Precisely

JULY 31, 2023

The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Let’s explore more of the report’s findings around data integrity maturity, challenges, and priorities.

Data Integration 52

Data Integration Telecommunication Data Governance Government 52

Precisely

JUNE 22, 2023

As the financial services landscape has become more complex and sophisticated, the concept of data quality has evolved to imply a holistic approach that encompasses the overall trustworthiness of data. They have found ways to curate and manage data to instill confidence among decision-makers.

Data Integration 52

Data Integration Banking Data Governance Government 52

Precisely

JANUARY 16, 2024

A New Era of Data Governance Data governance is alive and well, having shifted from a “nice to have” function to a mainstream necessity – a “must” for any enterprise wanting to leverage data to its advantage. Without Data Governance, AI Remains a Huge Liability Everyone’s talking about AI. What’s the quality?”

Data Governance 67

Data Governance Government Metadata Data 67

Precisely

OCTOBER 12, 2023

The Need for Sustainable Compliance This swirl of macro trends has majorly impacted businesses in financial services and across industries – and the effects can be seen in data strategies. How can I get timely access (and scale) to all the data I need to ensure compliance? Data literacy.

Banking 62

Banking Data Integration Data Governance Government 62

Precisely

JANUARY 8, 2024

Location intelligence provides a critical link that joins existing data elements with the geospatial context surrounding them. By understanding where your customers live, for example, you can glean meaningful insights about their income level, lifestyle, and access to services.

Insurance 62

Insurance Telecommunication Retail Data Integration 62

AltexSoft

OCTOBER 30, 2021

Here, data scientists are supported by data engineers. Data engineering itself is a process of creating mechanisms for accessing data. They set up resources required by the model, create pipelines to connect them with data, manage computer resources, and monitor and configure the model’s performance.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98