Digital First Magazine – October 2022 – Data Management Trends in 2022

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Data Management Trends in 2022 Special

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The Ubiquity of Data in Decision Making

If change is the only constant, then technology waits for no one. It is in a continuous state of development and upgrades - no wonder it influences all the existing industries. For instance, we saw rapidly accelerating digital transformation when the pandemic disrupted every organisation’s operations. Remote working became the new norm.

Zoom, the video conferencing company, added an unprecedented number of free participants in the first half of 2020, making $328 million in revenue during its February–April quarter. Like Zoom, which became a household name practically overnight, many technology companies thrived during the pandemic while the world expected a shrink in economic growth.

In many ways, 2020 was a turning point for mankind and a boon for business leaders who embraced the latest technologies. Experts believe that in the next decade, we will experience more progress than the last 100 years combined as technology reshapes health and material science, energy, transportation, and a broad range of industries and domains. The emergence of technologies like Augmented Reality (AR), which is expected to grow by a CAGR of 100% in the period 2021–2025 to reach a total revenue of USD 20 billion in 2025, is enough to convince

any executive that tech is the go-to choice of the future.

Business leaders much keep an eye on the latest upgrades in technology to make their job easier. The phase of immediate crisis management is over. Now, it is time to ay solid foundations for future innovation and growth. Adopting the latest technology is the best solution. In this issue, we have identified ‘10 Must-Watch Tech Companies - 2022’ that are setting new trends and driving transformational changes across all business segments. On the cover, we feature Corent Technology, a global organisation known for automating some of the most complex, time and labourconsuming Cloud migration, modernisation, and management tasks. Enjoy Reading.

Sarath Shyam

Anand Subramanian,

President,

Vembu,

Shankar Bhaskaran,

Kunal Kislay,

Role of AI in Digital Marketing

Anand Subramanian, Vice President, India, Theorem

Over the years, companies have adopted newer ways of marketing, moving away from the traditional methods of marketing.

Advancements in Technology have been embraced by Digital marketers to help them achieve, if not over-achieve, the return on investment (ROI) on the marketing spend. Every Chief marketing officer (CMO) have their eye on key indicators around customer acquisition, customer retention and providing customer lifetime value.

If these are the key performance indicators (KPIs), how does technology help Marketing Directors and CMOs achieve these KPIs.

Digital marketing is an expertise that revolves around building insights using data and analytics. With the advent of social media and engaging the online audience new kind of data has become available to Marketers. All this humongous data

With the advent of social media and engaging the online audience new kind of data has become available to Marketers. All this humongous data has to be processed to generate insights

Anand Subramanian is an IT Services industry veteran with over 29 years of experience having worked with large IT service companies like Tata Consultancy Services, Wipro Technologies, and Tata Interactive Systems. Prior to joining Theorem, Anand managed India Delivery as a Chief Delivery Officer for Ness Technologies India PVT Ltd. Starting his career as a Software Engineer and eventually pivoting into Delivery Management roles, Anand has been involved in various strategic initiatives including leading Innovation initiatives throughout his professional tenure. Anand has managed customers including large Fortune 500 companies in their digital transformation journey. He is passionate about utilizing technology to solve customer pain points and he believes that value-addition is one of the key tenets in any customer engagement.

ANAND SUBRAMANIAN

has to be processed to generate insights. This is where technology plays an important role on how to make the best use of this data. Specifically, applying Artificial Intelligence (AI) and Machine Learning (ML) on this data sets helps identify behavioral patterns. Here are some insights that AI and ML algorithms help digital marketers

Customer acquisition: With the growth in the awareness amongst customers and with companies increasing digital marketing spend, customers have more choices than ever before. This becomes a challenge for businesses to remain competitive and continue to acquire more customers for their revenue growth. This is where technology has a role to play and AI can contribute in the different steps of the customer acquisition workflow. From creating awareness, building interest and then converting a prospect to customer AI can help in each of the steps. Optimizing the company’s

search engine ranking, understanding customer dropouts, providing seamless website and/ or customer support experience, generating interest in the products, providing cross-sell/ upsell opportunities are the areas where AI can help businesses reduce the customer acquisition costs.

Customer Experience: When customers want to make an online purchase the buying process has to be seamless. Using AI-enabled chatbots and using conversational AI, the customers’ online buying experience can be improved considerably. Customizing and personalizing content can be easily achieved through the information captured by the BOTs as well as through the feedbacks or reviews captured post the purchase. The chances of customers coming back to a brand increases significantly when brands customize content based on the context which is where technology can help.

Personalized marketing: Based on customer’s buying behaviours, their interests, and spending habits, different kinds of digital marketing engagements can be enabled. This includes sending personalized messages, notifications, emails, relevant offers and recommendations on brands that the customer is more likely to shop. There are various parameters that will need to be considered before determining these offers. For e.g. considering age, demographics, location, gender will not provide accurate insights on customers’ interests and predict future buying behaviours

Product Recommendations: By using the data collected and implementing intelligent learning mechanisms a recommendation engine can help push products based on their behaviors. Since human cannot process huge amounts of data, this is where technology will play a role to push this information to customers.

