After a long day at work, you are dazed but at the same time determined to watch the newest episodes of your favourite series. And just when you’re in the nail-biting scene, the video has paused, then begins to buffer and never resumes! After a few frustrating seconds later, the video is at its lowest resolution, hardly showing clarity.

How did an exhilarating affair end into an arduous one?

Say Hello, to Video Streaming challenges. Honestly, This is not how a video should be streaming. That is when the potential game-changers like AI and Deep Learning comes into action. They can ensure Zero-delay, High Definition, enhanced streaming OTT making the user experience more enjoyable and worthy.

Adaptive BitRate

OTT or Over the Top content makers, as well as the providers, are launching intriguing content every single week. But the Quality of experience which is generally referred to as QoE is one main issue that’s not been able to tackle well enough by the OTT platforms.

QoE is about a video’s overall resolution, startup time, stalls and pauses that happen through the relay. Seamless delivery of video is highly reliable on internet speed or the so-called bandwidth which can drastically affect the user experience. That is when this ABR comes into play tackling the challenges with an innovative algorithm.

The parameters of a video have been “adapted” dynamically by the performance of your network. Therefore, compromising the quality of the video. But, it is still dependent on internet speed. It is 2021, and a lot of talks is happening around Deep Learning.

Intelligent Solutions

Videos have been the new type of data communication, literally an extension to an email or text. This has created a necessity for developments in video streaming with optimization of network and data. Most of the happening these days are unperceptive to the content it holds. The bitrate of a scene where the characters are just having a conversation differs from that of an action scene. Imagine seeing the wrinkles on Dumbledore’s face when he raises his wand, but unable to see clearly when Harry is fighting Voldemort.

And that needs an answer to the following questions

• What if a scene’s content could be comprehended?
• What if streaming algorithms learn the QoE by every scene?
• What if the encoding is instructed with relative importance to all frames?
• What if the streaming is friendly on any device?

The best possible answer is Deep Learning.

Deep Learning

Artificial Intelligence and Deep Learning are advancing technologies to improve video content and user’s QoE. Content conscious AI can remarkably improve the viewing experience making it personalized, immersive and novel. DNN (Deep Neutral Network) can be made device-aware, dynamically computing it with the strategic resources to scale up the performance of streaming.

An intelligent and notable method to enhance QoE is when the encoder looks at the entire video than looking at the pixels in individual frames. This helps for

• A better understanding of content
• Delivering the highest quality stream
• Identifying the content redundancies.

How can the impact of Deep Learning take place?

Device Aware

The device quality plays a major role in the entire process of decoding. Making it device friendly by all means, says that people can watch it anytime, anywhere and by any device. A click on the app of their smartphones. Because smartphones are the new laptops. Having the track on which device is targeted by the user can make his/her personal experience most engaging. The unique potential of a device can be utilized as its soul ability to redefine the entire streaming.

Context-Aware 

It doesn’t end there at being device-aware. Deep Learning and Artificial Intelligence can be made to pay specific attention to the entire context of the video, search preferences, genre interest. For instance, a user is streaming a particular series every week. Deep learning can use this data to show the user the preference show, the similar genres, other shows from the director and cast. This not only improves the QoE of the user but also makes it a Win-Win for the OTT providers as well.

Understanding the business of Streaming  

One might wonder why an OTT platform has to invest in such user engaging technologies? The answer to this is a recent survey done across 1000 customers who are using the OTT streaming in Urban India. As many as 62% of people viewing through the OTT face issues like buffering, pausing and hang of the app whilst used on travelling.

More than ever, In recent times, the customers are making it clear by immediate feedbacks on official and social media sites regarding any dissatisfaction While using the OTT. This shows a clear intolerance in regards to lower QoE which can affect the revenue. Long load times, buffering, pixelation and stalled videos should no longer be seen to sustain this business.

The emerging trends, the pandemic situation has given new dynamics to online streaming. The limitations to short films or fiction videos have widened up to live stream of sports, gaming and releasing movies. Thereby generating more revenue.

