Why DevOps Matters: Things You Need to Know

DevOps creates an agile relationship between system development and operating departments, so the two collaborate in providing results that are technically effective, and work well for customers and users. This is an improvement over the traditional model where development delivers a complete design ? and then spends weeks and even months afterwards, fixing client side problems that should never have occurred.
Writing for Tech Radar Nigel Wilson explains why it is important to roll out innovation quickly to leverage advantage. This implies the need for a flexible organisation capable of thinking on its feet and forming matrix-based project teams to ensure that development is reliable and cost effective.
Skirmishes in Boardrooms
This cooperative approach runs counter to traditional silo thinking, where Operations does not understand Development, while Development treats the former as problem children. This is a natural outcome of team-centred psychology. It is also the reason why different functions pull up drawbridges at the entrance to their silos. This situation needs managing before it corrodes organization effectiveness. DevOps aims to cut through this spider web of conflict and produce faster results.

The Seeds of Collaboration

Social and personal relationships work best when the strengths of each party compensate the deficiencies of the other. In the case of development and operations, development lacks full understanding of the daily practicalities operating staff face. Conversely, operations lacks ? and should lack knowledge of the nuances of digital automation, for the very reason it is not their business.
DevOps straddles the gap between these silos by building bridges towards a co-operative way of thinking, in which matrix-teams work together to define a problem, translate it into needs and spec the system to resolve these. It is more a culture than a method. Behavioural change naturally leads to contiguous delivery and ongoing deployment. Needless to say only the very best need apply for the roles of client representative, functional tester and developer lead.

Is DevOps Worth the Pain of Change?

Breaking down silos encroaches on individual managers? turf. We should only automate to improve quality and save money. These savings often distil into organisational change. The matrix team may find itself in the middle of a catfight. Despite the pain associated with change resistance, DevOps more than pays its way in terms of benefits gained. We close by considering what these advantages are.

An Agile Matrix Structure ? Technical innovation is happening at a blistering rate. The IT industry can no longer afford to churn out inferior designs that take longer to fix than to create. We cannot afford to allow office politics to stand in the way of progress. Silos and team builds are custodians of routine and that does not sit well with development.

An Integrated Organization ? DevOps not only delivers operational systems faster through contiguous testing. It also creates an environment whereby cross-border teams work together towards achieving a shared objective. When development understands the challenges that operations faces ? and operations understands the technical limiters – a new perspective emerges of ?we are in this together?.

The Final Word ? With understanding of human dynamics pocketed, a DevOps project may be easier to commission than you first think. The traditional way of doing development – and the waterfall delivery at the end is akin to a two-phase production line, in which liaison is the weakest link and loss of quality inevitable.

DevOps avoids this risk by having parties work side-by-side. We need them both to produce the desired results. This is least until robotics takes over and there is no longer a human element in play.

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Energy efficiency- succeed and benefit

Energy is neither created nor destroyed; it is only transformed. This being the law of conservation of energy, and given that the process of transforming energy is inefficient resulting in loss of usable energy in the process of transforming one form of energy into another form, Energy Efficiency finds a home.
Talking of Energy efficiency, think of how much useful energy can be obtained from a system or a particular technology. It is also about the use of technology that requires a lesser amount of energy to carry out the same task.

Energy efficiency is the responsibility of both demand side and supply side. Supply-side energy efficiency refers to a set of actions taken to ensure efficiency through the electricity supply chain. Supply side efficiency measures are about efficiency in electricity generation; be it operation and maintenance of existing equipment or upgrading existing equipment with state-of-the-art energy-efficient generating equipment.

The demand side energy efficiency on the other hand refers to the actions taken to use less/demand less energy. Think of less energy usage in relation to improvement of energy efficiency in buildings, solar water heaters, energy efficient lighting systems such as Compact Fluorescent Lamps, conducting energy audits to identify potential energy saving opportunities, efficient water heating systems and the list is endless.

Success of energy efficiency is a win ? win to YOU-ME-US – the energy consumers, to THEM the energy producers and suppliers and to our precious ENVIRONMENT.
Gain to energy suppliers: – Less energy usage and better energy usage patterns among consumers consequently reduces the customer load which reduces losses on the supply side. Less energy loss creates capacity on the system to serve more customers.

Gain to you-me-us: – Less energy usage and better energy usage patterns Benefits the customer through reduced Electricity bills / $ savings through lower bills.

Benefits to the environment: – Usage of less energy reduces use of fossil fuels, hence reduction in GHG emissions hence conserving our environment. Companies look at means to make rational use of their least efficient generating equipment. The objective is to improve the operation and maintenance of existing equipment or upgrade it with state-of-the-art energy-efficient technologies. Some companies have on-site electricity generation alternatives and thus tend to consider the supply side in addition to demand-side energy efficiency.

Failure Mode and Effects Analysis

 

Any business in the manufacturing industry would know that anything can happen in the development stages of the product. And while you can certainly learn from each of these failures and improve the process the next time around, doing so would entail a lot of time and money.
A widely-used procedure in operations management utilised to identify and analyse potential reliability problems while still in the early stages of production is the Failure Mode and Effects Analysis (FMEA).

FMEAs help us focus on and understand the impact of possible process or product risks.

The FMEA method for quality is based largely on the traditional practice of achieving product reliability through comprehensive testing and using techniques such as probabilistic reliability modelling. To give us a better understanding of the process, let’s break it down to its two basic components ? the failure mode and the effects analysis.

Failure mode is defined as the means by which something may fail. It essentially answers the question “What could go wrong?” Failure modes are the potential flaws in a process or product that could have an impact on the end user – the customer.

Effects analysis, on the other hand, is the process by which the consequences of these failures are studied.

With the two aspects taken together, the FMEA can help:

  • Discover the possible risks that can come with a product or process;
  • Plan out courses of action to counter these risks, particularly, those with the highest potential impact; and
  • Monitor the action plan results, with emphasis on how risk was reduced.

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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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