As a data analyst, you will be responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. To become a professional data analyst, you will need to have strong math and computer skills and be able to effectively communicate your findings. While a bachelor’s degree is not required, it can certainly give you an edge over the competition. If you are interested in becoming a professional data analyst, there are a few things you can do to help you get started.

1. Graduate degree or certificate in data analytics

While a bachelor’s degree is not required, completing a graduate degree or certificate program in data analytics can help you stand out from the competition. These programs will give you the skills and knowledge you need to effectively collect, clean, and analyze data. For example, if you have a graduate certificate in analytics, you’ll be able to list that on your resume and show potential employers that you have the skills they are looking for. Additionally, many graduate programs offer internships or fellowships, which can give you real-world experience working with data. It’s important to note that many data analyst positions require at least a bachelor’s degree, so if you don’t have one, you may want to consider completing a degree or certificate program before applying for jobs.

2. Strong math skills

Data analysts need to have strong math skills in order to effectively analyze data. If you’re not confident in your math skills, there are a few things you can do to improve. First, consider taking some online courses or brush up on your math skills with a tutor. Additionally, many data analyst positions require proficiency in statistical software, so if you’re not familiar with any programs, now would be a good time to learn. If you have strong math skills, you’ll be able to effectively analyze data and find trends that businesses can use to make better decisions. This is a critical skill for data analysts, so it’s important to make sure you’re as strong in math as possible.

3. Proficiency in statistical software

As a data analyst, you’ll need to be proficient in at least one statistical software program. This could be SAS, SPSS, or R. If you’re not familiar with any of these programs, now would be a good time to learn. Many data analyst positions require proficiency in at least one statistical software program, so it’s important to make sure you’re familiar with at least one before applying for jobs. Additionally, many graduate programs offer courses in statistical software, so if you’re interested in becoming a professional data analyst, completing a degree or certificate program can give you the skills you need.

4. Ability to effectively communicate findings

Data analysts need to be able to effectively communicate their findings to those who are not familiar with data. This means being able to explain trends and patterns in a way that is easy to understand. Additionally, data analysts need to be able to present their findings in a way that is visually appealing. If you’re not confident in your ability to effectively communicate your findings, consider taking some public speaking courses or practicing presenting your findings to friends and family. Being able to effectively communicate your findings is a critical skill for data analysts, so it’s important to make sure you’re as strong as possible in this area.

5. Experience working with data

While experience working with data is not required, it can certainly give you an edge over the competition. Many data analyst positions require at least two years of experience working with data, so if you have any experience, be sure to list it on your resume. Additionally, if you have experience working with a specific type of data or in a specific industry, that can also be helpful. For example, if you have experience working with financial data, you may be a good fit for a position as a financial analyst. Experience is not required for all data analyst positions, but it can certainly give you an advantage. 

6. Strong problem-solving skills

Data analysts need to have strong problem-solving skills in order to effectively find trends and patterns in data. If you’re not confident in your problem-solving skills, there are a few things you can do to improve. First, consider taking some online courses or brush up on your problem-solving skills with a tutor. Additionally, many data analyst positions require proficiency in statistical software, so if you’re not familiar with any programs, now would be a good time to learn. If you have strong problem-solving skills, you’ll be able to effectively analyze data and find trends that businesses can use to make better decisions. This is a critical skill for data analysts, so it’s important to make sure you’re as strong in problem-solving as possible.

7. Organizational skills

The ability to manage and organize data is critical for any data analyst. This includes the ability to collect data from various sources, clean and prepare data for analysis, and organize data in a way that is easy to understand and use. Additionally, data analysts must be able to effectively communicate their findings to others, which requires excellent written and verbal communication skills. If you want to become a professional data analyst, be sure to brush up on your organizational skills! This is an important skill to have in any career, but it is especially important for data analysts.

8. Critical thinking skills

Critical thinking skills are important for any career, but they are especially important for data analysts. Data analysts must be able to examine data and see beyond the surface level. They must be able to identify patterns, trends, and relationships in data, and then use this information to make recommendations or predictions. Critical thinking skills are not only important for analyzing data, but also for communicating findings to others. If you want to become a professional data analyst, start honing your critical thinking skills today! 

Becoming a professional data analyst requires more than just a love for numbers. Data analysts must be able to effectively communicate their findings, have strong problem-solving skills, and be able to navigate different types of data. If you have the skills and experience required, consider pursuing a career in data analysis! With the right skill set, you can be a valuable asset to any business.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Want to Become a Professional Data Analyst? Here’s How appeared first on OpenBusinessCouncil Directory.

But with so much data being captured, this brings us to an important point on how it is being stored. While collecting data is great, the key to ensuring it will help you are mastering data organization.  This means that you have the right tools and software in place to keep up to speed with the enormous amounts of data that is flooding in and ensure that your company can make the most of it all.

With the evolvement of AI, data warehousing and organization is no longer a manual gig. In fact, it can all pretty much be generated automatically and minimize the role that humans have to play to get the right type of data sets in the end.

When you are able to fully tap into the capabilities of data organization, you will be able to easily collect, sort, and generate data sets that directly align and support all your visions and goals. They become a great baseline for your business going forward—especially because your whole organization will be able to access and use your company’s data!

It is essential that you get up to speed with the latest trends and evolvements of data warehouses and data organizations. This is why we have rounded up all there is to know, what it is and how you can now ensure you are making the most of your data and safeguarding it well into the future.

What is Data Warehousing?

