How Data Is Helping To Resolve Supply And Demand Challenges

Perhaps one of the most sweeping outcomes of the 2020 pandemic has been its effect on the global supply chain. From consumer goods to raw materials, products are either unavailable for purchase or take excessively long to reach their destinations. Even common grocery items like baby formula are becoming hard to find, as reported by CBS in an April 2022 report.

Analysts predict that the major supply and demand crunches will have less impact in the future, per CNBC. However, businesses and buyers aren’t content to wait until early 2023 to feel less of a pinch. They want answers now, and they’re getting them in the form of innovative uses of data and technology.

As it turns out, data—when utilized thoughtfully—has value in smoothing out supply chain hiccups. Below are several examples of how data is being tapped to tackle post-pandemic procurement and delivery issues.

1. Data is revealing where companies should focus their resources to satisfy customers.

Nothing is as frustrating for shoppers as being unable to get what they want. To better allocate resources and anticipate needs, some brands are leveraging real-time data analytics. Understanding in-the-moment demands enables teams to pivot and respond.

An example of this type of process is Chipotle’s use of Semarchy’s data management tool. After “The Great Carnitas Shortage of 2015,” the company realized that it needed to make adjustments to its supply chain. By aligning operations, communications channels, and ordering platforms, Chipotle found it could more easily stay ahead of supply chain issues. This has helped the company meet customer experience assumptions and avoid snags.

2. Data is reducing friction from delays in service industries.

Many services that followed more traditional in-person models were forced to embrace digitization during Covid. Many found that their internal processes weren’t ready for the challenges or consumer expectations of online transactions, though. For instance, some small to mid-sized financial lenders realized that they didn’t have the workflows or tools to streamline application processing. As a result, they risked falling behind their bigger competitors.

Data-driven software solutions from entities like publicly traded MeridianLink have helped fill this gap. MeridianLink, valued at over $2 billion, designed a data-rich platform to gather and process loans rapidly. Their platform has enabled nearly 2,000 financial institutions to swiftly turn around consumer loan applications without causing friction.

Due to the improvement in efficiency backed by data, banks, credit unions, and mortgage lending houses can keep pace. In today’s strong real estate market, that’s a huge supply and demand advantage.

3. Data is freeing employees to concentrate more fully on supply chain management.

Overcoming major supply chain hurdles can only happen when thought leaders have the bandwidth to brainstorm. Regrettably, far too many of them are bogged down by repetitive tasks. If those tasks can be automated, they can take up far less time. The result is teams who can concentrate on solving high-level concerns.

For instance, consider digital pioneering company IBML and its Cloud Capture software. The software captures, identifies, and classifies information from any source such as a complex invoice or a standard customer return form. Once appropriately logged, the information becomes available to authorized users. This type of consistent data capture facilitates a less clunky document processing.

It also frees executives, managers, and supervisors to divert attention toward pressing supply chain concerns. The supply chain conundrum won’t be fixed overnight or even in a few months. Yet fresh, data-driven solutions can help companies undergo fewer stressors as a result of supply and demand interruptions.

Many businesses have yet to digitize their supply chain processes, but rather rely on paper-based exchanges. This can lead to very limited visibility and coordination, and processes being heavily disrupted in times of crisis. This can lead to a failure to anticipate and meet demand and consequent loss of revenue.

Digitization requires investment and change management, but if properly leveraged it supports visibility, collaboration and communication. Access to real-time data compared with historical data can help businesses to identify cost drivers, support demand-supply balancing, manage warehouse cost by way of stock optimization, optimize processes, and in turn, identify opportunities to lower costs.

This can result in an ecosystem which makes digitization and data sharing pay by improving economic and financial performance.The collection and analysis of data creates valuable visibility and understanding within the supply chain but also greater confidence in the analysis and decision making process.

It enables businesses to introduce governance mechanisms and business models to measure the demand signal across the supply chain. Data can be used to oil the wheels of the supply chain but to achieve these benefits collaboration and the sharing of data is required amongst participants across the supply chain or at least between critical parts of the chain.

Collaboration and data sharing require trust. This can be challenging, particularly where the parties in the supply chain are competitors.

