Article | December 10, 2020
Saurav Singla is a Senior Data Scientist, a Machine Learning Expert, an Author, a Technical Writer, a Data Science Course Creator and Instructor, a Mentor, a Speaker.
While Media 7 has followed Saurav Singla’s story closely, this chat with Saurav was about analytics, his journey as a data scientist, and what he brings to the table with his 15 years of extensive statistical modeling, machine learning, natural language processing, deep learning, and data analytics across Consumer Durable, Retail, Finance, Energy, Human Resource and Healthcare sectors. He has grown multiple businesses in the past and is still a researcher at heart.
In the past, Analytics and Predictive Modeling is predominant in few industries but in current times becoming an eminent part of emerging fields such as health, human resource management, pharma, IoT, and other smart solutions as well.
Saurav had worked in data science since 2003. Over the years, he realized that all the people they had hired — whether they are from business or engineering backgrounds — needed extensive training to be able to perform analytics on real-world business datasets.
He got an opportunity to move to Australia in the year 2003. He joined a retail company Harvey Norman in Australia, working out of their Melbourne office for four years.
After moving back to India, in 2008, he joined one of the verticals of Siemens — one of the few companies in India then using analytics services in-house for eight years.
He is a very passionate believer that the use of data and analytics will dramatically change not only corporations but also our societies. Building and expanding the application of analytics for supply chain, logistics, sales, marketing, finance at Siemens was a very fulfilling and enjoyable experience for him.
Siemens was a tremendously rewarding and enjoyable experience for him. He grew the team from zero to fifteen while he was the data scientist leader. He believes those eight years taught him how to think big, scale organizations using data science.
He has demonstrated success in developing and seamlessly executing plans in complex organizational structures. He has also been recognized for maximizing performance by implementing appropriate project management tools through analysis of details to ensure quality control and understanding of emerging technology.
In the year 2016, he started getting a serious inner push to start thinking about joining a consulting and shifted to a company based out in Delhi NCR.
During his ten-month path with them, he improved the way clients and businesses implement and exploit machine learning in their consumer commitments. As part of that vision, he developed class-defining applications that eliminate tension technologies, processes, and humans. Another main aspect of his plan was to ensure that it was affected in very fast agile cycles. Towards that he was actively innovating on operating and engagement models.
In the year 2017, he moved to London and joined a digital technology company, and assisted in building artificial intelligence and machine learning products for their clients. He aimed to solve problems and transform the costs using technology and machine learning. He was associated with them for 2 years.
At the beginning of the year 2018, he joined Mindrops. He developed advanced machine learning technologies and processes to solve client problems. Mentored the Data Science function and guide them in the development of the solution. He built robust clients Data Science capabilities which can be scalable across multiple business use cases.
Outside work, Saurav associated with Mentoring Club and Revive. He volunteers in his spare time for helping, coaching, and mentoring young people in taking up careers in the data science domain, data practitioners to build high-performing teams and grow the industry. He assists data science enthusiasts to stay motivated and guide them along their career path. He helps fill the knowledge gap and help aspirants understand the core of the industry. He helps aspirants analyze their progress and help them upskill accordingly. He also helps them connect with potential job opportunities with their industry-leading network.
Additionally, in the year 2018, he joined as a mentor in the Transaction Behavioral Intelligence company that accelerates business growth for banks with the use of Artificial Intelligence and Machine Learning enabled products. He is guiding their machine learning engineers with their projects. He is enhancing the capabilities of their AI-driven recommendation engine product.
Saurav is teaching the learners to grasp data science knowledge more engaging way by providing courses on the Udemy marketplace. He has created two courses on Udemy, with over twenty thousand students enrolled in it. He regularly speaks at meetups on data science topics and writes articles on data science topics in major publications such as AI Time Journal, Towards Data Science, Data Science Central, Kdnuggets, Data-Driven Investor, HackerNoon, and Infotech Report. He actively contributes academic research papers in machine learning, deep learning, natural language processing, statistics and artificial intelligence.
His book on Machine Learning for Finance was published by BPB Publications which is Asia's largest publisher of Computer and IT Books. This is possibly one of the biggest milestones of his career.
Saurav turned his passion to make knowledge available for society. Saurav believes sharing knowledge is cool, and he wishes everyone should have that passion for knowledge sharing. That would be his success.
Article | December 10, 2020
In recent years, artificial intelligence research and applications have accelerated at a rapid speed. Simply saying your organization will incorporate AI isn’t as specific as it once was. There are diverse implementation options for AI, Machine Learning, and Deep Learning, and within each of them, a series of different algorithms you can leverage to improve operations and establish a competitive edge. Algorithms are utilized across almost every industry. For example, to power the recommendation engines in all media platforms, the chatbots that support customer service efforts at scale, and the self-driving vehicles being tested by the world’s largest automotive and technology companies. Because of how diverse AI has become and the many ways in which it works with data, companies must carefully evaluate what will work best for them.
Article | December 10, 2020
We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly.
Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics.
According to Gartner, augmented analytics is the future of data analytics and defines it as:
“Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.”
Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next.
Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics.
