Menu Close

Expanding the spectrum of analytics with the help of data diversity: A special focus on data quality management

Introduction

There is a difference between predictions and outcomes. While predictions are based on certain facts to forecast coming events, outcomes testify to these predictions and mark the endpoints of an event. The nature of forecasting has undergone a rapid change in the present time. Driven by various types of machine learning methodologies and advanced algorithms, we can predict the outcome of an event with a high level of accuracy. Our nature of trend prediction has moved from qualitative to quantitative analysis. This has allowed us to perform customer analytics with a high degree of precision by harnessing the power of data diversity.

The contours of data diversity

In the last decade, we have seen various systems driven by data in the spotlight. The evolution of various machine learning techniques in the last decade has been spontaneously driven by data diversity. Other types of STEM techniques like science, technology, engineering, and medicine are directly or indirectly related to data diversity. In addition to this, the various contours of data diversity provide countless opportunities in analytics, and machine learning provides a platform for the development of various soft skills.

Data quality management

The various equations for data quality management can be satisfied by using DataOps. Our reports of data quality management are directly related to the elements of privacy. Data development and data quality management are interrelated as both methodologies require systematic compliance with the norms that have been laid out. For instance, we have seen data breaches and other threats arising due to data mismanagement. This has also proved to be a threat to knowledge discovery and knowledge management. As such, data mismanagement also has its repercussions on knowledge marketing which fails to take off if our data sets are not backed by concrete evidence and security shields.

Forensic analysis

Data analytics, data quality, and data management are intricately linked to data privacy. Performing forensic analysis on different data sets means diagnosing various types of small and minute problems in our data sets. Forensic Analysis of data also allows us to examine its compliance with various privacy governance norms. In addition to this, analytics of data is pivotal for compliance with Global data protection rules. The advanced type of analytics allows us to understand the underlying elements of data that may arise during the process of collection, processing, and transformation.

Use case of e-commerce analytics

The expansion of e-commerce analytics in 2021 has proved that our digital domains are going to be the greatest platform to tap for future marketing. This means that social media platforms would not only become digital shops but would also act as a repository to carry out customer analytics. Data from the various transactions carried out by customers would be used as a raw material to understand their preferences and recommendations. For instance, Google Analytics powered by the Google search engine is used to get glimpses of various aspects of customer activity in a phased manner.

Data visualization

The applications of data visualization are increasing on a large scale in the present times. Credit goes to applications of machine learning that are enabling 3D visualization of data. Machine learning techniques are also leveraged for performing the next level of advanced analytics which can then be represented in a lucid manner. In simple terms, data visualization enables us to represent even the complex data sets in a manner easily comprehensible to the user.

Data dashboards

Data dashboards serve as a prime matrix for understanding data in various formats. Data dashboards not only help in understanding the various intricacies of data but also help in communicating necessary information effectively. Such dashboards also help to minimize ambiguity in various data sets and help in the creation of data profiles. Such profiles help to chalk out different types of data sets and help in the clustering of similar data sets.

Data sharing

Data sharing in the present times is a crucial element of privacy. While it is extremely important for analytics of data, its misuse can expose the sensitive information of the user to various miscreants. Data tampering and miss interpretation are not very uncommon in the present times. This demands a secure transaction of data for the purpose of analytics. When it comes to data collaboration, there needs to be a concurrent agreement between the parties to protect various aspects of the data pipeline.

Concluding remarks

The spectrum of Data analytics is constantly expanding with the help of data diversity. The only caveat in this expansion is a special focus on data safety, data privacy, and data management, the misuse of which can perforate the entire data fabric.

To learn more, contact us at askus@algoscale.com