Serenity Gibbons


Source: How Data Is Helping To Resolve Supply And Demand Challenges

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How Business Intelligence Can Fuel Your Efforts

Big data holds big potential. According to IDC, businesses spent $215 billion on big data and business intelligence solutions in 2021 alone. That represents a 10% increase compared to 2020. Job growth in data analytics and business intelligence (BI) also remains strong. It’s clear that the future is doubling down on data. But all its power and glory mean very little until you can answer one question: What can data-driven intelligence do for you?

Having a theoretical understanding of how to foster an intelligent business is one thing. Putting your BI tools to work and generating results is quite another. Too many organizations fail to bridge the gap and successfully use business data to transform their operations. The effectiveness of BI data can suffer from:

  • Failure to involve the right people in the decision-making process
  • Limitations within the BI software itself
  • Poor adoption processes

So, let’s fix that. Here’s a closer look at BI, along with some steps you can take to ensure it’s broadly adopted and benefits all users.

BI Definition: What Is Business Intelligence?

Let’s start with some clarity. First, what is a good business intelligence definition? Business intelligence, or BI, refers to software that turns data into usable insights. To make sense of information, it uses:

  • Data collection tools
  • Business analytics
  • Data visualization
  • An organized data warehouse

When you wrap your data up in a neat and tidy package, you can make better business decisions based on real-world insights.

What Is Business Analytics?

So what is business analytics, then? There’s a little overlap here. Business analytics uses historical data to identify potential trends and patterns that help companies make predictions for the future.

An easy way to think about it is that business analytics is a small slice of BI. Both are important, especially for informed decision-making. Both can work together to drive better business outcomes.

Why Are BI Reporting and Business Data Important for Companies?

Imagine you are shopping for a new car. You go to the lot and see they have several of the same model in stock. On the surface, they all look the same except for the color. But after you buy one, you learn that it was actually a year older than the others on the lot and had thousands more miles than them. It had also been in a previous accident, but it still costs the same as the newer cars.

With a little more insight into what you were buying, you may have chosen differently. Since all the cars cost the same in this example, you could have gotten more value by getting a newer, less-driven model that was in better condition. BI and analytics work in a similar manner. They provide users with data they might have overlooked or might not realize is available. With those insights, users can improve business operations and data-driven decision-making.

To be clear, BI data doesn’t tell companies what to do or what will happen if they make certain decisions. Its value lies in presenting business leaders with simplified data insights related to a specific area of business. It helps to remove some of the guesswork of an endless list of what-if questions. It streamlines the process of searching for and combining various data sets to speed up the decision-making timeline.

How Do Companies Use BI Software?Typing on a laptop.

Business intelligence software sounds helpful in theory. In reality, the possible applications are nearly endless. For instance, a retail store or logistics company might use BI for predictive purposes. AI is useful for identifying supply chain risks and may help companies plan for unexpected surges in demand or delays in transport.

Sales teams will often use a BI platform to visualize their pipelines and see where all of their deals are in real-time. Take Meltwater client AxiaOrigin, for example. This consultancy specializes in best-in-class data discovery and analysis, with a particular focus on unused data that its clients struggle to unlock value from.

Much of its work is bespoke to each client, so having a flexible solution that can address a wide range of requests is critical. AxiaOrigin can explore large sets of raw data without manually mining and extracting insights. And it’s all because of our AI-powered business intelligence and analytics tools.

Another common use case is to predict future trends, which is how Fund for Peace uses Meltwater. This non-profit works to prevent conflict. It relies on easy-to-use BI to track trends and get early warnings of potential conflict. This forward-thinking approach allows the organization to respond quickly to escalating situations. Using an easy-to-understand, end-to-end solution reduces the time it takes to research and track events, which has enriched the organization’s data even more.

Specifically, BI reporting can be useful in several ways:

  • Spot trends
  • Benchmark competitors
  • Increase sales and profit
  • Optimize operations
  • Uncover problems or issues
  • Track performance
  • Predict future trends and successes
  • Understand your customers

When used to its potential, BI reporting can help to improve just about any aspect of your business.

What Kinds of Business Intelligence Tools Should You Use?

The right BI tools let you go from theoretical benefits to tangible value. To make this leap, you must first explore your options for choosing and implementing BI solutions.

Types of Intelligent Business ToolsA hand points to charts and graphs displayed on a transparent screen.

A range of tools and solutions are part of the BI market. Examples include:

  • Dashboards
  • Data visualization tools
  • Reporting features
  • Data mining
  • ETL (extract transfer load)
  • OLAP (online analytical processing)

Among the most common are dashboarding and visualization tools. Dashboards can be customized to display certain types of data at a glance. These are most often used when business leaders need to access the same information on an ongoing basis. Visualization tools turn data into visual images or models for easier information processing.