You have probably heard this term being thrown around quite a bit, but do you know what it really means? The name has a literal meaning—as it is essentially a software tool that is able to store data you collect in a cloud database.

When you set up your data warehousing correctly, it will not only be stored securely, but is able to be cataloged in a way that makes it super simple for nearly anyone to find and access.

Thank goodness for data warehousing, really. With an incredible amount of metadata out there, the last thing you want to do is get bogged down trying to manually find it yourself. Instead, you will be able to find it in a couple of quick seconds! 

Plus, the best tools for data warehousing ensure that anyone can use it—including those in your organization who may not be technology experts. This also means that your marketing department or sales teams can access essential data sets for their own business purposes without having to take time away from your IT team who are busy implementing other tasks.

Why Do You Need Data Warehousing?

So why exactly do you need data warehousing? In addition to it simply making life so much easier for you, we have rounded up all the proven results that other organizations have experienced when they utilize all that data warehousing can offer them.

1. Maximize Efficiency

Time is money. So when it comes to ensuring that your organization is maximizing the efforts of their time while on the job, data warehousing can really have a positive impact on streamlining your operations.

When your team is able to make the most of their daily hours and get tasks done better and more efficiently, then your business is going to become way more streamlined. Data warehousing provides an easy-to-use tool that lets each and every member of your team get the type of data they need with minimal effort or help provided.

This is because the data warehousing tools basically do it all themselves! They use artificial intelligence and automation to tag and sort data on your behalf, often also predicting what data sets you will need to be generated. 

2. Secure Data Safely

There are more and more laws coming out around data privacy. So you do not want to ever put your company in a situation where you risk the data you have collected getting breached or stolen—and you ultimately being liable for it.

Data warehousing tools provide you with an extremely secure way to store your data while keeping it easily accessible to your internal team. This just means you have one less thing to worry about and can have the reassurance that your data is in safe hands.

3. Supports Decision Making

Ultimately, data sets can drive important decision-making processes and develop strategies for success. Data warehousing lets your company rely on data for this whenever it is needed.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Data Warehousing: What Is It & Why Is It Important? appeared first on OpenBusinessCouncil Directory.

Sundeep Reddy Mallu pursued Electrical Engineering at the undergraduate level and began his journey by selling seeds to the farmers in his native place. Grasping the basics from his early years, he postgraduate in management and business administration with Marketing and IT as his majors. He gives credit to this stellar journey that made him adept with Data Science skills and being able to deliver business values to Fortune 100 clients globally.

Sundeep is currently serving as SVP Analytics at Gramener to advise executives on aligning their data science strategy with business vision. He is also responsible for building teams to apply analytics and data visualisation, while also helping businesses adopt a data-driven culture. His keen interest in the application of Data Science and Machine Learning in ESG makes him believe that the action of humanity in the next decade would be critical.

“It’s no longer within 1.5ºC of temperature rise. It’s the case of how far are we overshooting it. This means ESG, as an overall ecosystem is gaining traction. This is evident in the number of companies who are making public commitments to carbon-neutral initiatives. This means that the climate effects are slowly starting to show up in the company top lines and bottom lines.”, he indicates.

More Interviews

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Susan Oh, Future of AI, Blockchain; Blockchain For Impact Global Summit @ UN HQ, Top 10 Frontier Women in Digital

Hilton asked Sundeep about the drivers that helped him apply his skills to agriculture in India. He fondly replies that though India is a country with its own share of challenges, he has been able to provide leverage by introducing simple yet effective technological concepts to farming.

While citing the exemplary work he has done in the country, Sundeep adds, “The kind of solutions that we could bring into the Indian context was way different from the ones in the US, particularly due to the huge difference in the percentage of population dependent on farming as their primary source of income. Therefore, while the AI and ML applications have been in practice for a very long time in the US, India is still experimenting with tailor-made solutions. With startups budding up in the country, there seems to be a whole lot of optimal progress being made here”. 

On this note, Hilton was interested to know what prompted Sundeep to join Gramener. In the words of Sundeep, “The vision of Gramener is about the public impact. While the organisation is a commercial entity, the underlying goal that the founder’s support is the impact it makes on society. The intent is to take the help of data to narrate inside stories to create action so that impossibilities can come to life. Gramener focuses on custom solution-building in pharmaceutical and life sciences, logistics, and ESG.”

He further adds, “Given that our ESG solutions span not just across climate or agritech alone, it’s the fusion of these multiple problems that interlap with each other. And we, being at a position, where we can understand the science part of it the climate standpoint, understanding the solution point of it from a technology standpoint, we are fortunate that we are able to play a small role in this big picture of assisting in the ESG space to our clients.”

On being asked by Hilton about how data is managed, Sundeep tells him that to reduce the noise and create relevant data, technology helps them connect to various data sources. They are also able to aggregate the data, and standardise the data cleaning access and data quality checks, thus reducing the manual effort.

Sundeep told Hilton that three basic ways to maintain data ethics at Gramener involve the collection of only the required data, maintaining standards even while procuring the data from the third party, and aligning the policies while complying with industry regulations.

Explaining the challenges while providing solutions that meet ESG goals, Sundeep says, “The popular culture, folklore, is for companies like big companies consuming every data that they can collate and use it. They have the means and resources to spend on these. When it comes to the ESG space, you already have a limited monetary means that you have. That is a space where we invest a lot of time to optimize that stretch the dollar to the longest possible impact. And getting the data right is a big step in how much you invest. Because each of these solutions has to be maintained in a longer haul. It’s n0t that you built that and it’s done. These are the systems that have to survive and fight any of the windows. Unlike other engagements, ESG engagements don’t give you results in a quarter, they have their long tail where they play and out and they generate value, which means every system that you build has to withstand the test of the time. And that’s where creating a long pull- the total cost of ownership of a solution goes a long way. And hence, starting with data is key”. 

