Big Energy Data Management

Recent times have seen the advent of cloud based services and solutions where energy data is being stored in the cloud and being accessed from anywhere, anytime through remote mobile devices. This has been made possible by web-based systems that can usually bring real-time meter-data into clear view allowing for proactive business and facility management decisions. Some web based systems may even support multi utility metering points and come in handy for businesses operating multiple sites.

Whereas all this has been made possible by increased use of smart devices/ intelligent energy devices that capture data at more regular intervals; the challenge facing businesses is how to transform the large data/big volume of data into insights and action plans that would translate into increased performance in terms of increased energy efficiency or power reliability.

A solution to this dilemma facing businesses that do not know how to process big energy data, may lie in energy management software. Energy management software?s have the capability to analyse energy consumption for, electricity, gas, water, heat, renewables and oil. They enable users to track consumption for different sources so that consumers are able to identify areas of inefficiency and where they can reduce energy consumption, Energy software also helps in analytics and reporting. The analytics and reporting features that come with energy software are usually able to:

? Generate charts and graphs ? some software?s give you an option to select from different graphs

? Do graphical comparisons e.g. generate graphs of the seasonal average for the same season and day type

? Generate reports that are highly customisable

While choosing from the wide range of software available, it is important for businesses to consider software that has the capacity to support their data volume, software that can support the frequency with which their data is captured and support the data accuracy or reliability.

Energy software alone may not make the magic happen. Businesses may need to invest in trained human resources in order to realise the best value from their big energy data. Experts in energy management would then apply human expertise to leverage the data and analyse it with proficiency to make it meaningful to one?s business.

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The General Data Protection Regulation & The Duty to use Encryption

The General Data Protection Regulation, abbreviated to GDPR, raised a storm when it arrived. In reality, it merely tightened up on existing good practice according to digital security specialists Gemalto. The right to withhold consent and to be forgotten has always been there, for example. However, the GDPR brings a free enforcement service for consumers, thus avoiding the need for third party, paid assistance.

The GDPR Bottom Lines for Data Security
Moreover, the GDPR has penalties it can apply, of the order that might have a judge choking on his wig. Under it, data security measures such as pseudonymisation (substitution of identifying fields) and encryption (encoding including password protection) have become mandatory. Businesses must further respect their client data by:

a) Storing it in a secure environment supported by robust services and systems

b) Having proven measures to restore availability and access after a breach

c) Being able to prove frequent effectiveness testing of these measures.

The General Data Protection Regulation places an onus on businesses to report any data breaches. This places us in a difficult situation. We must either face at least a wrist slap upon reporting failures. Alternatively, pay a fine of up to ?10 million, or 2% of total worldwide annual turnover.

The Engineered Weak Link in the System
Our greatest threat of breach is probably when the data leaves our secure environment, and travels across cyberspace to an employee, stakeholder, collaborator, or the client themselves. Since email became open to attack, businesses and individuals have turned to sharing platforms like Dropbox, Google Drive, Skydrive, and so on. While these do allow an additional layer of password protection, none of these has proved foolproof. The GDPR may still fine us heavily, whether or not we are to blame for the actual breach.

How Hacking is Approaching Being a Science
We may make a mistake we may regret, if we do not take hacking seriously. The 10 worst data hacks Identity Force lists are proof positive that spending lots of money does not guarantee security (any more than having the biggest stock of nuclear weapons). We have to be smart, and start thinking the way that hackers do.

Hacker heaven is finding an Experian or a Dun & Bradstreet that may have shielded 143 million, and 33 million consumer records respectively, behind a single, flimsy cyber-security door. Ignorance is no excuse for them. They should simply have known better. They should have rendered consumer data unreadable at individual record level. The hackers could have found this too demanding to unpick, and have looked elsewhere.

How Data Encryption Can Help Prevent Hackers Succeeding
Encrypting data is dashboard driven, and businesses need not concern themselves about it works. There are, however, a few basic decisions they must take:

a) Purge the database of all information held without explicit permission

b) Challenge the need for the remaining data and purge the nice-to-haves

c) Adopt a policy of encrypting access at business and customer interfaces

d) Register with three freemium encryption services that seem acceptable

e) After experimenting, sign up for a premium service and be prepared to pay

Factors to Consider When Reaching a Decision
Life Hacker?suggests the following criteria although the list is a one-size-fits-all

a) Is the system fast, simple, and easy to operate

b) Can you encrypt hidden volumes within volumes

c) Can you mass-encrypt a batch of files easily

d) Do all other files remain encrypted when you open one

e) Do files automatically re-encrypt when you close them

f) How confident are you with the vendor, on a scale of 1 to 10

It may be wise to encrypt all the files on your system, and not just your customer data. We are always open to a hack by the competition after our strategic planning. If we leave the decision up to IT, then IT, being human may take the easy way out, and encrypt as little as possible.

