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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Monitoring Water Banks with Telemetrics

Longstanding droughts across South Australia are forcing farmers to rethink the moisture in the soil they once regarded as their inalienable right. Trend monitoring is an essential input to applying pesticides and fertilisers in balanced ratios. Soil moisture sensors are transmitting data to central points for onward processing on a cloud, and this is making a positive difference to agricultural output.

Peter Buss, co-founder of Sentek Technology calls ground moisture a water bank and manufactures ground sensors to interrogate it. His hometown of Adelaide is in one of the driest states in Australia. This makes monitoring soil water even more critical, if agriculture is to continue. Sentek has been helping farmers deliver optimum amounts of water since 1992.

The analogy of a water bank is interesting. Agriculturists must ?bank? water for less-than-rainy days instead of squeezing the last drop. They need a stream of online data and a safe place somewhere in the cloud to curate it. Sentek is in the lead in places as remote as Peru?s Atacamba desert and the mountains of Mongolia, where it supports sustainable floriculture, forestry, horticulture, pastures, row crops and viticulture through precise delivery of scarce water.

This relies on precision measurement using a variety of drill and drop probes with sensors fixed at 4? / 10cm increments along multiples of 12? / 30cm up to 4 times. These probe soil moisture, soil temperature and soil salinity, and are readily re-positioned to other locations as crops rotate.

Peter Buss is convinced that measurement is a means to the end and only the beginning. ?Too often, growers start watering when plants don’t really need it, wasting water, energy, and labour. By monitoring that need accurately, that water can be saved until later when the plant really needs it.? He goes on to add that the crop is the ultimate sensor, and that ?we should ask the plant what it needs?.

This takes the debate a stage further. Water wise farmers should plant water-wise crops, not try to close the stable door after the horse has bolted and dry years return. The South Australia government thinks the answer also lies in correct farm dam management. It wants farmers to build ones that allow sufficient water to bypass in order to sustain the natural environment too.

There is more to water management than squeezing the last drop. Soil moisture goes beyond measuring for profit. It is about farming sustainably using data from sensors to guide us. ecoVaro is ahead of the curve as we explore imaginative ways to exploit the data these provide for the common good of all.

Total Quality Management

Total Quality Management (TQM) is another business management approach that focuses on the involvement of all members of the organisation to participate in improving processes, products, services, and the culture in which they work in. It is important that every team member realises how each individual and each activity affects, and in turn is affected by, others.

With the use of combined quality and management tools, TQM also aims to reduce losses brought about by wasteful practices, a common concern in most companies. Using the TQM strategy, business would also be able to identify the cause of a defect, thereby preventing it from entering the final product.

Deming’s 14 Points

At the core of the Total Quality Management concept and implementation is Deming’s 14 points, a set of guidelines on quality as conceptualised by W Edwards Deming, one of the pioneers of quality. Deming’s 14 points are as follows:

  1. Create constancy of purpose for improving products and services.
  2. Adopt the new philosophy.
  3. Cease dependence on inspection to achieve quality.
  4. End the practice of awarding business on price alone; instead, minimise total cost by working with a single supplier.
  5. Improve constantly and forever every process for planning, production and service.
  6. Institute training on the job.
  7. Adopt and institute leadership.
  8. Drive out fear.
  9. Break down barriers between staff areas.
  10. Eliminate slogans, exhortations and targets for the workforce.
  11. Eliminate numerical quotas for the workforce and numerical goals for management.
  12. Remove barriers that rob people of pride of workmanship, and eliminate the annual rating or merit system.
  13. Institute a vigorous program of education and self-improvement for everyone.
  14. Put everybody in the company to work accomplishing the transformation.

But if you were to reduce to bare bones the TQM philosophy from Deming’s 14 points, it would all come down to two simple goals:

  1. To make things right the first time; and
  2. To work for continuous improvement.

As with all other quality management process, the end goal is to be able to offer products and services that meet and even exceed customer’s expectations.

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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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