Article 8 of the EU Energy Efficiency Directive ? Orientation

Following in-depth discussion of the UK?s ESOS response, we decided to backtrack to the source, especially since every EU member is facing similar challenges. The core purpose of the directive is to place a pair of obligations on member states. These are

To promote the availability of energy audits among final customers in all sectors, and;
To ensure that enterprises that are not SMEs carry out energy audits at least every four years.

Given the ability for business to look twice at every piece of legislation it considers unproductive, the Brussels legislators took care to define what constitutes an enterprise larger than an SME.

Definition of a Large Undertaking

A large undertaking meets one or both of the following conditions:

It employs 250 or more people
Its annual turnover is more than ?50 million and its balance sheet total exceeds ?43 million

Rules for Energy Audits

If accredited / qualified in-house specialists are unavailable then independent experts should supervise audits. The talent shortage seems common to many EU businesses. In hindsight, the Union could have ramped up slower, especially since the first compliance date of 5 December 2015 does not leave much swing room.

ecoVaro doubts there was a viable alternative, given the urgent imperative to beat back the scourge of carbon that is threatening the viability of our planet. The legislators must have been of a similar mind when laying down the guidelines. Witness for example the requirement that penalties be ?effective, proportionate and dissuasive?.

In order to be compliant, an energy audit must

Be based on twelve months of verifiable data that is
- over a continuous period beginning no more than 24 months before the beginning of the energy audit, and;
- identifies energy saving opportunities including paths to their achievement
Analyse the participant’s energy consumption and energy efficiency
Have not been used as the basis for an energy audit in a previous compliance period

Measurement of current status and progress tracing are at the core of energy saving and good governance generally. EcoVaro has a powerhouse of software tools available on the cloud to help project teams save time and money.

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Disadvantages of Spreadsheets – Obstacles to Compliance in the Healthcare Industry

Most of the regulatory compliance issues we talked about concerning spreadsheets have been related to financial data. But there are other kinds of data that are stored in spreadsheets which may also cause regulatory problems in the future.

In the US, a legislation known as HIPAA or Health Insurance Portability and Accountability Act is changing the way health care establishments and practitioners handle patient records. The HIPAA Privacy Rule is aimed at protecting the privacy of individually identifiable health information a.k.a. protected health information (PHI).

Examples of PHI include common identifiers like a patient’s name, address, Social Security Number, and so on, which can be used to identify the patient. HIPAA covers a wide range of health care organisations and service providers, including: health plan payers, health care clearing houses, hospitals, doctors, dentists, etc.

To protect the confidentiality, integrity, and availability of PHI, covered entities are required to implement technical policies such as access controls, authentication, and audit controls. These can easily be implemented on server-based systems.

Sad to say, many health care organisations who have started storing data electronically still rely on spreadsheet-based systems. Those policies are hard to implement in spreadsheet-based systems, where files are handled by end-users who are overloaded with their main line of work (i.e. health care) and have very little concern for data security.

In some of these systems, spreadsheet files containing PHI may have multiple versions in different workstations. Chances are, none of these files have any access control or user authentication mechanism whatsoever. Thus, changes can easily be made without proper documentation as to who carried out the changes.

And because the files are normally easily accessible, unauthorised disclosures – whether done intentionally or accidentally – will always be a lingering threat. Remember that HIPAA covered entities who are caught disclosing PHI can be fined from $50,000 up to $500,000 plus jail time.

But that’s not all. Through the HITECH Act of 2009, business associates of covered entities will now have to comply with HIPAA standards as well. Business associates are those companies who are performing functions and services for covered entities.

Examples of business associates are accounting firms, law firms, consultants, and so on. They automatically need to comply with the standards the moment they too deal with PHI.

More Spreadsheet Blogs

Spreadsheet Risks in Banks

Top 10 Disadvantages of Spreadsheets

Disadvantages of Spreadsheets – obstacles to compliance in the Healthcare Industry

How Internal Auditors can win the War against Spreadsheet Fraud

Spreadsheet Reporting – No Room in your company in an age of Business Intelligence

Still looking for a Way to Consolidate Excel Spreadsheets?

Disadvantages of Spreadsheets

Spreadsheet woes – ill equipped for an Agile Business Environment

Spreadsheet Fraud

Spreadsheet Woes – Limited features for easy adoption of a control framework

Spreadsheet woes – Burden in SOX Compliance and other Regulations

Spreadsheet Risk Issues

Server Application Solutions – Don’t let Spreadsheets hold your Business back

Why Spreadsheets can send the pillars of Solvency II crashing down

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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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