Improve customer retention: Customer churn cannot be avoided, however, how do we reduce it. Using technology one can determine how frequently and why customers leave. Customers leave brand due to different reasons. Reasons can be numerous – demographics, feedbacks, customer satisfaction scores, amount of time spent on customer support, etc. Analysing the online customer engagement using AI can help businesses determine their best marketing strategies, production capacity to meet customers’ demands. Once the reasons are identified, using different AI and ML algorithms one can identify when the customer is likely to leave. This will give brands an

opportunity to take remedial actions to retain these customers.

Customer lifetime value: Businesses have come to realize that determining the customer lifetime value is important in order to be successful. To put it in simpler terms, customer lifetime value is the money a customer is willing to spend on a brand. Indirectly, it also shows will a customer become a repeat customer. AI will help businesses determine this value and in turn determine their most valuable customer(s) and help determine ways of how to convert a low valued customer to a high-valued customer. Businesses can then plan their loyalty programs, incentivizing repeat customers using different techniques like more targeted/personalized customer messaging, multi-channel marketing, over invest in repeat and continued buyers, etc.

What to watch-out for: One thing that has been discussed quite often is the authenticity of the prescriptions made by AI tools. How can AI tools be free of biases? An AI bias occurs when the algorithms deliver biased results due to incorrect assumptions. It also depends on the data sets on which the ML is being trained. More often than not ML processes get trained on training data and depending on the data quality the machine delivers the results. Hence it is important to question the AI results without which AI could end up being counterproductive.

Conclusion: AI has become mainstream to large brands and businesses who integrate technology to their business growth. Breaking the different stages of marketing and identifying the relevance of AI in each of these stages will help businesses propel forward their growth.

OPINION

Dating the Data or Being ‘Daring’ with the Data?

Shajesh Menon, CEO & Founder, Younion

Data might be the new buzzword of the decade with everyone wanting to swipe right at it, but with great power comes great responsibility. It’s not just about dabbling with numbers, it’s about making sure things are done with intent, done well and done right. With the world soaked in technology, organizations and individuals are truly embracing the power and competitive edge that good data can unlock. The mined insights can be influential, and the impact can be revolutionizing. What we do with it, how we use it, whom we share it with, and handle all of this information is a responsibility that’s essentially in our hands.

Shajesh Menon is the Founder and CEO of Younion. With more than two decades of diverse experience in Marketing and Brand Management, he has closely worked with companies like Google, Microsoft, Philips, Salesforce, AWS, Dell, Vmware, HPE, AutoDesk, RedHat, Intel, etc. Before the establishment of Younion, Shajesh held different leadership roles. From working in general selling, to opting for media sales, marketing services, business administration and management, Shajesh has done it all. Under the sheer guidance and leadership of Shajesh, Younion has successfully served several brands for twelve years as a brand experiences partner. The company has had a long association with many respected global brands and is known for providing structured & effective delivery practices, value-centric and result-oriented systemic framework, to its clients. Shajesh strongly believes that inspiration can strike anywhere, anytime and that solutions are always rooted within problems. For Shajesh, food & travel go hand in hand, he lives for the drive, he travels for the food and is on a perpetual quest for stuff that’s authentic and local.

To understand how to deal with data the right way, let’s take a step back and look at the journey of data.

From cradle to grave

It all starts with data creation, what’s collected is classified and validated, then stored – onpremises, in data centers, across devices, in the cloud. Next in the life cycle comes data sharing, be it with internal stakeholders or with partners/clients outside the organization. Data sharing safeguards involve access control, rights management to prevent data loss prevention and unauthorized access to sensitive information. When dealing with third-party vendors and their data, clear measures need to be set in place for verification, integration, dissemination, archiving, backup, and removal after services are delivered.

The pandora’s box

While the data lifecycle might sound straightforward on paper, every phase is ridden with challenges. Database security is arguably the most challenging of them all, because it is touched by all possible aspects of information security technologies and practices. It’s also naturally

In a world where the value and volume of data are growing exponentially, data privacy is juxtaposed as both a potential issue as well as a source of competitive advantage

at odds with database usability. While on one hand, when databases are built to be more accessible and usable, they tend to be vulnerable to security threats. On the other hand, databases being invulnerable translates to them being more difficult to access and use. A paradox all parties dealing with data, at some level or the other, have to live with! Circling back to the topic of data responsibility, security controls, awareness training, penetration testing and vulnerability assessment should be part and parcel of formal data security policies.

The price of privacy

Today, companies face heightened scrutiny from regulators in the area of improving how they collect, use, store and delete personal information and manage privacy. With time, the pressure is only expected to grow with innovations such as the Internet of Things, big data, always-on virtual assistants, etc., generating more and more data and insights about everything people say and do.

Is more always better?

