The government has appointed Alberto Palomo Lozano as Spains first chief data officer. A number that already exists in other countries such as France, the United States, the United Kingdom, and Canada. Palomo will be responsible for launching the Data Office, which relies on the Secretary of State for Digitization and Artificial Intelligence, which must respond to the major challenges of the data economy, and define legal and policy frameworks for engagement and governance.

On Tuesday, the government clarified that the aforementioned office should establish principles and standards to ensure data flows across sectors, ensuring privacy and respecting the rights of citizens. The Executive Director emphasized that the authority was born with the purpose of becoming a key agent for strengthening a framework ensuring data sovereignty in Spain and at the European level, promoting the construction of data centers, as well as the processing and storage of this data in Spain.

Specifically, data office functions go through the design of strategies and frameworks of reference in the field of data management, and the creation of spaces for sharing data between businesses, citizens, and public administrations in a secure, well-governed and usable way. Data in the productive sectors of the economy through big data technologies and artificial intelligence.

They insisted from the government that this figure will be very relevant to the establishment of a clear management of the data ecosystem in Spain, where the public sector and the private sector can cooperate with confidence to spread the Spanish data economy.

The new data manager will also be responsible for representing Spain at GAIA-X, a community-based European cloud project. They noted that the Data Office, together with the GAIA-X Regional Center in Spain, will support the deployment of sectoral data spaces, with a particular focus on the sectors of tourism, health, agri-food or sustainable mobility.

In terms of training, Palomo Lozano holds a PhD in Theoretical Physics from the Autonomous University of Madrid and CSIC, and has training in data mining and statistical learning. He worked at Iberdrola and at Huawei; Finally, in the R&D lab deep learning (Deep Learning) at the Canadian branch, where he worked on hardware design to improve the training phase of neural networks based on artificial intelligence.

Carme Artigas, Minister of State for Digitalization and Artificial Intelligence, announced a year ago that she would appoint a data manager to advance the governments strategy in this area.

After knowing the appointment, some companies in the technology sector celebrated it. In a statement, Sothis CEO Raul Martinez stated, Having a figure to coordinate a national strategy for data management and use is a major boost to making it easier for our companies and public administrations to adopt methodologies that allow them to extract the most value possible from data and, ultimately, be better able to competition as a country.

The director added that in the digital economy, information is the most important resource and this appointment will help promote the digitization of business processes and public administration, thus strengthening the culture of real data in Spain.

In it, Rafael Quintana, general manager of consultancy Quilk for Spain and Portugal, noted that the data would be a pillar of Spains economic recovery and that it was vital to have a number that coordinates the strategy.

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Spain already has its first Chief Data Officer to respond to the challenges of the data economy | comp - thedailyguardian.net

Process mining helps organisations discover and improve the performance of their processes, identifying bottlenecks and other areas of improvement

When implemented correctly, process mining reduces costs, improves service level agreements (SLAs) and empowers teams to solve inefficiencies quickly. Its become an asset for Business Analysts and Operations Excellence Consultants, but crucially, it provides management with rich insights about the performance of their operation.

For those organisations who have successfully implemented process mining, creating and analysing current state processes is entirely automated. So, process discovery is no longer a time-consuming and resource intensive practice. But what about those organisations who havent yet seen the benefits of process mining?

If you employ people to execute business processes using IT systems, then you should be exploring process mining.

How does process mining work?

Process mining combines data science and process analytics to mine data from information systems. For example, Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) tools, create event logs with every transaction which provides an audit trail of processes. This shows what work is being done, when, and by who.

Process mining software then uses this information to create a process model. This allows the end-to-end process to be examined, showing the detailed steps taken and any variations. Built in machine learning models then help give insights into any root cause of deviations. For example, it might point out that every time a new customer needs proof of address, the process is slowed down. These models enable management to see if their processes are performing efficiently, or if they arent, they provide the information needed to optimise them.

