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Method of teaching has been a subject of discussion and debate for a long time. The effectiveness of training and skilling has been questioned and deliberated time and again globally, irrespective of the field, stream, sector, or specialisation.

The education sector, of late, has been witnessing a move away from traditional teaching techniques. Rote learning is slowly becoming expendable, especially in practical fields like analytics and data science.

The pace at which these fields are evolving coupled with the rapidly increasing demand globally across all industry verticals has created a significant gap in the right talent supply with the skillset to apply themselves for a given business context and create impact.

While the academic institutes, MOOCs, and the likes are doing a tremendous job in creating awareness and equipping the talent with theoretical concepts and knowledge, there is a gap that is widening around enabling the talent with the right experience to be impactful on-the-job quickly. The rising attrition of experienced talent is adding to the pressure on the system.

There is no doubt that theoretical learning is foundational for analysts and data scientists, but the work entails individuals to critically understand business problems and create innovative solutions. This demands them to be continuous prolific learners, creative thinkers, and quick problem solvers. The way to achieve these desired qualities is through learning by experience.

The learning-by-doing method allows learners to be engaged and actively participate in the learning process by working and reflecting on the projects done. This form of learning is proving to be the most effective in becoming successful in the analytics and data science landscape. We will discuss how one can and should upskill oneself through experiential learning in analytics and data science.

Before delving into the essence of experiential learning, there are two fundamental concepts that every aspiring analyst and data scientist must ingrain themselves with to become successful in the field.

1. Do not believe data without reasoning

Data is the basis for your trendsetting, analysis, prediction, and business solutions. If the data is faulty, the entire project will fail. One must question the existence of the data and reason with the data to ensure its validity and quality before moving on to any other step. For instance, last year, Italy had the highest number of COVID-19 deaths at one point. But a part of this situation owed itself to every death in an Italian COVID-19 hospital being counted as a COVID-19 death, regardless of the real reason. If one were to base ones predictions and trends on just the former statement, the results would be faulty.

2. Do not arrive at conclusions without critically examining the data

Complementing data reasoning this step entails examining the data and its correlation to causation. Go a step further into ensuring that the claims made by the data are backed by facts and information. For instance, citizens in the UK shop more during winter than summer. At face value, this proves seasonal consumer preferences, but in reality, winter coincides with Christmas and New Year sales, pushing customers to go on shopping sprees. Basing your analysis on the first statement would lead to an incorrect business solution.

The most fundamental aspect at which all three streams of analytics, descriptive, predictive, or prescriptive are built on, is the clarity around correlation v/s causation. Several of the analytics and data science applications fail to address business problems due to a lack of critical examination leading to the faulty judgment of interchanging correlation with causation and vice versa.

The methods of teaching and learning are undergoing a significant change in the modern era. The traditional classroom approach, based on the foundations of listening to lectures and reading out of textbooks, is not proving to be successful in readying professionals for todays workplaces. An increasing number of researchers with empirical pieces of evidence is proving the advantage of experiential learning on learners over conventional methods.

Setting the foundation for todays classrooms, Edgar Dales Cone of Experience, or his Learning Pyramid (1940), illustrates how the depth of a persons understanding depends on the medium leveraged and the senses involved in the learning process. Dales research identifies that direct, purposeful, or on-field experiences are the most effective method, resulting in 90 per cent retention of the information. In contrast, it revealed the least effective learning method through presented information like verbal and visual symbols.

As Dale explains, people learn best when they are present in action and learn from their experience. In the world of data science, opening up the learners sensory channels to interact with the information at hand is bound to produce better results. Moreover, analytics and data science are practical fields, entailing practitioners to work on models, deal with data, and make engineering decisions. For instance, a data scientist cannot learn a hackathon solution without brainstorming the possibilities or building an intelligent model right from the textbook.

