What Makes A Project?

An activity must therefore meet various conditions to be defined as a project. These conditions are:

• No routine: A project represents a new challenge and lies outside the everyday areas of activity.
• Goal-oriented: A project has a defined goal. Only when this is achieved can the project be completed.
• Time limit: Every project has a defined beginning and end.
• Specified resources: All resources such as personnel, budget or materials are limited in quantity and should not be exceeded for successful project completion.
• Required expertise: Several employees, usually from different departments, are involved in a project. This ensures broad technical knowledge of the content and optimal solutions.

Also Read: What Does PMO or Project Management Office Mean

The Concept Of Project Management

In project management, projects and their goals are planned and controlled. The project goals should be achieved within the defined framework for the company – in terms of costs, quality and time. For this purpose, project managers guide each project through the areas of project definition, project implementation and project completion. 

Depending on the complexity of the tasks, project management is about the entirety of

• management tasks

• organization

• techniques and 

• middle

To ensure the successful completion of a project.

Project definition: To achieve the project goal, the project planning is done with the client in the first step.

The project goal and the process are defined with the help of various methods. These methods include SWOT analysis, project structure plans or network plans. In addition, resources such as costs, materials and personnel are planned and determined for the project in this phase.

This is followed by project implementation.

In the second step, the previously defined goals are pursued. Each project’s progress is documented and compared with the previously created plans. Regular target/actual comparisons are used to identify deviations and counteract the risk of delaying the achievement of goals. From this step, the objective can only be changed by agreement with the client since the cost and time factors are already coordinated with the project objective.

The final step in process management is project closure.

Here all completed project steps must correspond to the objective. This way, the result can be handed over to the client in the agreed quality. All data is then passed on to revision: with the help of a post-calculation, possible deviations can be considered in subsequent project planning.

Practical Tools For Project Management

There are many different project management tools on the market. You support the planning and implementation of projects and make an important contribution to the success of projects in companies. There are software tools that 

• present all tasks of the project.
• Implement projects in an optimized way.
• Ensure control and transparency of complex projects.
• Simplify teamwork.

Conclusion

The goal of project management is to control projects in a meaningful way and thus shape change processes in companies successfully. Be it the switch to mobile working for your employees, redesigning your website, or considering offering webinars for existing and potential customers. None of these projects can do without project management.

The post Perfect Projects – What Is Project Management? appeared first on Web Updates Daily.

But don’t get to brooding. Not yet! Instead, it is more advisable to first understand the reasons why most other ML projects end up failing. Once you are aware of the possible and obvious pitfalls, you can simply go back to your project, eliminate them right away, and get your development back on track.

Also, as per VentureBeat, almost 87% of AI projects failed to make it through 2020, owing to a host of intrinsic factors. And as intelligent projects hinging on technologies like Computer Vision and Natural Language Processing cost a lot of money, failing isn’t always an option.

So before we talk about the ways to make your ML project a roaring success, let us delve right into the reasons that enhance fallibility:

Lack of Expertise

Well, the first reason doesn’t need any validation. Machine learning projects require algorithms, data procurement, high-quality annotation, and other complex aspects taken good care of. Having an inexperienced team or data analytics person take care of these intricate ML concepts and developments is waiting for a mishap to happen. Final deployment, continuous monitoring, and successful predictive testing are far more critical and require the assistance of end-to-end service providers.

Subpar Data Volume and Quality

If you evaluate closely, the success of every ML project hinges on Data. Starting from data identification, aggregation, cleansing, augmentation to data labeling, the entire data-specific circle comprises 80% of the total time allocated to developing any machine learning model. 

Image idea: Role of data and how 80% of the time is due to different data-specific tasks

But then, most organizations, in an effort to launch the MVP at a lighting pace, end up compromising on data volume. The lack of diversity makes the models linear and far less intuitive than expected. Data quality also takes a hit if experienced AI training data collection services aren’t requested. 

In the end, it is all about how well Data Science is implemented, and not many ML projects can get this part right.

Erroneous Labeling

Lack of properly annotated data can kill off even the most obvious projects. While labeled data can still be bad data at times, it actually covers the skill sets of your model provided the data collection vendors have done their job to perfection.

Did you know that almost 76% of global organizations end up annotating training data in-house, which stalls some of the more promising AI projects?

