Overloaded infrastructure and information silos no longer have to block your data management strategy. Effective big data analysis today requires power that only a flexible cloud computing environment can provide, eliminating scalability barriers and meeting rigorous security requirements such as NIS 2. In this article, you will learn how to implement multifaceted analysis to uncover business dependencies that are not visible at first glance and effectively optimize IT operational costs.What is multifaceted analysis and what are its benefits?
Multifaceted analysis refers to data analysis techniques that consider multiple variables and different types of data simultaneously to understand the complex relationships between them. They often include both quantitative and qualitative data. The advantages of multifaceted analysis are numerous. Such analyses:
- increase the accuracy and detail of results and reports,
- allow for a better understanding of complex dependencies that may not be visible in one-dimensional analysis,
- identify key factors influencing results, which supports making more informed and significantly better business decisions,
- provide better predictions of future trends.
Challenges associated with processing and analyzing large datasets in corporate infrastructure
Processing and analyzing large datasets within a company’s infrastructure carries many challenges that can affect the effectiveness and efficiency of an organization’s analytical activities. Such datasets can often be safely called big data – they are extremely large and diverse sets of data that continue to grow over time. Big data is often structured, unstructured, and semi-structured simultaneously, which also limits the ability of basic tools to analyze this data. Challenges associated with processing and analyzing large datasets include, among others:
- Performance – large datasets require significant computing and memory resources that standard infrastructure may not be able to handle.
- Scalability – company infrastructure may not cope with a sudden and significant increase in data volume.
- Data management – the variety and multitude of data pose a significant challenge for daily data handling and maintaining consistency, cleanliness, and integrity.
- Data quality – large datasets may contain duplicates, errors, or missing values that spoil analysis results, which we might not catch while working with them daily in the company’s internal infrastructure.
- Data security – large datasets are more vulnerable to breaches and leaks.
- Data integration – data coming from different sources and having different formats makes it difficult to consolidate and utilize their full potential during analysis.
Advantages of moving multifaceted analysis to the cloud
Given the difficulties mentioned above and the fact that large datasets are unlikely to shrink – on the contrary, they will continue to grow – we should consider moving our multifaceted analysis and
big data to the cloud. Why? First of all, the cloud has many advantages and will address the challenges described in the point above almost immediately – thanks to the ability to dynamically adjust computing resources to needs, it will increase scalability, offloading the company’s IT architecture. Moreover,
the cloud offers a wide selection of tools and services that are flexible and can be tailored to the specific needs of the company; it also has high computing power and will significantly speed up our analytical processes. Data availability increases dramatically in the cloud because it enables collaboration on the same dataset from virtually anywhere and at any time. This eliminates information silos, which often occur in offices and limit analytical capabilities. Data integration and management in the cloud become much easier, ensuring higher quality of analyzed information. Data security in the cloud is also well taken care of. Currently, cloud providers offer advanced security solutions, especially in the era of emerging new restrictions and regulations in the EU and worldwide, such as the NIS 2 directive.
As we have already written, moving multifaceted analysis to the cloud can significantly improve the efficiency, scalability, and flexibility of analysis. Cloud platforms also allow for dynamic adjustment of computing resources, data management, and conducting advanced analysis without the need to invest in physical hardware, which significantly lowers operating costs. However, the choice of appropriate tools and platforms depends on the specific needs and requirements of the company – there are many options, so we have plenty to choose from. These tools include fast data warehouses such as Amazon Redshift, Google BigQuery, and Oracle Autonomous Data Warehouse, which enable data analysis on a massive scale. ETL (Extract, Transform, Load) services such as AWS Glue and Azure Data Factory facilitate preparing, processing, and loading data from various sources. Tools for analyzing and visualizing large datasets (Amazon Athena, Google Dataflow, Azure Synapse Analytics, IBM Watson Studio) offer interactive environments for data exploration and building and training analytical models. Cloud platforms also combine data processing capabilities with advanced analytical tools,
enabling the building and deployment of AI models. If you feel that moving your resources and multifaceted analysis to the cloud is a step that will bring many benefits and support your business, do not hesitate to
contact our experts at Mindbox – we will help you choose the best options and successfully implement them in your organization.
