Saving Energy Step 2 ? More Practical Ideas

In my previous blog, we wrote about implementing a management system. This boils down to sharing a common vision up and down and across the organisation, measuring progress, and pinning accountability on individuals. This time, we would like to talk about simple things that organisations can do to shrink their carbon footprints. But first let’s talk about the things that hold us back.

When we take on new clients we sometimes find that they are baffled by what I call energy industry-speak. We blame this partly on government. We understand they need clear definitions in their regulations. It’s just a pity they don’t use ordinary English when they put their ideas across in public forums.

Consultants sometimes seem to take advantage of these terms, when they roll words like audit, assessment, diagnostic, examination, survey and review across their pages. Dare we suggest they are trying to confuse with jargon? We created ecoVaro to demystify the energy business. Our goal is to convert data into formats business people understand. As promised, here are five easy things your staff could do without even going off on training.

  1. Right-size equipment? outsource peak production in busy periods, rather than wasting energy on a system that is running at half capacity mostly.
  2. Re-Install equipment to OEM specifications ? individual pieces of equipment need accurate interfacing with larger systems, to ensure that every ounce of energy delivers on its promise.
  3. Maintain to specification ? make sure machine tools are within limits, and that equipment is well-lubricated, optimally adjusted and running smoothly.
  4. Adjust HVAC to demand ? Engineers design heating and ventilation systems to cope with maximum requirements, and not all are set up to adapt to quieter periods. Try turning off a few units and see what happens.
  5. Recover Heat ? Heat around machines is energy wasted. Find creative ways to recycle it. If you can’t, then insulate the equipment from the rest of the work space, and spend less money cooling the place down.

Well that wasn’t rocket science, was it? There are many more things that we can do to streamline energy use, and coax our profits up. This is as true in a factory as in the office and at home. The power we use is largely non-renewable. Small savings help, and banknotes pile up quickly.

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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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Scrumming Down to Complete Projects

Everybody knows about rugby union scrums. For our purposes, perhaps it is best to view them as mini projects where the goal is to get the ball back to the fly-half no matter what the opposition does. Some scrums are set pieces where players follow planned manoeuvres. Loose / rolling scrums develop on the fly where the team responds as best according to the situation. If that sounds to you like software project management then read on, because there are more similarities?.

Isn’t Scrum Project Management the Same as Agile?

No it’s not, because Scrum is disinterested in customer liaison or project planning, although the team members may be happy to receive the accolades following success. In the same way that rugby players let somebody else decide the rules and arrange the fixtures, a software Scrum team just wants the action.

Scrum does however align closely ? dare I say interchangeably with Agile?s sprints. Stripping it of all the other stages frees the observer up to analyse it more closely in the context of a rough and tumble project, where every morning can begin with a backlog of revised requirements to back fit.

The 3 Main Phases of a Scrum

A Scrum is a single day in the life of a project, building onto what went before and setting the stage for what will happen the following day. The desired output is a block of component software that can be tested separately and inserted later. Scrumming is also a useful technique for managing any project that can be broken into discreet phases. The construction industry is a good example.

Phase 1 – Define the Backlog. A Scrum Team?s day begins with a 15 minute planning meeting where team members agree individual to-do lists called ?backlogs?.

Phase 2 – Sprint Towards the Goal. The team separates to allow each member to complete their individual lines of code. Little or no discussion is needed as this stage.

Phase 3 – Review Meeting. At the end of each working day, the team reconvenes to walk down what has been achieved, and check the interconnected functionality.

The 3 Main Phases of a Scrum ? Conclusions and Thoughts

Scrum is a great way to liberate a competent project team from unnecessary constraints that liberate creativity. The question you need to ask yourself as manager is, are you comfortable enough to watch proceedings from the side lines without rushing onto the field to grab the ball.

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The Connection between Big Data and MDM

Master Data is information that is critical to your business. This could include contracts, proprietary information, intellectual capital and a whole lot more besides. Because this often reposes in a variety of different places, you need a master data management / MDM policy to control it. That way, you can link it all together in a single, secure, backed up file.

This Sounds Like Big Data

Not necessarily: big data refers to extremely large data sets that are best stored and analysed on a cloud using big technology, in order to uncover trends, patterns and associations often relating to human behaviour. Of course, if you run a niche restaurant your critical master data might be limited to a few recipes and the books you do not care to show your accountant.

The distinction is largely a question of size: think of your master data as the subset of big data that you already have your mind around. According to John Case of IBM this is probably already in a structured format and available to share. He goes on to present a cogent case for using this as a peg point around which to systematise the rest. This is because the average organisation already has master data recording customers? and prospects? behaviour.

Do I Still Need My Master Data?

Yes you do, because real people created it with the benefit of human insight. Retain it as a separate set. Then compare it with the results of big data processing for even richer insights. Two heads are better that one and that goes for data processing too.

Trends in CRM Big Data

Adding data via location-aware devices like smartphones and tablets is adding a new dimension to customer information. We now know where they were when they made the enquiry or punched in the information. Use this geo-location data to hone the way you interact with customers and service their accounts. Do not phone a customer who makes decisions at work when they are at home.

Does My Master Data Belong on a Cloud?

There are a number of ?ifs? to consider. How comfortable are you with your service provider. What would happen if someone hacked their server? There are many advantages to cloud technology. Denizon knows of solutions you can rely on, and makes sure its clients have contingency plans to protect them at all times.

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