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Transforming Data into Knowledge: Defining the Six Steps of Information ManagementSummaryWe are living in the information age! Farmers and their advisors have access to ever increasing amounts of data. However, data does not equal knowledge. To be effectively used in making decisions, data must go through a transformation process that involves six basic steps: 1) data collection, 2) data organization, 3) data processing, 4) data integration, 5) data reporting and finally, 6) data utilization. Through this process data is transformed into information, which becomes knowledge, if interpreted correctly. However, mistakes at any level of the process compromise the entire system, resulting in useless information. The Dairy Herd Improvement program is a premiere example of the process. Standardized data collection procedures, and the definitions and formulas which organize the data, are the foundation of the program. The weak link with DHI data is in the utilization step, as many producers fail to review their reports. Applying the process to financial data is needed to supplement the efforts of the Farm Financial Standards Council, which has done an excellent job in developing standards for certain phases of the process. Educators and industry professionals in Pennsylvania and the Northeast collaborated to develop a standardized chart of accounts for dairy accounting systems, which is now available as a PDF document or in a QuickBooks backup file. Analysis and planning tools that can automatically integrate data from accounting systems using this chart of accounts will enable producers to utilize their accounting and other information systems to make well informed decisions. Presentation MaterialsDetails What is data transformation? What are the benefits of data transformation? What are the processes? Learn all this and more! The last few decades have seen a renaissance in data collection and processing— today’s data teams have more information at their disposal than ever before. While this has led to a proliferation of data analytics & science, it’s presented a number of problems for engineers and business teams. Raw data can be challenging to work with and difficult to filter. Often, the problem isn’t how to collect more data, but which data to store and analyze. To curate appropriate, meaningful data and make it usable across multiple systems, businesses must leverage data transformation. What is Data Transformation?Data transformation is the mutation of data characteristics to improve access or storage. Transformation may occur on the format, structure, or values of data. With regard to data analytics, transformation usually occurs after data is extracted or loaded (ETL/ELT). Data transformation increases the efficiency of analytic processes and enables data-driven decisions. Raw data is often difficult to analyze and too vast in quantity to derive meaningful insight, hence the need for clean, usable data. During the transformation process, an analyst or engineer will determine the data structure. The most common types of data transformation are:
In addition, a practitioner might also perform data mapping and store data within theappropriate database technology. What is Data Preparation? | Zuar This article discusses what data preparation entails and how it is done. Zuar | BlogTeam Zuar The Data Transformation ProcessIn a cloud data warehouse, the data transformation process most typically takes the form of ELT (Extract Load Transform) or ETL (Extract Transform Load). With cloud storage costs becoming cheaper by the year, many teams opt for ELT— the difference being that all data is loaded in cloud storage, then transformed and added to a warehouse. The transformation process generally follows 6 stages:
These steps are meant to illustrate patterns of data transformation— no single “correct” transformation process exists. The right process is the one that works for your data team. That is to say, other bespoke operations might occur in a transformation. For example, analysts may filter data by loading certain columns. Alternatively, they might enrich the data with names, geo-properties, etc. or dedupe and join data from multiple sources. Want to automate your ETL processes to enable all of your data to flow into a single destination? Learn more about Zuar’s Mitto platform that allows you to transform, model, report, and manage your data more effectively. Data Transformation TypesThere are two common approaches to data transformation in the cloud: scripting-/code-based tools and low-/no-code tools. Scripting tools are the de-facto standard, with the greatest amount of customization, flexibility, and control over how data is transformed. Nonetheless, low-code solutions have come a long way, specifically in the last few years. We’ll briefly discuss both options. Scripting ToolsThe most common data transformations occur using SQL or Python. At the simplest, these transformations might be stored in a repository and executed using some orchestrator. More commonly, platforms like dbt are used to orchestrate and order transformations using a combination of SQL/Python. These tools or systems often boil down to programmatically creating tables or transformations using some scripting language. The Python Mitto SDK is also useful for scripting and automation. Enabling remote interactions with schedules, jobs, and business functions has never been easier. Want to see Mitto in action? Schedule a demo of the Python Mitto SDK. Low-/No-Code ToolsThese tools are the easiest for non-technical users to utilize. They allow you to collect data from any cloud source and load it into your data warehouse using an interactive GUI. Over the past decade, many low-code solutions have proliferated. Zuar's Mitto is an example of a product that has ETL/ELT capabilities, but also helps you manage data at every step in its journey. Mitto can be hosted either on-premise or in the cloud and has code and no code options. Data Cleaning: Benefits, Steps & Using Clean Data | Zuar This article discusses data cleaning, its benefits, and how to create and use clean data. Zuar | BlogTeam Zuar Data Transformation TechniquesThere are several data transformation techniques that can help structure and clean up the data before analysis or storage in a data warehouse. Here are some of the more common methods:
Data Transformation: BenefitsTransforming data can help businesses in a variety of ways. Here are some of the biggest benefits:
While the methods of data transformation come with numerous benefits, it’s important to understand that a few potential drawbacks exist.
Nonetheless, data transformation is an essential part of any data driven organization. Implementing tests and following the best-practices of software development will help to minimize errors and improve confidence in data. Without experienced data analysts with the right subject matter expertise, problems may occur during the data transformation process. While the benefits of data transformation outweigh the drawbacks, it's necessary to take appropriate caution to ensure sound transformation. Data Transformation ImplementationOrganizing, transforming, and structuring data can be an overwhelming task for many organizations, but with the right research and planning it's possible to integrate a data-driven culture into your business. But first, it’s crucial to have a long-term strategy for analysis and transformation. Zuar offers several products (e.g. our data pipeline tool Mitto) and services that can enable more efficient and accurate data management, by automating many steps in the process! Next Steps: Learn about...
What is Data Integration? | Zuar Data integration systems bundle together your data from all of your different programs into cohesive information that helps your business intelligence. Zuar | BlogTeam Zuar Everything You Need To Know About Data Mapping | Zuar Employ data mapping to combine/migrate all of your desperate data into a single destination. Learn how! Zuar | BlogTeam Zuar Database Migration: What to Know | Zuar Database migration is a decision most businesses will need to make at somepoint. Before the advent of the internet, firms traditionally kept their datapipelines and storage in-house, centralized within their local area networks(LAN). This system was simple, efficient, and allowed companies to fun… Zuar | BlogTeam Zuar What is the process of transforming data into a useful information?A transformation process is needed to convert data into information that once analyzed, is valuable. In recent years, a variety of names that refer to data analysis have emerged. Traditional business intelligence now includes concepts such as data discovery, visual analysis, agile BI, and business analytics.
What is data transformation in science?Data transformation is the process of converting data from one format to another. The most common data transformations are converting raw data into a clean and usable form, converting data types, removing duplicate data, and enriching the data to benefit an organization.
What is science in data science?Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
Why is data transforming into information important?Data is transformed to make it better-organized. Transformed data may be easier for both humans and computers to use. Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats.
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