Gone were the days of gaming with Personal Computers. But, games like PUBG having pulled greater crowds just from a smartphone. The new gaming platform from Google, stadia gave an announcement stating as low bandwidth as 25mbps is enough for exceptional streaming.

This tells us how cost-effective ideas are emerging to change the phase of this business using AI and deep learning. A recent survey showed that 53.98% of viewers were willing to upgrade to premium subscriptions for intriguing content that has minimal streaming challenges.

Finally, The big picture of delivering high-quality videos at lower cost subscriptions to generate greater revenues is in view. The considerable benefits obtained from the usage of AI and the impact of deep learning in creating a greater streaming experience has solved many practical conditions. The biggest innovations of all this process are DNN based advancements. So, very soon this is going to change all the demographics to matchless QoE and incredible video consumption.

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Better customer experience, lower costs, enhanced accuracy, and new features are a few advantages of applying machine learning models to real-world applications.

According to a survey conducted by Rackspace Technology, 34% of respondents project having up to 10 artificial intelligence and machine learning projects in place within the coming two years. Meanwhile, 31% see data quality as a primary challenge to preparing actionable insights into AI and machine learning projects.

Before applying the power of machine learning to business and operations, companies must overcome various obstacles.

Let’s dive into some of the primary challenges businesses encounter while integrating AI technologies into business operations in data, skills, and strategy.

The Importance of Data Quality

Data still remains a significant barrier in various stages of planning and utilizing an AI strategy. According to the Rackspace survey, 34% of the respondents said low data quality is the foremost cause of machine learning research and development failure, and 31% stated that they lacked production-ready data.

The AI research community has access to several public datasets for practice and testing their latest machine learning technologies, but when it comes to implementing those technologies to real applications, gaining access to quality data is challenging.

To overcome the data challenges of AI strategies, businesses must fully evaluate their data infrastructure, and breaking down silos should be a top priority in all machine learning initiatives. Furthermore, organizations should also have the right methods to filter their data to boost the performance and accuracy of their machine learning models.

Soaring Demand for AI Talent

The next area of struggle for most businesses is access to machine learning and data science talent. However, with the evolution of new machine learning and data science devices, the talent problem has grown less severe.

Before starting an AI initiative, it is advised that all businesses should perform a thorough evaluation of in-house expertise, available devices, and integration opportunities. Additionally, businesses must consider if re-skilling is a logical course of action for long-term business goals. If it’s feasible for businesses to up skill their engineers to take data science and machine learning projects, they will be better off in the long run.

Outsourcing AI Talent

Another area that has seen extensive growth in recent years is the outsourcing of AI talent. According to the Rackspace survey, just 38 % of the respondents depend on in-house talent to improve AI applications. Others either completely outsource their AI projects or use a mixture of in-house and outsourced talent.

A successful strategy requires close communication between AI experts and subject matter specialists from the company executing the plan.

AI projects not only require strategy and technical expertise but also a strong partnership with the company and the leadership. Outsourcing AI talent should be done meticulously. While it can expedite the process of creating and executing an AI strategy, businesses must ensure that their experts are wholly committed to the process. Ideally, organizations should make their in-house team of data scientists and machine learning engineers work with outsourced specialists.

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In the digital era, businesses incorporate big data business analytics to enhance decision-making, increase accountability, boost productivity, make better forecasts, determine success, and gain aggressive advantages. However, various companies have difficulties practicing business intelligence analytics on a strategic level.

This year seems to be an excellent year for big data analytics, yet there are some challenges to overcome. According to Gartner, 87% of businesses have low Business Intelligence (BI) and analytics maturity, requiring data guidance and assistance. The obstacles with business data analysis are associated with analytics and can also be caused by extensive system or infrastructure challenges.

Thus, it is time to dive deeper into the most prevalent big data analytics problems, examine possible root causes, and highlight the possible solutions to those problems. Here are the top data analytics difficulties businesses face.

Inaccurate Analytics

Nothing could be more detrimental to a business than incorrect analytics, and this issue needs to be addressed at the earliest.