All of the above can fall into one of two buckets. There’s the “classic” BI that focuses only on in-house transactional data. And then there’s “modern” BI that takes internal and external data from a variety of sources into account. Modern BI offers additional advantages to completing and enriching data sets, which allows for faster and easier analysis.

Today’s BI solutions are largely cloud-based software-as-a-service (though some are still on-premise). They span a range of features and functionality. They’re enterprise-grade in terms of power. But even non-technical users can benefit from the approach that many BI applications take. Having your own data analysts or team of data scientists is great, but it is no longer required for deploying BI.

Going Beyond a Business Intelligence System

A person with long hair smiles while sitting at a table having a meeting with colleagues.It’s not just a matter of choosing software and tools to make BI solutions work. This is where a lot of companies go wrong. You cannot simply “solution-ize” your business. You must factor in other considerations that can make or break your BI implementation.

First, companies need to instill the right culture. Technology itself isn’t enough if the people using it can’t make heads or tails of it. Staff empowered to make decisions who know the right questions to ask are ideally suited for BI. They’re usually skilled in finding patterns in sales data or social media mentions, for example. They also view a BI solution as more than just a data tool. They see it as a valuable way to investigate how past actions triggered results and to better predict the results of future actions.

Also, companies need buy-in from the top down. BI software isn’t just a one-off activity. On the contrary, its value grows over time as it collects more data for deeper and more reliable insights. With this in mind, don’t expect BI solutions to perform miracles overnight, and don’t try to implement them quickly.

Doing so most often means that some aspects of the organization were not taken into account. Using an implementation consultant can shore up these little bumps in the road that could ultimately derail your BI program. They ask hard questions to achieve short-term goals and long-term value.

The data you feed your BI software will also contribute to your success. You need to consider structured and unstructured data sources to gauge customer sentiment and relate it to other data points. This is becoming critical as customers engage with businesses through a variety of channels. Also, data should come from verified, reliable sources to maintain its integrity and reap all the benefits of BI.

Lastly, organizations need to establish specific goals for their BI system. Goal creation ensures that companies are collecting and analyzing the right data. You’ll likely have a mix of metrics related to customer satisfaction, sales, and internal user adoption. Your BI system must be flexible and customizable to accommodate future goals and priority shifts.

By: TJ Kiely

Source: How Business Intelligence Can Fuel Your Efforts


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What An Ethical Data Strategy Can Look Like

That’s according to Angela Benton, the founder and CEO of Streamlytics, a company that collects first-party consumer data transparently and aims to disrupt the current model of third-party mining of data from cookies and other methods that raise privacy and ethics concerns. Most recently, she was named one of Fast Company‘s Most Creative People for helping consumers learn what major companies know about them and paying them for the data they create while using streaming services like Netflix or Spotify.

In the latest Inc. Real Talk streaming event, Benton explains that she founded the company with minorities in mind, particularly the Black and Latinx communities, because of the disproportionate way they’ve been affected by data and privacy. For example, she points to the recent controversy over facial recognition data being sold to the police, which has a much higher error rate when comparing data of Black and Asian male faces, which could potentially lead to wrongful arrests.

“That becomes extremely important when you think of what artificial intelligence is used for in our day-to-day world,” she says, noting that AI is used for everyday interactions like loan applications, car applications, mortgages, and credit cards. Using her company’s methods, Benton says, clients can secure ethically sourced data, so that algorithms won’t negatively affect communities that have historically suffered from discriminatory practices.

Here are a few suggestions from Benton for finding data ethically without relying on third-party cookies.

Do your own combination of data sets.

“How [Streamlytics] gets data is very old school,” Benton says. Instead of relying on tech to combine data points, she says, you can manually compare data you already own and make assumptions using your best judgment. You may have data from a Shopify website, for example, about the demographic of your customers, and then you can go to a specific advertiser, like Hulu, for instance, to then target people that fit that profile.

Use your data to discover new products.

You can also look to your data to find common searches or overlapping interests to get ideas for new products, Benton says. Often, she says, she receives data requests from small business owners to discover ideas that aren’t currently on the market, for example, a vegan searching for a vitamin.

This combination method surprised Benton when she presented clients with data. “I thought it was going to be more focused on just like, “How can I make more money?” she says. “But we are hearing from folks that they want access to data to use it in more creative ways.”

