By John Scutz
In this digital era, data has proliferated like never before. It has not only increased in size but also variety, volume, and veracity. Studies indicate that over 2.5 quintillions of data are being created each day. Such a massive pace suggests that 90% of data has generated over the past two years alone.
A large part of this enormous growth is fuelled by business ecosystems that bank of a variety of processes, technologies, systems, etc. to carry out B2B operations.
Data is available in a number of forms generated from a number of sources whether marketing data, consumer data, or operations data. Marketing data includes data produced by prospect targeting, web traffic data, market segmentation, website log data, etc, consumer data includes transactions, banking records, insurance claims, employee benefits, etc, and operations data includes data generated from sales data, online transactions, pricing data, etc.
The giant evolution of unstructured, semi-structured data is called big data. When this humongous streams of data are optimally processed, it helps to produce or extract valuable insights to allow companies make better business decisions. Carefully processing big data plays a vital role in analysing the needs and requirements of customers which, in turn, allow organizations to improve their branding and reduce churn. But, with the presence of 4 attributes of data, volume, velocity, veracity, and variety, extracting insights for business is a mammoth task. These attributes, also called 4Vs, make the big data interpretation difficult and time-consuming.
Let us delve into the 4Vs to become aware of the intricacies of big data.
Volume: Big data is huge in terms of value. It has to be measured in GB, TB, and Exabytes. With the growth in Big data, the volume will only increase.
Velocity: Big data’s velocity or frequency is extremely high which slows the processing speed, resulting in downtimes and breakdowns.
Veracity: veracity is a measure of the accuracy of data. Business operations can get corrupted if the data is inaccurate and comprises anomalies.
Variety: data can be of myriad variety including, semi-structured, unstructured and heterogeneous data that can be too disparate for enterprise B2B networks. Pictures, Videos, etc. fall under this category.
Owing to the 4Vs of Big data, the quality of processing of big data is hampered in terms of speed and quality. As a result, application failures and data flow breakdowns occur which, in turn, result in delays and information losses during the course of mission-critical operations. In addition, a huge amount of time, effort, and resources are required to discover, extract, prepare, and manage rogue data sets. This weakens the organization’s ability to recognize new market realities and capitalize on market opportunities.
Understanding the Architecture of Big Data
Big data revolution and the inability of companies to process it can be evaluated by using a layered architecture. Big data consists of different layers where every layer performs a definite set ff function. Let us unlock the facts about the 6 layers for better understanding.
- Data ingestion layer: This is the first layer of Big data architecture where data is prioritized as well as categorized. This data ingestion layer ensures that data flows smoothly in the following layers.
- Data collector Layer: Data collector plays a central role in transporting data from the first layer, ie data ingestion to the rest of the data pipeline. So, it collects data and transports it further.
- Data processing layer: In this layer, data is processed.
- Data storage layer: Data storage layer stores the data.
- Data query layer: In this layer, active analytic processing takes place and value is created.
- Data visualization layer: The true value of data is unlocked in this layer.
Need for data ingestion
To create value out of big data, data has to be ingested properly. Data ingestion software can be used to ingestion data, unstructured or structured, to move data from the point of origination into a system where it is stored and analyzed for further operations. Ingesting data with the data ingestion software not only alleviates manual effort involved in handling large streams of data but also reduces overhead costs, reducing delivery time. It also improves the quality of data, thus helping companies extract better insights. This accelerates the operational efficiency and performance of business ecosystems. Hence, the degree of accuracy of data processing and analyzation greatly depends on how good your data ingestion software is. Evaluate your software today to find whether your data ingestion software needs an upgrade or replacement?