If the system relies on data with bugs, errors, or is incomplete, it’s highly likely to get poor results. Data quality management and mandatory data validation process, including every stage of the ETL process, can help businesses ensure incoming data quality at various levels. This will help organizations to identify errors and ensure that a modification in one area quickly results in pure and accurate data.

Utilizing Big Data Analytics is Challenging

The level of complexity of the reports is too high and time-consuming. It can be fixed by hiring a UI/UX expert to help businesses develop a robust and flexible user interface for easy navigation and workflow.

It’s advisable to get the team together and define critical metrics to identify what functionality is often used, what needs to be focused, measured, and analyzed. Involving an external expert from the business domain would be an excellent option to help the business with data analysis.

Long System Response Time

The system takes plenty of time to analyze the data. It may not be so important for batch processing, but for real-time systems, such delay can be costly.

The problem with data analytics infrastructure and resource utilization is that it has reached its scalability limit. Also, it could be that the hardware infrastructure is no longer adequate.

The easiest solution is to append more computing resources to the system. It’s useful if it helps improve the system response within an affordable budget and there is proper utilization of the resources.

Costly Maintenance

Every system needs continuous investment for its maintenance and infrastructure. Moreover, business owners are constantly looking for ways to reduce these investments. Therefore, it’s always a good idea to frequently assess the systems to avoid overpaying.

New emerging technologies process more data volumes in a faster and economical way. The best solution is to shift to new technologies to improve reliability, scalability, and availability.

Besides, for not using most of the system capabilities, businesses pay for the infrastructure they utilize. Therefore, improving business metrics and optimizing the method according to business needs will be helpful.

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There has been substantial progress in building a Machine Learning (ML) methodology for Earth Observation (EO) data analysis; however, experts worldwide face many challenges while using ML algorithms on EO data.

For ML models to work, two processes work simultaneously. First, tons of data is captured from EO satellites, which is processed to make it application-ready. This data is called application-ready data (ARD), put in Cloud and organized into different datasets called data cubes. Secondly, the training data is collected to train models. Once both datasets are organized, an appropriate ML model is selected to classify, smoothen, and process the data to get valuable insights.  

Using multiple ML algorithms on large volumes of EO data ensures reliable and conclusive results, thereby easing the process to prove or disprove a given hypothesis. While the benefits are many, EO satellite data’s abundant availability makes it tricky to run ML models and algorithms efficiently. Currently, we have a ton of datasets like Sentinel 2, Sentinel 3, Landsat 8, and SkySat, to name a few, which provide more than 2 petabytes (PB) of data every day. Thus, while many ML models operate efficiently on sample models, they fail to represent actual reality.

One of the most critical challenges faced in deploying ML models appropriately is the massive volume of data collected. Prof. Dr. Gilberto Camara, Secretariat Director, GEO, mentioned during the discussion that the data derived from EO satellites should be enough to cover all the categories and details a project requires explicitly. However, data labeling of the number of categories is crucial, which defines the behavior of the classifier modeling the data.

Data labeling

ML requires labels to understand data better, but natures’ diversity limits the application of ML algorithms. The currently available categorization is often found not enough to label data. For a basic understanding, we take the example of using ML algorithms for EO data of forests –

As depicted in the above image, how one defines a forest label is different for different places. Forest is a single label, but it has several variations, ranging from Boreal forest to Tropical forest. One may think this problem can be easily solved by breaking the label down into several small labels. Supposedly, if one were to break the term forest into eight different labels, the problem of finding good samples to train the ML algorithm is multiplied by eight. Hence, if we required 1,000 samples for the forest, in the above scenario, we would require 8,000 samples for the same, which complicates the matter. Thus, to describe nature appropriately, it is essential to ensure whether the labels used to define nature are consistent with the ML models.

Time as an element

In the case of forests, EO data is being used to monitor a forest’s condition – particularly deforestation. Deforestation is not a one-time process but is the result of a series of steps happening over time. In the ML context, it involves working with both Space and time. To understand it better, we can look at the diagram below, which explains how a forest evolves. A forest can grow in any of the ways mentioned below; for instance, it can be conserved throughout time, as in Fig A (1). There can be deforestation, or there can be afforestation in deforested land with time, as in Fig A (3).

