How to Better Develop Large Enterprise Data Strategies
Big Data strategies will fail without proper planning. It’s time to address this critical issue.
Companies are now learning how to integrate ERP (Enterprise Resource Planning) and other business applications to eliminate silos that hinder efficient business processes. Service-oriented architecture, software as a service, and cloud computing have played significant roles in facilitating large-scale application integration. However, today’s organizations face new challenges in managing vast amounts of data that are not merely a continuous stream but a complex array of separate data streams. These data streams are often isolated, similar to how previous enterprise applications were.
In the realm of large-scale structured data environments, most of the challenges posed by the surge in data can be mitigated through extensions, redundancy, and advanced analytics. However, in the Big Data era, these challenges represent just a fraction of the broader business problems that need to be addressed. Today, the types of data collected span a wide range of sources, including embedded sensors, RFID chips, documents, images, social media feeds, and even large data shared between business partners. Attempting to define or specify these data forms would significantly diminish their inherent value. Instead, enterprises can only predict the potential number of events or reactions. No matter how many check boxes or data files they create, data overflow remains a constant challenge.
From a competitive standpoint, ignoring the implications of non-traditional data can be catastrophic. According to a McKinsey Global Institute study titled “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” companies that fail to fully leverage existing data risk losing hundreds of billions of dollars.
Relational databases alone cannot provide a comprehensive solution due to the sheer volume and variety of data. Managing unstructured data using conventional tools and techniques becomes extremely difficult. Non-relational NoSQL, XML, and key-value data storage systems can help enterprises overcome scalability and accessibility issues associated with big data. Solutions like Hadoop and MapReduce, along with Hive Query Language, offer a starting point for managing big data and extracting business intelligence. MongoDB and Cassandra, among others, have achieved NoSQL-Hadoop integrations, making it easier to handle multiple data streams with a unified client interface.
In today’s enterprise environment, data has become more flexible. Tools like JitterBit, designed for parallel data flows and intelligent data blocks, facilitate the transmission of data from one application to another while ensuring data quality. This is particularly crucial for time-sensitive business activities involving immediate analysis. Such analysis often requires querying both current and historical data to identify emerging trends, which frequently necessitates the use of SQL.
The advent of new data formats does not negate the importance of carefully collected and organized internal corporate data stored in SQL databases. While there is a significant difference in data accuracy and relevance, most organizations recognize the need to retain the SQL enterprise data architecture to support best business practices. Simply transforming everything into unstructured data formats is not practical, and efforts to forcibly convert structured data to unstructured data are often futile.
From a business perspective, the goal should not be to focus on integrating structured data but rather to prioritize the organization’s needs. Tools like Oracle Data Integrator aim to strike a balance between loading and transforming data via Hadoop, making it easier to analyze traditional corporate data. This approach enables the integration of data from multiple sources and stores, thereby increasing the demand for data integration. This compromise allows for a more flexible handling of raw data, preserving its intrinsic value for future analysis methods.
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