Don’t take social media data at face value.

Benton and her company purposely do not source social media data because she thinks the data leave too much out of the full picture. You may get a customer’s age and “likes” from a social media page, but that doesn’t tell you what they’re searching for or what their habits are.


Data Privacy: 4 Things Every Business Professional Should Know

5 Applications of Data Analytics in Health Care

Data Science Principles

“That’s not, to me, meaningful data. That’s not where the real value lies,” she says. “We’re not focused on what people are doing on social media, we’re focused on all of the activities outside of that.” She gave a scenario where a consumer is watching Amazon Prime, while also scrolling on Uber Eats to find dinner.

Data signals are happening at the same time, but they’re not unified. It’s up to businesses to connect the dots. To Benton, that’s more meaningful than what you’re posting and what you’re liking on social media.

Source: What an Ethical Data Strategy Can Look Like |



“Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism”.

“Who Owns the Data? Open Data for Healthcare”.

“Note – The Right to Be Forgotten”.

“Big Data ethics”.

“Data workers of the world, unite”.

“Challenges and Opportunities of Big Data in Health Care: A Systematic Review”

Personal Data trading Application to the New Shape Prize of the Global Challenges Foundation

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

“Methodology – Global Open Data Index

The Open Knowledge Foundation

Why Is China Cracking Down on Ride-Hailing Giant Didi?

Just days after Didi Global Inc., China’s version of Uber, pulled off a $4.4 billion initial public offering in New York, the Chinese cyberspace regulator effectively ordered it removed from app stores in its home market, citing security risks. The ruling doesn’t stop the company from operating -– its half-billion or so existing users will still be able to order rides for now. But it adds to the uncertainty surrounding all Chinese internet companies as regulators increasingly assert control over Big Tech.

1. What’s Didi?

It’s China’s biggest ride-hailing company. Didi squeezed Uber out of China five years ago, buying out the American company’s operations after an expensive price war. Its blockbuster IPO on June 30 was the second-biggest in the U.S. by a company based in China, after Alibaba Group Holding Ltd, giving Didi a market value of about $68 billion.

Accounting for stock options and restricted stock units, the company’s diluted value exceeds $71 billion — well below estimates of up to $100 billion as recently as a few months ago. The relatively modest showing reflects both investors’ increasing caution over pricey growth stocks, and China’s recent crackdown on its biggest tech players.

2. What is this investigation about?

The specifics are still very unclear. Two days after the IPO, the Cyberspace Administration of China said it’s starting a cybersecurity review of the company to prevent data security risks, safeguard national security and protect the public interest. Two days after that it said Didi had committed serious violations in the collection and usage of personal information and ordered the app pulled. There are no details on what precisely the investigation centers on, when or where the alleged violations occurred or whether there will be more penalties to come.

3. Are there any hints?

The Global Times, a Communist Party-backed newspaper, wrote in an editorial that Didi undoubtedly has the most detailed travel information on individuals among large internet firms and appears to have the ability to conduct “big data analysis” of individual behaviors and habits. To protect personal data as well as national security, China must be even stricter in its oversight of Didi’s data security, given that it’s listed in the U.S. and its two largest shareholders are foreign companies, it added.

4. Is it just Didi?

No. The Chinese internet regulator has widened its probe to two more U.S.-listed companies, targeting Full Truck Alliance Co. and Kanzhun Ltd. soon after launching the review into Didi.

5. Was this out of the blue?

No. In May, China’s antitrust regulator ordered Didi and nine other leaders in on-demand transport to overhaul practices from arbitrary price hikes to unfair treatment of drivers. More broadly, Beijing is in the process of a sweeping crackdown on the nation’s Big Tech firms designed to curb their growing influence.

In November 2020 the authorities derailed the planned IPO of fintech giant Ant Group Co. and in April hit Alibaba with a record $2.8 billion fine after an antitrust probe found it had abused its market dominance. Didi, however, said on Monday it was unaware of China’s decision to halt registrations and remove the app from app stores before its listing.

