How do you simplify data in a big data environment?

One way to define data requirements is to conduct a current state assessment of the organization's business data (and technical data) needs. This assessment can be focused on a domain / subject area, a function, or the entire enterprise. Identifying data sources, their level of quality (accuracy, completeness, etc.) and the associated metadata (definitions, formats, transformation rules, etc.) the organization can understand what they have now and how they can leverage it - and enhance it.

One more of way to define data requirements is to conduct JAD sessions and come up with different useful use cases as per the business data needs, in the form of user stories and swim line diagrams, by different modules/processes etc., which could serve as inputs to BAs in writing BRDs and Architects in building Data Models.

Big data is categorized with the concept of 5V's, which are volume, velocity and variety, veracity, and value. Volume: It is the amount of data that is generated and collected on a daily basis, which ranges from terabytes to petabytes of data. Velocity: It is the rate of speed at which data is generated, processed, and analyzed. Variety: It means different types of data, such as structured, semi-structured data, and unstructured data. Veracity: It means the accuracy and reliability of the data for drawing conclusions. Value: It refers to the insights from the data that can be used to make more profitable decisions to benefit businesses.

Data models are important for business as well as technical reasons. Start with business level models (conceptual - just business concepts and their relations) and then take them to the logical level (more detailed business requirements - attributes, definitions, etc.) These can help build the physical models that are used to develop the databases and file structures to support both business and technical needs. Data models are essential to understanding business data needs!

My experience of several years as Enterprise Architect has led me to believe that data pipelines not only integrate or connect data but they are meant to serve a larger good of Orchestration. of data. This will help hugely reduce one-time effort as well as operational cost to keep the data flowing seamlessly.

Paired with delivering data pipelines, is the need to automate collection and monitoring of logs. The logs allow teams to understand how to optimize the pipeline and the level of data consumption. The level of data consumption is a way to understand where to prioritize the investment of time and money. It also helps identify costly areas that need attention. Sound data operations help control, govern and simplify not just big data but all data.

Incorporating automated data pipelines is a pivotal step in simplifying big data environments, but it goes beyond just efficiency gains. By embracing automation, data architects can unlock valuable time and resources, allowing them to delve deeper into data analysis and uncover meaningful insights. These pipelines not only streamline data workflows and ensure reliability but also foster a proactive data-driven culture. As data becomes more readily available and reliable, teams can proactively address challenges and seize opportunities, ultimately driving innovation and staying ahead in the competitive landscape.

Data compression techniques act as the catalyst for unleashing hidden potentials within big data environments. Beyond reducing data size without compromising quality or usability, they offer a gateway to enhanced data organization and accessibility. By skillfully selecting appropriate compression algorithms and formats, data architects open up avenues to optimized data structures. Furthermore, exploring the nuances of compression benefits, like improved data transfer speeds, reduced storage footprint, and accelerated processing efficiency, allows for a deeper understanding of its transformative power. As the puzzle pieces of data fit together seamlessly, a new era of streamlined operations and cost-effectiveness emerges.

Data cleansing is an integral part of supporting high data quality. After identifying the issues with data quality, it is important to correct them - and to ensure they do not recur. High level data quality increases the trust business people have with the data - and helps to support confident decisions and improved operations.

With the advent of AI, ML and ML Operations, it's absolutely imperative that there's a robust Data Quality framework in place. Along with DQ Framework, strong focus on continued data hygiene and operationalization of Data Quality to ensure consistently clean data in essential. Without , adequate data quality, your Machine Learning algorithms will break-up and ML will need frequent remodeling with skewed results.

Organize data sets into various partitions buckets and tiers. It helps if you create data repositories divided according to the data sources, year month day and time of ingestion etc whichever approach makes more sense to your use case. The goal is to retrieve as relevant information as possible from the data repository with minimal amount of data queried by the consumer applications. Also, you need to consider proximity of data residence to your consumers. This is to reduce any network latency issues. Once you finalized the data partition strategy, focusing on data compression and formats will make a great deal of difference in your day to day data operations.

A well-designed and ambitious set of metrics and measures must be part of architecture strategy, instead of soft benefits like “faster”, “easier”, and “better”. A measurement plan's elements are capacity assessment, gaps, execution plan, and metrics to ensure a smooth strategy implementation, ensuring effective roadmap implementation and, more importantly, stakeholders' buy-in and engagement. Measurement plans will serve as a strategic basis for measuring progress and benchmarking toward business strategy and use cases among architecture and cross-functional departments. “If you can't measure it, you can't manage it”.

Data Sampling: Instead of processing the entire dataset, a representative sample is taken. This reduces the data volume while preserving key characteristics. Sampling techniques may include random sampling, stratified sampling, or cluster sampling. Data Aggregation: Aggregating data involves combining multiple data points into a summary or higher-level representation. For example, data can be aggregated at the customer level instead of analyzing individual transactions. Dimensionality Reduction: Dimensionality reduction techniques aim to reduce the number of variables or features in the data while retaining important information. Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are commonly used.