Daily Tech Digest - September 10, 2020

Does An Analytics Head Require A Doctoral Degree?

Obviously, researchers in business are not expected to publish papers or guide students as their academic counterparts do. They are looked up to analyze complex business problems methodically as a scientist does. They are expected to make suitable approximations and define some simple parts in the complex whole and attack them using known repeatable, robust principles and techniques. ... Let us say, a large IT services company wants to fill leadership roles in the data science consulting practice. This person should have enough technical depth and the ability to identify the business gaps, communicate with the clients and most importantly build solutions that provide measurable business value (interestingly, this last skill is never considered a core competency in any traditional PhD in AI or other Masters and Bachelors courses). Let us say, an IT product company decides to smarten its application and wants leadership that can take them to the market quickly and profitably. The leaders should have the skill to define the product, design the technicalities, and lead the data science and DevOps teams compassionately and efficiently for rapid design and development. Hence, A leader in data science is not necessarily a technical expert who worked in the company long enough or a business leader who is a taskmaster!

Ripple20 Malware Highlights Industrial Security Challenges

Since availability is critical to ICS systems, and since the systems themselves can be fragile and quirky, these are generally the responsibility of operational technology (OT) teams. The information technology (IT) team usually manages the corporate network. OT employees are familiar with process technology and the systems they manage, but they do not generally know a great deal about information security, which can lead to insecure deployments. One fairly common situation for manufacturers is a divide, sometimes adversarial, between the IT and OT staff within a company.  OT employees do not want the IT staff to tamper with their systems out of fear of downtime that can cost the company. From what we have seen, these relationships often resemble red team versus blue team attitudes at many organizations. The blue team can resent the efforts of the red team because those efforts create more work for the blue team and can be considered a criticism of their work. OT employees also often don't want to consult with their IT counterparts when making arrangements such as remote access, leading to situations such as RDP on control networks commonly being exposed to the public Internet.

India can soon be the tech garage of the world

The government has a crucial role to play in positioning India as the Tech Garage of the World. It should act as a catalyst, and bring together the synergies of the private sector with the aim of innovating for India and the world. It has the potential to provide an enabling environment and a favourable regulatory ecosystem for the development of technology products and provide the size and scale necessary for their rollout. The product development should ideally be undertaken through private entrepreneurship, with the government acting as a facilitator. The key principles of product design should incorporate transparency, security and ease of access. The products must have open architecture, should be portable to any hosting environment and should be available in official and regional languages. The irrevocable shift brought about by covid-19 presents opportunities to develop new technology platforms. In this process, data integrity, authenticity and privacy should be embedded into the design of a product. A balance needs to be struck between regulation and product design through a dynamic collaboration between the government and technology entrepreneurs.

The State of Chatbots: Pandemic Edition

Generally speaking, there are two types of chatbots right now. The first kind is the more primitive kind that is based on simple question and answer rules. This kind is the easiest to deploy quickly, in response to some catastrophic event, like, for instance, a pandemic. It has a scripted set of answers. The problem with this kind of chatbot is that it is very limited, and it can't be enhanced or expanded. It's a one-trick chatbot. "The deterministic-rules based approach chatbots are easy to stand up quickly," Ian Jacobs, a principal analyst at Forrester Research, told InformationWeek. That means there was a huge number of these deployed during the pandemic. "There was an increase in call volume, and you were doing anything you could to get answers to customers without hiring another thousand call center agents," he said. These bots were doing very simple things, but "We are getting to the point where the value that brands are getting out of those very simple bots has already been achieved." One example of this type of bot was deployed by a credit union in the northwestern United States in April when stimulus checks were on the way, Jacobs said. This organization stood up a simple bot designed to answer basic questions that people were asking about the checks.

Digital Transformation Success Elusive For Financial Institutions

When financial institution executives were asked about the importance of alternative digital transformation strategies, improving the overall customer experience was considered to be of high or very high importance by 88% of organizations. The importance of improving the customer experience was followed closely by the need to improve the use of data, AI and advanced analytics (76% rated high or very high). Illustrating the perceived broad scope of digital transformation initiatives at most financial institutions, the majority of the other possible digital transformation strategies were each rated almost identically by financial institution executives in the Digital Banking Report research. Innovation agility, improving marketing and sales, improved efficiency, improved risk management and reducing costs were each rated high or very high by roughly six in ten executives. It is a bit concerning that the need to change the existing business model and transforming legacy core systems were considered the least important strategies despite research that indicates these strategies are of significant importance for transformation success.

