Tips

GDPR rules can spur broader steps to protect sensitive data

Companies need to make compliance with GDPR's requirements on managing personal data a priority, but they should also work to implement wider data protection efforts in the future. Continue Reading
GDPR mandates push data quality improvement into IT spotlight

Data quality issues cost companies significant amounts of money in lost revenue and added expenses, and their impact will only get bigger when the EU starts enforcing its GDPR law. Continue Reading
Seven steps to a successful data lake implementation

Flooding a Hadoop cluster with data that isn't organized and managed properly can stymie analytics efforts. Take these steps to help make your data lake accessible and usable. Continue Reading
Data analytics architecture must break down higher ed silos

Siloed data isn't only a problem for businesses. It's also a big issue for many large universities -- one that their data infrastructures need to resolve for effective analytics. Continue Reading
Three ways to turn old files into Hadoop data sets in a data lake

Hadoop data lakes offer a new home for legacy data that still has analytical value. But there are different ways to convert the data for use in Hadoop depending on your analytics needs. Continue Reading

Database architecture design has to guard against DBMS chaos

The proliferation of database technologies gives organizations more options to meet data processing needs. However, a strong architecture strategy is a must to avoid a DBMS free-for-all. Continue Reading
Make data usability a priority on data quality for big data

To help make big data analytics applications more effective, IT teams need to augment conventional data quality processes with measures aimed at improving data usability for analysts. Continue Reading
Database protection methods expand to shield data from attackers

Database vendors have beefed up the security tools in their software -- and that's a good thing because attackers are increasingly targeting database systems to steal sensitive data. Continue Reading
Ingesting data into a data lake may give you data indigestion

Big data vendors routinely push the notion of ingesting all of your data into a data lake. But in many cases, doing so is an unnecessary step that could cause data ingestion problems. Continue Reading
Blooming of master data sources complicates customer MDM efforts

As companies collect customer data and other types of information from more and more sources, the master data management process is becoming even more complex than it already was. Continue Reading
NoSQL data stores offer a mixed menu for managing diverse data sets

NoSQL databases provide alternatives to relational software for managing pools of big data. But prospective buyers need to discern between the various types of NoSQL technologies. Continue Reading

Data governance initiatives get more reliant on data lineage info

Agile development and big data applications complicate efforts to govern data, but a sharper focus on tracking and managing data lineage can help smooth the governance process. Continue Reading
Metadata management tools help data lake users stay on course

Effective metadata management processes can prevent analytics teams working in data lakes from creating inconsistencies that skew the results of big data analytics applications. Continue Reading
Streaming, connectivity new keys to data integration architecture

The growing use of the cloud, big data and data from outside sources has complicated data integration, making the addition of data streaming and broader connectivity a must. Continue Reading
Top issues for data management programs include big data, IoT, cloud

It's time for big data systems to prove their business value, consultant Andy Hayler says. He also sees growing roles for IoT, the cloud and machine learning in the data management process. Continue Reading