10 Reasons to be excited About Data Analytics in 2018

| March 9, 2018

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With the advancement in technology and accelerated innovation, there are various opportunities for data analytics professional. The growth of data in an environment of Internet connectivity is taking us towards digital business transformation. As we are already in 2018, we can expect a boom in digital business evolution with business analytics. Cognitive Computing – It was in 2016 when cognitive and natural language technology started being adopted. We can expect a more strategic analytics technology in 2018. Embedded Analytics – There has been a continuous need for bringing analytics close to the users. We can expect a more predictive and prescriptive analytics in the line-of-business apps.

Spotlight

Globant

We are a digitally native company where innovation, design and engineering meet scale. We use the latest technologies in the digital and cognitive field to empower organizations in every aspect. - Globant has more than 8,300 professionals and we are present in 14 countries working for companies like Google, Linkedin, BBVA, EA and Coca Cola among others. - We were named a Worldwide Leader of Digital Strategy Consulting Services by IDC MarketScape report (2016 and 2017)

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A Tale of Two Data-Centric Services

Article | April 13, 2020

The acronym DMaaS can refer to two related but separate things: data center management-as-a-service referred to here by its other acronym, DCMaaS and data management-as-a-service. The former looks at infrastructure-level questions such as optimization of data flows in a cloud service, the latter refers to master data management and data preparation as applied to federated cloud services.DCMaaS has been under development for some years; DMaaS is slightly younger and is a product of the growing interest in machine learning and big data analytics, along with increasing concern over privacy, security, and compliance in a cloud environment.DMaaS responds to a developing concern over data quality in machine learning due to the large amount of data that must be used for training and the inherent dangers posed by divergence in data structure from multiple sources. To use the rapidly growing array of cloud data, including public cloud information and corporate internal information from hybrid clouds, you must aggregate data in a normalized way so it can be available for model training and processing with ML algorithms. As data volumes and data diversity increase, this becomes increasingly difficult.

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How can machine learning detect money laundering?

Article | December 16, 2020

In this article, we will explore different techniques to detect money laundering activities. Notwithstanding, regardless of various expected applications inside the financial services sector, explicitly inside the Anti-Money Laundering (AML) appropriation of Artificial Intelligence and Machine Learning (ML) has been generally moderate. What is Money Laundering, Anti Money Laundering? Money Laundering is where someone unlawfully obtains money and moves it to cover up their crimes. Anti-Money Laundering can be characterized as an activity that forestalls or aims to forestall money laundering from occurring. It is assessed by UNO that, money-laundering exchanges account in one year is 2–5% of worldwide GDP or $800 billion — $3 trillion in USD. In 2019, regulators and governmental offices exacted fines of more than $8.14 billion. Indeed, even with these stunning numbers, gauges are that just about 1 % of unlawful worldwide money related streams are ever seized by the specialists. AML activities in banks expend an over the top measure of manpower, assets, and cash flow to deal with the process and comply with the guidelines. What are the punishments for money laundering? In 2019, Celent evaluated that spending came to $8.3 billion and $23.4 billion for technology and operations, individually. This speculation is designated toward guaranteeing anti-money laundering. As we have seen much of the time, reputational costs can likewise convey a hefty price. In 2012, HSBC laundering of an expected £5.57 billion over at least seven years.   What is the current situation of the banks applying ML to stop money laundering? Given the plenty of new instruments the banks have accessible, the potential feature risk, the measure of capital involved, and the gigantic expenses as a form of fines and punishments, this should not be the situation. A solid impact by nations to curb illicit cash movement has brought about a huge yet amazingly little part of money laundering being recognized — a triumph rate of about 2% average. Dutch banks — ABN Amro, Rabobank, ING, Triodos Bank, and Volksbank announced in September 2019 to work toward a joint transaction monitoring to stand-up fight against Money Laundering. A typical challenge in transaction monitoring, for instance, is the generation of a countless number of alerts, which thusly requires operation teams to triage and process the alarms. ML models can identify and perceive dubious conduct and besides they can classify alerts into different classes such as critical, high, medium, or low risk. Critical or High alerts may be directed to senior experts on a high need to quickly explore the issue. Today is the immense number of false positives, gauges show that the normal, of false positives being produced, is the range of 95 and 99%, and this puts extraordinary weight on banks. The examination of false positives is tedious and costs money. An ongoing report found that banks were spending near 3.01€ billion every year exploring false positives. Establishments are looking for increasing productive ways to deal with crime and, in this specific situation, Machine Learning can end up being a significant tool. Financial activities become productive, the gigantic sum and speed of money related exchanges require a viable monitoring framework that can process exchanges rapidly, ideally in real-time.   What are the types of machine learning algorithms which can identify money laundering transactions? Supervised Machine Learning, it is essential to have historical information with events precisely assigned and input variables appropriately captured. If biases or errors are left in the data without being dealt with, they will get passed on to the model, bringing about erroneous models. It is smarter to utilize Unsupervised Machine Learning to have historical data with events accurately assigned. It sees an obscure pattern and results. It recognizes suspicious activity without earlier information of exactly what a money-laundering scheme resembles. What are the different techniques to detect money laundering? K-means Sequence Miner algorithm: Entering banking transactions, at that point running frequent pattern mining algorithms and mining transactions to distinguish money laundering. Clustering transactions and dubious activities to money laundering lastly show them on a chart. Time Series Euclidean distance: Presenting a sequence matching algorithm to distinguish money laundering detection, utilizing sequential detection of suspicious transactions. This method exploits the two references to recognize dubious transactions: a history of every individual’s account and exchange data with different accounts. Bayesian networks: It makes a model of the user’s previous activities, and this model will be a measure of future customer activities. In the event that the exchange or user financial transactions have. Cluster-based local outlier factor algorithm: The money laundering detection utilizing clustering techniques combination and Outliers.   Conclusion For banks, now is the ideal opportunity to deploy ML models into their ecosystem. Despite this opportunity, increased knowledge and the number of ML implementations prompted a discussion about the feasibility of these solutions and the degree to which ML should be trusted and potentially replace human analysis and decision-making. In order to further exploit and achieve ML promise, banks need to continue to expand on its awareness of ML strengths, risks, and limitations and, most critically, to create an ethical system by which the production and use of ML can be controlled and the feasibility and effect of these emerging models proven and eventually trusted.