In a world where the value and volume of data are growing exponentially, data privacy is juxtaposed as both a potential issue as well as a source of competitive advantage. When it comes to data, the mindset of the-morethe-better is fast changing, both companies and regulators are slowly realizing that it’s possible to have too much of a good thing, as it makes way for a host of other problems – cost, security, and compliance, to name a few.

Devil is in the details

With increasing conversations and controversies surfacing, stringent laws and regulations are essential to make a paradigm shift in the minds of the general audience and its growing concerns about private data. The frequent headlines shining a spotlight on the potential misuse of consumer data, organizations dealing and sharing the data with third parties, etc., lead to significant reputation damage and data breaches that do a disservice to the ecosystem at large. There’s a lack of stringent personal data protection laws in India but aligning to GDPR policies is not something organizations can overlook. Companies need to adopt a more proactive method while leveraging inventory of third-party relationships, including data collected, stored, or shared in order to address issues related to quality, use, privacy, and security. From using the right tools that handle requests in a timely manner, keeping authentication and permissions for affiliate marketing in mind, gearing up for reputational risks that extend beyond noncompliance. Having a GDPR-compliant privacy policy, seeking consent from data subjects, securing the processing of personal data, non-adherence can lead to big financial implications even for Indian businesses. Without a comprehensive approach to information governance, database management and data privacy remain a pressing compliance challenge and a potential ticking time bomb.

Are Retailers Doing Enough to Secure Their Data?

Kumar Vembu, CEO & Founder, GOFRUGAL

Kumar Vembu is a visionary with more than two decades of entrepreneurial experiences of identifying and riding the technology transformation wave to doing business in India. He is an also angel investor in some of the most promising start-ups such as Pick Your Trail and Urban piper. Driven by a determined goal to build a worldclass software product company in India, Kumar co-founded Zoho Corp., in 1995 with his brother Sridhar & Delium in 2016. GOFRUGAL is his brainchild and was established in 2004 with a vision to make ERP easy. GOFRUGAL’s solutions are helping over 30,000 brands across 40+ business formats transform trade business operations digitally for improved customer experience helping them sell more & serve their customers. In conversation with Kumar Vembu.

SMB and MSME retailers have just started or increased adoption in technology since pandemic. Why is it important for them to invest in a realtime data back-up solution?

India ranks second in the world in incidents involving ransomware. The number of organisations that paid ransom increased from 26% in 2020 to 31% in 2021. The pandemic has accelerated digital adoption among SMEs and businesses are generating more data now than ever. It is crucial for businesses of all sizes to encrypt, protect and backup their data regularly. When valuable business data gets

deleted or affected by virus, there is a significant impact to business operations. Impact includes business downtime, orders lost with increased operational costs to get their business back running.

Most businesses still prefer traditional backup methods that are highly people-dependent, error-prone, labor intensive, time-consuming, and skill intensive to back-up and restore correctly. Its time for businesses to realize the benefits of a real-time back-up as a service BaaS that offers fully automated back-up on cloud. As the entire back-up is automated, it is errorfree, back-up time is instant and restoration is

Its time for businesses to realize the benefits of a real-time back-up as a service BaaS that offers fully automated back-up on cloud

in a matter on minutes based on the size of data. This is because data is continuously synced to the cloud and is automated. No additional skill, people costs not required.

How has the pandemic increased vulnerability for these retailers?

The retail industry has seen massive investments coming in from tech giants since the pandemic. According to a study , COVID has accelerated the digital transformation in retail/ecommerce by 6.1 years, subsequently rising the competition. Competition and ravaging chaos are the roaring threat of ransomware attacks in retail. Hackers attack a business and encrypt their files, threatening to publish their stolen data for a ransom. Apart from locking the business and blocking its operations, data leakage leads to a strained reputation, sullied image and legal complications. Also, data theft results in failed transactions, falling delivery times and fashions a feeling that the business is unsafe and unworthy. Customers shift to competition that’s more secure. Reputation and credibility especially play an integral role in the growth of an SME.

Security was always a concern for organisations embarking on a digital journey and now it has become a prerequisite to not just secure yourself but also build a competitive edge over contemporaries.

What are the benefits of back-up as a service and how will it disrupt data back-up methods?

Back-up-as-a-service addresses some of the key challenges existing in the incumbent system and

enhances business experience for entrepreneur and his team.

Lowered total cost of ownership: No upfront investment in infrastructure, no skill or expertise required to manage backup. In the legacy back-up, owners have to run the software on the interface with the help of a IT professional and is constantly dependent on them for any hassles. Back-up-as-a-service simplifies this by eliminating process. Backing up business information is now as simple as restoring phone data from cloud with a single click!

Completely automated: The process is automated and once the set-up is complete, there is no additional effort expected from the user. It keeps running in the background. Automating this process also removes room for error and helps the business owner collate valuable insights from data. It is simple DIY tool that automates the data back-up and restore Less time consuming: Data backup is periodic and is done every 3 minutes. This allows the owner to focus on other important decisions and employees to attend to their customers without any ad hock.