Concluding the interview that promises to enlighten the audiences with its deeper impact and awareness about the challenges in creating a sphere of intersection between technological advancements (AI, Data Sciences, and ML) with ESG, Hilton asks Sundeep to give his views on the future that lies here. “The hunger for data is constant. What we will see increasingly happen in my view is that the access to data would become uniform. The differentiation would not be a lot more different. How data gets put to use for digital making is what will go into a drastic change”, he shares.

About citiesabc.com
https://www.citiesabc.com/​​​​​​​​​​​
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https://www.openbusinesscouncil.org/

4IR: AI Blockchain Fintech IoT Reinventing a Nation by Dinis Guarda and Rais Hussin (4irbook.com)

Dinis Guarda citiesabc openbusinesscouncil Series is also available as podcast on

Apple Podcasts: https://podcasts.apple.com/us/podcast/dinis-guarda-citiesabc-openbusinesscouncil-series/id1510330391

On Spotify: https://open.spotify.com/show/1vA8KaDaRpJ0mAfpNbfTSF?si=H_WngL4RSOyu1W7VAmM41w&dl_branch=1

Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xMDMyMzg4LnJzcw==

Amazon Music: https://music.amazon.com/podcasts/953a5156-823c-4e86-baeb-4fda1128e44c/DINIS-GUARDA-CITIESABC-OPENBUSINESSCOUNCIL-SERIES

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post citiesabc Podcast: Hilton Supra With Sundeep Reddy Mallu On Sustainable Application Of Data For Business appeared first on OpenBusinessCouncil Directory.

Imagine that data could be shared seamlessly with partners, governments, and other organisations, without breaking any data protection law, to facilitate innovation. How will it be possible to use closely guarded customer data while still maintaining the highest privacy and safety standards? Is it possible to monetise data without compromising the sensitivity of the information (or data)? The following write-up spills it all.

Data is the fuel for the rapidly progressing Artificial Intelligence (AI) industry -as it is for almost all other industries. Digitisation, interconnection of the network channels, and IoT generate mountainous volumes of data at an unimagined and unprecedented scale. IDC predicts that more than 175 zettabytes of data will be available by 2025, growing at an exponential rate. Thus, unbelievable data would be available, and accessible to all. However, there are still a huge number of AI innovations and projects that do not reach the stage of viability, owing to an insufficient amount of data.

The inaccessibility of authentic data for innovation and AI

Accurate data collection and processing is extremely straining in terms of expenses and time. While certain sectors like financial services, telecom, healthcare, internet companies, retail, etc, are in direct contact with the customer data, this access is restricted only to these touchpoints.

Then there is another category of data that is fragmented and siloed. Stringent regulations like GDPR watch over the sharing of data that can be processed only with user consent, strictly for lawful purposes. Data security measures are another reason that restricts data access over a larger scale. The data related to Research and Development requires regular hypothesis testing. This is, thus, another challenge pertaining to the presence of any real-time data from the field.

While AI projects require deep learning and innovation are constantly ravenous for large volumes of data, both structured and unstructured, to train models. Scarcity and huge expenses for labelled training data make it difficult to make it available for AI.

These challenges decelerate authentic data monetisation and realisation of benefits (from innovation and business). This is a major reason for many great projects to never even see the light of the day. While alternatives like data masking, anonymisation, and obfuscation ensure data security and privacy to abundant data, synthetic data is a preferred choice when data is scarce.

Synthesising data

Data generated as a result of using computer algorithms and simulations to the real-time data is synthetic data. Digitally synthesising data reflects the real-world either statistically or mathematically. Simply put, the data is generated by reproducing the statistical properties and patterns of the existing real-time datasets. This is done by modelling the probability distribution of these datasets and sampling them out. Essentially, the algorithm creates a new dataset with the same characteristics as the original data.

Although this synthesised data lead to the same answers, is it almost impossible that the original data can be ever reconstructed from either the algorithm or this synthesised data. Thus, synthetic data is almost as potent as the original data with equal predictive power. It further carries no baggage of privacy concerns or restricted usage of any kind.

Synthetic data is being increasingly employed for a wider range of applications. For instance, Syntegra is using its synthetic data generator to create and validate an anonymous replica of NIH’s database. This database has a record of more than 2.7 million individuals that have been screened for COVID-19 and more than 413,000 patients that have tested positive. This synthetic data set duplicates the real-time data quite precisely. Due to its anonymous nature, the data can be conveniently shared and used by researchers and medical professionals worldwide. A remarkable step to accelerate the progress in research for COVID-19 treatment and vaccines.

The AI team at Amazon uses synthetic data on Alexa for training its NLU system (National Language Understanding). Consequently, new versions of Alexa in three new languages have come out: Hindi, Brazilian Portuguese, and US Spanish. This invalidates any further use of large customer interaction data. Synthetic data is also being used by Waymo, a Google company, to train its autonomous vehicles. Synthetic data technology is extremely useful for American Express to enhance its fraud detection capabilities.

Synthetic data could, therefore, fill in the gaps that are hindering the evolution of AI technology. These are proving increasingly helpful in creating inexpensive, yet accurate, AI models. According to MIT Technology Review, Synthetic data for AI is among the top 10 breakthrough technologies in 2022. Analyst firm Gartner predicts: “By 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated. The fact is you won’t be able to build high-quality, high-value AI models without synthetic data.”

