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.

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2015 ESOS Guidelines Chapter 3 ? The ESOS Assessment

ESOS operates in tandem with the ISO 50001 (Energy Management) system that encourages continual improvement in the efficient use of energy. Any UK enterprise qualifying for ESOS that has current ISO 50001 certification on the compliance date by an approved body (and that covers the entire UK corporate group) may present this as evidence of having completed its ESOS assessment. It does however still require board-level certification, following which it must notify the Environment Agency accordingly.

The Alternate ESOS Route

In the absence of an ISO 50001 energy management certificate addressing comprehensive energy use, a qualifying UK enterprise must:

  1. Measure Total Energy Consumption in either kWh or energy spend in pounds sterling, and across the entire operation including buildings, industrial processes and transport.
  2. Identify Areas of Significant Energy Consumption that account for at least 90% of the total. The balance falls into a de minimis group that is officially too trivial to merit consideration.
  3. Consider Available Routes to Compliance. These could include ISO 500001 part-certification, display energy certificates, green deal assessments, ESOS compliant energy audits, self-audits and independent assessments
  4. Do an Internal Review to make sure that you have covered every area of significant consumption. This is an important strategic step to avoid the possibility of failing to comply completely.
  5. Appoint an Approved Lead Assessor who may be internal or external to your enterprise, but must have ESOS approval. This person confirms you have met all ESOS requirements (unless you have no de minimis exceptions).
  6. Obtain Internal Certification by one of more board-level directors. They must certify they are satisfied with the veracity of the reports. They must also confirm that the enterprise is compliant with the scheme.
  7. Notify the Environment Agency of Compliance within the deadline using the online notification system at snapsurveys.com as soon as the enterprise believes is fully compliant.
  8. Assemble your ESOS Evidential Pack and back it up in a safe place. Remember, it is your responsibility to provide proof of the above. Unearthing evidence a year later it not something to look forward to.

The ESOS assessment process is largely self-regulatory, although there are checks and balances in place including lead assessor and board-level certifications. As you work through what may seem to be a nuisance remember the primary objectives. These are saving money and reducing carbon emissions. Contact Ecovaro if we can assist in any way.

Directions Hadoop is Moving In

Hadoop is a data system so big it is like a virtual jumbo where your PC is a flea. One of the developers named it after his kid?s toy elephant so there is no complicated acronym to stumble over. The system is actually conceptually simple. It has loads of storage capacity and an unusual way of processing data. It does not wait for big files to report in to its software. Instead, it takes the processing system to the data.

The next question is what to do with Hadoop. Perhaps the question would be better expressed as, what can we do with a wonderful opportunity that we could not do before. Certainly, Hadoop is not for storing videos when your laptop starts complaining. The interfaces are clumsy and Hadoop belongs in the realm of large organisations that have the money. Here are two examples to illustrate the point.

Hadoop in Healthcare

In the U.S., healthcare generates more than 150 gigabytes of data annually. Within this data there are important clues that online training provider DeZyre believes could lead to these solutions:

  • Personalised cancer treatments that relate to how individual genomes cause the disease to mutate uniquely
  • Intelligent online analysis of life signs (blood pressure, heart beat, breathing) in remote children?s hospitals treating multiple victims of catastrophes
  • Mining of patient information from health records, financial status and payroll data to understand how these variables impact on patient health
  • Understanding trends in healthcare claims to empower hospitals and health insurers to increase their competitive advantages.
  • New ways to prevent health insurance fraud by correlating it with claims histories, attorney costs and call centre notes.

Hadoop in Retail

The retail industry also generates a vast amount of data, due to consumer volumes and multiple touch points in the delivery funnel. Skillspeed business trainers report the following emerging trends:

  • Tracing individual consumers along the marketing trail to determine individual patterns for different demographics and understand consumers better.
  • Obtaining access to aggregated consumer feedback regarding advertising campaigns, product launches, competitor tactics and so on.
  • Staying with individual consumers as they move through retail outlets and personalising their experience by delivering contextual messages.
  • Understanding the routes that virtual shoppers follow, and adding handy popups with useful hints and tips to encourage them on.
  • Detecting trends in consumer preferences in order to forecast next season sales and stock up or down accordingly.

Where to From Here?

Big data mining is akin to deep space research in that we are exploring fresh frontiers and discovering new worlds of information. The future is as broad as our imagination.?

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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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