In the present times, it is difficult to deal with big data projects without adequate knowledge of the data analytics lifecycle. The data analytics lifecycle comprises six critical phases. One of the notable things about the life cycle is that it operates bidirectionally which means in both forward and backward directions. The bi-directionality of the data science life cycle connotes that this is an iterative process tasked with uncovering new information and operationalizing the process of data management. The research related to data analytics and data management is still in a juvenile phase. However, some of the best institutes for data science have already started researching the life cycle of data science given the pivotal importance it will assume in the near future. In this article, we aim to understand the various phases of the data analytic lifecycle from the perspective of project management.
A gist of the data analytics lifecycle
The first stage in the data analytics lifecycle is that of data discovery. This is more of a preparatory phase in which the business team learns about the various projects and other necessary toolkits that are required for the process of analytics. The assessment phase is a critical part of this stage. It is in this stage that we formulate the hypothesis and also fabricate a road map of the entire data tracking process. The second phase is called the data preparation phase. An analytic sandbox and a toolkit form the critical machinery of this phase. ETL process, that is, the extraction, transformation, and loading of data is done at this stage. In the third phase, model planning takes place. To summarise model planning briefly, we form a detailed workflow and chalk out the methods and techniques that need to be followed in the subsequent phases. It is at this stage that we establish the relationships between various variables and zero in on the most suitable data models. After the model planning has been done, the process of model building starts to take shape. At this stage, we test and train our data set for the purpose of model building. The main goal at this stage is to execute model planning in a foolproof manner. The toolkit plays an important role in constructing a robust environment for the functioning of the model. After the model has started working, we can shift to the fifth phase of the data analytics lifecycle. This stage is all about communicating the results and collaborating with various stakeholders. It is not at the fourth stage but at the fifth stage that the success or failure of a model is determined. This is because the fifth phase provides a feedback report about the satisfaction of the stakeholders and the functioning of the model. Finally, it is during the operationalization phase that various reports related to the functioning of the model are prepared. Before the model is brought into the formal industrialization process, a testing environment can be formed to run the project on a pilot basis.
Glimpses of a case study
Global Innovation Network and Analysis, a team of data analysts, wanted to determine the levels of innovation within the ecosystem of a particular company. For this, they needed to go through the six phases of the data analytic lifecycle and employ them to determine the levels of innovation. The group conducted a detailed survey about the innovation activity in various sectors. This was followed up by data preparation to check the sufficiency of data related to various innovation factors. They tested the ideas of knowledge expansion and knowledge transfer during the model planning phase. In the model-building phase, they constructed a social graph that gave a clear picture of the top innovation influencers. For the communication of the result, internal presentations were organized and the results were published via social media networks. In the operationalization phase, the team concluded that the levels of innovation were very high in organizations but there was a need to identify some of the hidden innovators within the ecosystem of a particular company.
In the present times, there is continuous research going on in the field of design thinking and predictive analytics of data-based products. The main aim is to reform the life cycle of data analytics by introducing various types of social graphs and social network analysis. Machine learning techniques like clustering and regression analysis can also play a significant role when it comes to the design and development of data-related products in the near future.