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HOW TO PREPARE FOR A CAREER IN DATA SCIENCE?

Article | February 17, 2020

The continuous advancements in technology and the increasing use of smart devices are leading tremendous growth in data. Considering reports, more than 2.5 Quintilian bytes of data are generated on a daily basis and it is expected that 1.7 Mb of data will be produced every second in the near future. This is where data scientists play an influential role in analyzing these immense amounts of data to convert into meaningful insights. Data science is an overriding method today that will remain the same for the future. This drives the need for skilled talent across industries to meet the challenges of data analytics and assist delivering innovation in products, services and society.

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Soft Skills in Data Science

Article | April 29, 2021

We live in a world convulsed by new technologies and we are witnessing how more and more processes are automated in order to be executed with the same skill or even with better results than if they were carried out by a human, all this in order to be more efficient and effective. In this context the world of work is becoming increasingly competitive, because to remain employable we need to learn to manage or find a way to adapt our knowledge and skills to new technologies. With the spread of e-learning platforms and the tutorials that we can find available on the internet, acquiring new knowledge is within everyone's reach. For this reason, it is necessary to differentiate ourselves in order to stand out from other professionals, who have the hard skills similar to ours and this is precisely where Soft Skills play a very important role. What are Soft Skills? Soft skills are actually a combination of individual social skills, communication skills, personality traits, attitudes, social intelligence and emotional intelligence. Which facilitate relationships with others, making us more effective when interacting with other people. We could say that Soft Skills are the human interface that allow us to adapt to different working environments and industries. They are powerful tools for personal and professional growth. Why are Soft Skills key in our professional growth? Nowadays, standing out in the world of work is getting increasingly difficult, regardless of whether you are part of a corporation or work independently, due to the great competition within the labor market. That is why we must develop certain skills and attitudes that help us to function properly and successfully meet professional demands. Soft Skills are the point of differentiation that allows us to be selected for a position. The reason is very simple, we could be applying for a position and competing with people that are equal or even more qualified than us at a technical level, but to achieve the collaborative objectives of the company, more is required than just the technical and rational part. Also the way of communicating, values, ethics, as well as personality traits are highly valued factors since they help to drive organizations through high-performance teams, guaranteeing the achievement of their objectives. The background of the Soft Skills that we have trained throughout our lives make us unique, because it is unlikely that two people have the same combination of Soft Skills and been trained in a similar way, and that makes us more competitive against certain job opportunities where perhaps many will have the same Hard Skills, but where our Soft Skills will be the ones that will make us stand out to continue advancing in our professional career. How to sharpen our Soft Skills? To perform in any job we necessarily need to interact with other people, even if we work independently or remotely, so we must have the necessary skills that allow us to connect successfully with our teammates and stakeholders. Starting from the fact that Soft Skills are human skills, we can say that we have them pre-installed and the way to start using them (installing them) is through the experiences we undergo every day. Imagine being able to communicate assertively in your work environment and in your personal life. Master the use of tools installed in you to improve your interpersonal relationships within your work teams and reduce conflict. This would allow you to foster a healthy working environment and be able to lead any team in any environment in a strategic and effective way. Think of Soft Skills as a set of Apps that are ready to be used (like a toolbox) and that according to the experiences that are presented in our personal and / or professional lives, we are going to choose to use these applications to achieve our goals. Every time we access one of these applications, we are giving it the opportunity to collect data that will allow it to personalize its insights according to our needs and to fine-tune its effectiveness each time we use it. One of the best ways to train our Soft Skills is by leaving our comfort zone, because that will allow us to 'install' more and more Soft Skills. Another way to refine our Soft Skills is by participating in activities that involve people we do not know and even better if we involve people from other cultures, because we will achieve a beneficial exchange of experiences and knowledge for both parties that