To sum it up, one needs to measure what exists in a certain place at a certain point in time and determine the events that have happened in that particular location over time. Hence, to work with Space and time, we require spatial-temporal models. Modeling events and time is key for big EO data analysis, but ML has a hard time dealing with the change.

A solution to the above challenge is to use Geospatial Semantics for EO data analysis. Herein, EO data is organized using a logical view, including indexing and/or ingestion, rather than arranging it in three dimensions: time, longitude, and latitude. The significance of ingesting data is that it can be collected in a query-optimized way. Certain access patterns can be achieved more efficiently, such as spatial analysis or time series analysis.

Other technical challenges

The other technical challenges that data analysts and processors face while feeding the images to the ML models, like:

• Resolution– Different satellites provide different resolutions of images ranging from 500m provided by MODIS to 0.3 m by WorldView. Additionally, different datasets have different formats, such as JPEG2000 and GeoTIFF, among others. Thus, the processor must learn to work with different resolutions and formats. This problem can be partly solved by third-party software like Sentinel Hub, which harmonizes the Earth Observation data in one single format. 

• often partly or fully covered by clouds. The Clouds make it difficult for any algorithm and processor to derive useful insights from the satellite imageries. Therefore, the processor should mask these clouds so that these white spots or shadows do not distort the signals.
• Geometrical accuracy– Satellite images often twitch because geo-referenced points used for georeferencing the image are not perfect. While this has gotten better over the past few years, but it still could not be expected that one pixel will represent one point of the world.

Conclusion

Artificial Intelligence experts face many challenges while applying ML algorithms on EO data, affecting every phase of the data processing and analysis, ranging from collecting training data to deriving valuable insights from it. Recently, MKAI Technical Forum conducted a webinar on using AI on EO data. The webinar discussed how ML models are used to classify, smoothen and post-process the humongous volumes of EO data. A few ways to deal with these challenges would be to build robust and geographically diverse training data sets, include Geospatial Semantics in the process, and harmonize the data using various third-party software available in the market.

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The use of robotics has gradually gathered momentum over the past decade or so. If you were to step into a factory or even a warehouse right now it’s likely you’d see robots in full flow. But to date that’s just where they’ve stayed: firmly on the factory floor.

However, we’ve recently seen huge advances in AI, sensors, speech recognition and computer vision. All these technologies are combining with shrinking hardware costs and the rollout of 5G to make robotics more accessible than ever before. In our Technology Vision 2020 report, released in February earlier this year, we predicted that the convergence of these technologies would lead to a surge in the use of robotics ‘in the wild’ – non-enclosed public spaces – over the next three to five years.

This prediction was dramatically altered though by one of the biggest events to occur in our lifetime: Covid-19.

An accelerated immediate future

The pandemic has had a monumental impact on every walk of life and virtually every industry and business has been affected in some way. With social distancing in force and workers health the number one concern, this dramatic change of environment has paved the way for an acceleration in robotics in all aspects of society. As people have been asked to stay at home, robots have become critical to the ‘contact-less’ solutions businesses and governments have been striving for.

In the short-term, one area robotics is able to tackle effectively lies in managing volatility in demand and workforce available, while also creating a safe working environment for workers. Within warehouses, robotics are being used to manage uncertain demand and helping companies dynamically scale up or down their site productivity.

Robots are taking on new responsibilities during the pandemic ‘in the wild’ too, in many cases joining frontline workers in the fight against the virus as quickly as they can be produced. In Shenxhen, a start-up called Youlbot built an antivirus robot in just a couple of weeks. Its six ultraviolet bars can sanitize surfaces and its infrared camera scans for fevers among patients and the public. Similarly, Danish firm UVD Robots has developed a self-driving disinfection robot that uses UV light to completely disinfect rooms in just 10 minutes. It’s easy to see how useful these and similar robots would be in helping communities come out of lock-down, from public transport and schools to hotel rooms and restaurants. 

