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Data Fragmentation-Structure Your Fragmented Data To Gain Valuable Insights

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Fragmented Data is the new oil. Big Data companies explain us why Data Fragmentation is important. How many times have you read about the significance of data in modern business? Still, enterprise data is often strewn across a disconnected matrix of data islands, greatly reducing its ability to provide insights.

Common data storage problems for business owners include large data volume, fragmented data, extraneous duplicate copies and dark data, which offers little visibility into what it is or where it lives.

It’s important to keep safe your data but most important is to analyze your data. This is the reason that Big Data companies are getting popular.

Data fragmentation and the “3 V’s of Big Data”

Data fragmentation happens when a collection of stored data is split into many pieces. This type of data separation makes it nearly impossible for businesses to protect, locate and manage their most important digital asset. Common causes of data fragmentation include data being siloed to unique systems, data being spread across multiple locations, and multiple data copies being stored and replicated.

One framework for understanding the nature of data fragmentation is the “3 V’s of Big Data”:

  • Volume: represents the sheer amount of the data in storage – often in the terabytes or petabytes – that becomes too large to process within a traditional data system.
  • Velocity: represents the increasing speed at which new data arrives into a data system, which becomes a challenge due to the rise of real-time feeds and analytics.
  • Variety: represents the array of structured and unstructured data types that become harder to process, particularly with an abundance of text, image and video-based content.

Data fragmentation is expensive. Data anomalies, support costs for various systems and software platforms, and duplicated data storage costs are all avoidable expenses caused by data fragmentation. A study commissioned by Cohesity found that 63% of businesses have 4-15 copies of the same exact data, and 35% of businesses use six or more unique vendors to manage data workflow.

Common data consolidation structures

The chaos and uncontrollable costs of data fragmentation can be reined in with careful data system structure and design. Three common structures for consolidating enterprise data are the data warehouse, data mart and data lake.

Data warehouse

The data warehouse is the foundation of enterprise data management. It’s an integrated and permanent structured database solution. The warehouse consolidates fragmented data with diverse formats across multiple sources and is generally optimized for data analysis and query processing. Its contents can be extracted by front-end tools to support decision making. The data stored in the warehouse helps managers better understand the business’s activities across all areas. Therefore, you should consider unifying all of your data into a single location with the help of an expert data warehouse consultant.

Data mart

An important subset of the data warehouse is a data mart, a unit of the warehouse used by a specific business function or team. Unlike fragmented data silos, data marts inherit similar structure and properties of the data warehouse. This prevents individual departments from being bogged down by the sheer volume of data.

Data lake

An alternative or complementary solution for managing and consolidating large volumes of data is a data lake. Unlike a data warehouse, which is a carefully managed system for structured data, a data lake primarily consists of raw, unstructured data.

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Turn your Fragmented data into your most valuable asset

Adaptive businesses turn their fragmented data into their most valuable asset. The first step to do this is to consolidate and structure all data sources. From there, businesses can use data mining and analytics to gain insights.

AI, supported by machine learning, can navigate through a data warehouse or data lake to analyze data patterns and relationships. Data mining technology uses these structured systems to identify problems, opportunities and relationships, and even create models to project future events.