The potential of data is (almost) unlimited. It is revolutionising business operations and boosting performance by providing businesses with useful and actionable insights. Most companies, however, are not actively embracing it in a way that enables them to stay ahead in the race for a competitive edge.
As data is becoming more and more important it is now crucial for businesses to extract value from it. This is where data preparation comes in. What is it ? What are its major steps, tools and uses? Read our FAQ to make the most of your data.
Data preparation is the process of collecting, cleaning and analysing raw data so business analysts and data professionals can extract actionable information to be shared with operational teams. Preprocessing data is an imperative aspect of modern business practices as implementing inaccurate data only leads to negative consequences.
Business users must be able to rely upon and trust the quality, the accuracy and the dependability of datasets to guarantee productive decision making. By extracting valuable information from various data sources and analysing using data preparation tools, it shall assist any business ensure successful operational strategies and decisions are being made.
Cleaning data is the process of detecting, deleting or modifying irrelevant or inaccurate data from datasets so that a high quality of data can be extracted and used by data scientists to create algorithms. A high quality of data is imperative for effective analysis and implementation of KPIs and actionable information.
Data preparation is crucial otherwise business intelligence tools will not provide an accurate analysis. Quality data is essential for improved outcomes, supporting business objectives and enabling informed business decisions in regards to marketing, sales, production and distribution.
Data science can make searching for patterns or trends in monumental lakes of complex data a more refined and successful endeavour, enabling major insights and delivering new business opportunities. It ensures that effective technologies foolproof business strategies and outcomes.
Effective data analytics is impossible without the use of high quality data sources so data scientists can create reliable service models and algorithms. If poor or misleading data is used for analysis, operational teams will provide inaccurate information that will result in harmful business decisions being made. Data preparation is essential for successful business outcomes.
Data discovery is the process of detecting patterns or trends in collected datasets which can be analysed and used as actionable insights. Understanding data allows a business user to obtain high quality visual data which can be prepared and integrated for shared business purposes.
A data warehouse is a technical stack which can store large amounts of prepared data from multiple sources. A data warehouse stores, structures, compares and analyses both current and rapidly-updating real time data which reduces analytical issues. Data warehouses cannot however manage raw data, so data preparation is essential to an operational data warehouse.
Although data wrangling and data preparation are similar in regards to ensuring the unification and process of datasets for analysis, there are some key differences. Data wrangling is actually one of the techniques used during data preparation to preprocess data so it is converted from raw data into a more suitable format.
As useful as Data preparation must be for your business, you may have a real hard time conducting its implementation. Need help and advice?