Potential challenges

With compelling benefits, authentic and accurate synthetic data generation requires truly advanced knowledge and specialised skill sets. Further, the required sophisticated frameworks should be put up to enable its validation and alignment with the objective.

It is critical that the synthetic data generated does not relate to or expose the original data set in any way, while it should match the important patterns in the original. Failing would result in either overlooking potentially larger opportunities or generating inaccurate insights for any subsequent efforts to model the data.

For the AI models that have been trained on synthetic data generated by simply copying the original one, there is always a risk that inherent historical biases might creep in. Complex adjustments are, therefore, necessary for a fairer and more representative synthetic data set. Hard, yet achievable.

For the synthetic data generated that has been optimised on a predetermined abstract of fairness, the resulting dataset accurately reflects the original one while still maintaining inherent fairness. So, no bias mitigation strategies are needed, with no compromise on predictive accuracy.

The certainty of synthetic data in AI’s future

“There is a risk of false, early-stage perceptions surrounding the use of synthetic data in some circles. This is most likely due to the naming of the term itself, as anything ‘synthetic’ might naturally be thought of as plasticized, non-organic, or in some way fake. But, of course, there should be nothing more natural than machine learning tuition being driven by machine intelligence. Properly generated, managed, maintained, and secured, synthetic data’s level of bias handling, safety, privacy, and cadence represent a significant accelerator and enabler for the AI capabilities of tomorrow”, Nelson Petracek, CTO, TIBCO Software.

Already being used in healthcare (for training the machines to monitor a patient’s post-op recovery), security and surveillance (for detecting a suspicious object or behavioural pattern), and delivery drones, synthetic data is progressively making an accelerated advancement in its evolution.

Synthetic data, surely, is synthetic in origin, but it has real-world DNA to it. Its validation and application are unbelievably tangible, pragmatic, and multifarious. Nevertheless, it is, in fact, a reality we all exist in.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post The Shift In The Paradigm To Synthesise Data For A Data-Driven AI Industry appeared first on OpenBusinessCouncil Directory.

“I’m a great believer in being heads-on. That’s probably the best way to be educated.”

Or Lenchner had a passion for developing software products since his early years. During his initial years, he founded and managed several internet-based products and businesses. After developing digital assets and online marketing programs himself, he joined Bright Data in 2015 as the Head of Product Development.

“I’m a product guy, that’s what I love to do also today. Even acting as a CEO, I’m still enjoying most of the time touching, building, and working with our own products”, he proudly says.

It is this ideology, he believes, that makes Bright Data successful in its ongoing drive to innovate. He further describes to Dinis the challenges that internet-based data is facing these days – lack of web transparency.

“The World Wide Web was designed to be the largest public database in the world – it should be open to anyone to use but in reality, it isn’t. This is the major challenge that we see. Though, the software that we develop overcomes this challenge and is able to collect this public data. This is why our products are so valuable to our customers. But, as a whole it’s not okay to block access to public data, it belongs to the public. This is why it is called public data”, he shares his concern.

On being prompted by Dinis about the ethical practices of web data extraction that the company follows, Lenchner explains that trusting the data starts with trusting the data partner or vendor. This is what forms a reliable basis for web data collection and ensures that it is used in an ethical manner.

“It’s a very strong culture in our company DNA to be accountable. We’ve taken unprecedented steps to ensure that our products and infrastructure address the highest industry standards. In 2021, we rejected over 830 cases of potential customers for simply not practising the use cases we support, even though everything is legal, everything is okay, but we don’t perceive it as ethical or they weren’t transparent enough when they came talking to us and went through our KYC process”, he indicates sincerely about the ethical and responsible culture at Bright Data.

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About Bright Data

A leading web platform, Bright Data, provides tools and products to extract, manage and utilise public web data. These could be used by any form and size of business, institutions, or even the public sector to research, monitor, or analyse the data and consequently make better and more informed decisions. Used worldwide by more than 15,000 customers, Lenchner states that the data is gathered in the most efficient, reliable, and flexible way.

“We build software for one purpose- to help our customers collect publicly available web data. So, if the internet today is probably the largest database in the history of mankind, it means that you can probably do a lot with that resource as a company, no matter what you are doing- selling stuff online, or developing medicine. This is a major resource you should take into consideration. We build the tools that allow you to collect the public web data you need, which is not an easy task”, explaining the power of data in the current world scenario.

Making public web data accessible to all keeps the markets open and in healthy competition. Major eCommerce companies and sites, finance, NGOs, research universities, public sector organisations, security operators, and travel sites are among the thousands of clients of this growing platform… With a strong customer base, Bright Data crossed the hundred-million-dollar revenue mark last year.

About The Bright Initiative

The Bright Initiative is a global program that uses public web data with the aim of driving positive change. This provides pro-bono access to leading web data technology for various public bodies, NGOs, Not-For-Profit Organisations, academic institutions, and others. What started as an exercise to help researchers during the initial stages of the COVID-19 pandemic in early March 2020, turned into an exciting venture for the company.

“We realised that we do really well as a commercial company, but you can actually also use these powerful tools for contributing to society in various ways. So, we just launched our new webpage, offering researchers and organizations the chance to approach us, if they think they should get these tools for free”, Lenchner says excitedly.

With over 400 partners already, The Bright Initiative has been contributing humbly to quite amazing causes. From being used for research purposes by universities, combating sex and human trafficking, to identifying victims of intrafamilial abuse (and helping them before it is too late)- The Bright Initiative has an extensive number of cases that have made a positive impact on society- all based on publicly available web data.