Secure and reliable: As data is backed up in a data center, this provides great protection to data and any malware and ransomware.

100% business continuity: In the event of a breach, restoration is fast and can be done with a single click and OTP authentication. Restoring data can be done by an entrepreneur from any educational background and doesn’t need a IT professional to intervene. Businesses can now enjoy peace of mind as they do not need to worry on data protection.

How does GoSecure help retailers to retrieve and secure data?

GoSecure real-time back-up as a service (BaaS) automates and disrupts data backup process by completely syncing it online real-time. This solution helped 2000+ businesses secure their data and 450+ businesses recover data seamlessly. GoSecure takes a measly duration of 30 minutes to retrieve years’ worth of information. A customer of GOFRUGAL from Tirupur was left devastated after a ransomware attack. However, GoSecure helped him retrieve information of 15 years in 30 minutes without any data loss.

More importantly it creates awareness on securing information and gives accountability to the owners. This will contribute in the rise of a generation of cautious business owners. The solution also helps businesses remain on the right side of the law. Be it GST or VAT, statutory law mandates generation and maintenance of electronic records and documents for at least three to seven years. GoSecure helps in maintaining important business information intact and remain statutory compliant.

Back-up-as-a-service addresses some of the key challenges existing in the incumbent system and enhances business experience for entrepreneur and his team

The How and Why of Data Science

Subramanyam Reddy, Founder & CEO, KnowledgeHut UpGrad

Data is a vast ocean of structured and unstructured information from a wide variety of sources. And a Data professional’s job is primarily to sift through it all and gather information and transform it into a form that can be easily understood, and then used by businesses. To achieve this, professionals like Data Scientists and Data Analysts must be well versed in data analysis, visualization, modelling, prototyping, and programming among other key areas. These essential functions lead to production and deployment of better products and services that solve real-world problems.

Subramanyam Reddy (Subbu) is the Founder and CEO of KnowledgeHut, the leading technology workforce development company that helps enterprises and individuals around the world move forward to the next level with the help of technology. in 2012, at the age of 28, Subbu, as he is known to everyone, started KnowledgeHut. He on-boarded 12 employees and with an initial investment of $10,000 (Rs 5 Lakh). They almost gave up after the first two months of no revenues and no more savings to invest. But then the leads started flowing in and by the end of third month, they generated revenue of around $28,000 a(Rs14 Lakh). Since then, there has been no looking back. The company has grown from strength to strength and is now expected to clock an Annual Revenue Run-Rate of Rs300 crore in March 2022.

A Data professional’s toolkit will consist of tools that enable them to explore, research, and analyze large volumes of data at high speeds and with accuracy, leading to impactful contributions to the project. Modern tools such as Matlab, SQL, Python, R and more are essential in the Data Science toolkit.

Beyond these tools, data professionals need curiosity, the skill to gauge patterns, and the ability to collaborate with a cross section of functions to succeed at their job.

Is Data Science a Career Worth Investing In?

It was just ten years ago that the Harvard Business Review quoted the then chief economist at Google saying that Data Science would be the sexiest job of the following decade. Today, a Data Scientist earns 25% more than a programmer, on average.

The World Economic Forum in their Industry 4.0 insight report has tagged Data Scientist to the top job in 2022, which is no surprise considering how every industry depends on data-driven insights to develop products and services. The Cybercrime Magazine has projected that by 2025, the global data storage will reach 200 zettabytes.

Competent Data professionals are the need of the hour to leverage this data and condense it into actionable information. There couldn’t be a better time to consider Data Science as a career.

So, You Want to be a Data Scientist. Now What?

A Data Scientist needs a significantly more varied skillset compared with other tech specialists. While tech skills do comprise of the essential skills that Data Scientists require, they’re not the only skills needed to be successful in the field. Other than familiarity with using programming languages for working with large datasets, you also need

Beyond these tools, data professionals need curiosity, the skill to gauge patterns, and the ability to collaborate with a cross section of functions to succeed at their job

to be able to wrangle big data, and deploy your statistical, analytical, and business skills to use the insights from that data for aiding in decision-making that helps the enterprise grow.

The key is to understand that the field of Data Science is as interesting as it is challenging, and it is also rewarding. Let’s look at the main skills you will need to master to thrive as a Data Scientist, whether you’re starting afresh in the field or switching to Data Science from another career path.

Statistical Acumen

Statistics has been the precursor to modernday Data Science with its penchant for finding patterns in data and continues to be relevant to how patterns in data are identified and then made available as usable information.

Programming Skills

Programming is essential to Data Science primarily for th e reason that an overwhelming majority of the data that Data Scientists will deal with comes from the digital realm. Termed Big Data for the sheer quantum of it, the data comes from various sources and there are powerful programming languages that make it possible to process it.

Visualizing Data

Data, in simple terms, is information collected through usage across multiple touchpoints. This information by itself would make no sense to somebody who isn’t trained to understand it. When the quantum of such information increases, it becomes even more important to be able to visualize the important information

from the data. Data visualization also makes it easier for people with no background in technology to be able to understand what the data is conveying.

