The Insight Story

Big Data Analytics in Media and Entertainment Industry

How is the Media and Entertainment Industry Leveraging Big Data Analytics for Disruptive Growth?

In today's scenario, Media and Entertainment organizations are seeking to distinguish themselves from their competitors by presenting entertainment and content that their viewers prefer. To accomplish this, they must have an in-depth understanding of their viewers, including when and how the audience selects content and offerings. This is where big data analytics plays a vital role.  Pre-eminent partakers in the media and entertainment industry, including Netflix, Amazon, and Disney, have already been leveraging big data analytics as part of their operational framework to enhance the viewer's experience. Hulu is known for using analytics for content acquisition and recommendation. Similarly, many entertainment companies also incorporate big data and AI analytics to improve their offerings and streamline their processes. As a result, the media and entertainment sector is at a massive advantage.  Data analytics in the media and entertainment (M&E) industry is helping businesses gain hidden insights into viewers' behavior while assisting in delivering personalized content. Additionally, data analytics in the media industry is driving digital transformation as organizations are able to get their hands on precise data insights on viewers. Due to this reason, big data holds the key to driving profitability across the media and entertainment sectors.  How Big Data Analytics is Making Media Companies Competitive  Attracting new viewers and retaining the older ones while keeping them engaged are the biggest challenges faced by almost all media and entertainment companies globally.  By integrating data analytics, entertainment corporations are able to gain detailed insights regarding their viewers as well as their systems and processes.  Read more: Emerging Trends in Media and Entertainment Industry in 2023  Media enterprises have been overcoming these challenges and achieving higher business outcomes. Some of the positive outcomes of data analytics are as follows:  Predicting Effective Media Investment Decisions  Media streaming platforms must ensure that the content they invest in will be well-received and successful among their viewers as well as deliver an adequate ROI. To assess the potential success of different media assets, media companies leverage data analytics to gain insights. By establishing the potential success of the content, digital platforms are able to generate huge dividends on their investments. Leading media company Warner Brothers invest in predictive analytics to predict the success of their movies. To refine the gathered insights delivered through analytics, media enterprises further automate reporting writing powered by natural language generation systems. As a result, they can convert insights into action and achieve better channel performance.  Increasing Viewer Retention  Media enterprises such as Viacom18 have been employing big data analytics to ensure viewer retention during break slots between program segments. This has enabled them to place the commercial breaks at the right time. As a result, Viacom18 has been able to retain its viewership during commercial breaks, thereby driving significant revenue for themselves as well as their advertisers. Following a similar path, other media enterprises have started using data analytics to drive similar initiatives to improve their customer engagement. By gaining an understanding of how well their content matches the demands of the viewers, M&E organizations can predict the different types of content recommendations to keep the users occupied on the site for extended periods.  Read more: How are Technological Advancements helping to Reshape Global Economic Growth?  Setting Precise Ad Targets  Advertising revenue is considered a major source of revenue generation for media streaming and entertainment corporations. To ensure that they display the most relevant ads to their users based on their different demography, media streaming enterprises use big data analytics for ad targeting and audience segmentation. Due to this, they are able to deliver greater ROI to their advertisers by helping them reach their target audience. By integrating big data analytics with machine learning, online streaming platforms, including YouTube, enhance the relevance of the displayed ads.  Performing a Detailed Performance Analysis  Network media and entertainment companies use analytics information in the form of language-based reports to study the performance of different channels and assets. With the help of this analytics data, media enterprises can gain real-time insights into the performance of their content as well as their competitors. This further assists in making enhanced strategies for growth.  Envisioning the Interest of the Viewer  The traditional method of media content development is being replaced by myriad media services like pay-per-view, live streaming, and much more. During this process of content delivery, media distributors are able to collect a vast amount of user data.  Media and entertainment companies are increasing their digital conversion rates by micro-segmenting their target audience across their advertising networks. Data analytics helps them in combining and making sense of all the collected user data from multiple sources, including social media.   But one of the critical challenges faced by the media and entertainment industry is the growing risk of customer churn. Big data in the M&E industry is further helping in seeking an in-depth understanding of viewers' behavior and preferences. With the help of analytics solutions, media organizations can also uncover reasons that drive viewers to subscribe and unsubscribe to a particular channel or platform.  Read more: The World of Virtual Reality: How Virtual Entertainment is Integrating Cutting-Edge Technology?  Optimizing the Scheduling of Media Streams  A recent Statista report highlighted that nearly 2.62 billion people have social media accounts. This exponential growth of media distribution across digital platforms has reduced the barrier between distributors and end-users. With data analytics in M&E,  service providers can reach their end-users directly without a conciliator. Furthermore, big data analytics is assisting media organizations in connecting and interacting with the audience directly through scheduled media streaming, thus maximizing their profits. Additionally, it has enabled the M&E industry to identify the exact content that customers wish to engage in on a scheduled basis.  Content Monetization  Media streaming platforms need to ensure that the content they invest in is well-received by the audiences and delivers an ample ROI. To assess the success across media assets and projects, media organizations are integrating big data analytics. Leading media conglomerate Netflix's investment in an American version of the British Show, House of Cards, proved successful, as they utilized insights captured from big data analytics and discovered the potential success of the show,   Leading media organizations are investing in predictive analytics technology to predict the success of the campaign and content. They are using technology to guide their decisions when investing in new strategies and ideas.  Read more: Trends 2023: Top Customer Experience (CX) Trends You Don't Want to Miss  Adapting the Ongoing Changes in the M&E Industry  Digitization of the media and entertainment sector has enabled enterprises to have unprecedented access to data on their viewers. Today's data is the new oil fueling powerful engines across industries to extract, refine, and harness operations efficiently. Organizations that build on a robust analytics foundation and a strong analytics culture are able to innovate and make wise decisions.  The ongoing changes in the media and entertainment business are likely to continue in 2023. Studios and video streamers are facing the reality of their own market disruption and exploring ways to gain profits in a less profitable business. Top social media services rely on user-generated video content (UGC), emphasizing the interests of viewers more than their connections. Today viewers are looking for more personalized content. The creator economy is supporting social media, and independent creators are getting closer to their audiences.  On the whole, the integration of big data analytics by media and entertainment companies is assisting them in delivering high-quality entertainment to viewers while ensuring better business outcomes.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.         A leader in the Media & Entertainment space, SG Analytics helps leverage advanced analytics capabilities to make accurate decisions and accelerate business growth. Contact us today if you are in search of media & entertainment solutions that enable businesses to solve problems by harnessing disruptive data, artificial intelligence, machine learning, and cutting-edge technologies. 