Organizations can scale beyond this issue either by outsourcing labeling to experienced service providers or by maintaining adequate training standards for the in-house data analysts, annotators, and scientists.

Lack of Proper Collaboration

Did you ever realize that AI and ML projects can even fail due to intrinsic factors? Surprised, right! Well, don’t be as every organization in charge of developing Machine Learning projects have BI specialists, Data Engineers, Data Scientists, DevOPs, and other professionals working in tandem. And finally, there is the core engineering team that takes the model to production.

However, if one segment fails to interact and collaborate with the other, data quality, volume, algorithms training data sets, testing sets, and other aspects can easily get compromised.

Dated Data Strategy

While a majority of concerns show up in between or at the fag end of the project conclusion, Data Strategy or the lack thereof is an issue that needs to be taken care of right at the onset. The factors included in a data strategy are often as follows:

• Total data requirement
• Can features be extracted from datasets
• What is the mode of data access?
• Does the procured data require cleaning, and if yes, to what end?
• Has the compliance been checked and verified?
• Pairing disparate datasets together for relevance and uniformity

Image idea: Volume of data preferred for organizations

Most organizations planning to launch an ML project follow an obsolete data strategy that only talks about the sources and annotation techniques, which then results in project failure.

Absence of Efficient Leadership

It is important that the crux of the project is defined by an AI leader, who would even understand the practical implications of the product. Having a team of engineers, data scientists, and analysts handling the job is fine but if efficient leadership is missing, the project gets restricted to a mere confluence of technologies and datasets. In the end, it’s all about what difference the project makes and what problem it ends up solving, and these aspects can only be iterated by the business leader itself.

Unanticipated and Unpleasant Data Bias

To be honest, an intelligent ML model is all about automating tasks based on the data that has been fed to it. If the data collection, labeled and trained with is one-dimensional, chances of bias coming into the picture grow significantly. Unsure as to what data bias in AI and ML means? Well, here is an example.

Imagine there is a resume filtering product that picks the best candidate for the job by shortlisting 5 resumes out of 5000. However, the model ends up picking 5 male candidates when there were at least 35 highly qualified female candidates that should have been prioritized. On closer scrutiny, it is identified that the model and subsequent algorithms were fed with male-specific data sets, thereby leading to gender bias and instant elimination of female candidates.

Wrap-Up

While there can be many other reasons for an ML project to fail, these are 7 of the most common yet underrated reasons. And if you look closely, it all comes down to the procurement, usage, deployment, cleansing, annotation, implementation, and transformation of data. Also, as data forms the backbone of any machine learning campaign, it is advisable to onboard a credible end-to-end service provider to handle every aspect of the ML project with precision and accuracy.

The post Why Machine Learning Projects Fail- 7 Reasons that can Take Your Efforts for a Ride? appeared first on Web Updates Daily.

In practice, a PMO is a knowledge centre that offers support services to projects and their teams. Some of the responsibilities or functions it performs are developing project management methodologies to establish a common language for managing those projects within the realization and training and training for project leaders and their teams. It also defines processes, tools and indicators.

One Of The Main Functions Of PMOs Is To Centralize Everything

A PMO usually has its department within an organization and what that department does is one among other things.

• It seeks to standardize the ways of working when it comes to projects.
• It seeks to ensure that best practices are implemented within the organization.
• It is a place where all projects are channelled and distributed accordingly.
• Make sure you are aligned with the strategic initiatives of the organization.

Project Management Office

He is in charge of keeping all the information on the project portfolio and preparing reports to be delivered to senior management to make decisions. Apart from all the functions in practice, we have seen that those project management offices that manage to understand the needs of senior management are working to be successful. The organization or department briefs the top executive, giving him the information he needs to make sure that things are on the right track and even the status of what is happening with all those major initiatives.

Who Should Work In The PMO

Some PMO leader is needed. It could be a manager or someone at a higher level, at the director level, who is part of the executive team because the PMO leader that office has to take a look at the strategy and guide the executive team on whether those strategies can be executed or not based on time, scope, budget and even resources. Who else do you need? You’re going to need project managers, project leaders.

Also Read: Why Is It Key To Integrate E-commerce And ERP In SMEs

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