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James M. Connolly
August 25, 2022
5 Min Read
Thibault Renard via Alamy Stock
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 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.
Artificial intelligence and machine learning are not “set and forget” technologies. They need quality assurance to operate, and continue to operate, as intended.
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.
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
A survey shows tech leadership's growing concern about AI bias, AI ethics, as negative events impact revenue, customer losses, and more.
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.
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
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 are prioritizing data quality, ROI from data and analytics investments, and data sharing.
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.
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.
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.
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.
Everyone in the organization needs to understand how to access data, keep it secure and think critically about its potential use cases and applications.
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.
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 says the deal to acquire the data observability startup will further Big Blue's own mission to provide ‘observability for business.’
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.
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.
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.
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.
About the Author(s)
Contributing Editor and Writer
Jim Connolly is a versatile and experienced freelance technology journalist who has reported on IT trends for more than three decades. He was previously editorial director of InformationWeek and Network Computing, where he oversaw the day-to-day planning and editing on the sites. He has written about enterprise computing, data analytics, the PC revolution, the evolution of the Internet, networking, IT management, and the ongoing shift to cloud-based services and mobility. He has covered breaking industry news and has led teams focused on product reviews and technology trends. He has concentrated on serving the information needs of IT decision-makers in large organizations and has worked with those managers to help them learn from their peers and share their experiences in implementing leading-edge technologies through such publications as Computerworld. Jim also has helped to launch a technology-focused startup, as one of the founding editors at TechTarget, and has served as editor of an established news organization focused on technology startups at MassHighTech.
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