Choosing the right places to apply process mining is important. Organisations who apply it to processes that have already been digitised, i.e. processes that use core IT systems, tend to see the best results. It provides an evidence-based view of how processes are performing and its an easy sell to senior management once they see where problems and opportunities lie.

Different Types of Process Mining

There are three basic types of process mining:discovery,conformance, andenhancement:

Why is it so Important?

Lean Six Sigma is a method that relies on a collaborative team effort to improve performance by systematically removing waste and reducing variation. Lean Six Sigma has proved itself to be an effective methodology for reducing operating costs and increasing return-on-investment. However, identifying opportunities and measuring the effects of improvements has been difficult. Process mining helps by identifying and quantifying the inefficiencies in processes and showing how effective any changes have been. The use of these processes not only reduces costs,but it also drives more innovation, quality, and better customer retention.

Process minings success can be demonstrated by the experience of a large insurance company. A major source of inefficiency and cost for the company was their end-to-end claims process: from FNOL (First Notice of Loss) through to claims assessment to final claims payout. Process mining was used to understand how approximately 300,000 claims were routed, which steps in the process had the longest lead times, which the most variation and why. The company found that a combination of manual data processing, handling various documents and managing multiple hand-offs between third parties during the claims assessment was adding operating costs whilst also impacting the customer experience negatively. Within eight weeks, process mining provided a rich map of the end-to-end claims process, with insights on pain-points and number of opportunities for improvement. A combination of user training, process automation and process improvement initiatives followed. The result was an astounding 43% improvement in cycle times and over 1200 hours of time savings generated each month.

Challenges to overcome

But process mining is still a relatively new discipline, so there are still some challenges to overcome, including:

Organisations that are striving to become digital businesses need to enhance the ability to investigate and analyse processes. The adoption of new automation technologies, such as RPA (robotic process automation), machine learning and NLP (natural language processing), has proven that business leaders want to invest in technologies that improve business performance. Process mining is another tool that organisations will increasingly lean on to achieve their business outcomes.

About the Author

Dan Johnson, Director of Automation, Future Workforce. Dans expertise in intelligent automation began in 2011 when he was part of the team delivering the first RPA projects into COOP Financial Services. He later joined Accenture as the Insurance Process Automation Lead for UK&I before leading an Automation and AI team at a UK Bank. Clients trust his exceptional experience to deliver their intelligent automation programmes.

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How Can Process Mining Help Your Organisation? - TechNative

Inside a Chinese bitcoin mine

New data shows Bitcoin mining in China was already in sharp decline before the latest crackdown by the government.

The research by the Cambridge Centre for Alternative Finance (CCAF) found China's share of mining fell from 75.5% in September 2019 to 46% in April 2021.

It also revealed Kazakhstan was now the third most significant Bitcoin mining nation.

Miners earn money by creating new Bitcoins, but the computing used consumes large amounts of energy.

They audit Bitcoin transactions in exchange for an opportunity to acquire the digital currency.

Global mining requires enormous computing power, which in turn uses huge amounts of electricity, and consequently contributes significantly to global emissions.

The CCAF's Cambridge Bitcoin Electricity Consumption Index shows that at time of writing Bitcoin consumed almost as much electricity annually as Colombia.

In June the Chinese authorities took strong action against Bitcoin.

The authorities told banks and payments platforms to stop supporting digital currency transactions causing prices to tumble.

The data from the CCAF covers a period before the crackdown, but it shows China's share of global mining power was already in significant decline prior to the action by the Chinese authorities.

The Cambridge researchers observed that the crackdown, once enacted, effectively led to all of China's mining power "disappearing overnight, suggesting that miners and their equipment are on the move".

Kazakhstan is a heavy user of coal-fired generation

Experts say the miners are highly mobile.

"Miners pack shipping containers with mining rigs", said David Gerard, author of Attack Of The 50 Foot Blockchain, "so that in effect they are mobile computer data centres, and they are now trying to ship those out of China".

It's not clear where they will go, but even before the crackdown the geography of mining was shifting.

Kazakhstan, a country rich in fossil fuels, saw an almost six-fold increase in mining - increasing its share from 1.4% in September 2019 to 8.2% in April 2021.