Benefits of Augmented Analytics
Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics:
Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them.
You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data.
Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output.
Grow into a Data-driven Company
It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset.
Essential Capabilities of Augmented Analytics
Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives.
Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability.
Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware.
Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently.
The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language.
Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data.
It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization.
Augmented Analytics: What’s Next?
Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis.
BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time.
Frequently Asked Questions
What are the benefits of augmented analytics?
Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs.
How important is augmented analytics?
Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors.
What are the examples of augmented analytics?
Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics.
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Article | December 10, 2020
Stephen Hawking, one of the finest minds to have ever lived, once famously said, “AI is likely to be either the best or the worst thing to happen to humanity.” This is of course true, with valid arguments both for and against the proliferation of AI.
As a practitioner, I have witnessed the AI revolution at close quarters as it unfolded at breathtaking pace over the last two decades. My personal view is that there is no clear black and white in this debate. The pros and cons are very contextual – who is developing it, for what application, in what timeframe, towards what end?
It always helps to understand both sides of the debate. So let’s try to take a closer look at what the naysayers say. The most common apprehensions can be clubbed into three main categories:
A. Large-scale Unemployment: This is the most widely acknowledged of all the risks of AI. Technology and machines replacing humans for doing certain types of work isn’t new. We all know about entire professions dwindling, and even disappearing, due to technology. Industrial Revolution too had led to large scale job losses, although many believe that these were eventually compensated for by means of creating new avenues, lowering prices, increasing wages etc.
However, a growing number of economists no longer subscribe to the belief that over a longer term, technology has positive ramifications on overall employment. In fact, multiple studies have predicted large scale job losses due to technological advancements. A 2016 UN report concluded that 75% of jobs in the developing world are expected to be replaced by machines!
Unemployment, particularly at a large scale, is a very perilous thing, often resulting in widespread civil unrest. AI’s potential impact in this area therefore calls for very careful political, sociological and economic thinking, to counter it effectively.
B. Singularity: The concept of Singularity is one of those things that one would have imagined seeing only in the pages of a futuristic Sci-Fi novel. However, in theory, today it is a real possibility. In a nutshell, Singularity refers to that point in human civilization when Artificial Intelligence reaches a tipping point beyond which it evolves into a superintelligence that surpasses human cognitive powers, thereby potentially posing a threat to human existence as we know it today.
While the idea around this explosion of machine intelligence is a very pertinent and widely discussed topic, unlike the case of technology driven unemployment, the concept remains primarily theoretical. There is as yet no consensus amongst experts on whether this tipping point can ever really be reached in reality.
C. Machine Consciousness: Unlike the previous two points, which can be regarded as risks associated with the evolution of AI, the aspect of machine consciousness perhaps is best described as an ethical conundrum. The idea deals with the possibility of implanting human-like consciousness into machines, taking them beyond the realm of ‘thinking’ to that of ‘feeling, emotions and beliefs’.
It’s a complex topic and requires delving into an amalgamation of philosophy, cognitive science and neuroscience. ‘Consciousness’ itself can be interpreted in multiple ways, bringing together a plethora of attributes like self-awareness, cause-effect in mental states, memory, experiences etc. To bring machines to a state of human-like consciousness would entail replicating all the activities that happen at a neural level in a human brain – by no means a meagre task.
If and when this were to be achieved, it would require a paradigm shift in the functioning of the world. Human society, as we know it, will need a major redefinition to incorporate machines with consciousness co-existing with humans. It sounds far-fetched today, but questions such as this need pondering right now, so as to be able to influence the direction in which we move when it comes to AI and machine consciousness, while things are still in the ‘design’ phase so to speak.
While all of the above are pertinent questions, I believe they don’t necessarily outweigh the advantages of AI. Of course, there is a need to address them systematically, control the path of AI development and minimize adverse impact. In my opinion, the greatest and most imminent risk is actually a fourth item, not often taken into consideration, when discussing the pitfalls of AI.
D. Oligarchy: Or to put it differently, the question of control. Due to the very nature of AI – it requires immense investments in technology and science – there are realistically only a handful of organizations (private or government) that can make the leap into taking AI into the mainstream, in a scalable manner, and across a vast array of applications. There is going to be very little room for small upstarts, however smart they might be, to compete at scale against these.
Given the massive aspects of our lives that will likely be steered by AI enabled machines, those who control that ‘intelligence’ will hold immense power over the rest of us. That all familiar phrase ‘with great power, comes great responsibility’ will take a whole new meaning – the organizations and/or individuals that are at the forefront of the generally available AI applications would likely have more power than the most despotic autocrats in history. This is a true and real hazard, aspects of which are already becoming areas of concern in the form of discussions around things like privacy.
In conclusion, AI, like all major transformative events in human history, is certain to have wide reaching ramifications. But with careful forethought these can be addressed. In the short to medium term, the advantages of AI in enhancing our lives, will likely outweigh these risks. Any major conception that touches human lives in a broad manner, if not handled properly, can pose immense danger. The best analogy I can think of is religion – when not channelled appropriately, it probably poses a greater threat than any technological advancement ever could.