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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Becoming Nimble the Agile Project Management Way

In dictionary terms, ?agile? means ?able to move quickly and easily?. In project management terms, the definition is ?project management characterized by division of tasks into short work phases called ?sprints?, with frequent reassessments and adaptation of plans?. This technique is popular in software development but is also useful when rolling out other projects.

Managing the Seven Agile Development Phases

  • Stage 1: Vision. Define the software product in terms of how it will support the company vision and strategy, and what value it will provide the user. Customer satisfaction is of paramount value including accommodating user requirement changes.
  • Stage 2: Product Roadmap. Appoint a product owner responsible for liaising with the customer, business stakeholders and the development team. Task the owner with writing a high-level product description, creating a loose time frame and estimating effort for each phase.
  • Stage 3: Release Plan. Agile always looks ahead towards the benefits that will flow. Once agreed, the Product Road-map becomes the target deadline for delivery. With Vision, Road Map and Release Plan in place the next stage is to divide the project into manageable chunks, which may be parallel or serial.
  • Stage 4: Sprint Plans. Manage each of these phases as individual ?sprints?, with emphasis on speed and meeting targets. Before the development team starts working, make sure it agrees a common goal, identifies requirements and lists the tasks it will perform.
  • Stage 5: Daily Meetings. Meet with the development team each morning for a 15-minute review. Discuss what happened yesterday, identify and celebrate progress, and find a way to resolve or work around roadblocks. The goal is to get to alpha phase quickly. Nice-to-haves can be part of subsequent upgrades.
  • Stage 6: Sprint Review. When the phase of the project is complete, facilitate a sprint review with the team to confirm this. Invite the customer, business stakeholders and development team to a presentation where you demonstrate the project/ project phase that is implemented.
  • Stage 7: Sprint Retrospective. Call the team together again (the next day if possible) for a project review to discuss lessons learned. Focus on achievements and how to do even better next time. Document and implement process changes.

The Seven Agile Development Phases ? Conclusions and Thoughts

The Agile method is an excellent way of motivating project teams, achieving goals and building result-based communities. It is however, not a static system. The product owner must conduct regular, separate reviews with the customer too.

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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How Accenture Keeps Rolling Out Sustainability

Multinational management-consulting and technology-services company Accenture has a good eye for sniffing out new business, with 305,000 employees advancing its interests in more than 200 cities in 56 countries evidence. Last year, it netted US$30 billion profit that is a tidy sum of money in anybody?s books.

Accenture also practices what it preaches. This is maximum business efficiency within moral standards. It tracks its carbon emissions from its offices around the world. Being a technology services company it is unsurprising that it automated the process. Being management consultants it can drill down to finest detail in its search for continuous improvement.

As a forward-thinking company Accenture is committed to transplanting its business skills into other organizations, in order to drive higher performance and sustain greater profits in the long term. It works with clients across borders and industries to integrate sustainability into their business models, and find effective ways to lighten carbon footprints.

The City of Seattle in Washington is a case in point. Following a proud history of nature and energy conservation, it engaged Accenture in 2013 to help it reduce downtown power consumption by 25%. Other project members were Microsoft supplying software, the local power utility for technical advice, and a non-profit to set up a smart building program. The initiative uses cloud services to process the big data generated by a host of building management services, plus a multitude of sensors, controls and meters.

The project is vital for the City. It wants to continue expanding but needs to avoid another power plant polluting its skyline. At the time of writing, the pilot sites had proved successful and the program was rolling out. Seattle?s next challenge is to acquire 15% of its energy from renewable sources by 2020.

The smart building solutions Seattle trialled in five downtown buildings, had a further welcome spinoff; by reducing operating times, facility managers can look forward to extended equipment life and fewer maintenance downtimes. The green building philosophy is alive and well in the City of Seattle, driven both by necessity and vision.

It is a no longer as question of if – but when – other urban communities follow suit. EcoVaro believes it is time long due for individual companies to start enjoying lower energy costs plus the prospect of profitably trading carbon credits. The process begins with measuring what you have and identifying cost-effective savings.

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