Theres a hoarder mentality lingering in the data collection and management business. No, they arent clinging to 30-year-old magazines or a roomful of plastic shopping bags. Its a strategy under which some enterprises insist on collecting every byte of data possible, just in case we need it someday.

The hoarder mentality isnt as prevalent as it was in the early days of big data, but some data professionals and marketers just cant let go of their virtual shopping bags.

Theres 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 youll see some of InformationWeeks articles on data quality, data management, and how that right data can drive company 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 Organizations Use of Data Analytics

Here are three best practices for leveling up your organizations 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.

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 todays data-rich world, its 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.

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

Todays 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. Heres 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.

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

Heres where were 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 dont always involve a data warehouse.

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Quick Study: How to Find the Right Data - InformationWeek

In todays data age, getting data analytics right is more essential than ever. A robust data analytics implementation enables businesses to hit key performance metrics, build data and AI-driven customer experiences (think personalize my feed) and capture operational issues before they spiral out of control. The list of competitive advantages goes on, but the bottom line is that many organizations successfully compete based on how effectively their data-driven insights inform their decision-making.

Unfortunately, implementing an effective data analytics platform is challenging due to orchestration (DAG alert!), modeling (more DAGs!), cost control (Who left this instance running all weekend?!) and fast-moving data landscapes (data mesh, data fabric, data lakehouses ). Enter DataOps. Recognizing modern data challenges, organizations are adopting DataOps to help them handle enterprise-level datasets, improve data quality, build more trust in their data and exercise greater control over their data storage processes.

DataOps is an integrated and agile process-oriented methodology that helps businesses develop and deliver effective analytics deployments. It aims to improve the management of data throughout the organization.

While there are multiple definitions of DataOps, below are common attributes that encompass the concept while going beyond data engineering. Heres how we define it:

We broadly define DataOps as a culmination of processes (e.g., data ingestion), practices (e.g., automation of data processes), frameworks (e.g., enabling technologies like AI) and technologies (e.g., a data pipeline tool) that help organizations to plan, build and manage distributed and complex data architectures. DataOps includes management, communication, integration and development of data analytics solutions, such as dashboards, reports, machine learning models and self-service analytics.

DataOps is attractive because it eliminates the silos between data, software development and DevOps teams. The very promise of DataOps encourages line-of-business stakeholders to coordinate with data analysts, data scientists and data engineers. Via traditional agile and DevOps methodologies, DataOps ensures that data management aligns with business goals. Consider an organization endeavoring to increase the conversion rate of their sales leads. In this example, DataOps can make a difference by creating an infrastructure that provides real-time insights to the marketing team, which can help the team to convert more leads. Additionally, an Agile methodology can be employed for data governance, where you can use iterative development to develop a data warehouse. Lastly, it can help data science teams use continuous integration and continuous delivery (CI/CD) to build environments for the analysis and deployment of models.

The amount of data created today is mind-boggling and will only increase. It is reported that 79 zettabytes of data were generated in 2021 and that number is estimated to reach 180 zettabytes by 2025. In addition to the increasing volume of data, organizations today need to be able to process it in a wide range of formats (e.g., graphs, tables, images) and with varying frequencies. For example, some reports might be required daily, while others are needed weekly, monthly or on demand. DataOps can handle these different types of data and tackle varying big data challenges. Add in the internet of things (IoT), such as wearable health monitors, connected appliances and smart home security systems, and that introduces another variable for organizations that also have to tackle the complexities of heterogeneous data as well.

OK, so, how can we make this a reality? First, to manage the incoming data from different sources, DataOps can use data analytics pipelines to consolidate data into a data warehouse or any other storage medium and perform complex data transformations to provide analytics via graphs and charts.

Second, DataOps can use statistical process control (SPC)a lean manufacturing methodto improve data quality. This includes testing data coming from data pipelines, verifying its status as valid and complete, and meeting the defined statistical limits. This enforces the continuous testing of data from sources to users by running tests to monitor inputs and outputs and ensure business logic remains consistent. In case something goes wrong, SPC notifies data teams with automated alerts. This saves them time as they dont have to manually check data throughout the data life cycle.

