Despite the fact that data and analytics have been linked to issues like data volume, variety, storage, and real-time data processing over the past few decades, they also present opportunities like improved customer service, increased revenue, a leg up on the competition, and analytics on the go. This article provides an overview of the data evolution path, focusing on publicly available data systems that have the means to extract, convert, and load data in large volumes and with a wide range of characteristics. Also, it uses parallel processing to speed up the execution of intensive operations like transformations and analyses. Data analytics provides a wide variety of answers to issues plaguing businesses. Today, businesses increasingly rely on data in their efforts to compete successfully. New entrants as well as existing competitors alike are employing methods derived from data analysis to compete, develop, and create value in most industries.
Data Analytics:
Data analytics is the practise of systematically acquiring, cleaning, merging, and analysing information with the purpose of gaining actionable insights and facilitating sound decision making. We recognise that our current, antiquated system lacks the capacity to handle unstructured, massive data sets for the same reason that it was never meant to handle such quantities of data in the first place: its design and implementation predated the advent of modern data storage and management. Not having to deal with such a massive amount of data was something we hadn't expected to have to deal with. How do we now evaluate information that requires specialised analytics tools?
Data is identified and tracked from a wide variety of sources, such as social media, machine-to-machine devices (IoT) or sensor data, and transactional data from core business applications.
Simple:
Simple designs tend to work best in terms of structure. This regulation necessitates an easy-to-understand data strategy, an unimpeded flow of information between various data sources and storage areas, and centralised control over who has access to which reports based on their roles within the organisation. In an effort to simplify things, we choose a unified platform that can serve as both a database and an assembly framework for data, as well as an analytics tool.
User-Centric:
A data analytics architecture should start with the issue description and requirements of the end-users, rather than the data or the technology needed to extract, collect, clean, ingest, transform, as well as distribute the information.
Adaptable:
Data in a Data analytics architecture moves like water from its various origins to a few key locations where it is stored in the form of small, crisp data that provides more context for analytics. The architecture's goal is to control that flow by setting up a network of data conduits tailored to certain users.
Automated:
All processes and the underlying infrastructure for data analytics should be fully automated for maximum efficiency. One of the most important roles of data catalogues is to profile and time stamp data as it is being ingested, then link that data to preexisting data sets and repositories. It needs to be able to spot outliers in real time and warn the right people or operational dashboards.
Flexible:
This design is open bus compliant and can accommodate a wide variety of business requirements.
Collaborative:
Users are able to work together in an organised fashion thanks to data analytics. It's not like in the old days when the agency put everything together. The Data analytics framework delegated the tasks of data acquisition and cleansing to several groups of business users inside the organisation. The agency in charge of putting this plan into action is still responsible for the laborious process of absorbing data from the various source systems and transforming it into modular, reusable data pieces.
Governed:
Four distinct groups of department users—data consumers, data explorers, data analysts, and data scientists—have their own unique set of access points defined by a data analytics architecture. For instance, the data catalog's raw data must be made available to data scientists.
Elastic:
Elastic design that adjusts to changing data processing requirements on demand. We should achieve on-demand scalability. Administrators are relieved of the burden of fine-tuning capacity, throttling consumption when appropriate, and constantly overbuying hardware with elastic designs.
Secure:
A data platform design is like a fortress that protects its citizens from attackers while allowing authorised users quick and easy access to their data. It follows all applicable rules and procedures and abides by all applicable security policies, regulations, and compliances.
Resilient:
The data architecture must be resilient with disaster recovery, high availability, as well as backup/restore capabilities. This is especially true in a cloud-based data architecture, where disruptions are routine on the massive server farms.
The ability to analyse and aggregate a large amount of data creates new prospects for businesses and even new types of businesses. These businesses have a wealth of data at their disposal, including product and service preferences, customer reviews, and more.
Businesses can benefit from data analytics approaches by gaining a 360-degree perspective of their customers, better categorising their products and services, conducting sentiment analysis, and predicting future sales. With the help of analytical and statistical modelling, a business may anticipate all of its portfolios and potential losses.