Experiential learning methodologies and their effectiveness can be illustrated through the essential skills under the hard-skill and soft-skill umbrella in the analytics and data science space. While hard skills provide a foundation for all solutions, soft skills help in creating innovative ideas and communicating them. A nurtured combination of the two is what sets apart a data scientist from their peers.

The need for practitioners to be skilled in the textbook technical concepts to ensure that the best possible analytical approach and models are built, while is necessary, is not sufficient. They need to be seasoned in applying the concepts in real-life problem situations.

The way to develop application-oriented hard skills is to focus on three essential components.

1. Applied knowledge of algorithms

While one may have mastered algorithms, it is essential to know how and when to apply them. There may be instances when one comes across a problem where conventional algorithms dont work. One will need to be fluent in writing a new/heuristic algorithm or creative in tweaking the old ones. Applied knowledge is learned from experience, so one must practice applying oneself in the right way.

2. Translation for business context

Data scientists often work with non-tech-based business professionals to find solutions to business problems or to create incremental business impact. It is paramount for them to understand the business context and translate those to data analytics problems, followed by building the right solution to map the context for timely implementation. This process also requires translating back the solution to business stakeholders in a language that they can comprehend. This is critical not only for a successful implementation of analytical solutions but to also set the stage for continuous improvement for incremental impact. Contextualisation leads to the adoption and growth of data-driven culture within organisations. The skills acquired by one through the experiential learning approach can help with the above endeavour.

3. Programming skills in Python or R

Python or R can handle applications from data mining and ML algorithms to running embedded applications under one unified language. Data scientists need to be skilled in one or both programming languages to be successful in the field. The application-oriented case study-based approaches enabled through experiential learning methodologies enable one towards industry readiness with this skill.

LinkedIns Future of Skills report from 2019 that studied behavioural insights based on millions of data points from member engagement identified soft skills to have increased value in enterprises. This, they reported, is given the expanding application of new technology that is broadening the job expectations for data scientists. The data science industry focuses majorly on hard skills, but it is time we lay enough importance on developing soft skills as well. There are three soft skills that are most important for a data scientist to nurture.

1. Critical thinking & problem-solving

Critical thinking and problem-solving skills assist data scientists in clarifying vague and broad problems. If the dataset has errors or is not understood correctly, the solution will be unsuccessful. Under the experiential learning framework, one can build these skills by participating in hackathons, building models for experimentation, or engaging with data.

2. Effective communication

Once one has solved the problem, it is important to communicate it to the stakeholders effectively. Data scientists inability to communicate with stakeholders is a pressing concern within the industry. If the receiver does not understand the solution, it will not be implemented. Individuals can hone this skill by putting themselves out there, explaining solutions to non-technical people, receiving feedback on it, and working on enhancing the skill with more practice.

3. Agility & flexibility

Agility and flexibility are two skills that are increasingly becoming more important. The agile approach to working empowers data scientists to prioritise and create roadmaps based on business needs and adapt to different goals. Agile individuals are always learning and growing from new practical experiences.

In summary, experiential learning is learning by doing with application orientation and contextualisation. The framework is poised to get wide adoption in the field of analytics and data science globally across enterprises, functions, and academia. The aspirants and practitioners in the field should benefit from the framework to be continuous and prolific learners to upskill themselves in the most effective way and be future-ready.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the formhere.

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Council Post: Experiential LearningAn Essence To Address The Skill Gap In The Field Of Analytics And Data Science - Analytics India Magazine

Athinia will bring semiconductor manufacturers and materials suppliers together to share, aggregate, and analyze data to unlock efficiencies. The platform will also enable industry actors to understand on a deeper level the interaction between materials and processes at semiconductor fabrication plants.

"The semiconductor industry is facing unprecedented disruption. This has created a critical need for a secure data collaboration platform that can provide the transparency and data intelligence companies need to solve challenges such as chip shortages and supply chain issues," said Kai Beckmann, Member of the Executive Board of Merck KGaA, Darmstadt, Germany, and CEO Electronics."Partnering with Palantir, we've combined our collective expertise in materials science, data analytics and security to increase our customers' efficiencies and time to innovation."