Around 18% of a data engineers time is spent on troubleshooting. DataOps enables automation to help data professionals save time and focus on more valuable high-priority tasks.

Consider one of the most common tasks in the data management life cycle: Data cleaning. Some data professionals have to manually modify and remove data that is incomplete, duplicate, incorrect or flawed in any number of ways. This process is repetitive and doesnt require any critical thinking. You can automate it by either setting customized scripts or installing a built-in data cleaning software tool.

Additional processes that can be automated via DataOps include:

To develop your own DataOps architecture, you need a reliable set of tools that can help you improve your data flows, especially when it comes to crucial aspects of DataOps, like data ingestion, data pipelines, data integration and the use of AI in analytics. There are a number of companies thatprovide a DataOps platform for real-time data integration and streaming that ensures the continuous flow of data with intelligent data pipelines that span public and private clouds. Looking to increase the likelihood of success of data and analytics initiatives? Take a closer look at DataOps and harness the power of your data.

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Where DataOps and Opportunities Converge - DevOps.com

Name: Angela Song

Position: Senior Director of the Office of Operational Strategic Initiatives (OSI); Leader of the OSI Tritonlytics Team

Years at UC San Diego: 20 years in January 2023

What she does at UC San Diego: As the senior director of OSI, Song helps spearhead strategic initiatives focused on continuously improving UC San Diegos operational efficiencies and strengthening the universitys commitment to a service- and people-oriented culture. Shes been with OSI since the beginning. When Chancellor Pradeep K. Khosla created the office in 2013, Song was one of three individuals selected from the Business and Financial Services (BFS) department to work with one of UC San Diegos external partners, McKinsey Consulting, on creating the universitys inaugural strategic plan.

Before OSI, Song served as the director of Administration and Metrics in Business and Financial Servicesa position she held since joining UC San Diego in 2003. She received her doctorate in Industrial and Organizational Psychology from UC Berkeley and her background in social science, data analysis, surveys and strategy management led to her role managing and overseeing internal performance metrics and campuswide surveys. Now, the surveys Song helped createincluding the Staff@Work Survey which opened Aug. 16, the Student Satisfaction Survey and the Academics@UCSD surveyare key metrics of the Chancellor's strategic plans.

Song also leads the Tritonlytics team, an OSI working group that aims to transform raw data into valuable insights and accelerate action. Her expertise is a key reason that Tritonlytics impact extends far beyond UC San Diego; the group assists other institutions nationwide in designing, administering and analyzing large-scale quantitative and qualitative surveys. Coast to coast, over 15 universities across four university systems participate in Tritonlytics higher education benchmarking program, which analyzes customer service, staff/faculty engagement and campus climate.

What she loves about UC San Diego: Song was inspired by how quickly and collectively the campus jumped into action in response to the COVID-19 pandemic.

Return to Learn is everything I love about UC San Diego; we collaborated, partnered, shared expertise and put aside our egos and titles to step upand step back, when necessaryso that our campus could do more than just survive, Song said. We were able to thrive during a global pandemic and let research and learning continue safely.

Even in the virtual era, Song felt the care and connections between staff, students and faculty at the university were strong. Many other campuses grew paralyzed; Song is proud of how our campus took the pandemic in stride.

UC San Diego shone as a beacon of hope that helped others to follow our lead, said Song. Not only do we do things right, we do the right things too.

Best advice received: Be grateful for everything, she shared with a smile. Sometimes our greatest blessings are in disguise.

Something unique in your workspace: Song is known around the OSI office as an avid dog lover. Her ten-year-old retired greyhound, Bali, and her five-year-old whippet, Hobbes, are never more than a few feet away from her desk.

