Trends driving demand for enterprise-scale data preparation
Data preparation is essential to drive effective self-service analytics and data science practices. Today, organizations know that data-informed decisions are key to successful digital transformation. Data-driven business intelligence offers a powerful way to gain a competitive advantage. To make strategic decisions, only trusted data can help. As the volume of data and diversity of data sources continue to grow exponentially, it’s challenging to realize the full value of data.
Are you ready to harness the potential value of trusted data in your organization? With trusted data, you can improve operational efficiencies and develop machine learning models that can provide accurate and expected outcomes. Trusted data analytics can spark collaboration in business processes.
Companies have invested a lot of time and money in consolidating their data. They are using cloud data warehouses or cloud data lakes to discover, access and use data and drive next-generation analytics use cases. But they soon realize that handling big data in the cloud can still be challenging. Data preparation tools are the missing ingredient.
Data preparation is the process of cleaning, standardizing and enriching raw data. This gets data ready to apply in advanced analytics and data science use cases. There are several time-consuming tasks required in preparing data so it can be moved to a cloud data warehouse or cloud data lake including:
In addition to the time-consuming steps of data preparation, data engineers still need to clean and normalize the underlying data. Otherwise, they won’t be able to understand the context of the data for analysis. They often accomplish this with small batches of Excel or Jupyter Notebooks data. But these data tools have their limitations. For one thing, they cannot accommodate large datasets. They don’t allow you to operationalize data. They can’t provide reliable metadata for enterprise flows. And the process to prepare datasets can take several weeks to months to complete. As a result, data customers spend as much as 80% of their time preparing data instead of analyzing the data and extracting value. 1
Flipping the 80/20 Rule
With the growth of unstructured data, DataOps spends more time than ever removing, cleansing and organizing data. Data engineers often miss critical errors, inconsistencies and anomalies. Business users have less time to wait to get the data they need. There’s greater demand than ever for high-quality, trusted data for analysis. The current approach to data preparation is simply not enough. Data engineers and data analysts are spending more than 80% of their time finding and preparing data they need. That leaves only 20% of their time to analyze the data and gain business value. This imbalance is called the 80/20 rule.
How can organizations flip the 80/20 rule to their advantage? For complex data preparation, an agile, iterative, collaborative and self-service approach to data preparation is needed. Enterprise-scale data preparation flips the 80/20 rule to a company's advantage. It enables IT departments to offer self-service capabilities on their data assets and empowers data analysts to efficiently:
Giving data analysts the right data at the right time means complex data can be prepared, data quality rules can be applied and business value can be delivered in less time.
There are two primary use cases for enterprise-scale data preparation solutions:
Data preparation to drive data science initiatives
Do your data scientists need to improve productivity and efficiency? AI-powered data preparation tools with an integrated data catalog can help. With these enterprise-scale data preparation tools in place, your team can discover and prepare complex data, without depending on standalone open-source tools with manual processes. Data scientists can now:
Data preparation tools for self-service analytics on cloud data lakes
Cloud data lakes have become the defacto platform for organizations. They make data available for advanced analytics workloads. But data lakes are in danger of becoming data swamps. Enterprise-grade data preparation tools help organizations avoid this. They refine the content of cloud data lakes and help IT teams learn what the data means. From there, they can extract business value from the data and collaborate. When the data is ingested, users can curate it for trusted data analytics through self-service.
Ready to empower your data scientists, data analysts and citizen data integrators? Informatica Enterprise Data Preparation opens the door to self-service analytics. It delivers code-free, agile data preparation on a cloud data lake, so, you can focus more on your artificial intelligence (AI) and machine learning (ML) use cases.
Here are eight ways data preparation can support your data management needs:
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. Also check out why customers prefer Informatica in the Constellation ShortList™ Metadata Management, Data Cataloging and Data Governance report.
Next Steps
Ready to explore more about enterprise-scale data preparation and business intelligence? Watch the webinar, “Data Preparation Done Right,” or read the blog, “8 Ways Data Preparation Helps to Meet the Data Needs of Self-service Business Users.” For more information, reach out anytime.
1https://www.dbta.com/BigDataQuarterly/Articles/Reversing-the-80-20-Ratio-in-Data-Analytics-140846.aspx
Jun 22, 2022
Jun 22, 2022