"We are excited to partner with market leaders in this space to create an ecosystem that will enable semiconductor companies and their suppliers to collaborate to make better decisions, combatting simultaneous demand and supply shocks," said Palantir COO Shyam Sankar. "Athinia will help companies across the value chain bring new products to market faster and accelerate their product differentiation and growth."

By harnessing Palantir's unique experience in building inter-organization ecosystems, Athinia will enable advanced data analytics, in turn limiting the costly impact of quality or performance excursions across the value chain, from supplier to semiconductor fabrication plants. It will also help fabs manage faster innovation in manufacturing processes in a single, secure platform that will support improved incoming material quality and increase supplier engagement. Suppliers will benefit from internal efficiency gains through smart data integration and can be a better partner for the fabs they serve. The partnership will help solve such challenges by creating a platform to analyze previously siloed data in a holistic way.

Merck KGaA, Darmstadt, Germany, and Palantir have already proven to be successful in using collaborative data analytics to help common customers minimize quality deviations and increase efficiencies. Athinia will build upon this experience and leverage Palantir's expertise to help customers improve their decision-making in optimizing semiconductor materials.

Merck KGaA, Darmstadt, Germany, has recently worked with leading semiconductor companies to leverage AI and data analytics for solving key challenges. "We worked with Merck KGaA, Darmstadt, Germany, to create a data sharing platform that enabled advanced predictive manufacturing for chemical mechanical polishing (CMP), a critical step in the semiconductor manufacturing process. Through this collaborative partnership, we implemented an AI-driven methodology to enable smart data collaboration that drove process and quality improvements. By extending this approach to the broader supply chain and enabling a data ecosystem, we believe advanced predictive manufacturing can be accelerated for the broader semiconductor industry,"said Raj Narasimhan, Corporate Vice President, Global Quality, Micron Technology, Inc.

The Athinia platform is powered by Palantir Foundry, which enables users to structure and analyze data from disparate sources, generate powerful insights and support operational decisions, all while helping to ensure that sensitive data is processed in accordance with applicable data privacy rules, regulations, and norms. Palantir Foundry is designed to provide world-class security, access controls, partitioning, auditing, and accountability functions to support responsible data use. Athinia acts independent from the Electronics business sector of Merck KGaA, Darmstadt, Germany, and enables data sharing only on codified and anonymized data and customers will retain full control of their data, including intelligent purpose-based access control management. The secure data collaboration environment will provide continuous feedback through a holistic view and a common operating picture of in-fab performance that can help solve quality deviations.

Merck KGaA, Darmstadt, Germany, and Palantir already started collaborating in 2017. Through the partnership "Syntropy"both companies are determined to unleash the power of biomedical data and revolutionize cancer therapy and accelerate research. Syntropy's aim is to provide researchers with intuitive analytics techniques to enable them to aggregate, analyze and then also share data from disparate sources.

For more information about Athinia, visit the website or social media channels: LinkedInYouTubeTwitter

All Merck KGaA, Darmstadt, Germany, press releases are distributed by e-mail at the same time they become available on the EMD Group Website. In case you are a resident of the USA or Canada please go to http://www.emdgroup.com/subscribe to register for your online subscription of this service as our geo-targeting requires new links in the email. You may later change your selection or discontinue this service.

About Merck KGaA, Darmstadt, Germany

Merck KGaA, Darmstadt, Germany, a leading science and technology company, operates across healthcare, life science and electronics. Around 58,000 employees work to make a positive difference to millions of people's lives every day by creating more joyful and sustainable ways to live. From advancing gene editing technologies and discovering unique ways to treat the most challenging diseases to enabling the intelligence of devices the company is everywhere. In 2020, Merck KGaA, Darmstadt, Germany, generated sales of 17.5 billion in 66 countries.