Understanding Data and Working with It

Like we discussed earlier, when we talk about data, we are referring to information. Consider buying clothes online. What you browse through is a data set, what you add to your wish list is another dataset, and what you finally end up buying is another. Each of these datasets contain several kinds of information ranging from the fabric to the brand to the size to the price and the shipping and the time of delivery promised.

Let’s assume you abandon your cart without completing the purchase, that creates yet another dataset. The platform you are browsing apparel on would have thousands of customers visiting and creating such datasets. How would the platform make sense of these? What is popular with the customers? What kind of display strategy works better? What kind of price point, what shipping policy increases sales?

Now consider other industries, healthcare, logistics, food & beverage, education, automobiles, finance, each of them has millions of such datasets that are collected in the form of numbers, values, and text that would confound anyone. This is the data that Data Scientists work with and here’s how they do that:

Data Collection and Storage

The raw data that is collected from various touchpoints is not suitable for analysis or

visualization until it goes through several processes. The collection and storing of such data is the first step in working with it.

Class Labelling

The splitting of the raw data into categories is the second step in working with data and creates more ease for further work.

Data Cleaning

This is one of the more tedious processes that Data Scientists encounter when working with raw data. This involves scanning data and getting rid of any anomalies such as missing values, incomplete fields, spelling errors, and anything else that makes the data unusable.

Data Balancing

Considering that data is gathered across several different touch points, it is often the case that data is not balanced in terms of representative values. Data Scientists employ data balancing methods such as extracting observations from categories to balance the collected data.

Data Shuffling

This is one the most crucial steps in working with data. This is also the stage where patterns start to emerge. This is also the stage where statistical methods such as randomization and different sampling methods are applied to data to draw out patterns and predictions.

Data Visualization

Finally, after several processes, the patterns drawn from the raw data can be presented in a visual form for everyone to understand and make decisions based on.

Through all these processes, several tools and technologies play a crucial role helping Data Scientists wrangle, clean, slice and dice, and present data. Among them, R, Matlab, SQL, and Python are some of the most popular. Clean datasets make for more efficient algorithms.

Looking to the Future

The US Bureau of Labour Statistics projects 11.5 million job opportunities in Data Science by the year 2026 with some of the highest salaries. For the last four years, Data Scientist has featured among the top three job roles in the USA on Glassdoor. The future sure looks bright for this essential function and people working to cultivate a strong skillset in the domain.

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AI-centric Workflows Can Help Companies Transform Data Into Actionable Intelligence

Shankar Bhaskaran, Managing Director, MetricStream India

As the fourth industrial revolution unfolds, India is rapidly scripting its own growth story. Leveraging the power of data, Indian businesses are awakening to the endless possibilities offered by an accelerating digital economy. Organizations are thriving on the back of next-generation technologies that are driving enterprises in a digital world. A Ministry report estimates that India’s GDP is poised to grow by 8% by 2022-23. Another report by an industry trade association states that Artificial Intelligence (AI) is expected to boost India’s annual growth rate by 1.3% by 2035, adding about 15% to the country’s current gross value. As organizations look to harness the power of nextgeneration technologies and thrive in the era of the

Artificial intelligence (AI) is a powerful tool that can enable organizations quickly get insights, identify patterns, avoid duplicate efforts, and apply the right actions in the decision-making process for the business

Shankar as the head of India field operations, is primarily focused on executing the strategy and operational plans aligned to the strategic vision of the company. Over the last 15 years at MetricStream, Shankar has leveraged the unique India-US business model for outsourced delivery to onsite locations, while setting up the sales and marketing infrastructure, customer advocacy, delivery management, as well as general administration teams to support scale and go-to market.

SHANKAR BHASKARAN

Fourth Industrial Revolution, the focus on data is now more critical than ever. It wouldn’t be wrong to say that it is data that runs the modern enterprise in today’s digitized world.

Data however cannot drive business value by itself. It is only when it is transformed into actionable intelligence that it can enable effective decisionmaking. That said, many businesses fall short of structured processes, producing unstructured data that is difficult to analyze and interpret. This is a major problem for teams working on IT, cybersecurity, risk, audit, and compliance. Most of their productive time is spent sifting through a large volume of data instead of analyzing it and using the information to help C-suite executives make strategic business decisions.

The power of data can only be harnessed by streamlining the processes and workflow and automating them with the right set of tools and technologies. Artificial intelligence (AI) is a powerful tool that can enable organizations quickly get insights, identify patterns, avoid duplicate efforts, and apply the right actions in the decision-making process for the business.