Organizations must rethink traditional IT strategy to succeed in the new normal

This newfound self-confidence, combined with IT pros’ achievements during this time, will completely transform how IT is viewed by the business in the future. IT may earn a more prominent voice in the C-suite, as 40% of surveyed IT pros believe they will now be involved in more business-level meetings. Likewise, IT’s role will be up-leveled due to the vast upskilling 26% of IT pros underwent during this experience. With 31% admitting there’s a need to rethink internal processes to better accommodate the rapid change of pace required post-COVID, it’s highly likely a focus on IT pros’ upskilling will continue into the future. “As always, with new responsibilities comes the need for new skills. While almost half of survey respondents felt they received the training required to adapt to changing IT requirements, nearly one-third experienced the opposite, and are at risk of being left behind as IT teams continue to grapple with how best to support the new normal,” said Johnson. IT pros said they’ve gained an increased sense of confidence in their expanded roles, responsibilities, and ability to adapt to unexpected change in the future, despite contending with more challenging working conditions over the course of the pandemic.

Why Linux still needs a flagship distribution

Now, imagine a single distribution has been chosen, from the hundreds of currently available distributions, to represent Linux to hardware manufacturers, vendors, and software companies. That one Linux distribution would be used by hardware manufacturers and software companies to create computers and software guaranteed to run on Linux. That distribution would have only one desktop environment, one package manager, one init system, and the current stable version of the Linux kernel. Users could also download this Linux distribution and use it at will, but the primary purpose of "Flagship Linux" would be to make things easier for manufacturers and developers. Set aside your affinity for the Linux distribution you use and ponder this for a moment: Would you rather argue over which distribution is the best, or would you rather see Linux enjoy massive growth on the desktop and laptop arenas? We've already seen a number of manufacturers start the rollout of preinstalled Linux laptops. Lenovo, Dell, HP are all joining in on the fun, but the process hasn't been easy. As you can see, those manufacturers are, for the most part, all winnowing down the selection of Linux distributions available.

Federated Machine Learning for Loan Risk Prediction

A model is only as strong as the data it’s provided, but what happens when data isn’t readily accessible or contains personally identifying information? In this case, can data owners and data scientists work together to create models on privatized data? Federated learning shows that it is indeed possible to pursue advanced models while still keeping data in the hands of data owners. This new technology is readily applicable to financial services, as banks have extremely sensitive information ranging from transaction history to demographic information for customers. In general, it’s very risky to give data to a third party to perform analytical tasks. However, through federated learning, the data can be kept in the hands of financial institutions and the intellectual property of data scientists can also be preserved. In this article, we will demystify the technology of federated learning and touch upon one of the many use cases in finance: loan risk prediction. Federated Learning, in short, is a method to train machine learning (ML) models securely via decentralization. That is, instead of aggregating all the data necessary to train a model, the model is instead sent to each individual data owner.

How to Protect Chatbots from Machine Learning Attacks

Chatbots are particularly vulnerable to machine learning attacks due to their constant user interactions, which are often completely unsupervised. We spoke to Scanta to get an understanding of the most common cyber attacks that chatbots face. Scanta CTO Anil Kaushik tells us that one of the most common attacks they see are data poisoning attacks through adversarial inputs. Data poisoning is a machine learning attack in which hackers contaminate the training data of a machine learning model. They do this by injecting adversarial inputs, which are purposefully altered data samples meant to trick the system into producing false outputs. Systems that are continuously trained on user-inputted data, like customer service chatbots, are especially vulnerable to these kinds of attacks. Most modern chatbots operate autonomously and answer customer inquiries without human intervention. Often, the conversations between chatbot and user are never monitored unless the query is escalated to a human staff member. This lack of supervision makes chatbots a prime target for hackers to exploit. To help companies protect their chatbots and virtual assistants, Scanta is continuously improving their ML security system, VA Shield.

The Expanding Role of Metadata Management, Data Quality, and Data Governance

After the data has been accurately defined, it is important to put in place procedures to assure the accuracy of the data. Imposing controls on the wrong data does no good at all. Which raises the question: How good is your data quality? Estimates show that, on average, data quality is an overarching industry problem. According to data quality expert Thomas C. Redman, payroll record changes have a 1% error rate; billing records have a 2% to 7% error rate, and; the error rate for credit records: as high as 30%. But what can a DBA do about poor quality data? Data quality is a business responsibility, but the DBA can help by instating technology controls. By building constraints into the database, overall data quality can be improved. This include defining Referential Integrity into the database. Additional constraints should be defined in the database as appropriate to control uniqueness, as well as data value ranges using check constraints and triggers. Another technology tactic that can be deployed to improve data quality is data profiling. Data profiling is the process of examining the existing data in the database and collecting statistics and other information about that data.

Quote for the day:

"Concentrate all your thoughts upon the work in hand. The Sun's rays do not burn until brought to a focus." -- A.G. Bell

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