The company holds the global rights to the name and trademark "Merck" internationally. The only exceptions are the United States and Canada, where the business sectors of Merck KGaA, Darmstadt, Germany operate as EMD Serono in healthcare, MilliporeSigma in life science, and EMD Electronics. Since its founding in 1668, scientific exploration and responsible entrepreneurship have been key to the company's technological and scientific advances. To this day, the founding family remains the majority owner of the publicly listed company.

About Palantir Technologies

Palantir Technologies Inc. builds and deploys operating systems for the modern enterprise. Additional information is available at http://www.palantir.com.

Who dares, wins.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantir's expectations regarding the terms and the expected benefits of the strategic partnership. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. Forward-looking statements are based on information available at the time those statements are made and were based on current expectations as well as the beliefs and assumptions of management as of that time with respect to future events. These statements are subject to risks and uncertainties, many of which involve factors or circumstances that are beyond our control. These risks and uncertainties include our ability to meet the unique needs of our customers; the failure of our platforms to satisfy our customers or perform as desired; the frequency or severity of any software and implementation errors; our platforms' reliability; and our customers' ability to modify or terminate their contracts. Additional information regarding these and other risks and uncertainties is included in the filings we make with the Securities and Exchange Commission from time to time. Except as required by law, we do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments, or otherwise.

SOURCE Athinia

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Athinia to Accelerate the Use of AI and Big Data to Solve Critical Semiconductor Challenges - PRNewswire

Summary

Implementing Artificial Intelligence (AI) has become a key challenge for organizations looking to create a competitive advantage through their data. Introducing a new technology (and associated process changes) is always a demanding task. According to ARC research, around 50 percent of respondents consider themselves to be in the piloting phase of an AI implementation. End users also expect 40-50 percent of all industrial applications to leverage AI by the year 2030. To reach this lofty goal, they need technology partners to help them address the main challenges that come with AI implementation: bridging the gap between multiple stakeholders and dealing with data. This ARC View will show how the use of RapidMiner as a partner for AI projects can help end users address these two key challenges.

Implementing an AI application is still a relatively new concept for many end users. As with any new technology or strategy, every step from proof-of-concept to lifecycle management can seem daunting. ARCs continuous research on artificial intelligence in manufacturing has uncovered the most common challenges that organizations face in their AI projectsyou can see the results in the chart below.

The blue bars represent challenges that are not just unique to end users, but to their organizations as a whole. In the case of AI, these challenges are amplified by the fact that it is such a cross-functional technology and has to address the needs of plant floor workers, data scientists, business executives, and many other groups. At times, the interests and goals of these groups can vary strongly, leaving many gaps to bridge.

The orange bar represents a common blocker thats preventing many organizations from even getting started with AI. Anyone who works with data knows that you must clean and prepare it to be usable for analysis. For those who arent lucky enough to have processes in place for data prep, this process can consume a lot of time and resources while having significant implications for the success of a project.

Still, as mentioned above, most of the respondents in our survey expect AI to become an integral part of production through 2030. Research also shows that early adopters of AI can typically expect a faster ROI and greater impact to their bottom line. So, what does it take to be part of the early adopter group?

The gap in question is characterized by different groups, their goals, and varying levels of knowledge in data science and manufacturing, respectively. The knowledge of these groups is often tribal knowledge that is only passed on within the group. As Sarma Malladi from the Swiss engineering and manufacturing company SWM International puts it, all manufacturers face the problem of tribal knowledge.

To bridge the gap, strong management, leadership, and technology are needed. In ARCs view, these fundamental internal requirements must be supported with a suitable tool that fulfills the following crucial demands:

RapidMiners tools specifically address the skills gap issue and support the need to bridge that gap.

The tailored user interfaces enable both beginners and experts to work with the same data. For example, beginners can use the AutoML solution RapidMiner Go to create basic models in just a few clicks, while experts can custom-code their own functions and share them with teammates using RapidMiner Notebooks.