Connected GRC – An AI-driven approach to transforming data into actionable intelligence Industry 4.0 has ushered in a complex and unsettled business environment with amplified digital interconnectedness of people, processes, systems, and

AI-enabled Connected GRC systems can drastically improve the speed at which risk, audit, compliance, and IT & cyber teams can locate relevant data and information, thereby expediting quick and factbased decision-making

organizations. Companies in India have to navigate through a rapidly evolving risk and regulatory landscape marked by geopolitical uncertainty. Here, AI can be a gamechanger. Using AI-enabled platforms to connect business governance, risk, and compliance functions (GRC), organizations can bridge operations and collate data from across functions, instead of operating in silos.

An integrated approach to GRC is the key, to bringing everything together. When powered by AI, a Connected GRC software provides an overarching framework for companies to work within—from compliance to IT security, legal functions, insights, and audits, AI creates a powerful mechanism for companies to best protect themselves. These integrated programs foster collaboration, sharp insights and intelligence gained both from machine learning and human observations. When utilizing AI correctly, employees can see the bigger picture, connecting the dots through large data sets that were previously overwhelming to manage.

Most importantly, AI-enabled Connected GRC systems can drastically improve the speed at which risk, audit, compliance, and IT & cyber teams can locate relevant data and information, thereby expediting quick and factbased decision-making.

AI-centric workflows can help integrate systems and power business growth

A connected GRC approach involves integrating governance, risk, and compliance for business, cybersecurity, and ESG functions in an organization. Software platforms like this can benefit an organization in several ways. For example:

C-suite executives and decision-makers get a connected view of risk, and the teams can internally collaborate seamlessly across Risk, Compliance, and Audit functions. They can use combined insights and turn them into a strategic advantage for business growth, competitive edge, and brand differentiation. They can benefit from real-time visibility and quantified risk insights across IT, cyber, and vendor risk. Companies can use the information to prioritize their cyber investments, and policies, and safeguard the organization with an active cyber risk management strategy.

They can use the system to develop, collect data and report on all industry and organizational-related sustainability reporting. Businesses can use the system to meet all ESG data, disclosure, and framework requirements and enhance customer, investor, and public brand perceptions.

AI-enabled systems will help companies thrive in Industry 4.0 For businesses to thrive in Industry 4.0 they should be able to make the right decisions based on timely information and insights. To enable this companies should be able to provide decision-makers with insights and learnings based on past events and accurately predict future outcomes. This is only possible through advanced data science techniques and AI-enabled systems. By using AIcentric workflows companies can leverage the power of data by transforming them into actionable intelligence, helping them to stay relevant as they ride the fourth wave of the industrial revolution.

Data Hygiene: The Surest Form of Security Against Cybersecurity Issues

Kunal Kislay, Co-founder & CEO, Integration Wizards Solutions

Kunal Kislay is a B.Tech IIT Bombay alumnus with 10-plus years of experience in enterprise mobility, Internet of Things, AI, Neural networks and Machine learning. Creating solutions for a vast array of verticals made him understand the pulse of technology and its changing paradigms. He used to work at Antenna Software as lead architect where he developed enterprise mobility platforms and solutions for some of the largest organizations in the world. After Pegasystems took over Antenna Software, Kunal, alongside his two Colleagues, Saquib Khan and Kumar Raman, bootstrapped Integration Wizards in 2014.

Cyber data hygiene is a mechanism to maintain the health and security of our systems to avoid any digital mishap. Threats like malware, phishing attacks, password theft, frauds, and cloud security breaches are on an all-time high.

Our lives have shifted to virtual spaces across devices. Passwords help us enter the world of entertainment, online shopping, social media, and banking, to name a few. A unique digital identity is constantly being created, ever-enriched with various personal credentials. An active digital footprint is generated by logging into multiple channels, posting on social media, and sharing data

access with third parties. A passive digital footprint is unintentionally generated when the touchpoint utilizes device location and other information sans access permit. The digital trail built is shaping our lives in numerous ways. Companies often seek social activities for character analysis of prospective employees. It even acts as evidence for relevant authorities in some instances. On the other hand, it also attracts hackers, who seek data for nefarious purposes. Once the data is leaked, it is not easy to protect one’s personal information from going public.

According to Cybersecurity Ventures, cybercrime losses are expected to exceed $6 trillion in 2021. A moment of weakness

A passive digital footprint is unintentionally generated when the touchpoint utilizes device location and other information sans access permit

can threaten and compromise sensitive data. However, certain practices may reduce, if not remove, the chances of such threats entering our devices. A sustainable, easy-to-follow cyber hygiene ritual done at regular intervals, coupled with the following, can be helpful.

Check your Digital Footprint: Some websites inform whether one’s data may have been exposed to unknown parties. The compromised data is not retrievable, but one will remain cautious in the future.

Document your Equipment: Know both the hardware and software of your devices. Reset the old ones that are no longer in use. Update the software on active devices. Track the recent update when provided by the authentic developer.

Run software updates timely: Out-of-date software contains a plethora of digital activity. Cybercriminals can easily mine this information if the software is not up-to-date. The updates shared by software providers close these vulnerabilities reported or identified in regular check-ups.