A growing impact long-term

Taking the long-term view, the entire robotics ecosystem is set to be dramatically accelerated as the case for robotics and automation becomes clearer in light of the pandemic. Industrial companies will increasingly turn to robotics to react to structural changes that will likely occur as a result of Covid-19. Take the supply chain, for example, the pandemic has shown how companies have to rethink their processes to be able to withstand macroeconomic shocks. Automation manufacturing and logistics processes can help to mitigate the sudden increased costs from having to move manufacturing capabilities quickly.

Again, looking beyond those controlled spaces, expect robotics to have a growing role ‘in the wild’. This will be backed up by the increase in 5G technology, just as 4G networks grew with the rising popularity of smartphones, with any robotics use cases requiring increased data transfer rates and low latency. This, in turn, will see the need for humans to maintain and control robots remotely grow, and new demand techniques and tools for teleoperations and VR training increase.

So, while today’s robotics leaders are stepping up to address the needs of businesses and society right now, those set to benefit long-term will be building the foundation of a more automated future. It’s key to form partnerships, enable new capabilities and work with governments to demonstrate new opportunities now.

Considerations

As robotics gather pace, there are still a number of considerations that businesses and developers must take into account in order to ensure the integration into society is a smooth one:

• Partnerships: It’s vital businesses look at the wider robotics landscape and build ecosystems that understand the standards, protocols, software and architecture already being developed to ensure they’re offering a competitive differentiator. Partnering with industry bodies or other companies can help to navigate this.
• Talent: A lack of skills in robotics across the industry could curtail efforts if businesses don’t focus on building their talent pipeline across patents, developers, technologists, architects and UX specialists. Using training, hiring new people, as well as collaborating with academia and industry is recommended to fill this gap.
• Automation anxieties: Concerns about automation have been temporarily pushed aside out of necessity. But what happens when we’ve got control over the pandemic – do robots continue to fulfil these tasks, or do businesses roll them back in storage? Whether it be through reskilling programs, or human-machine collaboration, businesses need to ensure they show considered, long-term thinking on how technologies will supplement their workforce. Otherwise, they face a significant backlash over putting profits before their people.

There are still questions to be answered about how businesses can best ease fears over the increase of robotics in society. However, as the world attempts to get back on its feet, robotics has shown that it does have a positive role to play. It should be an exciting time for businesses too. People being more used to daily robotic interactions could open a lot of new channels for businesses to interact with the world around them – this means increased customer interaction, data collection and even branding opportunities. Covid-19 has changed everything for everyone, but robotics is an area set to come out of the pandemic stronger than it was going into it.

The post Why Covid-19 will boost the use of robotics in the wild appeared first on Artificial Intelligence.

SEC Chief Economist and Director of the Division of Economic and Risk Analysis S.P. Kothari highlightedpolicy challenges of big data.

Speaking at the National Bureau of Economic Research (NBER) conference on big data and high-performance computing, Mr. Kothari stated that the SEC faces three main big-data policy challenges: security, technology and communications.

First, he said that the “volume, velocity, and variety of big data” create security risks. Mr. Kothari urged the SEC to be “mindful of the data it collects and its sensitive nature.” Second, he said that technology poses an “arms race” among firms, with the risk that those who have better technology will profit significantly at the expense of others. Third, he said that issues concerning big data are complex and “increasing require specialized training to understand.” He warned that the SEC focuses on retail investors, but there are numerous stakeholders including “pension funds, municipal bond issuers, brokerage firms, hedge funds, and Congress.” In communicating to each group, he said, “one size does NOT fit all.”

Mr. Kothari also described the potential of big data with respect to identifying and stopping bad actors. He pointed to the Consolidated Audit Trail, which, when completed by the self regulatory organizations, “will provide a single, comprehensive database enabling regulators to track more efficiently and thoroughly all trading activity in equities and options throughout the U.S. markets.”

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President’s message calls for tackling legal, security, governance challenges

President Xi Jinping called on Sunday for strengthened cooperation among countries to explore opportunities of digital, internet-based intelligent development and to properly address legal, security and governance challenges arising from big data industry development.

He made the remarks in a congratulatory letter to the China International Big Data Industry Expo 2019, which kicked off in Guiyang, Guizhou province.