“You just need tools to collect this public data and analyse it. So, we literally know lives are being saved just by using our tools effectively”.

“We support over 400 organisations like Universities, NGOs, and the UK government, for example, to help them make a positive social impact using the largest resource – Web Data.”

Relevance of data in Web 3.0

Being interested in Metaverse and Web 3.0 himself, Dinis poked Lenchner to explain the role of Big Data in the future of this space. Or explains that living in Web 3.0 does not just mean being in this world with AR VR sets. It just means a portion of one’s waking hours being spent inside the web itself. He insists on the beauty of mountain amounts of data being generated throughout the process of transition and while living in it.

“This will, for sure, push companies like Bright Data and others to build newer tools, more sophisticated tools, to be able to extract that data that will be added on that new layer of Web 3.0 or inside the Metaverse. We’re already there, we’re thinking this way, we’re innovating in this direction. It’s just going to be much bigger”, he exclaims.

Concluding an extremely knowledgeable session, Lenchner leaves a message for those who handle data (or are willing to enter data handling), “Start small, with a small data set, to validate your hypothesis, first. Then, scale up”.

About citiesabc.com
https://www.citiesabc.com/​​​​​​​​​​​
https://twitter.com/citiesabc__​​​​​​

About openbusinesscouncil.org
https://www.openbusinesscouncil.org/

4IR: AI Blockchain Fintech IoT Reinventing a Nation by Dinis Guarda and Rais Hussin (4irbook.com)

Dinis Guarda citiesabc openbusinesscouncil Series is also available as podcast on

Apple Podcasts: https://podcasts.apple.com/us/podcast/dinis-guarda-citiesabc-openbusinesscouncil-series/id1510330391

On Spotify: https://open.spotify.com/show/1vA8KaDaRpJ0mAfpNbfTSF?si=H_WngL4RSOyu1W7VAmM41w&dl_branch=1

Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xMDMyMzg4LnJzcw==

Amazon Music: https://music.amazon.com/podcasts/953a5156-823c-4e86-baeb-4fda1128e44c/DINIS-GUARDA-CITIESABC-OPENBUSINESSCOUNCIL-SERIES

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post CEO Of Bright Data, Or Lenchner, Explains The Challenges That The Data Industry Is Facing In Today’s World appeared first on OpenBusinessCouncil Directory.

Technology, as is the case with all industries, has made courier companies much more efficient to work with. Improved efficiency not only benefits couriers but the businesses that they work with too. Thanks to technology, customers can receive their parcels in a more timely and effective fashion. Software, in particular, has proven to be very useful for couriers and their partners, because it can be used to track and update parcels, as well as show proof of delivery, improving overall security. Security is a major concern for courier companies.

If you are a business owner and will be dealing with courier companies, then the software can help to make that process easier. This article will explain how: 

Customer Communication

Most businesses’ primary concern is customer satisfaction, which can be achieved through communication. If a business doesn’t update its customers regarding the status of their parcel delivery, their customers can become irritated and will go out of their way to avoid them in the future. Customers need to know exactly what’s going on, at every stage of the shipping process. The software can be used to provide them with this information. Every time a parcel is moved from one place to another, it can be scanned and the information added to your customer’s tracking page, which you can update and manage through shipping software.  

Data Retrieval

If at any point during a parcel’s time with its courier it is misplaced or goes missing, the software can be used to retrieve it. Most parcels have unique barcodes that are scanned at every stage during the shipping process. Uploading this information onto the shipment software program that you use will make finding parcels much easier if they do not reach their intended destination. In addition, if customers have any complaints about the way that their parcel was handled or treated, you will be able to use software to determine which packers, shippers, or staff from your chosen courier company handled it so that you can make a complaint.

Booking Duplication

When you are doing things manually, it’s very easy to make mistakes. This is especially true if you handle lots of parcels. Booking duplications is a very easy mistake to make. With shipping software, however, this thankfully isn’t the case. Shipping software will instantly notify you if you have booked a parcel into your system twice, allowing you to avoid costly duplication errors. With all of this said, in order to avoid booking duplications you do need to ensure that every parcel has its own unique barcode applied, otherwise you will be able to book a parcel twice.

Real-Time Tracking

Tracking is not just important for customers, it is also important for you. Unfortunately, theft and fraud are rampant in today’s world, even in courier companies. Delivery drivers are lucrative targets for sophisticated criminal gangs, and sometimes they even infiltrate them. By using software to track the company that you use and its drivers, you will be able to pinpoint the location of their van wherever they go, allowing you to track a parcel if it is stolen. In addition, you can also monitor your courier’s general behavior. If they are slacking off or aren’t delivering parcels on time, then you can use software to prove this to their boss. 

Administrative Tasks

One of the most time-consuming tasks businesses have is administrative work, including inputting and updating information on their system. Delivery software can be used to streamline and automate administrative tasks, saving them a lot of time. The time that they save by using software enables them to focus on other more important areas of their businesses, such as acquiring new clients and streamlining their operations in other ways. The software can also be purchased that automates HR and payroll tasks. Also, when companies use software to input information, there’s less of a chance of data mishandling.

Increased Productivity

Software ultimately leads to increased productivity. When staff are able to complete their work in an efficient and effective way, they will be more productive. Having to deal with the paperwork and manual administrative tasks will only slow them down and stop them from completing important tasks. Delivery drivers will also be more productive because instead of filling out forms and sheets for each delivery they complete, they will be able to breeze through the relevant documentation on their phone or handheld device.