This is all done on a single version of truth, e.g. a common database. This helps to bridge the aforementioned knowledge gap, as OT people from the plant floor can work with data quickly and create their own insights. Typically, the first aim is to re-create existing views and test the system against tribal knowledge. Then, after trust is built up, new insights and productivity gains follow.

In the following case study from the electronics industry, data from customer support was used by the data science teamgiven the size of the organization, the data represented millions of customers around the world.

As is the goal with any data science project, customers arent aware of whats happening behind the scenestheyre simply served better as a result of the right model being implemented. This case study below also shows how AI impacted the post-sales department as well as the production of spare parts. All parties involved rely on and trust the predictions of their developed machine learning models.

The organization that implemented this use case is a well-known, leading electronics manufacturer for the consumer and professional markets. Its diversified business includes consumer and professional electronics, gaming, entertainment, and financial services. The company needed to reduce overall customer support costs and tasked the data science team in their post-sales organization with achieving that goal.

The main project owner and their data science team understood the basic customer support statistics -- how many people called, how long people stayed on the phone, how many people visited the support website etc., but it was more difficult for the team to determine why people were calling. The reason for their lack of understanding was that the vast quantities of unstructured data that could help them had not previously been used.

A first step towards deeper analytical insight and greater business value was to focus on classification analyses -- why people are calling and document it in as much detail as possible (reasons and multiple layers of sub-reasons). To do this, the team first had to automate many of their existing business processes. An example of this was the translation process. With RapidMiner, the team could create workflows that allowed unstructured data in 26 different languages to be routinely translated for easier interaction and analyses.

This electronics manufacturer first used RapidMiner for web and text mining to support their classification analysis, which allowed them to identify trends and the reasons behind customer service calls. Today, the team is moving on to do more powerful analysis with RapidMiner, such as:

In a recent panel discussion at the ARC Europe Forum, it was stated that 80 percent of the work on AI is not about AI itself. One of the major challenges is lack of data and poor data quality. Imagine having to connect 40 years of equipment usage in a brownfield plant to gain access to the necessary data. Even if youre successful, the resulting data will likely vary in completeness, collection frequency, units, accuracy, availability, etc. Its also likely that most of the data has not been labeled correctly, which creates even more work.

Over the years, ARC has done a lot of research into the way organizations typically approach the problem of unlabeled data. Most rely on internal experts, often supported by some sort of tool, which can range from excel templates to more sophisticated software. This time-consuming process often increases the true cost of a project.

To support the data preparation and labeling, RapidMiner offers their data preparation tool Turbo Prep, which helps address the issue of inconsistent data. Its supporting functions are divided into five broad categories:

In this case study, the electronics manufacturer first experienced benefits by simply using the translation function. It created insights and tangible benefits without even getting to the core of AI.

The end user also mentioned that previously, the data science team operated in an 80/20 environment 80 percent of its time was spent on collecting and managing data, and only 20 percent on analyzing it. Now that all the tedious tasks of data cleansing and collection are automated with RapidMiner, the company flipped this ratio -- 80 percent of their time is spent on in-depth data analysis, and only 20 percent on collecting and managing data.

ARC has experienced it often in the past: End users and machine builders do not implement any new technology unless they understand it fully and are convinced it can help them achieve their goals. This has often resulted in the development of proprietary, in-house solutions, which can be effective but is extremely time and resource intensive. After solutions of this nature are implemented, they often stay in operation for sometimes up to 40 years, resulting in huge lifecycle costs.

While it is certainly true that an end user or machine builder needs to understand the technology fully as they are responsible for the safe operation of plants and equipment the right platform (such as RapidMiner) combined with industry expertise can help to kickstart the process, accelerate implementation, lower lifecycle costs, and even create opportunities that go well beyond the initial scope that your organization envisions.

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Keywords: Artificial Intelligence, AI, Machine Learning, AI Implementation, RapidMiner, Data Conversion, Anomaly Detection, ARC Advisory Group.

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Implementing AI: Bridging the Gap | ARC Advisory - ARC Advisory Group