The new generation of information technology, represented by the internet, big data and artificial intelligence, is booming at present and has significant and profound influence on various countries’ economic development, social progress and people’s lives, Xi said in the letter.

China attaches great importance to the development of the big data industry and is willing to share opportunities of the digital economy’s development with other countries and jointly explore new growth drivers and development paths by exploring new technologies, new business forms and new models, Xi added.

With increasingly wider applications of digital technologies in China, the country is expected to generate and store 27.8 percent of global online data by 2025, up from 23.4 percent last year, according to a report by market researcher International Data Corp and data storage firm Seagate.

In comparison, the US share will stand at 17.5 percent by 2025, a drop from its 21 percent share in 2018, the report added.

United Nations Secretary-General Antonio Guterres said that from medicine to transportation to farming, big data presents the world with a remarkable tool to advance global progress, but with that opportunity also comes risk.

“We must work together to ensure that big data, and the technologies that it enables, are harnessed for the benefit of mankind while minimizing the risks to development, peace and security and human rights,” Guterres said in a congratulatory letter to the expo.

Miao Wei, minister of industry and information technology, said China has already made significant progress in bolstering the big data industry with a string of big data platforms established in sectors such as manufacturing, commerce, finance, transportation and medical care.

“We will make a fresh push to integrate cutting-edge information technologies into the real economy, including establishing a national industrial data center, to better power the country’s sprawling manufacturing sector,” Miao said at the opening ceremony of the big data expo.

According to the ministry, China’s digital economy reached a total volume of over 31 trillion yuan ($4.5 trillion), or 34.8 percent of its GDP, in 2018.

Yang Xiaowei, deputy head of the Cyberspace Administration of China, also called for more efforts to develop the homegrown big data sector and highlighted that stepping up research and development is key to mastering core technologies.

Paul Romer, co-recipient of the 2018 Nobel Prize in economics and professor of economics at New York University, said he is impressed by China’s proposal in cyber sovereignty which he understands as: Each nation must be able to write and enforce its own laws that regulate cyberspace, and ensure that cyberspace works to the benefit of everyone in the nation.

“China’s articulation and implementation of cyber sovereignty means it is a chance for the world to see a different kind of organization for cyberspace, and a chance to see that with the right structure, we can get tons of benefits,” Romer added.

Lu Yong, vice-president of Huawei Technologies Co, said China’s digital economy has thrived on the basis that China has built the world’s largest 4G network.

“5G is not just a faster 4G. It will fundamentally reshape how enterprises run businesses and overhaul a wide range of industries by using data to create more value,” Lu said.

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Big data is now the basis for any activity in the business, government, and security sectors. However, in many cases the information streamed to law enforcement agencies and security organizations is not synchronized with other databases and is not analyzed in an optimized way due to difficulties in coping with the amount of information and with its coherent analysis. This scenario still exists and prevails today.

Databases that operate separately and do not communicate with other databases lead to a reality of partial information and difficulties in the understanding of the whole picture. In such a situation, security events and criminal activities materialize just because a piece of information was missed or wasn’t processed into clear insights.

In an era that draws from big data, its production and analysis, the major challenge lies in the fusion and integration of multiple information sources. Organizations are compelled to stream information from various systems, in different environments, in a structured and unstructured way, in multiple languages, formats, technologies, etc.

Big data analysis in the digital age is at the forefront of the current intelligence world. It is due to those challenges that analysts in all security and law enforcement systems are required to operate dozens of different systems in order to analyze and extract information, for the purpose of creating an accurate intelligence picture. This state of affairs also hampers both their instruction and their continuous work.

Technological capabilities are, therefore, demanded, in order to enable the integration of the various databases and simple analytic capabilities, for maximizing and making efficient the core operations of the security organizations in general, and the analyst’s work in particular.

Chen Vakrat Ben-Mordechai, VP Business Development of TA9 at Rayzone Group, asserts that “organizations don’t utilize more than 8% of the accessible information due to difficulties in the integration and operation of the intelligence systems.”