Parcel Mishandling

As we touched upon earlier, the software helps to improve security. Unfortunately, theft is very common nowadays. In addition, because of the large number of parcels that courier companies handle, parcels can go missing. The software allows companies to better track their parcels, giving them the opportunity to learn about any losses or thefts that occur. It can also be used to identify theft patterns. If parcels only ever go missing when a specific driver is on duty, then there’s a chance that they are to blame.

The software has dramatically improved the way that courier companies and businesses work together. It has led to improved productivity, efficiency, and customer satisfaction. If your company isn’t yet using software to handle its deliveries, then it’s time to.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post How Can Businesses Use Software Advancements For an Improved Delivery Service? appeared first on OpenBusinessCouncil Directory.

As 2021 draws to a close, Amrit Dhami, Thematic Analyst at GlobalData, offers her view on these themes:

M-commerce is fueling the development of super apps in the West, but China will continue to rule the roost

“The increased use of mobile wallets — a virtual wallet that stores card details on mobile phones — and mobile commerce (m-commerce) — purchasing online via a mobile phone — is driving the development of ‘super apps’ in the West, similar to China’s WeChat and Alipay. In 2022, the value of mobile wallet transactions will exceed $266 billion in the US and $60.5 trillion in China.

“Mobile wallets act as a gateway to a broader range of financial services without the need for consumers to leave the app. Mobile point of sale (POS) software usually requires biometric authentication and offers more security than contactless card payments. It’s also more convenient for users, as they carry their phones around everywhere with them anyway, and no additional hardware is needed. This will continue to encourage consumers to abandon cash and cards.

“Since it’s now easier than ever to make transactions using mobile phones, it’s no surprise that m-commerce is exploding to become the most popular method of online transaction. The convergence of mobile wallets, m-commerce, financial services, and social media will encourage payment companies such as PayPal to develop super apps that will streamline the consumer experience and provide a portal to a host of services.

“However, the West’s super apps will fall short of emulating China’s powerful digital ecosystems because western social media companies consistently fall short at building consumer trust — something that is pivotal to the success of super apps. In the absence of the regulatory hands-off approach in China that allowed Tencent and Alibaba to grow substantially, US social media giants must earn their users’ trust if they aspire to reach super-app status.

“GlobalData’s Thematic Scorecards score companies based on how they will perform in various themes. Almost all of the 35 companies on GlobalData’s Social Media Thematic Scorecard score less than two (out of five) for the ‘Data Privacy’ theme, including Meta and Snap. Data privacy is incredibly important to consumers and regulators, so this demonstrates that social media companies still have some way to go before they can be allowed to grow into super apps.”

The herd of fintech unicorns will continue to expand and engage in strategic partnerships

“Fintech unicorns—private companies valued at $1 billion or more— have now climbed to 116 in number, with an aggregate valuation of $529 billion. A surge of investment in start-ups has expanded the herd of fintech unicorns this year, and 2022 will be no different with the likes of Corvus Insurance and Navi Technologies set to join the cohort. Meanwhile, at least 10 current unicorns will become public, including Stripe, Monzo, and Chime.

“The sheer volume of highly valued start-ups operating in fintech demonstrates the extensive ongoing disruption of the financial services sector by digital technologies.

“Disruptors and incumbent institutions will continue to form dynamic partnerships to capitalize on the innovation of start-ups and large user bases of traditional financial institutions. Goldman Sachs and Visa are among those building an extensive fintech network.”

Openness in data ownership has played a critical role in fintech partnerships

“Open banking has encouraged partnerships between disruptors and incumbents by allowing accounts and data to be linked across institutions, important for services like smart budgeting, automated onboarding, and credit risk assessment. PSD2 (the amended Payment Services Directive) has facilitated openness by giving third parties access to banks’ infrastructure.

“Open banking —where banks open up data for regulated providers to access — has been a key driver of partnerships between unicorns and banks, and has been delivered top-down in Europe through PSD2. A more open model of data ownership, where data can be moved across different institutions, increases transparency in banking services, and is an important step in shifting the ownership of customer data from banks to the customer. Institutions will be battling to win our trust and confidence.

“Openness in data ownership will continue to be a key theme in the financial services sector, so discussions around PSD3 will continue in order to instigate the more aggressive measures needed for further networking across financial institutions.”

Generation Hashtag is driving fintech innovation

“Generation Hashtag refers to the cohort born between 1991 and 2005. Having experienced the uncertainty of both the Global Financial Crisis and COVID-19 recession, this generation will continue to redirect their savings towards commission-free stock trading and investment apps such as Robinhood and Freetrade. These apps target young investors with a firm grasp of technology, but limited wealth —Robinhood’s average user is aged 27.

“Generation Hashtag is also wary of credit card fees, so is driving innovation in interest free, buy now pay later (BNPL) services. BNPL is convenient for consumers and lucrative for retailers, reducing cart abandonment. However, more transparency around BNPL services is urgently needed to ensure consumers understand that they are accruing debt through BNPL, which can negatively affect their credit scores. Regulators will pay more attention to BNPL following its rapid growth in 2021, with the Financial Conduct Authority (FCA) already recommending rigorous affordability checks before consumers can use such services.”

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Predicts GlobalData, China To Dominate Super Apps, Data Ownership In Banking To Be More Transparent, And Mobile Wallet Transactions Set To Exceed $266 Billion In The US In 2022 appeared first on OpenBusinessCouncil Directory.