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What Is Technical Debt? A Complete Guide

You buy the latest iPhone on credit. Turn to fast car loan services to get yourself those wheels you’ve been eyeing for a while. Take out a mortgage to realise your dream of being a homeowner. Regardless of the motive, the common denominator is going into financial debt to achieve something today, and pay it off in future, with interest. The final cost will be higher than the loan value that you took out in the first place. However, debt is not limited to the financial world.

Technical Debt Definition

Technical debt – which is also referred to as code debt, design debt or tech debt – is the result of the development team taking shortcuts in the code to release a product today, which will need to be fixed later on. The quality of the code takes a backseat to issues like market forces, such as when there’s pressure to get a product out there to beat a deadline, front-run the competition, or even calm jittery consumers. Creating perfect code would take time, so the team opts for a compromised version, which they will come back later to resolve. It’s basically using a speedy temporary fix instead of waiting for a more comprehensive solution whose development would be slower.

How rampant is it? 25% of the development time in large software organisations is actually spent dealing with tech debt, according to a multiple case study of 15 organizations. “Large” here means organizations with over 250 employees. It is estimated that global technical debt will cost companies $4 trillion by 2024.

Is there interest on technical debt?

When you take out a mortgage or service a car loan, the longer that it takes to clear it the higher the interest will be. A similar case applies to technical debt. In the rush to release the software, it comes with problems like bugs in the code, incompatibility with some applications that would need it, absent documentation, and other issues that pop up over time. This will affect the usability of the product, slow down operations – and even grind systems to a halt, costing your business. Here’s the catch: just like the financial loan, the longer that one takes before resolving the issues with rushed software, the greater the problems will pile up, and more it will take to rectify and implement changes. This additional rework that will be required in future is the interest on the technical debt.

Reasons For Getting Into Technical Debt

In the financial world, there are good and bad reasons for getting into debt. Taking a loan to boost your business cashflow or buy that piece of land where you will build your home – these are understandable. Buying an expensive umbrella on credit because ‘it will go with your outfit‘ won’t win you an award for prudent financial management. This also applies to technical debt.

There are situations where product delivery takes precedence over having completely clean code, such as for start-ups that need their operations to keep running for the brand to remain relevant, a fintech app that consumers rely on daily, or situations where user feedback is needed for modifications to be made to the software early. On the other hand, incurring technical debt because the design team chooses to focus on other products that are more interesting, thus neglecting the software and only releasing a “just-usable” version will be a bad reason.

Some of the common reasons for technical debt include:

  • Inadequate project definition at the start – Where failing to accurately define product requirements up-front leads to software development that will need to be reworked later
  • Business pressure – Here the business is under pressure to release a product, such as an app or upgrade quickly before the required changes to the code are completed.
  • Lacking a test suite – Without the environment to exhaustively check for bugs and apply fixes before the public release of a product, more resources will be required later to resolve them as they arise.
  • Poor collaboration – From inadequate communication amongst the different product development teams and across the business hierarchy, to junior developers not being mentored properly, these will contribute to technical debt with the products that are released.
  • Lack of documentation – Have you launched code without its supporting documentation? This is a debt that will need to be fulfilled.
  • Parallel development – This is seen when working on different sections of a product in isolation which will, later on, need to be merged into a single source. The greater the extent of modification on an individual branch – especially when it affects its compatibility with the rest of the code, the higher the technical debt.
  • Skipping industrial standards – If you fail to adhere to industry-standard features and technologies when developing the product, there will be technical debt because you will eventually need to rework the product to align with them for it to continue being relevant.
  • Last-minute product changes – Incorporating changes that hadn’t been planned for just before its release will affect the future development of the product due to the checks, documentation and modifications that will be required later on

Types of Technical Debt

There are various types of technical debt, and this will largely depend on how you look at it.

  • Intentional technical debt – which is the debt that is consciously taken on as a strategy in the business operations.
  • Unintentional technical debt – where the debt is non-strategic, usually the consequences of a poor job being done.