TA9’s IntSight System is a big data intelligence and investigation system for intelligence and law enforcement agencies.

This flexible system focuses on the ability to solve the big data integration problem. The system does not only feature the whole range of capabilities required for a security organization. It also allows the organization to independently expand its use of the system while taking advantage of their own assets and their control over budgets and timetables.

It is an intuitive system based on an application configuration, just as in a smartphone, with no need for any instruction. It enables the analysts to analyze huge amounts of information in a simple, visual manner while integrating the various sources. Moreover, the analyst can process the data independently, add new data and create new queries, and in fact manage operations and investigations in the most efficient way.

TA9 is a software house developing an analytical off-the-shelf product designed for the federal and enterprise markets. The company’s team is made of tech-savvy veterans of the Israeli intelligence community.

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If you want to be a data scientist, the tech world is still rife with opportunities. As with most booming tech sectors, attracting enough talent to fill the demand is important.

One way of doing this is to give visibility to the talent that is already there, highlighting the importance of data science and bringing it to the forefront of people’s minds.

The DatSci awards is an initiative that offers visibility and recognises the efforts of individuals, educational bodies and forward-thinking organisations within the growing data science sphere.

Siliconrepublic.com spoke to Ana Peleteiro Ramallo, a senior data scientist with Zalando Technology and a finalist for Data Scientist of the Year at the awards.

“My colleagues encouraged me to apply,” she said. “My contributions within Zalando and also outside the company in the data science community in Dublin have been big, so I decided that I would apply.”

Peleteiro Ramallo works as part of the fashion content team at Zalando, which is the biggest fashion platform in Europe. “I work with machine learning, deep learning and different techniques in order to provide the insights to different people.”

She became an award finalist based on the impact of her work, her technical capabilities and her level of knowledge sharing. “It’s really important in science to share knowledge and to give feedback.”

She said that within Zalando, there is a lot of upskilling, mentoring and opportunity for meet-ups.

With data comes informed decisions

Peleteiro Ramallo wanted to be a data scientist because of her love of finding solutions. “I’ve always been interested in solving problems. Having data allows you to make more informed decisions,” she said.

“I love that by using data, you can empower the business to take data during decisions. So, you don’t only rely on your intuition but you rely on the insights you can obtain for the data.”

She also said she loves designing algorithms, building different things that will impact how other people work and research. “I’m an engineer by profession but I did a PhD in artificial intelligence.”

Peleteiro Ramallo spoke highly of the mentors she had that helped her on her journey as her career progressed. She said it’s what drives her to share her knowledge with the STEM community when she can.

“I think it’s really important to help people upskill and that’s why I’m really committed to sharing my knowledge in different venues because I think that if you can help other people to climb their career ladder, that would be beneficial for everyone because you get better professionals.”

It’s not a boy or girl thing

Peleteiro Ramallo is one of five finalists for the Data Scientist of the Year award but she is the only female one.

She told Siliconrepublic.com that she has always been in male-dominated environments, with her engineering background and her interest in sports, so she doesn’t even notice any more in her own career.

However, she said she was still conscious of the gender diversity issues within the tech industry. “That’s why I’m doing a lot of ‘women in tech’ events, trying to be a role model, give more visibility to women.”

Peleteiro Ramallo said some companies are already taking action to help close the gender gap, but for the rest of the industry, it’s about education at an early age.

“It’s about educating from the ground up; that there’s no such thing as ‘this is for girls’ and ‘this is for guys’ – this is just something that you do if you like it. It shouldn’t matter what you are.”

She mentioned that Zalando welcomed teenagers as part of work experience, enabling them to see that tech was something cool, applicable to real life and, most importantly, achievable.

“I think sometimes people get scared and think: ‘This is not something I can do, this is too difficult for me.’”

She also said it’s important to create a space where everyone feels safe to work, citing the sexism and sexual harassment allegations at Uber. “I think those are things that the company has to stop because I think there are individuals that don’t understand that we’re all equal.”

The ever-growing data science sphere

Given how long the data science sector has been growing, has it reached its peak yet? Peleteiro Ramallo doesn’t think so.