Research shows that marketing firms that achieve beyond their revenue goals are able to do so by applying personalization techniques 83% of the time.

Gone are the days when marketers used to plan and execute a campaign based on mere assumptions or a gut feeling about audiences. Today, marketing campaigns are driven by a more reliable method – audience data.

What does your target audience want? What do audiences think about your brand? Is your marketing campaign in alignment with your audience’s needs?

These are some key aspects that you need to know to make your marketing campaign a success.

There is no better way than getting the answers from the audiences themselves.

Businesses are now relying on big data resources to get accurate customer insights that can add value to their marketing efforts.

Why not give your marketing campaigns the power of such big data?

Here are some ways in which audience data can elevate your marketing strategies:

Helps you reach your target audience at the right time

A major proportion of your marketing success depends on timing. Your product/ service may be efficient at addressing an existing customer problem or may make their lives better in some way. To enable your product/service to attain its goal, it has to reach the target audience at the right time.

In traditional marketing, marketers leverage limited market research data accessible at that point of time, and their beliefs regarding the target audience, to make marketing decisions. This method may yield results, but not with consistency. There is a risk of failure in this trial-and-error-based approach.

Data-driven marketing businesses, on the contrary, support their marketing decisions with data. They may use the services of big data companies or employ in-house big data systems to get accurate, deeper, and comprehensive information about target audiences.

What’s more? They can even get access to real-time customer data that can be a complete game-changer for the campaign.

Gives you valuable insights for personalization

Successful marketing campaigns are those that engage with their audiences. Engagement is possible only when your marketing is customer-focused and relevant to their lives. You cannot inspire trust for your brand or marketing message in audiences unless you personalize.

Audience data helps you overcome the drawback of insufficient or inaccurate audience data by giving you key insights such as audience demography, online behavior, purchase history, and pain points.

These insights, among others, enable you to personalize your marketing message and convert it into a one-to-one brand communication with your target audience.

Does this strategy work?

Research reveals that companies that conceive marketing strategies based on their audience data earn anywhere from 5 to 8 times more revenue on their allotted budgets. With fact-based personalization, you can expand the potential of your strategies for maximum marketing success.

Gives you access to customer information across marketing channels

Data-driven marketing strategies are based on data collection from multiple channels where your customers may have a presence. You get access to streams of data across sources. These include television, radio, and news sources; search engines; social media; apps; and media such as videos and podcasts.

This is high-quality data because it is coming straight from your target audience. You can convert this huge volume of data into crucial intelligence for your marketing campaigns. For example, you can identify channels that have the best customer presence.

You can apply your marketing campaigns to these channels. Your marketing will have the maximum possible reach.

Data-driven campaigns will be more in tune with your customer expectations. With efficient big data planning, you can even create a highly one-to-one marketing experience for your customers. 

Provides insights into your most valuable customers

The beauty of data-driven marketing is that the marketing and brand success you experience is not temporary. Big data systems can be integrated with your existing CRM systems or predictive analysis systems to create customer profiles and predict their purchase behaviors.

The entire infrastructure gives you critical customer insights, such as their purchase patterns and sales potential. You can identify customer profiles that mean revenue for your business and those that are superficial leads.

This is critical business intelligence that helps you focus on your most important customers. Data-driven insights also enable you to acquire new customers and strengthen relationships with existing customers.

Helps you deliver super-performing products/services

There is no better marketing than a high-quality product/service.

Deliver the best product/service that your customers are happy about, and watch your customers become your brand ambassadors.

Audience data empowers you with insights required to develop such products or services. Inputs such as target audience needs, demography, and expectations can help you improve your product development. You will increase your chances of product or service success with a fact-based approach to development.

Helps deliver outstanding customer experiences

With data-driven marketing, you can predict your customers’ brand expectations. You can reach out to them with the right marketing message at the right time. You can communicate brand messages that you know will make the right impact.

This is also an opportunity to upsell or cross-sell other products/services. But you will be doing so knowing that your customers are in need of a specific product/service. As a result, the experience will be outstanding for your customers.

When you do so consistently, you create loyal brand followers.

In conclusion

87% of marketing professionals believe that their organization’s data assets are poorly utilized. 64% believe that marketing efforts based on data are critical to their organization’s success.

These insights are an opportunity to realize the value of your data assets. Leverage your company’s big data assets for developing well-planned marketing strategies. It is a valuable opportunity to make your brand stand out!

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post How Audience Data Can Help Your Company with Marketing Strategies appeared first on OpenBusinessCouncil Directory.

But according to ActiveOps, a leading provider of digital operations management solutions, there are three areas where workforce data can help organisations tackle burnout. These deal with the current state of play where organisations have burnt-out employees that they need to support and help protect the workforce from burnout in the future.

Richard Jeffery, Group CEO, ActiveOps, stated: “Finding meaningful solutions to employee burnout isn’t just a social responsibility for organisations –though that is incredibly important if organisations want to live up to their values and be an employer of choice for the best talent out there. Beyond that, reducing burnout is vital to an organisation’s top and bottom lines.”

Data enables better workload management

Though the causes of employee burnout are varied, the most significant cause of burnout is overwork – or too much of the wrong kind of work for a specific employee. Organisations must be aware of how much work they are expecting of their employees, what they are getting through, and the work coming down the line. Armed with this data, managers can divert workload away from burnt-out employees while they recover and gradually increase their workload as they regain their wellbeing. By getting a holistic view of the whole team, those managers can see whether they should be diverting that work within the team – perhaps to an underworked team member – or whether they should be looking at additional short-term resources to fill the gap.