Theyre always watching me and waiting for me to take a break so I can take them on a W-A-L-K-I-E, she shared with a laugh, spelling the word so her nearby pups wouldnt hear.

Favorite spot on campus: Its hard not to be a sunset lover, and Song is no exception. Near Scripps Pier, a few benches dot a patch of grass that looks out over the ocean. When she can, Song loves to catch a sunset there.

Something most people don't know about her: Where we spend our free time is an indicator of what were passionate about, she said. Her passions have led her to work with several non-profit organizations. As an elected board member of the Network for Change and Continuous Innovation, she helps higher education institutions across the country be transformative and agile lifelong models of learning. Song also serves as a board member of the Greater San Diego Whippet Association and volunteers with the Greyhound Adoption Center.

If she had one day to do anything she wanted: I would go to Disneyland! she said, grinning. I havent been to visit since the new Star Wars Land openedI cant wait to go!

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Take 10 With a Triton: Meet Angela Song, a Lover of Dogs, Data Analytics and Disneyland - University of California San Diego

A new commercial partnership between UNSW, PEXA and Frontier SI is launching a data-driven toolkit that rapidly calculates property valuations to inform urban development. The toolkit, hosted by the new venture Slate Analytics, allows users to visualise diverse infrastructure and planning scenarios and their effect on surrounding property values. It will facilitate data-driven solutions to support better city planning.

The digital toolkit, called the Rapid Analytics Interactive Scenario Explorer (RAISE), uses big data, artificial intelligence and advanced analytics to calculate property valuations across Australias residential property market. Its interactive, what-if scenario capability represents an industry-first for planning support systems.

RAISE provides a cyber-secure, scalable and commercially viable solution that reduces the risks and costs associated with land value analysis, he says.

This new commercial partnership brings together PEXAs expertise in data and digital property settlements, and Frontier SIs specialisation in spatial mapping and geodesy with UNSWs leading research on data science and the future of our cities to deliver a world-class platform, Pettit said.

Read more: Why cycling lanes should be on the fast track for cities

Scott Butterworth, ChiefData and Analytics Officer atPEXA, says: With its evidence-driven scenario modelling, RAISE will revolutionise the way we approach urban planning and development. It could also prove highly beneficial to Australias lending community,potentially expediting the mortgage approvals process on behalf of Australian homebuyers.PEXA is delighted to be leading this venture, delivering on real-world needs.

The digital toolkit is the result of a longstanding research partnership between Frontier SI and UNSW CFRC.

Graeme Kernich, CEO FrontierSI says,Through Slate Analytics, FrontierSI and UNSW has realised its vision to bring cutting-edge AI into the property valuation products and services space. The investment by PEXA will be critical to furthering this vision and expanding the impact that this capability will have for Australia and beyond.

The venture builds on previous research with UNSWs CFRC, Frontier SI, Commonwealth Bank of Australia, Liverpool City Council, the NSW Office of the Valuer General and property data management company Omnilink, that was funded by an Australian Government Cooperative Research Centres Projects (CRC-P) grant.

A new commercial partnership between UNSW, PEXA and Frontier SI is launching a data-driven toolkit that rapidly calculates property valuations to inform urban development.

The toolkit cleans, links and embeds diverse geospatial and property data from trusted government databases and industry partners, including from such sources as the Australian Bureau of Statistics (ABS), Geoscape, Valuer Generals data and transport, education and crime statistic agencies.

It includes aproperty market dashboard developed in response to COVID-19 that provides a daily snapshot of how the property market is performing in comparison to prior to the pandemic. It has an interactive map of Australia, identifying COVID case numbers based on data from state departments of health.

The dashboard provides graphs and charts of key property market metrics, including median property prices, auction clearance rates, the house value index and the performance of the ASX 200 real estate sector, to assist government, industry and communities in understanding the impacts COVID-19 is having on the property market.

The toolkit was used in recent research evaluating the proposed high-speed rail network on the east coast of Australia. It revealed that land value growth around the high-speed stations, estimated to increase by up to $140 billion, could be used to fund the network.