6. Why does Didi matter?

You can’t really overstate just how dominant Didi is in ride hailing in China, accounting for 88% of total trips in the fourth quarter of 2020. When Didi bought Uber’s Chinese operations in 2016, Uber took a stake in the company that currently stands at 12%. Didi’s U.S. IPO was shepherded by a who’s who of Wall Street banks. Its largest shareholder is Japan’s SoftBank Group Corp. with more than 20%, and others include Chinese social networking colossus Tencent Holdings Ltd. However, due to Didi’s ownership structure, Chief Executive Officer Cheng Wei and President Jean Liu control more than 50% of the voting power.

7. How’s the company doing?

While Didi had a net loss of $1.6 billion on revenue of $21.6 billion last year, according to its filings with the U.S. Securities and Exchange Commission, its diversity cushioned it against the worst of the pandemic downturn. The company reported net income of $837 million in the first quarter of 2021. With growth in its core market beginning to slow, it has expanded rapidly into fields from car repairs to grocery delivery and has pumped hundreds of millions into researching autonomous driving technology. It’s also said to be planning to expand services into Western Europe.

8. What happens now?

On Didi specifically the critical question is what the review regarding user data finds. But analysts are already looking at the likely wider impact. Key issues are whether the action is likely to discourage other Chinese tech firms from embarking on an overseas listing, and whether the action marks a new direction for the regulatory crackdown. Didi itself said in a statement in would fully cooperate with the review. It warned though that the removal of the app for new users may have an adverse affect on revenue.

Based on the laws cited by the regulators, Didi is probably being investigated over its purchase of certain products and services from other suppliers, which may threaten national data security, according to analysts from Shenzhen-based Ping An Securities. “Didi will inevitably have to check its core network equipment, high-performance computers and servers, large-capacity storage equipment, large databases and application software, network security equipment, and cloud computing services, sort them out and make necessary rectifications to meet regulatory requirements,” the analysts wrote in a note on Monday.

Yang Sirui, chief analyst for the computer industry at Bank of China International, said that Didi went for its public listing in the US hastily, probably due to investor pressure. “Listing Didi as soon as possible meets the demands of the capital,” he said. “But if [Didi] had arbitrarily collected user privacy data, abused it, or monetized it illicitly, it will inevitably be punished by Chinese regulators.” Since its founding in 2012, Didi has undergone a number of private fundraising rounds, raising tens of billions of dollars from venture capital or major tech firms. According to its IPO prospectus, SoftBank Vision Fund is currently the largest shareholder of Didi, with a 21.5% stake. Uber (UBER) and Tencent (TCEHY) followed with a 12.8% and 6.8% stake respectively.

The Reference Shelf

— With assistance by Coco Liu, Molly Schuetz, Abhishek Vishnoi, and Colum Murphy


Source: Why China is Citing Security Risks in Crack Down on $UBER rival $DIDI – Bloomberg



Didi is a Chinese vehicle for hire company headquartered in Beijing with over 550 million users and tens of millions of drivers. The company provides app-based transportation services, including taxi hailing, private car hailing, social ride-sharing, and bike sharing; on-demand delivery services; and automobile services, including sales, leasing, financing, maintenance, fleet operation, electric vehicle charging, and co-development of vehicles with automakers.

In March 2017, the Wall Street Journal reported that SoftBank Group Corporation approached DiDi with an offer to invest $6 billion in the company to fund the ride-hailing firm’s expansion in self-driving car technologies, with a significant portion of the money to come from SoftBank’s then-planned $100 billion Vision Fund.

DiDi claims that it provides over tens of millions of flexible job opportunities for people, including a considerable number of women, laid-off workers and veteran soldiers. Based on a survey released by DiDi in March 2019, women rideshare drivers in Brazil, China and Mexico account for 16.7%, 7.4% and 5.6% of total rideshare drivers on its platforms, respectively. DiDi supports more than 4,000 innovative SMEs, which provides more than 20,000 jobs additionally.

40% of DiDi’s employees are women. In 2017, DiDi launched a female career development plan and established the “DiDi Women’s Network”. It is reportedly the first female-oriented career development plan in a major Chinese Internet company.


Why Your Workforce Needs Data Literacy

Organizations that rely on data analysis to make decisions have a significant competitive advantage in overcoming challenges and planning for the future. And yet data access and the skills required to understand the data are, in many organizations, restricted to business intelligence teams and IT specialists.

As enterprises tap into the full potential of their data, leaders must work toward empowering employees to use data in their jobs and to increase performance—individually and as part of a team. This puts data at the heart of decision making across departments and roles and doesn’t restrict innovation to just one function. This strategic choice can foster a data culture—transcending individuals and teams while fundamentally changing an organization’s operations, mindset and identity around data.