Device Protection: A password or a lock pattern is a must on all devices. If accidentally misplaced, this secures unwarranted access. Avoid opening suspicious emails. Keep track of applications downloaded. Remove the ones not in use anymore. While choosing to download an app, the developers disclose what kind of information will be collected. Make conscious choices when it comes to sharing the access.

Choose Passwords wisely: A general rule of thumb is to create strong and memorable passwords. Upper/lower case letters, symbols, and numerals, uniquely mixed, can be decoded only by the maker. Passwords like birthdates, anniversaries, and the names of close ones should be avoided as they might be easily detectable.

Password managers help store, manage and protect all passwords in one place. It can generate unique and complicated passwords that shall be only accessible to its rightful owner

Remembering unique passwords for multiple channels can be difficult. A password manager is an excellent tool to be deployed.

Use Password Managers: These are automatic management tools that prioritize security and time. The password managers, inbuilt within web browsers, do not guarantee comprehensive security. Password managers help store, manage and protect all passwords in one place. It can generate unique and complicated passwords that shall be only accessible to its rightful owner.

It can also suggest a time to create a new one and comply with crucial cybersecurity practices.

Multi-Factor Authentications: The most commonly known is two-factor authentication. Another method of login is generating a OneTime-Password. It creates additional steps to get access to a particular channel. This may be a question that only one person may know an answer to. In some cases, one may be able to set the question as well as the answer.

Virtual Private Network (VPN): VPN protects our data by creating a private network, providing anonymity to the home/public network. The digital footprint is protected as the external website cookies installed are unable to decipher the original address of the user. Internet browsing and activity are, hence, harbored with a strong barrier.

Back-Up Data: All data should be backed up regularly to a secondary source. Hard Drives for offline backup or cloud storage for online backup should be used as dependable storage units. This also assures fewer chances of personal/business data tampering if an offline backup version exists elsewhere.

Don’t Accept All Cookies: We have come across a notification box asking us to accept website cookies. Various misleading consent notices are put with such notifications. The option to decline access is usually not given directly. Otherwise, selective freedom is given to people when choosing what the channel can access. By deploying such ‘dark patterns,’ websites mislead visitors to create a pool of information to run their business decisions.

Select the options opposite to ‘accept all.’ Mostly, nothing has to be changed; only the ‘save as preference’ option has to be clicked. The user usually procrastinates this extra step.

The Future Virtual Disposable Machines:

If your laptop/ computer is affected by malware, it is difficult to remove the same from the device. VDMs act like mini PCs in the original PC. If malware affects the VDM, it can be reset and ready to use again. No other data outside the VDM is lost. These systems might be revolutionary for sectors like e-commerce and banking, which generate and maintain large amounts of sensitive data.

Conclusion

Cyber data hygiene is nothing less than an exercise that provides immunity to battle threats. The fragility of all systems is such that if any barrier is broken, there is no way back to security. The more obstacles we create, the better. Moreover, prevention is in our hands, and the consequences might be nearly unbearable if one loses control of their data. It is, hence, imperative to mindfully build our online practices, monitor and protects the same.

Why Modern Data Analytics is Substituting Traditional Data Analytics

Saket Agarwal, Founder & CEO, Onnivation

There is an old adage in statistics that states “correlation does not imply causation”. In business intelligence, the thing that could completely turn things upside down is Assumption. Let me help explain this with an example.

The Toronto Transportation Commission was trying to find the reason between number of complaints and the bus delays on specific routes. One might think, the most obvious reason would be Tardiness. But on using data to analyse and arrive at the exact issue, it was found that Weather was to be blamed for these spikes in complaints. The days on which there would be heavy rains or snow would see an increase in the no of complaints.

SaketAgarwal,theFounder & CEO of Onnivation started off with building and running 2 startups after finishing education, including failing at building Aao Pao Khao, a Vada Pav chain based out of Calcutta.

He founded Onnivation in 2015 and grew from an individual consultant to a leader in the Israel-India tech corridor. Onboarded 15+ partners across Cloud, Data and Security domains from Israel and have helped 100+ tech unicorns, enterprises and high growth consumer companies transform their tech stack. He evolved his role from day-to-day operations to strategy, government partnerships, USA & Singapore expansion & strategic relationship building earning himself the moniker of #YourIsraelGuy.

Such use cases will continue to crop up for businesses if Big Data is not analyzed in a more scientific manner so to speak and people run after the wrong issues.

What is Big Data. It’s not simply “lots of data”, it is rather defined by the 4VsVolume (the quantity of data)

Velocity (the speed at which data is generated)

Variety (text, audio, video, images, etc.)

Veracity (trustworthiness of data).

Traditional Business Intelligence tools were designed for low volumes and structured data and the issues of speed always remained. Traditional data operations were not meant to be agile and therefore cannot adapt or incorporate the new sets of data that are now available due to modern logging and monitoring solutions

for operations. Furthermore, they run in silos, by the same person, and generally in a local environment. On top of this, traditional analytics operations are heavily reliant on the human-in-the-loop paradigm, meaning the validation and interpretation of data requires human intervention.

