Data is the new oil. In fact, data is so valuable that companies are spending billions to acquire it, store it, and then analyze and understand it. But all of this data comes at a price: processing power. It takes massive computing resources to ingest and analyze information from multiple sources. Data preparation can help manage this process by ensuring that your data is clean and ready for analysis.
Data Ingestion and Integration
Data integration is the process of combining data from multiple sources into a single repository. Data preparation is the first step in preparing your data for analysis, and it includes three main activities:
- Data ingestion – The process of loading data into a data warehouse or database
- Data cleansing – The process of correcting errors in your dataset (e.g., misspellings) and removing superfluous information that isn’t needed for analysis purposes
- Data transformation – The process of changing the format or structure of your dataset so that it matches what you need for analysis purposes
Data preparation is the process of transforming raw data into a form that can be analyzed. It’s an important step in the data analytics process, and it’s done in two steps: Data integration and data profiling.
Data integration involves combining multiple datasets into one unified dataset–for example, combining multiple files from different sources into one file with all of your information in it. This can be done manually or through automated tools like Excel VLOOKUPs (a function that allows you to pull information from another spreadsheet) or SQL queries (a language for querying databases).
Data profiling refers to cleaning up your data so that it looks nice and neat before you start analyzing it; this includes things like correcting spelling errors or fixing missing values by guessing what those values could possibly be based on other nearby values.
Data profiling is the process of analyzing your data to find out what it contains. It’s a subset of data quality assessment, which means that it helps you understand if your data is accurate and reliable enough for use in analysis. It’s important to know what your data contains before you analyze it because some types of analysis require certain kinds of information and not others. Data profiling can be done manually or automatically–manual methods involve inspecting each field individually while automatic methods use algorithms that perform a number of tests on every field at once (e.g., checking if all values are integers between 0-100).
Data Quality Assessment and Enhancement
Data quality is a critical part of an analytics process. It’s not just about completeness, but also accuracy, timeliness and consistency. Data quality checks can also include business rules that must be applied to your data before it can be used in analysis. For example:
- Does the customer have an active subscription?
- Is this transaction being done by someone who has authorization to make it?
Big data is a lot of data.
Big data is a lot of data. It can be structured, unstructured or semi-structured; it comes from a variety of sources and uses different types of storage systems. The term “big” refers not just to the size of your dataset but also its volume — how much information flows through it — and velocity: how fast that information is generated.
Big data has become more common thanks to advances in technology that allow us to collect more information than ever before and store it more efficiently than ever before (and cheaply!). This means there’s an abundance of potential insights waiting behind every click, swipe or tap — if only someone could figure out how to get at them!
The process of preparing data for analysis is a crucial step in any analytics project. It’s important to remember that big data is a lot of data, and it takes time and effort to make sure that all your information is clean and ready for analysis. With the right tools, though–and some patience–you can make sure that your data is ready before you start crunching numbers!