New Requirements Spur Data Quality and Data Integration

A combination of factors is heightening the need for high-quality, well-governed data. These include the need for trustworthy data to support AI and machine learning initiatives, new data privacy and data management regulations, and the appreciation of good data as the fuel for better decision making.

Multiple Data Sources, Governance, and High Volume Are Top Data Quality Challenges

  1. The top 3 challenges companies face when ensuring high quality data are multiple sources of data (70%), applying data governance processes (50%), and volume of data (48%).
  2. About three-quarters (78%) of companies have challenges profiling or applying data quality to large datasets.
  3. 29% say they have a partial understanding of the data that exists across their organization, while 48% say they have a good understanding.

Source: Syncsort’s 2019 Enterprise Data Quality Survey

The Data Prep Market is Growing

The global data prep market, including tools for data curation, data cataloging, data quality, data ingestion, data governance—whether on-prem or in the cloud, is growing. The market is expected to rise from its initial estimated value of $3.38 billion in 2018 to an estimated value of $21.48 billion by 2026, registering a compound annual growth rate of 26% in the forecast period to 2026.

There are 4 key drivers for the increase:

  1. The rising need for adhering to regulatory and compliance requirements
  2. The need for on-time qualified data
  3. The benefits of streamlined business operations
  4. The fact that data prep tools help companies in predictive business analytics

Source: “Global Data Prep Market—Industry Trends and Forecast to 2026” from Data Bridge Market Research

The Need for Data-Driven Insights

Maintaining a competitive business edge depends on the ability to leverage accurate and reliable data to make informed and strategic decisions.

85% of organizations see data as one of the most valuable assets to their business.

Key reasons for having a strategy to maintain high-quality data are:

Increased efficiency 60%

Increased customer trust 44%

Enhanced customer satisfaction 43%

Enabled more informed decisions 42%

Cost savings 41%

Source: “2020 Global data management research,” produced by Insight Avenue for Experian

Top business uses of data driving organizations’ data strategies

Although there are many business objectives that are driving data strategies, the most frequently mentioned are improving the decision making of end users and uncovering customer preferences and patterns.

To inform decision making 92%

To understand customers and trends 82%

To improve internal operations 78%

To provide smarter services and products 77%

To support a better customer experience 73%

Top challenges being encountered with your machine learning projects

The adoption of machine learning—which has been described as “getting computers to act without being explicitly programmed”—is increasing. This is due to the vast and rapidly growing volumes of data and the associated challenges of finding value from that data. However, fundamental data issues underlying machine learning models is a problem, pointing to challenges with data quality and access to the right data.

Operationalizing machine learning models and pipelines 74%

Quality issues with data 57%

Lack of access to the right data 56%

Source: Profiling the Data-Driven Business, 2019” produced by Unisphere Research, a division of Information Today, Inc., and sponsored by Pythian.



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