9 Stages of the Big Data Analytics Life Cycle

Big data analysis is primarily distinguished from traditional data analysis on account of velocity, volume, and variety of the data. The characteristics of the data in question hold paramount significance in this regard.

Big data analysis is primarily distinguished from traditional data analysis on account of velocity, volume, and variety of the data. The characteristics of the data in question hold paramount significance in this regard. A step-by-step methodology is put into action while performing analysis on a distinctly large data.

The essential measurements needed to organise the tasks and activities of the acquiring, analysing, processing, and the repurposing of data are part of this methodology. Hence, to organise and manage these tasks and activities, the data analytics lifecycle is adopted.

While training for big data analysis, core considerations apart from this lifecycle include the education, tooling, and staffing of the entire data analytics team. Hence, it can be established that the analysis of big data can’t be attained if it is imposed as an individual task. Instead, preparation and planning are required from the entire team.

There are essentially nine stages of data analytics lifecycle. Like every other lifecycle, you have to surpass the first stage to enter the second stage successfully; otherwise, your calculations would turn out to be inaccurate.

The first stage is that of business case evaluation which is followed by data identification, data acquisition, and data extraction. Once you’ve extracted the data correctly, you will validate it, and then go through the stages of data aggression, data analysis, and data visualisation. Finally, you’ll be able to utilise the analysed results.

Business Case Evaluation

The evaluation of big data business case aids in understanding all the potent aspects of the problem. It allows the decision-makers to properly examine their resources as well as figure out how to utilise them effectively. This way, the business knows exactly which challenges they must tackle first and how. In addition to this, the identification of KPIs enables the exact criteria for assessment and provides guidance for further evaluation.

Business Case Evaluation

In case, the KPIs are not accessible; the SMART goal rule should be applied. This means that the goals should be specific, measurable, attainable, relevant, and timely. It is also crucial that you determine whether the business case even qualifies as a big data problem. For this, you should evaluate whether or not there is a direct relationship with the aforementioned big data characteristics: velocity, volume, or variety. Another important function of this stage is the determination of underlying budgets. If there’s a requirement to purchase tools, hardware, etc., they must be anticipated early on to estimate how much investment is actually imperative.

Data Identification

The identification of data is essential to comprehend underlying themes and patterns. This step is extremely crucial as it enables insight into the data and allows us to find correlations. Depending on the scope and nature of the business problem, the provided datasets can vary. Hence, the sources of these datasets can either be internal or external, so, there shouldn’t be any fixed assumptions.

For example, if the source of the dataset is internal to the enterprise, a list of internal datasets will be provided. This includes a compilation of operational systems and data marts set against pre-defined specifications. In contrast, when it comes to external datasets, you’ll be provided third-party information.  Prominent and everyday examples of regular external dataset are blogs available on websites.

Data Acquisition and Filtering

After you’ve identified the data from different sources, you’ll highlight and select it from the rest of the available information. The idea is to filter out all the corrupt and unverified data from the dataset. Remove the data that you deem as invaluable and unnecessary. Many files are simply irrelevant that you need to cut out during the data acquisition stage

Now all the files that are invalid or hold no value for the case are determined as corrupt. However, one shouldn’t completely delete the file as data that isn’t relevant to one problem can hold value in another case. Hence, always store a verbatim copy and maintain the original datasheet prior to data procession. In case you’re short on storage, you can even compress the verbatim copy.

To improve the classification, the automation of internal and external data sources is done as it aids in adding metadata. It is of absolute necessity to ensure that the metadata remains machine-readable as that allows you to maintain data provenance throughout the lifecycle. This guarantees data preservation and quality maintenance.

Data Extraction

When you identify the data, you come across some files that might be incompatible with the big data solutions. In external datasets, you might also have to disparate it. In the data extraction stage, you essentially disparate data and convert it into a format that can be utilised to carry out the juncture of big data analysis.

How much data you can extract and transform depends on the type of analytics big data solution offers. For instance, the extraction of delighted textual data might not be essential if the big data solution can already process the files. Furthermore, if the big data solution can access the file in its native format, it wouldn’t have to scan through the entire document and extract text for text analytics.

Data Validation and Cleansing

Make no mistake as invalid data can easily nullify the analysed results. Data is pre-defined and pre-validated in traditional enterprise data. However, big data analysis can be unstructured, complex, and lack validity. Due to excessive complexity, arriving at suitable validation can be constrictive. Hence, it can be established that the data validation and the cleansing stage is important for removing invalid data.

