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Database managementStructured vs. Unstructured Data: Which One Gives Better Insights?Data is how we make sense of the world around us. With data estimated to reach 181 zettabytes by 2025, finding the right information from the data we generate can be challenging. And data comes in two primary forms: Structured and unstructured. Structured data has been in use for decades. Ever since System Query Language (SQL) was developed in the early 1970s, the potential benefits of structured data in a relational database were unlocked. And in the modern era, as machine learning (ML) and artificial intelligence (AI) come into their own, the power of raw, unstructured data is also within reach. Let's dive deeper into the differences between these two data types. Structured Data vs. Unstructured Data: Overview StructuredUnstructuredAnalysisQuantitativeQualitativeSearching SQL-based methodsMay need special tools and expert consultationStorageTakes up vertical storage based on hardware capacityAmount of storage needed depends on the nature of the dataFormatPredetermined format that typically uses alphanumeric charactersCan be represented by various media, such as text, video, and audio What is Structured Data? Structured data is highly organized and is stored in systems with fixed schemas known as data warehouses. Its rigid organizational structure makes it easy for ML algorithms to decipher. This stored data can be efficiently searched and manipulated through an SQL database. Examples of Structured Data NamesPhone numbersAddressesAccounting remittancesCredit card numbers ✶ Want to know more? Try this beginner's guide to improving document management What is Semi-structured Data? The middle ground between structured and unstructured data is semi-structured data. It has no predefined data model, but it sorts data into preset fields using metadata like internal tags and markers. These metadata identifiers leverage search tools to make navigating and analyzing semi-structured data easier, even if it is mostly unstructured. Your email inbox is one of the best examples of semi-structured data. Metadata tags enable users to search for keywords within hundreds of emails quickly and efficiently. What is Unstructured Data? According to a survey by Accenture, nearly 80% of companies' data is 50-90% unstructured. This data doesn't follow any predetermined schemas and is stored in ever-growing data lakes, where the data remains unstructured until data scientists extract what they need and organize it. Managing your company’s unstructured data can grant you a competitive edge. Examples of Unstructured Data PDFsImagesVideo filesSocial media postsAudio files Challenges of Unstructured Data Until computing technology became capable, unstructured data was nearly impossible to analyze meaningfully. Now, with tools like ML and AI, it’s possible to comb through vast amounts of unstructured data to find exactly what you’re looking for. However, there are challenges to dealing with unstructured data: Requires expertise: Data science knowledge is required, which can make tackling unstructured data intimidating to many smaller businesses that may not have the technical know-how.Needs special tools: Unstructured data is a relatively newer field compared to structured data. So, examining it requires specialized tools designed to handle various formats and volumes.Limits product choices: Special tools and skills for analyzing unstructured data are still being refined. Therefore, there isn't a wide selection of options for data managers when dealing with this type of data. ✶ Find out more about data silos and data lakes Sift Through Data Seamlessly With Unleash While structured and unstructured data are distinct forms, they are often found together—businesses can gain insights from both structured and unstructured data. Internal and external sources combine to create a stored, growing body of knowledge. The larger that body of knowledge grows, the more time-consuming it becomes to navigate. Enter Unleash—the enterprise search tool that streamlines productivity across your organization. It spares you and your colleagues from digging through files and folders by searching entire databases in a flash. It’s simple and easy to use, so you can gather insights with fast data navigation.
Database managementData Silos vs Data Lakes: Which Strategy Drives Data Management?In today's digital era, businesses are inundated with data, making effective data management essential for making sense of this deluge of information. Data management solutions can provide businesses with the answers they need, or at least a starting point, to determine how to handle all this data. Data silos and data lakes are two different approaches to managing and storing large volumes of data or big data. Here is a comprehensive outline of both concepts and how they drive data management. What is a Data Silo? Data silos in big data refer to isolated data repositories created within an organization as different departments or teams generate and store their own data. This type of data management makes it challenging for businesses to search, access, and analyze data across departments, hindering effective collaboration and reducing process efficiency. They can also affect data integrity, as the same information may be stored in different databases, leading to inconsistencies and data synchronization issues. As such, breaking down data silos is crucial for unlocking effective and unified data management. Breaking Down Data Silos There are several ways to break down data silos and enhance information sharing. These include: Changing Company Culture: Data silos may result from organizational structures that encourage departments to store data locally or keep information within their confines. Organizations need to communicate with employees and teams about the problems associated with data silos and the benefits of information sharing for better data management and analysis.Centralizing Company Data: Organizations need to pool all company data into a cloud-based data lake where it will be consolidated and easily accessed. This will provide a centralized location for all data and eliminate the need for departmental databases.Fostering Data Integration: Data integration involves combining data from different sources into a single unified view, providing a consistent and consolidated view of data. ETL (Extract, Transform, Load) is a popular approach to data integration, involving extracting data from source systems, transforming it to conform to the target system's format, and loading it into the target system.Creating a Data Governance Framework: Establishing a data governance framework that centralizes data access and control can prevent the creation of data silos. The framework should include policies and procedures for managing data quality, security, privacy, and compliance with using and accessing data. This way, organizations can break down data silos, enhance information sharing, and promote collaboration across departments. What is a Data Lake? A data lake is a centralized repository that stores, processes, and secures data in various formats, including structured, semi-structured, and unstructured data. It facilitates the centralization of information, helping to break down data silos in organizations. Data lakes offer an intelligent approach to data management, enabling cost-effective storage of large amounts of raw data in its native format, making it ready for AI/ML analytics. How to Build a Data Lake? Here is a broad outline of how to build a data lake: Identify Your Organization’s Data Goal: Before you start building a data lake, identify the data analytics goals of your organization. This will help you to determine the relevant data you need to store in the data lake.Set Up Cloud Storage: Data lakes are typically cloud-based and designed to support big data processing and analytics. Solutions like Amazon S3, Microsoft Azure, or Google Cloud offer scalability and cost-effectiveness.Prepare the Data: Ensuring data quality, consistency, and accuracy before loading it into the data lake is essential. Raw data can be messy and unstructured, making searching, analyzing, and deriving insights difficult. Data preparation involves data profiling, data cataloging, data backup, and archives.Enforce Security Policies: Data lakes can contain sensitive information. Therefore, it's important to implement access controls, encryption, and auditing to keep sensitive data safe and track access to the data.Make Data Available For Analytics: Analyzing and learning from the data can provide insights into business operations and improve decision-making. ✶ Find out how federated search integrates with data lakes Overcoming Data Management Challenges While breaking down data silos and embracing data lakes can help organizations become data-driven, it is easier said than done. Challenges include the learning curve associated with data lakes and their susceptibility to malware pollution. When malware "seeps" into data lakes, it can turn them into gold mines for malicious actors. However, enabling uninhibited search capabilities can serve as an alternative to breaking down data silos, as it allows users to search through and across data repositories within an organization. To this end, tools like Unleash offer a simple and easy-to-use solution that enables users to search every folder and application. This streamlines productivity and allows employees to quickly access the information they need.