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How to Put Data to Work for Self-Service Analytics and Data Science With AI-Powered Enterprise Data Preparation By Preetam Kumar

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Trends driving demand for enterprise-scale data preparation Today, organizations are excited about the potential value of data to drive data-informed decisions for competitive advantage. But gaining the full value from the rapidly growing data in volume and diversity is increasingly challenging. There is also a growing demand for trusted data from every user to make… more
The post How to Put Data to Work for Self-Service Analytics and Data Science With AI-Powered Enterprise Data Preparation appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.

Trends driving demand for enterprise-scale data preparation

***NOTE TO INSPECTOR: All visible graphics are our own design, and were produced for this particular shoot.***

Today, organizations are excited about the potential value of data to drive data-informed decisions for competitive advantage. But gaining the full value from the rapidly growing data in volume and diversity is increasingly challenging. There is also a growing demand for trusted data from every user to make strategic decisions, improve operational efficiency, develop machine learning models, and collaborate on business processes. To achieve these objectives, many companies have invested a lot of time and money in consolidating all their data into a cloud data warehouse or data lake, thinking that this will solve their data problem. However, they soon realize that data in the cloud is messy, and it is challenging to discover, access, and use the data for driving next-gen analytics use cases despite all their efforts.

These trends make the data preparation activities essential for organizations for driving self-service analytics and data science practices.

What is data preparation and what are its challenges?

Preparing data for analytics and machine learning involves several necessary and time-consuming tasks, including data extraction, cleaning, normalization, loading, and the orchestration of ETL workflows at scale. Once the data has been reliably moved to the cloud data lake or data warehouse, the underlying data still needs to be cleaned and normalized by data analysts and data scientists to understand the context of the data. Today, this is done with small batches of the Excel or Jupyter Notebooks data, which cannot accommodate large datasets, cannot be operationalized, or cannot provide reliable metadata for enterprise flows. This process to prepare datasets can take several weeks to months to complete. As a result, customers spend as much as 80% of their time preparing data instead of analyzing the data and extracting value.

Reference architecture for cloud data lake and data warehouse with data preparation | Informatica

Flipping the 80/20 rule The approach to data preparation in many companies is still not efficient. Data analysts and data scientists can spend 80% of their time and effort finding and preparing the data and only 20% of their time analyzing it. On top of that, due to the rapid growth of unstructured data, the Data Ops team is spending more time removing, cleansing, and organizing data to uncover errors, inconsistencies, and anomalies.

Simultaneously, with an increased focus on the data-driven approach for decision-making, the dependency on high-quality, trusted data stresses the importance of bringing standardization and efficiency to data preparation. Plus, business users have less time to wait for IT to provide the data, and they require a self-service capability in data preparation to accelerate decision making.

One way to speed up the data preparation process is through an agile, iterative, collaborative, and self-service approach to data preparation. This modern self-service approach to data preparation can help an enterprise flip the 80/20 Rule to its advantage. It enables IT departments to offer self-service capabilities on their data assets while empowering analysts to discover the right data asset, prepare, apply data quality rules, collaborate with others, and deliver the business value in significantly less time.

Diagram showing 7 stages in a modern enterprise data preparation process | Informatica

Use cases for modern enterprise data preparation

There are two primary use cases for enterprise-scale data preparation solutions.

  • Data preparation to improve analytics and data science development

    AI-powered enterprise data preparation embedded with an enterprise data catalog can improve data scientists’ productivity and efficiency who work manually using open-source tools to find and prepare data. Data scientists spend most of their time in data discovery and preparation that delays data science projects. With integrated enterprise data preparation and data cataloging, they can work with a large volume of structured and unstructured data sets stored in a cloud data lake. This can accelerate their model development and discover hidden nuggets from the data for predictive and prescriptive analytics.

  • Data preparation for self-service analytics on cloud data lakes

    Cloud data lakes have become the de-facto platform for organizations to make data available for advanced analytics workloads. However, data lakes are in danger of becoming a data swamp unless organizations have the right technologies to learn what the data means and extract value. Enterprise data preparation can help refine the content of cloud data lakes once the data is ingested and curate the data so users can use trusted data for self-service analytics.

How does Informatica help?

Informatica Enterprise Data Preparation allows data scientists, data analysts, and citizen data integrators to do code-free, agile data preparation on a cloud data lake to drive self-service analytics and AI/ML use cases. Here are eight ways Informatica Enterprise Data Prep helps meet data needs:

  1. Increase trust by improving data quality: Informatica Enterprise Data Preparation applies intelligence and automation to improve data quality and reduce manual work. It helps enhance data quality standardization across the enterprise as well as verify and enrich customer data such as email addresses, postal addresses, and phone numbers.
  2. Establish an enterprise data catalog: Informatica Enterprise Data Catalog enables data analysts and data scientists to understand what data they have, how the data is defined, its location, and lineage information about its origin and use, and how the data is related to other data. Using AI/ML and automation capabilities of the CLAIRE AI engine, Informatica Enterprise Data Catalog can help organizations curate data for pipelines by exposing which datasets are available with relevant context. This reduces the time it typically takes for users to find and understand trusted, relevant, and available data.
  3. Increase user agility and efficiency: Informatica Enterprise Data Preparation empowers the IT department to offer self-service capabilities on their data assets, while in turn enabling data analysts to discover the right data asset, prepare, apply data quality rules, collaborate with others, and deliver business value in significantly lesser time.
  1. Improve analytics and data science: Informatica Enterprise Data Preparation provides intelligent and automated data preparation that helps data scientists and data analysts increase their productivity and focus on analytics, AI/ML, and achieve business outcomes. It can help reduce dependency on manual coding skills and reduce pressure on organizations to hire data scientists.
  2. Increase the value of cloud data lakes: Informatica Enterprise Data Preparation shortens the path to value for cloud data lakes.  It helps in transforming, cleansing, preparing, and enriching raw data once it lands into a cloud data lake and makes it ready for advanced analytics and AI/ML use cases. Informatica Enterprise Data Catalog tags the information that describes their data lineage. Cataloging of the data at scale increases consistency across all the data, which is not possible with siloed self-service tools.
  3. Enhance operationalization with DataOps: Scalable, AI-powered data preparation from Informatica can help you to achieve the following DataOps goals:
  • Continuous integration and collaboration to quickly find relevant data
  • Continuous delivery and easily mapped governed, trusted datasets to define business terms for increasing speed and quality of data pipelines
  • Continuous deployment of datasets for pipelines
  1. Gain a holistic view to streamline data prep: Informatica Enterprise Data Preparation enables organizations to get an end-to-end holistic view of workloads to see common recurring problems and use AI and automation to replace unnecessary manual work.
  2. Improve data governance: With Informatica’s Enterprise Data Preparation, Data Catalog, Data Quality and Axon Data Governance, customers can establish governance while ingesting data into a data cloud data lake. Infusing CLAIRE, the industry’s first metadata-driven AI engine in a data catalog, can increase scalability and accuracy to govern data across cloud data lakes and data warehouses.

Informatica Enterprise Data Preparation is recognized, for the second time in a row, on the Constellation “ShortList” for Self-Service Data Prep

The latest Constellation ShortList report has recognized Informatica Enterprise Data Preparation as one of the leading products in the Self-Service Data Preparation solutions category. This Constellation ShortList is determined by client inquiries, partner conversations, customer references, vendor selection projects, market share, and internal research. To learn more, download the analyst report.

The post How to Put Data to Work for Self-Service Analytics and Data Science With AI-Powered Enterprise Data Preparation appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.