Story continues

According to the US Department of Commerce, 87% of Kazakhstan's electricity "is generated from fossil fuels" with coal accounting for more than 70% of generation.

The country is now the third largest miner of Bitcoins, behind the US, which saw its share of global mining power also rise significantly - to 16.8%.

The data also revealed the close ties between sources of cheap electricity and Bitcoin mining.

Researchers found a seasonal movement of mining between Chinese provinces in response, it was suggested, to the availability of hydro-electric power.

Mining moved from the coal-burning northern province of Xinjiang in the dry season, to the hydro-abundant southern province of Sichuan in the rainy season.

The researchers noted that "this seasonal migration has materially affected the energy profile of Bitcoin mining in China", adding that it illustrated "the complexity of assessing the environmental effects of mining".

Sichuan banned Bitcoin mining in June.

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Cambridge data shows Bitcoin mining on the move - Yahoo News

Like many of us, schools in the United States are active on social media. They use their accounts to share timely information, build community and highlight staff and students. However, our research has shown that schools social media activity may harm students privacy.

As a researcher who specializes in data science in education, I and my colleagues came to the topic of student privacy unintentionally. We were exploring how schools used social media during the early days of the COVID-19 pandemic, specifically March and April of 2020. In the course of this research, we noticed something surprising about how Facebook worked: We could view the posts of schools including images of teachers and students even when not logged in to our personal Facebook accounts.

The ability to access pages and pictures even when we were not logged in revealed that not only could schools posts be accessed by anyone, but they could also be systematically accessed using data mining methods, or new research methods that involve using computers and statistical techniques to discover patterns in large often publicly accessible datasets.

Since practically all U.S. schools report their websites to the National Center for Education Statistics, and many schools link to their Facebook pages from their websites, these posts could be accessed in a comprehensive manner. In other words, not only researchers but also advertisers and hackers could use data mining methods to access all of the posts by any school with a Facebook account. This comprehensive access allowed us to study phenomena like violations of students privacy at a massive scale.

The easy access to student photos that we encountered comes despite broader concerns about individuals privacy on social media. Parents, for instance, have expressed concerns about teachers posting about their children on social media.

Fortunately, our search of news coverage and academic publications did not reveal any harms that have come to students because their schools posted about them. However, there are a number of possible risks that identifiable posts of students could pose. For instance, would-be stalkers and bullies could use the postings to identify individual students.

Also, there are newer threats that students may face. For instance, the facial recognition company Clearview collects internet data and social media data from across the World Wide Web. Clearview then sells access to this data to law enforcement agencies, who can upload photos of a potential suspect or person of interest to view a list of potential names of the individual depicted in the uploaded photo. Clearview already accesses identifiable photos of minors in the U.S. from public posts on Facebook. It is possible that photos of students from schools Facebook pages could be accessed and used by companies such as Clearview.

Even though we are not aware of these things actually happening, that is not reason to not be concerned about it. At a time when our privacy is often threatened in surprising ways, as technology journalist Kara Swisher writes, only the paranoid survive. My fellow researchers and I think this cautious view even a paranoid view is particularly justified when it comes to students as minors who may not provide their explicit permission to be included within posts.

In our study, we used federal data and an analytical tool provided by Facebook to access posts from schools and school districts. We use the term schools to refer to both schools and school districts in our study. From this collection of 17.9 million posts by around 16,000 schools from 2005 to 2020, we randomly selected sampled 100 and then coded these publicly accessible posts. We determined whether students were named in the post with their first and last name and whether their faces were clearly depicted in a photo. If both of these elements were present, we considered a student to be identified by name and school.

For example, a student in a Facebook post whose photo includes a name in the caption, such as Jane Doe, would be deemed identified.

We determined that 9.3 million of the 17.9 million posts we analyzed contained images. Within those 9.3 million posts, we estimated that around 467,000 students were identified. In other words, we found nearly half a million students on schools publicly accessible Facebook pages who are pictured and identified by first and last name and the location of their school.

