Top 3 Steps to Be Followed by Big Data Analytics Project Managers
The projects of Big Data Analytics are the top priorities of most of the IT companies, who are looking for getting the business advantages out of all the data — structured, semi-structured and unstructured — flowing into their systems. However, with every initiative which gives big rewards, there are some big risks also. This is the reality of big data implementation, which makes managing and planning deployments efficiently.
There are several way to go either right or wrong. Well, a list of steps is there every big data analytics project manager should follow for setting their programs in right way to get the expected business value and of course a solid return on their investment.
1. Choose business sponsors with strong business objectives:
With all buildup surrounding big data analytics, business executives should be good in sponsoring a project. Select the business sponsors who have a clear set of business plans with a realistic timeline. If you have a well-defined target of the business results you want to achieve, you can build a scope for the analytics and data management systems which should be established alongside the supporting technology which should be installed. If someone starts a project without that type of scoping, the project is possibly to spin out of control.
2. Learn from your mistakes — a part of the project plan:
New methodologies, techniques and technologies will be introduced by big data analytics in your company, and possibly will need new skills. Additionally, big data technologies are still developing; sometimes a considerable amount of custom development work is need; and there is a serious lack of those needed new skills, for both the data scientists and IT developers and other analytics experts who will lead this work of data analysis. This way, your project team will learn and users and business managers will measure what actually big data analytics means to them. You must create project budgets and schedules based on a long learning, with inclusion of the unavoidable mistakes which will be made in the procedure of that learning.
3. Treat data scientists as artists:
Skilled analysts and data scientists play a key role in pulling business understandings out of big data buildup. Making those understandings through applications such as data mining and predictive analytics is an iterative and incremental procedure. A data scientist will devise, test, refine, validate and ultimately run an analytical model and internally publish the results. He might test out many variables using different statistical methods in doing so. People somehow mislead the term ‘data science’. They treat the data scientists as common labors. They should treat them as talented artists to get better productivity and better results as well.
It’s a fact that there are both big risks and big rewards in implementing a big data analytics project. However, if you acquire knowledge in big data analytics or data science by pursuing data scientist or big data courses in India, it will be more helpful for you. With proper knowledge and attention related to projects, every big data analytics project manager can minimize the flaws and make deployments a big business opportunity for their companies.