Big data often receives redundant information that can be exploited to find interconnected datasets—this aids in assembling validation parameters as well as to fill out missing data. With the help of offline ETL operation, data can be cleansed and validated. However, this rule is applied for batch analytics. In the case of real-time analytics, an increasingly complex in-memory system is mandated. To determine the accuracy and quality of the data, provenance plays a pivotal role. In addition to this, you must always remember to maintain the record of the original copy as the dataset that might seem invalid now might be valuable later. You can always find hidden patterns and codes in the available datasheets.   

Data Aggression and Representation

An ID or date must be assigned to datasets so that they remain together. Either way, you must assign a value to each dataset so that it can be reconciled. Hence, it can be said that in the data aggression and representation stage, you integrate different information and give shape to a unified view.

Multiple complications can arise while performing this step. To begin with, it’s possible that the data model might be different despite being the same format. Furthermore, the likeliness of two files resonating similar meaning increases if they are assigned similar value or label it given to two separate files.

Data aggregation can be costly and energy-draining when large files are processed by big data solution. For reconciliation, human intervention is not needed, but instead, complex logic is applied automatically. Whether or not this data is reusable is decided in this stage. However, the important fact to memorise is that the same data can be stored in various formats, even if it isn’t important. Additionally, one format of storage can be suitable for one type of analysis but not for another. For instance, the data that is stored as BLOB would not hold the same importance if access is mandated to individual data fields. A standardised data structure can work as a common denominator when used for a variety of analysis techniques.

Data Analysis

Now comes the stage where you conduct the actual task of analysis. Here, you’ll be required to exercise two or more types of analytics. This stage has the reputation of being strenuous and iterative as the case analysis is continuously repeated until appropriate patterns and correlations haven’t tampered. The process becomes even more difficult if the analysis is exploratory in nature.

On the one hand, this stage can boil down to simple computation of the queried datasets for further comparison. On the other hand, it can require the application of statistical analytical techniques which are undoubtedly complex. The second possibility can be excruciatingly challenging as combining data mining with complex statistical analytical techniques to uncover anomalies and patterns is a serious business.

This technique is mostly utilised to generate the statistical model of co-relational variables.

When it comes to exploratory data analysis, it is closely related to data mining as it’s an inductive approach. Instead of generating hypotheses and presumptions, the data is further explored through analysis. This permits us to understand the depths of the phenomenon. By doing so, you can find a general direction to discover underlying patterns and anomalies. 

Data Visualization

If only the analysts try to find useful insights in the data, the process will hold less value. Therefore, in the data visualisation stage, the optimisation of data visualisation techniques becomes important as powerful graphics enable the users to interpret the analysis results effectively.

This is essential; otherwise, the business users won’t be able to understand the analysis results and that would defeat the whole purpose. This way, they can not only obtain value from the data analysis but also provide constructive feedback. The results procured from data visualisation techniques allow the users to seek answers to queries that have not been formulated yet.

The interesting thing here is that the analysed results can be interpreted in different ways. However, it is absolutely critical that a suitable visualisation technique is applied so that the business domain is kept in context.

Moreover, simple statistical tools must be utilised as it becomes comparatively difficult for users to understand the aggregated results when they’re generated. Hence, the idea is to keep it simple and understandable. Keep in mind the business users before you go on to select your technique to draw results.

Utilisation of Analysis Results

Before you hand-out the results to the business users, you must keep in check whether or not the analysed results can be utilised for other opportunities. The results provided will enable business users to formulate business decisions using dashboards.

The analysed results can give insight into fresh patterns and relationships. Hence, depending on the nature of the problem, new models can possibly be encapsulated. You might also find new relationships that didn’t exist earlier. Now it must be realised that these models will come across in the form of mathematical equations or a set of rules. These models are later used to improve business process logic and application system logic. These ties and forms the basis of completely new software or system.

Commons areas that are explored during this time are input for an enterprise system, business process optimisation, and alerts. Hence, the results gathered from the analysis can be automatically or manually fed into the system to elevate the performance.

The identified patterns and anomalies are later analysed to refine business processes. And finally, the data results can be applied as input for existing alerts. For example, these alerts can be sent out to the business users in the form of SMS text so that they’re aware of the events that require a firm response.  

In conclusion, the lifecycle is divided into the nine important stages of business case evaluation, data identification, data acquisition, and filtering, data extraction, data validation and cleansing, data aggregation and representation, data analysis, data visualisation, and lastly, the utilisation of analysis results. Therefore, it can be established that the nine stages of the Big Data Analytics Lifecycle make a fairly complex process.

It’s not as simple and lenient as any traditional analytical approach. In this lifecycle, you need to follow the rigid rules and formalities and stay organised until the last stage. Failure to follow through will result in unnecessary complications.