“I think companies are realising that they need to leverage their data, so they have some data that they can use to make more informed decisions, or to improve customer experience.”

She said it’s a really good time for those in the data science sphere because of the big opportunities and challenges that are out there right now.

She also advised those who want to become data scientists to always keep learning. “Always keep refreshing your knowledge because it’s a field that changes really fast and it’s important to keep up to date with what’s the newest algorithm or the new approaches.”

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Big Data could not be described just in terms of its size. However, to generate a basic understanding, Big Data are datasets which can’t be processed in conventional database ways to their size. This kind of data accumulation helps improve customer care service in many ways. However, such huge amounts of data can also bring forth many privacy issues, making Big Data Security a prime concern for any organization. Working in the field of data security and privacy, many organizations are acknowledging these threats and taking measures to prevent them.

WHY BIG DATA SECURITY ISSUES ARE SURFACING

Big data is nothing new to large organizations, however, it’s also becoming popular among smaller and medium sized firms due to cost reduction and provided ease to manage data.

Cloud-based storage has facilitated data mining and collection. However, this big data and cloud storage integration has caused a challenge to privacy and security threats.

The reason for such breaches may also be that security applications that are designed to store certain amounts of data cannot the big volumes of data that the aforementioned datasets have. Also, these security technologies are inefficient to manage dynamic data and can control static data only. Therefore, just a regular security check can not detect security patches for continuous streaming data. For this purpose, you need full-time privacy while data streaming and big data analysis.

PROTECTING TRANSACTION LOGS AND DATA

Data stored in a storage medium, such as transaction logs and other sensitive information, may have varying levels, but that’s not enough. For instance, the transfer of data between these levels gives the IT manager insight over the data which is being moved. Data size being continuously increased, the scalability and availability makes auto-tiering necessary for big data storage management. Yet, new challenges are being posed to big data storage as the auto-tiering method doesn’t keep track of data storage location.

VALIDATION AND FILTRATION OF END-POINT INPUTS

End-point devices are the main factors for maintaining big data. Storage, processing and other necessary tasks are performed with the help of input data, which is provided by end-points. Therefore, an organization should make sure to use an authentic and legitimate end-point devices.

SECURING DISTRIBUTED FRAMEWORK CALCULATIONS AND OTHER PROCESSES

Computational security and other digital assets in a distributed framework like MapReduce function of Hadoop, mostly lack security protections. The two main preventions for it are securing the mappers and protecting the data in the presence of an unauthorized mapper.

SECURING AND PROTECTING DATA IN REAL TIME

Due to large amounts of data generation, most  organizations are unable to maintain regular checks. However, it is most beneficial to perform security checks and observation in real time or almost in  real time.

PROTECTING ACCESS CONTROL METHOD COMMUNICATION AND ENCRYPTION  

A secured data storage device is an intelligent step in order to protect the data. Yet, because most often data storage devices are vulnerable, it is necessary to encrypt the access control methods as well.

DATA PROVENANCE

To classify data, it is necessary to be aware of its origin In order to determine the data origin accurately, authentication, validation and access control could be gained.

GRANULAR AUDITING

Analyzing different kinds of logs could be advantageous and this information could be helpful in recognizing any kind of cyber attack or malicious activity. Therefore, regular auditing can be beneficial.

GRANULAR ACCESS CONTROL

Granular access control of big data stores by NoSQL databases or the Hadoop Distributed File System requires a strong authentication process and mandatory access control.

PRIVACY PROTECTION FOR NON-RATIONAL DATA STORES

Data stores such as NoSQL have many security vulnerabilities, which cause privacy threats. A prominent security flaw is that it is unable to encrypt data during the tagging or logging of data or while distributing it into different groups, when it is streamed or collected.

CONCLUSION

Organizations must ensure that all big data bases are immune to security threats and vulnerabilities. During data collection, all the necessary security protections such as real-time management should be fulfilled. Keeping in mind the huge size of big data, organizations should remember the fact that managing such data could be difficult and requires extraordinary efforts. However, taking all these steps would help maintain consumer privacy.

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