its facility management policies?

The journal Premises and Facilities Management interviewed MD Martin Bouygues on his personal opinions concerning managing energy consumption in facilities. He began by commenting that this was hardly a subject for the C-Suite in years gone by. Low-level clerks simply paid the bills following which the actual amounts were lost in the general expenses account. That of course has changed.

Early pressure came from soaring energy bills, which were pursued by a whole host of electricity-saving gadgets. However, it was only after the carbon crisis caught business by surprise that the link was forged to aerial pollution, and the social responsibilities of big business to help with the solution. The duty to have an energy strategy became an obligation eagerly policed by organisations such as Greenpeace.

Unsurprisingly, Martin Bouygues? advice begins with keeping energy consumption and its carbon footprint as high up on the agenda as health and safety. ?It needs bravery and a lot of hard work to get it there,? he says, ?so perseverance is the key?. 

The company has developed proprietary software that enables it to pull data from remote sensors in more than 80 countries every fifteen minutes. A single large building can contribute 50 million data items annually making data big business in the system. Every building has an allocated energy performance contract against which results are reported monthly, as a basis for reviewing progress.

The system is intelligent and able to incorporate low-occupancy periods such as weekends and public holidays. What is measured gets managed. We all know that, but how many of us apply the principle to our energy bills. With assistance from ecoVaro, the possible becomes real.

We offer a similar service to the Bouygues model with one notable exception. You don’t buy the software and you only pay when you use it. Our systems are simply designed for busy financial managers.

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Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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Energy Cooperation Mechanisms in the EU

While the original mission of the European Union was to bring countries together to prevent future wars, this has spun out into a variety of other cooperative mechanisms its founders may never have dreamed of. Take energy for example, where the European Energy Directive puts energy cooperation mechanisms in place to help member states achieve the collective goal.

This inter-connectivity is essential because countries have different opportunities. For example, some may easily meet their renewable targets with an abundance of suitable rivers, while others may have a more regular supply of sunshine. To capitalise on these opportunities the EU created an internal energy market to make it easier for countries to work together and achieve their goals in cost-effective ways. The three major mechanisms are

  • Joint Projects
  • Statistical Transfers
  • Joint Support Schemes

Joint Projects

The simplest form is where two member states co-fund a power generation, heating or cooling scheme and share the benefits. This could be anything from a hydro project on their common border to co-developing bio-fuel technology. They do not necessarily share the benefits, but they do share the renewable energy credits that flow from it.

An EU country may also enter into a joint project with a non-EU nation, and claim a portion of the credit, provided the project generates electricity and this physically flows into the union.

Statistical Transfers

A statistical transfer occurs when one member state has an abundance of renewable energy opportunities such that it can readily meet its targets, and has surplus credits it wishes to exchange for cash. It ?sells? these through the EU accounting system to a country willing to pay for the assistance.

This aspect of the cooperative mechanism provides an incentive for member states to exceed their targets. It also controls costs, because the receiver has the opportunity to avoid more expensive capital outlays.

Joint Support Schemes

In the case of joint support schemes, two or more member countries combine efforts to encourage renewable energy / heating / cooling systems in their respective territories. This concept is not yet fully explored. It might for example include common feed-in tariffs / premiums or common certificate trading and quota systems.

Conclusion

A common thread runs through these three cooperative mechanisms and there are close interlinks. The question in ecoVaro?s mind is the extent to which the system will evolve from statistical support systems, towards full open engagement.

The Better Way of Applying Benford’s Law for Fraud Detection

Applying Benford’s Law on large collections of data is an effective way of detecting fraud. In this article, we?ll introduce you to Benford’s Law, talk about how auditors are employing it in fraud detection, and introduce you to a more effective way of integrating it into an IT solution.

Benford’s Law in a nutshell

Benford’s Law states that certain data sets – including certain accounting numbers – exhibit a non-uniform distribution of first digits. Simply put, if you gather all the first digits (e.g. 8 is the first digit of ?814 and 1 is the first digit of ?1768) of all the numbers that make up one of these data sets, the smallest digits will appear more frequently than the larger ones.

That is, according to Benford’s Law,

1 should comprise roughly 30.1% of all first digits;
2 should be 17.6%;
3 should be 12.5%;
4 should be 9.7%, and so on.

Notice that the 1s (ones) occur far more frequently than the rest. Those who are not familiar with Benford’s Law tend to assume that all digits should be distributed uniformly. So when fraudulent individuals tinker with accounting data, they may end up putting in more 9s or 8s than there actually should be.

Once an accounting data set is found to show a large deviation from this distribution, then auditors move in to make a closer inspection.

Benford’s Law spreadsheets and templates

Because Benford’s Law has been proven to be effective in discovering unnaturally-behaving data sets (such as those manipulated by fraudsters), many auditors have created simple software solutions that apply this law. Most of these solutions, owing to the fact that a large majority of accounting departments use spreadsheets, come in the form of spreadsheet templates.

You can easily find free downloadable spreadsheet templates that apply Benford’s Law as well as simple How-To articles that can help you to implement the law on your own existing spreadsheets. Just Google “Benford’s law template” or “Benford’s law spreadsheet”.

I suggest you try out some of them yourself to get a feel on how they work.

The problem with Benford’s Law when used on spreadsheets

There’s actually another reason why I wanted you to try those spreadsheet templates and How-To’s yourself. I wanted you to see how susceptible these solutions are to trivial errors. Whenever you work on these spreadsheet templates – or your own spreadsheets for that matter – when implementing Benford’s Law, you can commit mistakes when copy-pasting values, specifying ranges, entering formulas, and so on.

Furthermore, some of the data might be located in different spreadsheets, which can likewise by found in different departments and have to be emailed for consolidation. The departments who own this data will have to extract the needed data from their own spreadsheets, transfer them to another spreadsheet, and send them to the person in-charge of consolidation.

These activities can introduce errors as well. That’s why we think that, while Benford’s Law can be an effective tool for detecting fraud, spreadsheet-based working environments can taint the entire fraud detection process.

There?s actually a better IT solution where you can use Benford’s Law.

Why a server-based solution works better

In order to apply Benford’s Law more effectively, you need to use it in an environment that implements better controls than what spreadsheets can offer. What we propose is a server-based system.

In a server-based system, your data is placed in a secure database. People who want to input data or access existing data will have to go through access controls such as login procedures. These systems also have features that log access history so that you can trace who accessed which and when.

If Benford’s Law is integrated into such a system, there would be no need for any error-prone copy-pasting activities because all the data is stored in one place. Thus, fraud detection initiatives can be much faster and more reliable.

You can get more information on this site regarding the disadvantages of spreadsheets. We can also tell you more about the advantages of server application solutions.

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