Seeing where your employees are spending their time may also help you identify whether they are in the right kind of work.

Data helps managers engage employees more effectively

The company attains employee engagement by understanding and providing what employees want from their work – whether involved in decisions that affect them, measurable and actionable feedback, ongoing development, or a sense of satisfaction and purpose. Disengaged employees are at an increased risk of burnout.

Software that aggregates workforce data can identify employees who are off-task for significant periods or who spend a lot of time using training sections of programmes – both of which may be signs that they are disengaged and struggling. That data can be used as the basis for a productive conversation between employees and managers to help analyse and resolve the issues affecting those employees.

Data brings hybrid work to life

The move to hybrid working styles has presented both opportunities and challenges for employee wellbeing. Many organisations lack a transparent view of the time employees spend working, especially during their non-working hours.

Technology enabling organisations to accurately track when employees log on, showing managers how well they balance work and rest, will help managers proactively intervene to protect their employees from overwork and thus burnout.

“Organisations with a high level of burnout will soon (if they haven’t already) see their retention rates plummet as employees look for other roles where they can recover themselves. Burnout damages the company brand and incurs high costs as the organisation has to hire and train replacements for those employees they lose. Eliminating burnout, therefore, can reduce the cost of churn for organisations. 

“Key to the success of any organisation’s efforts to reduce burnout is to revisit how they measure employee productivity. It’s no longer enough to look simply at outcomes or the volume of tasks completed. As work has grown more complex, the methods of measuring success have also increased in complexity – and often, organisations find it very hard to measure performance without technological intervention,” concluded Richard Jeffery.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Three Ways Data Can Help Resolve Employee Burnout  appeared first on OpenBusinessCouncil Directory.

Why this book matters:

• Clearly demonstrates the growing impact of machine learning and data analytics and how this is transforming customer insights
• Combines an academic overview of machine learning with industry information providing a unique bridge between the two
• Includes practical case studies from well-known companies from a variety of industries such as marketing, IT, banking, and telecommunications
• Reveals the opportunities and challenges of the transformation process for businesses in general and for customer insights in particular
• Gives support for those who consider the machine age of customer insight as a big opportunity and convinces those who are still skeptical by means of practical use cases
• Short, concise chapters written by outstanding thought leaders from renowned universities in Europe & the US and from innovative firms in different industries
• An essential read for professionals, researchers, and students alike

We are now entering a new machine age. Emerging machine learning technologies are reaching the mainstream, transforming the way business value is created, and the race to harness the benefits for better insight-based decision making is on.

Across industries, players are affected by the pace of progress of machine learning tools, developing technologies and the abundance of data, but businesses must master these new capabilities to generate the insights that will enable them to thrive in this new age.

New book, The Machine Age of Customer Insight offers a short pit stop in the race for better, faster customer insights from machine learning tools. It explains the transformation, its growing impact, and the necessary capabilities for organisations to invest in. It provides a comprehensive, practical overview which addresses both academics and practitioners providing a unique bridge between the two.

The Machine Age of Customer Insight illustrates the opportunities and challenges of the transformation process for businesses in general, and specifically for customer insights which has been accelerated by the pandemic, drawing upon case studies of how well-known companies have gained a competitive advantage by transforming customer insights into business value. It also discusses the success factors in using machine learning tools including using data privacy for competitive advantage and data collection and the experience economy.

Written by leading thought leaders from renowned universities in Europe and the United States as well as from innovative firms, this book is divided in three sections which explore key areas:

. How the field of customer insights is being transformed

. Analytical tools which can generate customer insights

. How the management of customer insights can lead to success

Each section includes short, concise chapters written by the different thought leaders on topics of their expertise. It is an essential read for business leaders, students, researchers, and professionals alike.

The Machine Age of Customer Insight by Martin Einhorn, Michael Löffler, Emanuel de Bellis, Andreas Herrmann, and Pia Burghartz, published by Emerald, available on Amazon.

EXCLUSIVE ARTICLES AND INTERVIEWS AVAILABLE:

The authors of The Machine Age of Customer Insight are highly respected thought leaders in their field, and are available for expert comment, guest articles and interviews on a range of customer insight and AI topics including:

. 5 ways AI is transforming customer insights

. How voice and facial coding can be used in market research

. Leveraging customer insights with 5G

. Machine learning tools: a brief overview

. A step-by-step guide for data scraping

. How data privacy will be a drive for competitive advantage

. What is the experience economy and should your company care?

. How to turn business data into business value

. How the pandemic has accelerated AI customer insights

About the authors:

Dr. Martin Einhorn is Director of Customer Evaluation and Analytics at Porsche AG and lecturer at Sigmund Freud University Vienna.

Prof. Dr. Michael Löffler is Vice President for Sales Planning and Strategy at Porsche AG, leading several departments responsible for sales and marketing strategy, worldwide training, organisational development, and innovation.

Prof. Dr. Emanuel de Bellis is Assistant Professor of Marketing at the University of Lausanne. His research explores how consumers perceive and use autonomous products and other AI-based technologies.

Prof. Dr. Andreas Herrmann is Director for Marketing and Research Methods at the Institute for Customer Insight at the University of St. Gallen and Visiting Professor at the London School of Political Science.

Pia Burghartz is a PhD candidate and research associate at the Institute for Customer Insight at the University of St. Gallen. Her research deals with consumers’ acceptance of autonomous driving and shared mobility.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Welcome to the age of better, faster, more valuable customer insights appeared first on OpenBusinessCouncil Directory.