The digitization of data and the rise of Big Data have led to a massive influx of information into companies. A lot of information is produced, but also a lot of information to process. And yet this mass of data represents a real economic and strategic challenge for companies, provided they know how to process and use it properly, by deploying the right methods for their big data projects.
Too much information kills information, or so the saying goes. And this seems to be borne out by the rise of Big Data.
With the multiplication and development of digital media, companies are faced with an influx of data to manage. While companies are beginning to understand the importance of processing these volumes of data and their value, it is not always easy for them to process this data quickly. Fast data processing means immediate availability of Big Data tools, which is why it’s so important to know which project methodologies to deploy for an effective Big Data project.
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What specific methods can you use to ensure the success of your Big Data projects?
Big data project management calls on a range of different methodologies, depending on the business teams involved, and also on the modes used.
The CRISP method in Big Data projects
The CRISP method, known as CRISP-DM when IBM created it in 1996, was originally designed for data mining projects. Totally independent of the tools and technologies used in business, this method owes its success and generalization to all Big Data projects to its standard application scheme. Built around six steps, the CRISP method is ideal for solving a defined problem, as it focuses on identifying business needs and objectives:
- step 1: understanding the business problem ;
- step 2: understanding the data ;
- step 3: data preparation ;
- step 4: data modeling ;
- step 5: evaluation ;
- step 6: deployment.
With its cyclical, iterative approach, the CRISP method allows you to adapt as the project progresses, as it encourages collaboration and communication between the project team and business experts, and favors backtracking. Compatible with agile methodologies, the CRISP method is widely used for Big Data and predictive analysis projects.
Agile methods in Big Data
Originally designed for data projects, the CRISP method can nevertheless lack agility in certain projects, in the sense that it does not place the customer and the production of value at the center of the process. This is why different variants have emerged over the years:
- AgileKDD: this method is based on the OpenUp lifecycle, founded on four phases: inception, elaboration, construction and transition. Each phase contains one or more iterations divided into sequences, or sprints, planned to fixed deadlines.
- ASUM-DM: developed by IBM, the ASUM-DM method is an extension of CRISP-DM, combining agility and traditional project management. It is divided into 3 main phases: analysis and understanding of the business and data, design, configuration and construction, deployment and operation and optimization.
Methods specific to Big Data projects
Methods for Big Data projects These include the Stampede method and the AABA method.
- Developed by IBM for its customers, the Stampede method is based on the provision of expert resources. It should enable any company to quickly launch a project generating value from Big Data.
- Stampede is applied within the framework of a pilot project defined after an intensive work session lasting around four months to define the big data project, identify the necessary resources, organize a work plan and define the value generated for the company.
- The AABA method, for Architecture-centric Agile Big Data Analytics, is centered on the DevOPs model. It integrates both AAA architecture and the database design of the big data system. Thanks to its agility, the AABA method enables rapid adaptation to changing needs and technologies.
As you can see, there’s no single ideal big data method for every project. As each company’s business expertise is different, it’s not always easy to find the right big data project method for each situation.
Each big data method has its own advantages and disadvantages, but what really counts is the involvement of all the forces involved, and the ability of both technical and business teams to adapt and evolve, with the aim of constantly improving the final project by creating value.
If you’re interested in big data projects, or data science in general, don’t hesitate to contact our team to discuss the job of Data Engineer, a profession that deals with all these issues!