Organizations can also instill a data culture by promoting data literacy—because in order for employees to participate in a data culture, they first need to speak the language of data. More than technical proficiency with software, data literacy encompasses the critical thinking skills required to interpret data and communicate its significance to others.

Many employees either don’t feel comfortable using data or aren’t completely prepared to use it. To best close this skills gap and encourage everyone to contribute to a data culture, organizations need executives who use and champion data, training and community programs that accommodate many learning needs and styles, benchmarks for measuring progress and support systems that encourage continuous personal development and growth.

Here’s how organizations can improve their data literacy:


Employees take direction from leaders who signal their commitment to data literacy, from sharing data insights at meetings to participating in training alongside staff. “It becomes very inspiring when you can show your organization the data and insights that you found and what you did with that information,” said Jennifer Day, vice president of customer strategy and programs at Tableau.

“It takes that leadership at the top to make a commitment to data-driven decision making in order to really instill that across the entire organization.” To develop critical thinking around data, executives might ask questions about how data supported decisions, or they may demonstrate how they used data in their strategic actions. And publicizing success stories and use cases through internal communications draws focus to how different departments use data.

Self-Service Learning

This approach is “for the people who just need to solve a problem—get in and get out,” said Ravi Mistry, one of about three dozen Tableau Zen Masters, professionals selected by Tableau who are masters of the Tableau end-to-end analytics platform and now teach others how to use it.

Reference guides for digital processes and tutorials for specific tasks enable people to bridge minor gaps in knowledge, minimizing frustration and the need to interrupt someone else’s work to ask for help. In addition, forums moderated by data specialists can become indispensable roundups of solutions. Keeping it all on a single learning platform, or perhaps your company’s intranet, makes it easy for employees to look up what they need.


Success Indicators

Performance metrics are critical indicators of how well a data literacy initiative is working. Identify which metrics need to improve as data use increases and assess progress at regular intervals to know where to tweak your training program. Having the right learning targets will improve data literacy in areas that boost business performance.

And quantifying the business value generated by data literacy programs can encourage buy-in from executives. Ultimately, collecting metrics, use cases and testimonials can help the organization show a strong correlation between higher data literacy and better business outcomes.


Knowledge Curators

Enlisting data specialists like analysts to showcase the benefits of using data helps make data more accessible to novices. Mistry, the Tableau Zen Master, referred to analysts who function in this capacity as “knowledge curators” guiding their peers on how to successfully use data in their roles. “The objective is to make sure everyone has a base level of analysis that they can do,” he said.

This is a shift from traditional business intelligence models in which analysts and IT professionals collect and analyze data for the entire company. Internal data experts can also offer office hours to help employees complete specific projects, troubleshoot problems and brainstorm different ways to look at data.

What’s most effective depends on the company and its workforce: The right data literacy program will implement training, software tools and digital processes that motivate employees to continuously learn and refine their skills, while encouraging data-driven thinking as a core practice.

For more information on how you can improve data literacy throughout your organization, read these resources from Tableau:

The Data Culture Playbook: Start Becoming A Data-Driven Organization

Forrester Consulting Study: Bridging The Great Data Literacy Gap

Data Literacy For All: A Free Self-Guided Course Covering Foundational Concepts

By: Natasha Stokes

Source: Why Your Workforce Needs Data Literacy



As data collection and data sharing become routine and data analysis and big data become common ideas in the news, business, government and society, it becomes more and more important for students, citizens, and readers to have some data literacy. The concept is associated with data science, which is concerned with data analysis, usually through automated means, and the interpretation and application of the results.

Data literacy is distinguished from statistical literacy since it involves understanding what data mean, including the ability to read graphs and charts as well as draw conclusions from data. Statistical literacy, on the other hand, refers to the “ability to read and interpret summary statistics in everyday media” such as graphs, tables, statements, surveys, and studies.

As guides for finding and using information, librarians lead workshops on data literacy for students and researchers, and also work on developing their own data literacy skills. A set of core competencies and contents that can be used as an adaptable common framework of reference in library instructional programs across institutions and disciplines has been proposed.

Resources created by librarians include MIT‘s Data Management and Publishing tutorial, the EDINA Research Data Management Training (MANTRA), the University of Edinburgh’s Data Library and the University of Minnesota libraries’ Data Management Course for Structural Engineers.

See also

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