Data preparation refers to the process of cleaning, standardizing and enriching raw data to make it ready for advanced analytics and data science use cases. Data analysts struggle to get the relevant data in place before they start analyzing the numbers. In fact, data scientists spend more than 80% of their time preparing the data they need before using it in various supervised and unsupervised machine learning models. It is called the 80/20 rule – with data analysts and data scientists spending 20% of their time on actual business analysis, while the rest is spent on finding, cleansing and organizing data.
Data preparation solves this problem for data analysts and data scientists through an agile, iterative, collaborative and self-service approach. It flips the 80/20 rule to a company's advantage. It enables IT departments to offer self-service capabilities on their data assets while empowering data analysts to discover the right data asset, prepare the data, apply data quality rules, collaborate with others and deliver business value in significantly less time.
Today's enterprises generate enormous data every day through their customer interactions, business processes and various transactional activities. Based on these data proliferation trends, we see that everyone has access to the same computing power. Most of the organizations have access to the same set of machine learning algorithms, too. The only difference is the data and how enterprises use the data to make business decisions. The data that enterprises store indifferent places, be it on-prem or in the cloud, is messy and incomplete. Because of this, the employees in the organization cannot use this data to run their analytics and data science projects – and this is where data preparation becomes critical.
Data preparation is fast becoming a massive challenge as organizations embark on data and analytics initiatives in their digital transformation journey. In a typical data infrastructure, data is distributed across data lakes, data warehouses, databases, flat files, XML files, CSV files, audio files, etc. This massive proliferation of data makes it extremely difficult to find, clean, transform and share data safely and timely for driving advanced analytics use cases.
An AI-powered data preparation tool with an easy-to-use user interface can improve data analyst productivity by reducing the cycle time to get to analytics – saving time and money while boosting productivity.
Hand coding and manually intensive approaches like using Excel spreadsheets for data preparation are time-consuming and redundant. Organizations are accelerating their machine learning initiatives to drive their digital transformation efforts. They have realized that machine learning and AI are critical for generating business value and making data-informed decisions.
Machine learning techniques have become pervasive across every industry (e.g., fraud analytics in the banking sector, customer engagement in retail, preventive maintenance in manufacturing, patient care in healthcare). They are used for almost all sorts of solutions using all data types – structured, semi-structured and unstructured data.
High-quality, trusted and governed data is key to the success of any machine learning initiative. Most machine learning projects fail due to untrusted data, making it garbage in and garbage out scenario. Due to these reasons, data preparation is essential for machine learning projects. But, the manual data preparation process becomes tedious due to the high volume and variety of data. Data scientists need high-quality training data to train the machine learning models. You need to infuse intelligence and automation into the data preparation process, provide the correct data set recommendations and automatically clean and transform the data for machine learning consumption. Data analysts and data scientists can improve their efficiency by focusing on building models rather than preparing data to train the model.
Historically, data preparation was complex for analysts to do themselves and often limited to IT through a complex hand-coding process. On top of that, IT doesn't have the necessary business context of the data to prepare the data efficiently.
Informatica Enterprise Data Preparation allows data scientists, data analysts and citizen data integrators to do low-code/no-code, agile data preparation on a cloud data lake to drive self-service analytics and AI/ML use cases.It is part of the Informatica Intelligent Data Management Cloud designed for hybrid and multi-cloud environments.Here are six different ways Informatica Enterprise Data Preparation helps meet data needs:
Industry use cases for data preparation
Data preparation customer success stories
Avis Budget Group uses Informatica to analyze terabytes of connected vehicle data from a fleet of 650,000 vehicles to drive innovation, increase efficiencies and improve customer experiences. They’re also supporting global vehicle analytics with end-to-end data pipelines and speeding time-to-insights.
With Informatica Enterprise Data Preparation, Avis Budget Group preps and analyzes data at scale for many use cases, including customer 360, fleet management, mileage optimization and preventative repairs. Informatica helps them increase productivity with rapid data discovery as well as understand data assets and lineage to create fully governed data pipelines at scale.
Meanwhile, a large global automobile company processes, prepares and catalogs large volumes of data landing in Hadoop data lake to provide fast, easy and governed access for analytics users across business use cases: connected car, dealer data quality, credit analytics, incentive data, etc.
Their data analysts use Informatica Enterprise Data Preparation for self-service semantic search, data wrangling and data sharing, helping them reduce time to insights for data scientists and lower the burden on IT.
Get started with data preparation today
To learn more, download Accelerating Time to Value with Enterprise Data Preparation, an 8-step plan that outlines how you can spend less time preparing data – and more time using it.