A significant portion of this could be dedicated to funding its construction and supporting the creation of liveable and vibrant cities and regions, Prof. Pettit says.

Read more: UNSW launches Cities Institute to help future-proof our cities

One of its aims is to improve users understanding of land economics, in particular, the influence that infrastructure provision and other key variables play in land values, Prof. Pettit says.

The tool considers structural attributes, such as the buildings quality, age and size; neighbourhood attributes, such as crime levels and socio-economic profile; and accessibility, such as the nearest transport connections, jobs centres and services.

It is accessed through a web browser allowing multiple users in diverse locations to compare and analyse different scenarios. Users can alter key assumptions within the tool, such as access to transport, schools, hospitals and zoning plans, and map their effect on property values.

For example, users can drag and drop new train stations into urban areas and predict the anticipated increase in value (value uplift) on surrounding properties associated with the accessibility benefits of improved transport infrastructure.

New and improved transport including new stations, faster trips, more frequent trips, upgraded services or higher volume services can connect residents to jobs and services and also connect businesses to labour, supplier and customer markets, Prof. Pettit says. As such, there is an expectation that residents and businesses will pay a premium for property serviced by this infrastructure.

The toolkit was used in recent research evaluating the proposed high-speed rail network on the east coast of Australia. Image: Shutterstock

Governments are interested in measuring value uplift to determine the viability of using value capture (taxing this uplift) to fund current or future transport infrastructure projects through policies, such as developer charges, stamp duty,land tax and betterment tax, he says.

Value capture is, in essence, a tax on the increase in land values associated with new or upgraded infrastructure, and these taxes can be used to offset infrastructure costs and fund supporting initiatives, such as new parks or increased affordable housing stocks.

There has been renewed national policy interest in value capture in Australia in the debate around the National Settlement Strategy as well as the national Smart Cities Plan (2016) and the Greater Metropolitan Sydney Planning Strategy (2018), and internationally, he says.

The expense involved in engaging specialist valuation consultants means that fewer options are typically explored by planners and decision-makers, and effective value capture mechanisms are seldom employed in Australian infrastructure planning, he says.

UNSW will retain a stake in the new venture, supporting it through ongoing research and development.

The tool makes an important contribution to building more equitable and productive future cities, helping planners, councils and governments maximise investments to get the best outcomes, including better public transport, additional green space and more affordable housing stock for the whole community, Prof. Pettitsays.

Read more:Family-friendly apartments are key to a compact city

RAISE was co-designed through a series of workshops with research partners, state government-based valuers and local government urban planners to ensure its functionality and user experience aligned with end-user expectations.

The workshops evaluated rapid prototypes to optimise the design according to its data inputs, modelling and scenario formulation and visualisation, interface and workflow. Feedback shaped further development, extending the tools base functionality, model and data integration, and collaborative features through a series of iterations.

For example, feedback from stakeholders in the co-design workshops prompted the introduction of school-quality indicators into the modelling, Prof. Pettit says. Properties in catchments of schools performing well, as measured by NAPLAN results, were found to be valued higher.

The need for digital technologies, such as data-driven scenario tools, will continue to grow with rapid urbanisation and global population growth,however, the inherent complexity of big data as well as the diverse players and conflicting agendas mean the tools work best in tandem with expert knowledge, he says.

These tools and technologies work hand in hand collaboratively with planners and those key decision-makers who shape the future of the city Were all striving towards more liveable cities, more sustainable cities, more resilient, productive [and] inclusive cities. The tools are used to support those sorts of big-picture challenges.

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New land valuation tool uses big data to inform urban planning and investment - UNSW Newsroom

Correlation One forms strategic partnership with nation's leading coalition committed to hiring and advancing Black Americans without four-year degrees into family-sustaining jobs

NEW YORK, Aug. 25, 2022 /PRNewswire/ -- Correlation One today announced that it has joined forces with OneTen, a coalition designed to close the opportunity gap for Black talent in the United States by working with America's leading executives, companies, and talent developers to hire and advance one million Black Americans without four-year degrees into family-sustaining roles over the next 10 years. This strategic partnership will allow Correlation One to share and shape best practices for sourcing, developing, and hiring Black talent.

