Data Preparation software

Updated: July 30, 2023

Data Preparation software is a crucial tool for organizations to clean, transform, and structure raw data into a usable and meaningful format for analysis and decision-making. This software automates data cleaning processes, removing duplicates, handling missing values, and resolving inconsistencies, ensuring data accuracy and reliability. It also offers a range of data transformation and manipulation features, allowing users to merge, split, and reshape datasets as needed. With intuitive data visualization capabilities, Data Preparation software empowers users to explore and understand their data visually, identifying patterns and trends that can inform business strategies. Additionally, advanced algorithms and machine learning techniques assist in automating data preparation tasks, saving time and effort for data analysts and data scientists. By streamlining the data preparation process, organizations can improve data quality, accelerate data analysis, and enhance the overall effectiveness of their data-driven initiatives.

See also: Top 10 Business Intelligence software

2022. Document onboarding startup Flatfile nabs $50M



Data cleansing, the process of preparing data for applications like predictive analytics, can be time-consuming. However, Flatfile, a platform that utilizes AI, is streamlining this task by automatically learning how imported data should be structured and cleaned. The company has successfully secured a $50 million Series B funding round. Flatfile's AI algorithms have been trained on over 25 billion "data decisions," enabling them to map and resolve schemas within files such as spreadsheets and CSVs. In cases where anomalies or unsupported data types are encountered, customers are prompted to make decisions, which are then added to a database for future reference. Additionally, Flatfile recently introduced a software development kit (SDK) that empowers developers to leverage Flatfile's components for accessing import, matching, merging, and export functions.


2020. Truthset raises $4.75M to help marketers score their data



Marketing data quality startup Truthset has successfully secured $4.75 million in seed funding. The company specializes in evaluating the accuracy of consumer data purchased by marketers, assigning it a score ranging from 0.00 to 1.00. To generate these scores, Truth{set} verifies the data against various independent sources, as well as utilizing first-party data and panels. The platform goes even further by allowing the "suppression" of consumer IDs that fail to meet a marketer's standards, preventing their use in targeting efforts. Moreover, Truthset seamlessly integrates with demand-side platforms, data management platforms, and customer platforms. It also offers compatibility with leading data providers, such as Facebook, LiveRamp, and The Trade Desk.


2020. Data dashboard startup Count raises $2.4M



Count, a startup aiming to create a comprehensive data platform, has successfully secured $2.4 million in funding. Count focuses on providing cost-effective data pipeline building tools to early-stage teams. It offers a centralized solution for aggregating data and generating reports that can be accessed by the entire team. With Count's powerful notebooks, team members can share insights within the relevant context and query data without the need to learn SQL. Count competes with various solutions, including data warehouses like Snowflake, data cleaning tools like DBT, and analytics platforms such as Looker.


2020. Funnel raises $47M for its marketing data preparation software



Funnel, a startup based in Stockholm, has secured $47 million in Series B funding. Funnel provides technology solutions that assist businesses in preparing their marketing data for enhanced reporting and analysis, enabling them to become "business-ready." The company's initial goal was to enable marketers to automate the collection of online marketing data from various platforms in real time, facilitating more accurate analysis of marketing expenditures. While Funnel initially focused on visualizing marketing data, it has now shifted its focus to consolidating data from different marketing channels, cleansing and normalizing it, and making it compatible with popular business intelligence tools for in-depth analysis.


2018. Tableau gets a new data preparation tool



Data analytics platform Tableau has introduced a new data preparation tool. The primary objective is to provide users with a visual means to shape and cleanse their data, which is particularly crucial as businesses increasingly gather data from diverse sources. While Tableau Prep offers automation capabilities, its most significant feature is the visual interface it affords users to create such workflows. Prep supports all standard Tableau data connectors and enables users to perform calculations as well. Additionally, the company has introduced a server plan for businesses seeking on-premises or cloud-based deployment, along with a fully hosted online plan. Pricing for these options ranges from $35 to $70 per user per month.


2015. Big Data analytics platform Platfora scores $30M



Platfora, a company dedicated to assisting customers in processing and comprehending big data, has announced a $30 million investment. Platfora is specifically designed to handle large-scale data (petabyte or larger) across diverse platforms, including Amazon Web Services, Microsoft Azure, and Hadoop. It empowers business analysts to derive insights from various data sets without relying on data scientists or IT personnel. At its core, Platfora performs three primary functions: it prepares the data for analysis, processes the data in an in-memory database, and crucially, provides a visualization layer that enables business analysts and others to interpret the data meaningfully. Once the platform is set up by IT, users can delve into exploration. The data preparation and processing occur in the background as analysts select different sources to construct their desired dataset. Once this process is completed, they can visualize the data through charts and graphs in diverse formats, as expected.