While many of us already post photos of ourselves, friends and family and sometimes our children on social media, the posts of schools are different in one important sense. As individuals, we can control who can see our posts. If we want to limit it to just friends and family, we can change our own privacy settings. But people do not necessarily control how schools share their posts and images, and all of the posts we analyzed were strictly publicly accessible. Anyone in the world can access them.

Even if one considers the potential harm of this situation to be minimal, there are small steps that schools can take that could make a notable difference in whether that potential is present at all:

Not posting students full names would make it much more difficult for individual students to be targeted and for students data to be sold and linked with other data sources by companies.

Making school pages private means that data mining approaches similar to our own would be much more difficult if not impossible to carry out. This single step would drastically minimize risks to students privacy.

Opt-in media release policies require parents to explicitly agree to have photos of their child shared via communications and media platforms. These may be more informative to parents especially if they mention that the communications and media platforms include social media and more protective of students privacy than opt-out policies, which require parents to contact their childs school if they do not want their childs photo or information to be shared.

In sum, schools Facebook pages are different from our personal social media accounts, and posts on these pages may threaten the privacy of students. But using social media doesnt have to be an either-or proposition for schools. That is to say, it doesnt necessarily come down to a choice between using social media without considering privacy threats or not using social media at all. Rather, our research suggests that educators can and should take small steps to protect students privacy when posting from school accounts.

[Understand new developments in science, health and technology, each week. Subscribe to The Conversations science newsletter.]

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School posts on Facebook could threaten student privacy - The Conversation US

A representations of virtual currency Bitcoin is seen in front of a stock graph in this illustration taken May 19, 2021.

Dado Ruvic | Reuters

The price of bitcoin dropped below $30,000 late Monday night for the first time since Jun. 22, dragging other digital coins lower with it.

Bitcoin is trading in the $29,000 range, about 3% lower on the day, according to Coin Metrics. Ether is down 1.25% and XRP fell 4%. Even with the plunge bitcoin is up 2.3% for the year, according to CoinDesk data. Ether and XRP are both up about 140% for the year.

The plunge came amid news that the New Jersey Attorney General issued a Cease and Desist Order against the New Jersey-based crypto services firm BlockFi, ordering it to stop offering interest-bearing accounts, according to Forbes and later confirmed on Twitter by the company CEO.

It also came after a big sell-off in global stock markets Monday, when the Dow Jones Industrial Averagehad its worst day since last October, though the markets reclaimed much of their losses Tuesday.

"There's been a broad sell-off in global markets, risk assets are down across the board," Annabelle Huang, partner at cryptocurrency financial services firm Amber Group, said.

There are "concerns of the quality and strength of economic recovery" and "broader risk assets turned weaker including high yields," Huang said. "Coupled with recent BTC (bitcoin) weakness, this just sent crypto market down further."

At one point early Tuesday, about $89 billion was wiped off the entire cryptocurrency market in a 24-hour period. Since bitcoin's all-time high of nearly $65,000 in mid-April, its price has plunged over 50%.

Regulators around the world are also looking more closely at the crypto space.

Binance, the world's largest cryptocurrency exchange, last month was barred by U.K. authorities from carrying out any regulated activities in the country. Regulators in Japan, Canada and Thailand have also issued warnings about Binance.

"In general we're seeing more regulatory focus on crypto and bitcoin," said Vijay Ayyar, head of business development at cryptocurrency exchange Luno.

Bitcoin's fall below $30,000 could be important, according to Ayyar, who said the sell-off could go lower to test the $22,000 to $24,000 level.

From then on, bitcoin could trade in a range.

"I would see bitcoin between 20-40K ($20,000 to $40,000) for a while now before any bullishness returns," Ayyar said.

Kinetic Capital's Chu also sees potential selling ahead.

"Q1's crypto market momentum has stalled and is threatening further reversal potentially below the $25K levels," Chu said.

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Bitcoin drops back below $30,000, heads toward new low for the year - CNBC