This is further expounded in the Technical Debt Quadrant” put forth by Martin Fowler, which attempts to categorise it based on the context and intent:

Technical Debt Quadrant

Source: MartinFowler.com

Final thoughts

Technical debt is common, and not inherently bad. Just like financial debt, it will depend on the purpose that it has been taken up, and plans to clear it. Start-ups battling with pressure to launch their products and get ahead, software companies that have cut-throat competition to deliver fast – development teams usually find themselves having to take on technical debt instead of waiting to launch the products later. In fact, nearly all of the software products in use today have some sort of technical debt.

But no one likes being in debt. Actually, technical staff often find themselves clashing with business executives as they try to emphasise the implications involved when pushing for product launch before the code is completely ready. From a business perspective, it’s all about weighing the trade-offs, when factoring in aspects such as the aspects market situation, competition and consumer needs. So, is technical debt good or bad? It will depend on the context. Look at it this way: just like financial debt, it is not a problem as long as it is manageable. When you exceed your limits and allow the debt to spiral out of control, it can grind your operations to a halt, with the ripple effects cascading through your business.

 

Knowing the Caveats in Cloud Computing

Cloud computing has become such a buzzword in business circles today that many organisations both small and large, are quick to jump on the cloud bandwagon – sometimes a little too hastily.

Yes, the benefits of the cloud are numerous: reduced infrastructure costs, improved performance, faster time-to-market, capability to develop more applications, lower IT staff expenses; you get the picture. But contrary to what many may be expecting or have been led to believe, cloud computing is not without its share of drawbacks, especially for smaller organisations who have limited knowledge to go on with.

So before businesses move to the cloud, it pays to learn a little more about the caveats that could meet them along the way. Here are some tips to getting started with cloud computing as a small business consumer.

Know your cloud. As with anything else, knowledge is always key. Because it is a relatively new tool in IT, it’s not surprising that there is some confusion about the term cloud computing among many business owners and even CIOs. According to the document The NIST Definition of Cloud Computing, cloud computing has five essential characteristics, three basic service models (Saas, Paas and Iaas), and four deployment models (public, community, private and hybrid).

The first thing organisations should do is make a review of their operations and evaluate if they really need a cloud service. If they would indeed benefit from cloud computing, the next steps would be deciding on the service model that would best fit the organisation and choosing the right cloud service provider. These factors are particularly important when you consider data security and compliance issues.

Read the fine print. Before entering into a contract with a cloud provider, businesses should first ensure that the responsibilities for both parties are well-defined, and if the cloud vendor has the vital mechanisms in place for contingency measures. For instance, how does the provider intend to carry out backup and data retrieval operations? Is there assurance that the business’ critical data and systems will be accessible at all times? And if not, how soon can the data be available in case of a temporary shutdown of the cloud?

Also, what if either the company or the cloud provider stops operations or goes bankrupt? It should be clear from the get go that the data remains the sole property of the consumer or company subscribing to the cloud.

As you can see, there are various concerns that need to be addressed closely before any agreement is finalised. While these details are usually found in the Service Level Agreements (SLAs) of most outsourcing and servicing contracts, unfortunately, the same cannot be said of cloud contracts.

Be aware of possible unforeseen costs. The ability of smaller companies to avail of computing resources on a scalable, pay-as-you-go model is one of the biggest selling points of cloud computing. But there’s also an inherent risk here: the possibility of runaway costs. Rather than allowing significant cost savings, small businesses could end up with a bill that’s bound to blow a big hole in their budget.

Take for example the case of a software company cited on InformationWeek.com to illustrate this point. The 250-server cluster the company rented from a cloud provider was inadvertently left turned on by the testing team over the weekend. As a result, their usual $2,300 bill ballooned to a whopping $23,400 over the course of one weekend.

Of course, in all likelihood, this isn’t going to happen to every small and midsize enterprise that shifts to the cloud. However, this should alert business owners, finance executives, and CEOs to look beyond the perceived savings and identify potential sources of unexpected costs. What may start as a fixed rate scheme for on-demand computing resources, may end up becoming a complex pricing puzzle as the needs of the business grow, or simply because of human error as the example above shows.

The caveats we’ve listed here are among the most crucial ones that soon-to-be cloud adopters need to keep in mind. But should these be reasons enough for businesses to stop pursuing a cloud strategy? Most definitely not. Armed with the right information, cloud computing is still the fastest and most effective way for many small enterprises to get the business off the ground with the lowest start-up costs.

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