2022 Data preparation assignment help
Data preparation involves gathering the appropriate data for your application, organizing it into relevant content, then combining or structuring it into an informative format that is useful for your business solutions. The components of data preparation include data pre-processing, profiling, cleansing, validation and transformation; it often also involves pulling together data from different internal systems and external sources. Assignmentsguru is one of the leading content writing agencies in the world. We are a team of professional content writers who understand your needs, budget and constraints. We give you the best possible data preparation assignment at affordable prices to ensure that your project is completed on time and within budget. if you need any help of any kind do not hesitate to seek us!
Data preparation work is done by information technology (IT), BI, and data management teams to prepare data sets for loading into a data warehouse, NoSQL database or data lake repository or when new analytics applications are developed. In addition, data scientists, other data analysts and business users can use self-service tools to collect and prepare data themselves.
Data preparation is often referred to informally as data prep. It’s also known as data wrangling, although some practitioners use that term in a narrower sense to refer to cleansing, structuring and transforming data as part of the overall data preparation process, distinguishing it from the data pre-processing stage.
Purposes of data preparation
One of the primary purposes of data preparation is to make sure your raw data is accurate and consistent so your analytics applications will be valid. Data is commonly created with missing values, inaccuracies, or other errors. Additionally, separate data sets often have different formats that need to be reconciled. Correcting data errors, verifying the quality of the data and joining different sets of data constitute a big challenge.
Data preparation also involves finding relevant data to include in analytics applications to ensure they deliver the information that analysts or business users are seeking. The data can also be enriched and optimized to make it more informative and useful — for example, by blending internal and external data sets, creating new data fields, eliminating outlier values and addressing imbalanced data sets that could skew analytics results.
In addition, Business teams can use data preparation to organize data sets for all types of users. Having the input of managers, executives, and analysts helps streamline sales & marketing applications.
Steps in the data preparation process
The process of preparing data includes several distinct steps. There are variations in the steps listed by different data preparation vendors and data professionals, but the process typically involves the following tasks:
Data collection. Relevant data is gathered from operational systems, data warehouses and other data sources. Members of the BI team, other data professionals and end users should confirm that the data they are collecting is a good fit for the objectives of the planned application.
Data discovery and profiling. The next step is to explore the collected data to better understand what it contains and what needs to be done to prepare it for the intended uses. Data profiling helps identify patterns, inconsistencies, anomalies, missing data, and other attributes and issues in data sets so problems can be addressed.
Data cleansing. In this step, the identified data errors are corrected to create complete and accurate data sets that are ready to be processed and analyzed. For example, faulty data is removed or fixed, missing values are filled in and inconsistent entries are harmonized.
Data structuring. At this point, the data needs to be structured, modeled and organized into a unified format that will meet the requirements of the planned analytics uses.
Data transformation and enrichment. In connection with structuring data, it often must be transformed to make it consistent and turn it into usable information. Data enrichment and optimization further enhance data sets as needed to produce the desired business insights.
Data validation and publishing. To complete the preparation process, automated routines are run against the data to validate its consistency, completeness and accuracy. The prepared data is then stored in a data warehouse or other repository and made available for use.
Benefits of data preparation
Data scientists often complain that they spend a majority of their time locating and cleansing data rather than analyzing it. Implementing a data preparation process is a good idea for many reasons. It allows end users to spend more time on other important aspects of your business, such as data analysis and mining.related activities that deliver business value. For example, data preparation can be done more quickly, and prepared data can automatically be fed to users for repetitive analyses.
A well-managed data preparation program also helps an organization do the following:
ensure that the data used for BI, machine learning, predictive analytics and other analytics applications has sufficient quality levels to produce problematic results
avoid duplication of efforts in preparing data that can be used in multiple applications;
prepare data for analysis in a cost-effective and efficient way;
identify and fix data issues that otherwise might not be detected;
make more informed business decisions because executives have access to better data; and
get more business value and a higher return on investment (ROI) from its BI and analytics initiatives.
Effective data preparation is important in big data environments that are focused around Hadoop clusters, which can store unstructured, semi structured and structured information. This includes raw data.In many big data applications, data preparation is largely an automated task: Machine learning algorithms can speed things up by examining data fields and automatically filling in blank values, fixing errors or renaming fields to ensure consistency when data sets are being joined.
Data preparation tools and market
Data preparation is a time-consuming task that can pull skilled BI, analytics and data management practitioners away from more high-value work, especially as the volume of data used in analytics applications continues to grow. Knowledge of data is crucial for business success, but with self-service tools, it can be streamlined for convenience. By automating data preparation methods, you’ll be able to discover the best insights faster.
After data has been gathered and reconciled, data preparation software runs files through a workflow, during which specific operations are applied to them. For example, this step may involve aggregating existing data fields or applying a statistical formula. After going through the workflow, data is output into a finalized file that can be loaded into a data warehouse or other data store to be analyzed.
Self-service data preparation tools generally also feature graphical user interfaces (GUIs) that are designed to simplify the various steps in the data prep process.
In an April 2019 report on the data preparation market, consulting firm Gartner said the available tools have evolved from basic self-service capabilities to support the creation of BI and analytics data sets at enterprise scale. The market is complex, with choices ranging from data prep specialists to vendors that incorporate data prep software into BI, data science or data integration tools. Your choice of vendor depends on the type of pain relief you are seeking. Gartner advises organizations to evaluate tools extensively. They must have the ability to scale and provide features such as connectivity, machine learning automation, and data cataloging.
Vendors that focus specifically on self-service data preparation include Paxata and Trifacta. Alteryx, SAS, Tableau, Tibco Software and other BI and analytics vendors also support data preparation, as do various data integration and management vendors, such as Altair, Boomi, Datameer, IBM, Infogix, Informatica, SAP, Talend and Tamr.
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