will enrich and make the training of our Soft Skills even more valuable. Some examples of activities that will enhance your Soft Skills: • Participate in competitions (e.g. Hackathons) • Found or be a lead of a community that shares your interests, and organizes small or large projects. • Organize a study group aimed at carrying out a technical or business project in order to confront professionals from various fields or industries. • Find resources and experts to help you. There are Soft Skills trainers who know useful techniques and tips to develop/sharpen your skills. • Participate in volunteer activities. You will meet new people with whom to put your Soft Skills in action. These activities will train/sharpen your leadership skills, teamwork, delegation, interpersonal communication, persuasion, etc. These are skills that we do not have as much facility to train while we are students or when we have just started working after finishing our studies, and that are required in the labor market to continue climbing in our professional career. Why do Soft Skills matter in the Data Science universe? A consequence of the use of Artificial Intelligence and Data Science is that many of the jobs that we know today will be automated and this is a matter of concern for many professionals who see their careers are in danger, but the good news is that in the future many new jobs the Soft Skills will be the main protagonists, this is what John Thompson explains us in his book "Building Analytics Teams" In other words, it is precisely our human skills that will allow us to be more employable in the future, and they will be highly requested skills because according to what the experts envision which is, that the machines will not be able to match us in this field, and that is why training our Soft Skills becomes a priority because they will allow us to be the key players of the future. On the other hand, Data Science is an interdisciplinary field where Soft Skills such as cooperation and communication are essential to achieve the goals set. Denis Rothman, author of the book "Transformers for Natural Language Processing" in an interview that I conducted, mentioned that The Human Quality is the most important thing for him when choosing the members of his work team. These are some considerations to take into account to generate a culture of cooperation: • People work harder and need less supervision, when they themselves control their work and have more freedom to choose how to do it. When they work as a team, they show greater motivation, their sense of pride increases and productivity reaches higher levels. • Solid teams that seek quality and excellence correct themselves; that is, they identify problems and correct them very quickly. Thus, they gain work experience and increase their performance. • Forming a solid and efficient work team requires patience. You need to give them time to see your results. They will have to establish procedures to complete tasks, handle administrative functions and work together efficiently, they will even have to adapt to their own decisions and accept their consequences. • A manager or team leader must recognize the team building process without expecting immediate results. The group will have to go through a learning process and this will take longer in some groups than in others. Another key component to achieving high levels of cooperation is fluid communication among team members and stakeholders. For instance defining the communication channels and the contact points in the different teams involved, guarantees the constant flow of communication during the life cycle of a Data Science project. One of the most critical moments is the presentation of the results to the stakeholders. In some cases the results of a project are not taken into consideration not so much because the expected results are not achieved, but because the way in which these results are presented are not meaningful for the stakeholders, and this, in most cases, it is due to the existence of communication barriers that is a consequence of the use of a language (terminologies) used in the technical world but not in the business world. After taking a tour of the world of Soft Skills, we can conclude by saying that Soft Skills are like superpowers that are waiting for the opportunity to be put into action, to make you a superhero or superheroine. Keep climbing positions in your professional career depends on you, on how much you use these superpowers but above all on your skills to refine them and make them available to the work team of which you are part. Don't wait any longer and start discovering your potential, start training your Soft Skills! If you want to know more about Soft Skills, I invite you to visit The Soft Skills Show

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Spotlight

Globant

We are a digitally native company where innovation, design and engineering meet scale. We use the latest technologies in the digital and cognitive field to empower organizations in every aspect. - Globant has more than 8,300 professionals and we are present in 14 countries working for companies like Google, Linkedin, BBVA, EA and Coca Cola among others. - We were named a Worldwide Leader of Digital Strategy Consulting Services by IDC MarketScape report (2016 and 2017)

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