Businesses are now facing a unique set of challenges as big data continues to become more and more complex. Businesses are in need of more complex insights that may not be very easy to analyze and hence predict how various factors might affect their business.

That’s when modern data tools emerged. These tools could extract information from highly available but unclassified, unstructured data sets. Researchers in this field and the algorithms they develop help

data operations were not meant to be agile and therefore cannot adapt or incorporate the new sets of data that are now available due to modern logging and monitoring solutions for operations

teams manage data-intensive operations and play a crucial role in helping companies monitor, manage, and collect performance measures that can improve decision-making across the organization. For instance, when a business wants to upsell, data science tools can provide insights and predict which users to target. They can also be used to do a lot of A/B tests to determine who to target and how to communicate.

At the heart of it all, while traditional analytics help identify what happened, modern data science tools can go one step further and postulate why it happened and what businesses should do next.

Here are a few other factors that amplify the need for modern data science tools over conventional analytics tools: Integration capabilities

Despite the fact that traditional analytics tools are based on the same principles as modern data science tools, their inability to integrate with multiple sources of data, such as thirdparty applications and websites, limits their applicability. For example,in the social media space, for example, traditional analytics solutions cannot identify patterns in consumer behavior or decipher consumer sentiment. Modern data science tools can easily integrate sophisticated technology such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and Machine Learning to collect, structure, and analyze data, irrespective of where it resides; which eventually helps to predict outcomes faster. By connecting the right data at the right time, users can

smartly automate analytics processes to save time and energy, which can be redirected to business strategies.

Ease of access and availability

Until a few years ago, analytics tools were considerably expensive and complicated to use. Thanks to constant innovation in the big data space and large strides in computational capabilities of cloud computing, even small and medium businesses can now easily onboard sophisticated data science tools at much lower costs

High-performance Computing Power with Real-Time Automated Reporting

One of the biggest advantages of modern big data science tools is that they can compute large volumes of data at lightning-fast speeds in real-time. These capabilities help businesses keep a close eye on every related development, keeping them at pace with the industry, market, and client sentiments - without the need of an actual person to validate the findings.

By now, most businesses have realized that one cannot improve that one cannot measure. The next challenge now is to extract valuable and actionable insights from these data sets. Fortunately, the ability to do this has also become widely available due to the emergence of data science tools. So in order to reap rich rewards and establish compelling customer loyalty and brand reputation, businesses will have to leapfrog from traditional data analytics to modern data science tools by becoming data-driven, data-informed and data-inspired!

Data Privacy & Protection Post Covid-19

Virag Thakkar, Head - IT Governance, Risk & Compliance, Agoda Booking Holdings Company

In today’s challenging times of COVID that lead to the unexpected digital transformation of many companies, the question which ran into many board rooms was how we ensure the security of the infrastructure and privacy of data. On top of that, government agencies enforced contact tracing, leading to multiple applications and collection of personally identifiable data.

This led to unprecedented tinkering of existing process and evaluation of new risk vectors in conjunction with data privacy and governance. While most of the companies were just settling into GDPR, and various regulations depending upon the geography they operated upon, this came in as a new challenge for security & privacy professionals.

With 18+ years of leadership experience in Cyber & Information Security covering Audit Compliance, Privacy Frameworks, Risk Management & establishing Security Operations, Virag has attained the industry’s leading certifications like CISSP, ISO 27001 Lead Auditor, PCI-SI, ITIL Foundation & Cyber Laws.

Once this crisis gets over, in my opinion, this will start reflecting on how data privacy and protection is managed.

Government Compliance & Privacy Laws: All data so far collected, especially in conjunction with enforced law by government agencies, will undergo a lot of scrutiny. There will be landmark judgements passed by judicial in relations to how data is collected, stored and especially shared for emergencies. Governments who enforced usage of specific apps or collecting of data of existing under emergencies will need to be revaluated to see their effectiveness.

Fundamental Human Rights of Privacy: When united nations adjunct Rights of Privacy as a fundamental right, most countries were bound by it. While there are many definitions and grey areas especially on medical data (Covid Test Results Apps) & locational usage (Check-in to malls) everyone agrees the security of these details needs to be more

paramount. More stringent guidelines shall be formed and come out on a granular level of data than the current model of entire gambit.

Forced Digital Transformation: By working from home, companies are forced into digitization, drastically raising the data security and privacy risk levels. The new technologies lead to the creation of new processes, e.g. usage of video conferences or collaboration tools, as they were implemented without proper training to users or testing of technologies. Cyber attackers exploited this situation quite well to their advantages. There has been a 5 times increase in targeted attacks across organizations.

Overall, this has led to a rethinking of strategy not only in corporate bodies but also in government sections on how to have better laws and more secure ways of collection and usage of data. Whatever may be the outcome, it is an interesting time for data & privacy security professionals.

Beyond these tools, data professionals need curiosity, the skill to gauge patterns, and the ability to collaborate with a cross section of functions to succeed at their job