There’s a hoarder mentality lingering in the data collection and management business. No, they aren’t clinging to 30-year-old magazines or a roomful of plastic shopping bags. It’s a strategy under which some enterprises insist on collecting every byte of data possible, “just in case we need it someday.”

The hoarder mentality isn’t as prevalent as it was in the early days of big data, but some data professionals and marketers just can’t let go of their virtual shopping bags.

There’s a delicate balancing act that enterprises face in this era of advanced analytics and AI. Not enough data can leave a company eating competitors’ dust. Too much data can be hard to manage, even can make the company legally liable under myriad data privacy regulations. Good decisions, trustworthy AI apps, and efficient data management call for enterprises to collect, collate, and use the right data for them.

In this Quick Study you’ll see some of InformationWeek’s articles on data quality, data management, and how that right data can drive company success.

How Data Quality Contributes to Success

Data Quality: How to Show the ROI for Projects

Data quality is critical to enterprise success, but it also can be hard to quantify. Here are some key steps that you can take to measure and communicate the tangible return on investment for your data quality initiatives.

AI and Machine Learning Need Quality Assurance

Artificial intelligence and machine learning are not “set and forget” technologies. They need quality assurance to operate, and continue to operate, as intended.

How to Elevate Your Organization’s Use of Data Analytics

Here are three best practices for leveling up your organization’s use of analytics and attaining ROI with an enterprise analytics program. Think tools and company culture.

Seeking an Oasis in a Data Desert

Gaps in data quality, particularly due to supply chain issues during the pandemic, is becoming a serious influence on planning effective machine learning models.

When Data Does Bad

The Cost of AI Bias: Lower Revenue, Lost Customers

A survey shows tech leadership's growing concern about AI bias, AI ethics, as negative events impact revenue, customer losses, and more.

3 Ways Data Problems Can Stop Your Business

In today’s data-rich world, it’s important to not only focus on how data can be used to benefit business, but also where a flawed data strategy raises hurdles for the organization.

9 Ways to Reduce the Environmental Impact of Data

IT leaders can reduce the environmental impact of their data by considering a set of data sustainability principles, according to a Gartner analyst.

The Data Team and Your Organization

Priorities of Highly Successful Chief Data Officers

Is your data organization focused on the right areas? A survey of chief data officers looks at how these data executives can enable success in their organizations by the projects they choose to prioritize.

Chief Data Officers Help Steer Digital Transformations

Chief Data Officers are prioritizing data quality, ROI from data and analytics investments, and data sharing.

From AI to Teamwork: 7 Key Skills for Data Scientists

Today’s data scientists need more than proficiency in AI and Python. Organizations are looking for specialists who also feel at home in the C-suite.

Why to Create a More Data-Conscious Company Culture

The drive to greater transparency in data requires efforts beyond breaking down data silos. Here’s how and why to focus on cultivating a more data-literate workforce.

Data Science: A Guide to Careers and Team Building

Here's a collection of curated articles to help IT professionals learn how to make a career out of data science or how to build a team of data scientists.

CIOs Take Center Stage on ESG Strategies, Battling an Overflow of Data

With environmental, social and governance strategies forming a core part of organizational business plans, CIOs need to tap into various areas of expertise to ensure ESG efforts are organized and integrated enterprisewide.

Creating a Data Literate Culture in Your Organization

Everyone in the organization needs to understand how to access data, keep it secure and think critically about its potential use cases and applications.

An Insider's Look at Intuit's AI and Data Science Operation

Intuit's Director of Data Science speaks with InformationWeek about how the company's data operations have grown and evolved from just a few data scientists trying to sell executives on the value of data projects to becoming an AI-driven platform company.

Deployment Risk: AutoML & Machine Learning Without Expertise

Learn exactly what AutoML is, the value data scientists bring, and best practices on how to use AutoML to kickstart projects within your business.

The State of the Data Sector

IBM Databand.ai Acquisition Targets Data Observability

IBM says the deal to acquire the data observability startup will further Big Blue's own mission to provide ‘observability for business.’

GDPR Anniversary: Farewell to Global Data Lakes

Here’s where we’re at with the regulation and the data challenges organizations are faced with today. While individuals are concerned about privacy, organizations struggle to balance data privacy with the need to level AI, machine learning and analytics to compete.

Data Fabrics: Six Top Use Cases

A data fabric management architecture optimizes access to distributed data while intelligently curating and orchestrating it for self-service delivery. Here's a look at some ways a data fabric may be able to help your organization.

AI Set to Disrupt Traditional Data Management Practices

The growth of advanced analytics such as machine learning and artificial intelligence is set to drive a disruption in traditional data management operations, according to Gartner.

Beyond the Data Warehouse: What Are the Repository Options Today?

With the rise of unstructured big data, a new wave of data repositories has come into use that don’t always involve a data warehouse.