3 Ways
to Achieve Trusted Data Fast

The quality of your analytics is only as good as your data. As more companies pair internal data with external data, data integration and preparation are more important than ever.

How can you reduce the time and cost it takes to ingest and clean data while ensuring it meets privacy and regulatory requirements?

Did you know?


Without a modern approach to data governance, 80% of organizations will fail at scaling digital businesses1


60% of organizations say the need for data quality across data sources and environments is their biggest data management challenge3


67% of companies that met the GDPR compliance deadline worry about maintaining compliance2


Data scientists spend 45% of their time preparing and integrating data for analysis4

Fast access to clean, trusted data is a critical driver of business value.

Here are three ways to make that happen.


Speed up data ingestion

Developers spend significant time building pipelines to capture and transform data into formats required by target systems. And when data structures and system configurations change (which they often do) it takes even more time to build new connections or reconfigure ingestion rules to capture new data.

All this requires significant coding and ongoing maintenance, which contribute to data quality issues and delay business insights.

Did you know?

60% of companies say they have “too many data sources and inconsistent data.5

Hint: Use a meta-driven framework to automate data pipeline development to save developer time and cost.


Get faster insights about data quality

Once data reaches a data warehouse or data lake, it’s often inaccurate, duplicate, or incomplete. Organizations need earlier signals that something is wrong with the data. Left unchecked, poor-quality data leads to inaccuracies in reporting, delayed projects, and misguided business decisions.6

Did you know?

  • Data quality issues can delay analytics and AI projects by 40%
  • Organizations estimate the average cost of poor data quality at $1.28 million per year7

Hint: Adopting a data quality dashboard powered by machine learning can ensure you know exactly which data in the data warehouse is wrong and where to go fix it.


Automate data privacy compliance

Organizations worldwide are spending $8 billion on privacy tools, and yet most admit to being unprepared for emerging regulations. The effort required to manage changes to customer privacy requests by every global regulation in every database, table, and file that contains personally identifiable information is astounding. Organizations need automated solutions to help them get compliant and stay compliant – without spending a fortune doing it.

Did you know?

The average cost for organizations that experience non-compliance problems is $14.82 million8

Hint: A machine learning-based solution can help you easily catalog and protect sensitive information, ensuring you meet regulatory requirements.

Your path to fast, trusted data: Augment

Wavicle’s unified, augmented data management platform automates many of your data management tasks, shaving months off your project timelines and delivering trusted data at a lower cost.

faster development of data pipelines

less time spent on data quality checks

less time spent managing data privacy rules

Now you can quickly and easily integrate data from multiple sources, check your data quality, and meet data privacy requirements in a single, comprehensive cloud-based platform – no coding required.

6 Towards Data Science
7 Gartner