The strategic partnership will help advance Black Americans without four-year degrees into lucrative data careers.

As an endorsed OneTen talent developer, Correlation Oneis committed to increasing the hiring of Black talent without four-year college degrees into family-sustaining jobs by improving their hiring, retention, upskilling, and advancement practices to support a more diverse workforce and advance economic prosperity for all. As a OneTen coalition member and award-winning data skills training provider, Correlation One will train emerging data analysts and engineers and connect them with employers through a distinctive "last mile to jobs" approach.

"As part of OneTen's growing portfolio of career development providers bridging the gap between rising Black talent and top employers, we're proud to lend a hand in the advancement of Black Americans' financial and professional goals," said Sham Mustafa, Correlation One Co-Founder and Co-CEO. "At the same time, we know that by diversifying their data talent, our forward-thinking Employer Partners can leverage a wider range of capabilities and viewpoints to advance business goals."

Offered 100% free to learners from groups historically underrepresented in the data field, Correlation One training programs have received more than 150,000 applications and trained over 7,000 students. Over 60% of the trainees are Black a strikingly significant number given that Black professionals comprise only 7.3% of U.S. data analysts. The U.S. Bureauof Labor Statistics anticipates strong job growth within the data field at a rate of 27.9% through 2026, with employers willing to pay well for emerging talent.However, significant and pervasive structural barriers remain in place for rising data talent to acquire the technical skills necessary to secure professional positions in the lucrative, growing data economy.

While the job market continues to grow amid rising inflation and a looming recession, the racial wealth gap in America remains wide. This is largely due to the lack of access to quality, well-paying jobs that do not require college degrees: 79% of jobs paying more than $50,000 require a four-year college degree, which automatically excludes the 76% of Black Americans over age 25 with relevant experience who don't have baccalaureate degrees. With Black professionals representing less than 8% of the technology workforce, harnessing multi-stakeholder partnerships is vital to spearheading diversity and fostering pathways to success.

"Connecting Black talent through the kind of data skills training and upskilling Correlation One offers is critical to addressing the growing needs in today's data economy," said Maurice Jones, OneTen CEO. "We're thrilled to partner with Correlation One - to unlock workforce opportunities for Black talent in the data-driven jobs of the future and implement the skills that will enable Black talent to achieve generational wealth."

About Correlation One

Correlation One is a technology company whose mission is to create equal access to the data-driven jobs of tomorrow and believes that data literacy is the most important skill for the future of work. The company makes data fluency a competitive edge through global data science competitions, rigorous data skills assessments, and enterprise-focused data science education. Correlation One's solutions are used by some of the most elite employers all around the world in finance, technology, healthcare, insurance, consulting, and governmental agencies. Since launching in 2015, Correlation One has built an expert community of 250,000+ data scientists and 600+ partnerships with leading universities and data science organizations in the US, UK, Canada, China, and Latin America.

About OneTen

OneTen is a coalition of leading chief executives and their companies who are coming together to upskill, hire and promote one million Black individuals who do not yet have a four-year degree into family-sustaining jobs with opportunities for advancement over the next 10 years. OneTen connects employers with talent developers including leading nonprofits and other skill-credentialing organizations who support development of diverse talent. By creating more equitable and inclusive workforces, we believe we can reach our full potential as a nation of united citizens. OneTen recognizes the unique potential in everyone every individual, every business, every community to change the arc of America's story with Black talent. Join us at OneTen.org, where one can be the difference.

Media Contacts:Pamela Price[emailprotected]

SOURCE Correlation One

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Correlation One Partners with OneTen to Drive Economic Prosperity for Black Talent in the Data Field - PR Newswire