Managing Data Science Project

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Project management is always a difficult and time consuming task which requires solid skills, long-term experience, and deep expertise in a certain domain. What does each project manager have to remember when pushing forward a project related to Big Data industry?

 

 

Noting value and the importance of Data Science for the advanced software development, many IT companies are investing now more than ever in Big Data and related technologies. According to a SNS Research, the value of global investments in data science technologies will surpass the $57 billion mark by the end of 2017. Both business intelligence (BI) and analytics software market continuously grow and, by the end of this year, will generate global revenue value of $18.3 billion, as reported by Gartner.

However, it is important to wisely manage every stage of project development process and safely ensure success by applying the right techniques and leading a team properly. In this article, we will cover main concerns and their solutions in Big Data management.

 

What is CRISP-DM and Why You Need It

 

The most important task of Data Science management is to ensure the highest possible data quality. Various IT companies often try to reinvent the wheel and come up with their own approaches for data mining, but so far, there is still only one appropriate method for doing this which was introduced in Brussels in far 1999. And it’s called the Cross Industry Standard Process for Data Mining, commonly known as CRISP-DM.

The CRISP-DM process model is as follows:

  1. Business Understanding;
  2. Data Understanding;
  3. Data Preparation;
  4. Modeling;
  5. Evaluation;
  6. Deployment.

Each phase corresponds to the specific activities that usually exist in any project related to DS. Let’s consider the basic benefits you can get by following CRISP-DM principles.

 

Advantages of CRISP-DM

 

The main advantage of CRISP-DM is in its being a cross-industry standard. It means this methodology can be implemented in any DS project notwithstanding its domain or destination. Below, you will find the list of basic advantages of the CRISP-DM approach for Big Data projects.

Flexibility

No team can avoid pitfalls and mistakes at the beginning of the project. When starting a project, DS teams often suffer from the lack of domain knowledge or ineffective models of data evaluation they have. Thus, a project can become successful only if a team manages to reconfigure its strategy and is able to improve technical processes it applies. Another advantage of CRISP-DM approach is its flexibility. This makes it possible for models and processes to be imperfect at the very beginning. It provides a high level of flexibility that helps improve hypotheses and data analysis methods in a regular manner during further iterations.

Long-term Strategy

CRISP-DM methodology allows to create a long-term strategy based on short iterations at the beginning of project development. During first iterations, a team can create a basic and simple model cycle that can easily be improved in further iterations. This principle allows to ameliorate a preliminarily developed strategy after obtaining additional information and insights.

Functional Templates

The amazing benefit of using a CRISP-DM approach is a possibility to develop functional templates for DS management processes. The best way to take as many benefits as possible from CRISP-DM implementation is to create strict checklists for all phases of the work. Microsoft has already built that kind of checklist for DS teams.

 

Team Management In Data Science Software Development Project

 

As the DS market grows, IT companies hire more specialists to develop new projects. According to Evans Data Corporation, 6M developers are working on Big Data projects while you are reading this article. In fact, this number is one-third of all developers worldwide. That is why we need to consider the methods of DS team management.

Make Necessary Data Available to Each Specialist

DS specialists of every team have to be able to communicate effectively. Therefore, every team member has to have an access to data. It ensures the efficient data collection and obtaining analysis of high quality.

Make Sure Everyone Understands the Core Value of Your Company

It is crucial for team members to understand where they are going and what they are supposed to achieve. To run the race, you must know where the finish line is. Make sure that all the team members realize what is really important according to the core values company has.

Let Your Team Focus on One Task

Until the work starts, all roles and responsibilities have to be delegated accurately. Do not let your team members switch between several tasks. Instead, let them focus on one specific task till it is completed. It will help you create a core of in-house professionals specialized in an exact task completion.

Hire Responsibly

The presence of general DS experience is not enough to take someone aboard. The person, who is considered to be a potential team member, must have an expertise and convenient experience in the domain your project relates to.

Use the Right Tools

Data processing technologies are continuously improving and evolving. Therefore, it is important to implement centralized platforms that would be able to integrate with currently available tools and improve collaboration between hired talents.

Let Your Team Members Learn New Skills

When a specialist faces an issue he or she is not familiar with, do not try to delegate the task of finding a solution to another team member if the first one is ready to deal with the situation on his/her own. Let your employees improve their skills and learn new things.

 

Ensure a Timely Big Data Project Delivery

To apply Data science successfully to business, it is necessary to build an effective strategy and meet all the deadlines in order to timely perform established tasks. This is where Agile methodology comes in handy.

Agile for Data Science Projects

In this short paragraph, we will consider basic recommendations regarding using Agile for DS project management.

Solving Problems First, Building Features Second

The early sprints have to be aimed at understanding what can be controversial in what your team is about to do. Determining primary problems, which require immediate solutions, is a more effective approach in DS than creating additional features to get a wow-effect.

Use a Proper Sprint Structure

A sprint usually lasts for two weeks, but, sometimes, it can take less time to complete some tasks. Solid sprints do not allow to develop flexible strategies. Set the sprint length depending on the particular situation.

Develop a Culture of Fast Experimentation

Developing effective data analytics methods is all about getting insights, creating hypotheses and their testing. Yes, you are encouraged to experiment if it can improve your project. The only thing you have to remember that these experiments have to be performed quickly.

 

The effectiveness of any DS project lies in proper setting expectations and estimation of achieved results with regular correction of the previously set direction. Continuous improvement will ensure the success of any Big Data project.

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