New Focus on Monitoring Soil

There is nothing new about monitoring soil in arid conditions. South Africa and Israel have been doing it for decades. However climate change has increased its urgency as the world comes to terms with pressure on the food chain. Denizon decided to explore trends at the macro first world level and the micro third world one.

In America, the Coordinated National Soil Moisture Network is going ahead with plans to create a database of federal and state monitoring networks and numerical modelling techniques, with an eye on soil-moisture database integration. This is a component of the National Drought Resilience Partnership that slots into Barrack Obama?s Climate Action Plan.

This far-reaching program reaches into every corner of American life to address the twin scourges of droughts and inundation, and the agency director has called it ?probably ?… one of the most innovative inter-agency tools on the planet?. The pilot project involving remote moisture sensing and satellite observation targets Oklahoma, North Texas and surrounding areas.

Africa has similar needs but lacks America?s financial muscle. Princeton University ecohydrologist Kelly Caylor is bridging the gap in Kenya and Zambia by using cell phone technology to transmit ecodata collected by low-cost ?pulsepods?.

He deploys the pods about the size of smoke alarms to measure plants and their environment.?Aspects include soil moisture to estimate how much water they are using, and sunlight to approximate the rate of photosynthesis. Each pod holds seven to eight sensors, can operate on or above the ground, and transmits the data via sms.

While the system is working well at academic level, there is more to do before the information is useful to subsistence rural farmers living from hand to mouth. The raw data stream requires interpretation and the analysis must come through trusted channels most likely to be the government and tribal chiefs. Kelly Caylor cites the example of a sick child. The temperature reading has no use until a trusted source interprets it.

He has a vision of climate-smart agriculture where tradition gives way to global warming. He involves local farmers in his research by enrolling them when he places pods, and asking them to sms weekly weather reports to him that he correlates with the sensor data. As trust builds, he hopes to help them choose more climate-friendly crops and learn how to reallocate labour as seasons change.

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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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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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The Matrix Management Structure

Organizations exploit matrix management in various ways. A company, for instance, that operates globally uses it at larger scale by giving consistent products to various countries internationally. A business entity, having many products, does not assign its people to each product full-time but assign those to different ones on a part time basis, instead. And when it comes to delivering high quality and low cost products, companies overcome industry pressures with the help of many overseeing managers. In a rapidly changing environment, organizations respond quickly by sharing information through a matrix model.

Understanding the Matrix Management Structure

A basic understanding of matrix management starts with the three key roles and responsibilities that applies in the structure.

  • Matrix Leader ? The common person above all the matrix bosses is the matrix leader. He ensures that the balance of power is maintained in the entire organization by delegating decisions and promoting collaboration among the people.
  • Matrix Managers ? The managers cooperate with each other by defining the respective activities that they are responsible for.
  • Matrix Employees – The employees have lesser direct authority but has more responsibilities. They resolve differing demands from more than one matrix managers while they work things out upwards. Their loyalty must be dual and their relationships with managers must be maintained.

Characteristics of a Matrix Structure

Here are some features that define the matrix management structure:

  • Hybrid Structure ?The matrix structure is a mix of functional and project organization. Since it is a combination of these two, matrix management is hybrid in nature.
  • Functional Manager ? When it comes to the technical phases of the project, the functional manager assumes responsibility. The manager decides on how to get the project done, delegates the tasks to the subordinates and oversees the operational parts of the organization.
  • Project Manager ? The project manager has full authority in the administrative phases, including the physical and financial resources needed to complete the project. The responsibilities of a project manager comprise deciding on what to do, scheduling the work, coordinating the activities to diverse functions and evaluating over-all project performance.
  • Specialization ?As the functional managers concentrate on the technical factors, the project managers focus on administrative ones. Thus, in matrix management, there is specialization.
  • Challenge in Unity of Command ? Companies that employs matrix management usually experience a problem when it comes to the unity of command. This is largely due to the conflicting orders from the functional and project managers.

Types of Matrix Structure

The matrix management structure can be classified according to the level of power of the project manager. Here are three distinct types of matrix structures that are widely used by organizations.

  • Weak Matrix ? The project manager has limited authority and power as the functional manager controls the budget of the project. His role is only part-time and more like a coordinator.
  • Strong Matrix ? Here, the project manager has almost all the authority and power. He controls the budget, holds the full time administrative project management and has a full time role.
  • Balanced Matrix ? In this structure type, both the project and functional managers control the budget of the project. The authority and power is shared by the two as well. Although the project manager has a full time role, he only has a part time authority for the administrative staff to report under his leadership.

Successful companies of today venture more on enhancing the abilities, skills, behavior and performances of their managers than the pursuit of finding the best physical structure. Indeed, learning the fundamentals of the matrix structure is essential to maximize its efficiency. A senior executive pointed out that one of the challenges in matrix management is not more of building a structure but in creating the matrix to the mind of the managers. This comes to say that matrix management is not just about the structure, it is a frame in the mind.

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