Read more
ESG Data Providers

An Ultimate Guide: Understanding ESG Data Providers

ESG (Environmental, Social, Governance) investing has been growing significantly, and there are no signs of it slowing down. Today investors are majorly focusing on sustainability issues as well as demanding greater transparency from companies in the form of concrete data. This accumulated ESG data is being used to identify long-term investment opportunities with lower financial risk.   Organizations require accurate ESG data that can be used for ESG ratings, metrics, and reporting. However, access to reliable and real-time data is the only way for investors to grow alpha. As a result, a plethora of ESG data providers is on the rise, presenting organizations with a variety of solutions aimed at assisting and expanding their ESG programs.  ESG data providers today play a vital role in the investment process, as they assist with gathering and assessing real-time data about an organization’s ESG practices and present equity screens, portfolio analysis, competitive benchmarking, and risk analysis.  Read more: Investing in Energy Stocks: Hottest Green Energy Stocks to Invest in 2023  The Significance of ESG Data  ESG organizations are outperforming their peers. As per the Sustainalytics Global ESG Leaders Index, enterprises with strong environmental, social, and governance practices are likely to perform better financially than average companies.  While ESG data is vital, many are heavily focused on environmental sustainability and reporting as climate change is becoming a global risk, affecting the planet and disrupting economies. The data can generate useful insights to support environmental sustainability and help organizations enhance their business operations.  Tracking the supply value chain and keeping organizations accountable for their sustainability goals requires the use of big data analysis. With new tech innovations, companies are shifting to data providers to track and integrate their qualitative ESG data into operations. Navigating the ESG Data Landscape   The ESG landscape is evolving at a faster pace and compelling organizations to integrate additional resources to meet investor and regulatory expectations. But with the growing expectation on firms to meet environmental, social, and governance (ESG) standards, the expectations around ESG data are also increasing.   Investors, regulators, and internal stakeholders are anticipating more information on a company’s ESG initiatives. Asset managers are turning to external data providers to help them in evaluating the risks and opportunities. For many asset managers, this data plays a vital part in attracting and maintaining socially conscious investors.   While there are several data providers with different value propositions, there are several forms of ESG data providers in the market that focus on different value propositions. ESG data providers offer an assessment of the risks and impacts of an organization's activities related to environmental, social, and governance.   With ESG gaining the limelight, company-level ESG data is improving. However, it is still fragmented for smaller companies and emerging markets. It is, therefore, important for organizations to choose the right vendor who is capable of distilling disparate data and curating easy-to-understand scoring or ratings. And the key here is to understand that not all companies will require or benefit from a data provider.  Read more: The Role of Artificial Intelligence in Achieving Sustainability and Profitability  The Growing Anticipation Around ESG Data  Today investors, regulators, and internal stakeholders expect more insights concerning a company’s environmental, social, and governance (ESG) initiatives. Asset managers are turning to external data providers to assess the sustainability risks. For asset managers, this data is vital to attract as well as maintain socially conscious investors. Organizations need to incorporate ESG frameworks and operate in jurisdictions that require reporting data related to carbon emissions.  Identifying the Key Differences in ESG Data Vendors  There are also many disparities between ESG data vendors. ESG data is primarily gathered via publicly available information, and the sources often include sustainability reports, company filings, publicly available data, and news articles. While some ESG data providers search the web for data, others engage directly with companies to collect the required information. Consistency plays an equally vital role in accurate comparison.  Exploring Different Data Providers with Different Value Propositions  There are different ESG data providers in the market who focus on the different value proposition. While some data providers facilitate raw ESG data, some offer issuer-level ESG ratings, carbon footprint calculation, as well as private market coverage. An ESG data provider is a third-party vendor that presets organizations with an assessment of the impacts of a company’s activities on society and the environment. Some of the key value propositions that vary significantly from one provider to another include reporting methodologies, data outputs, and benchmarking.  Consistency for Accurate Comparison  Before an organization begins its process of choosing the right ESG data provider, organizational leaders involved in ESG decisions should outline the type of data they are looking to organize and identify the different use cases for ESG data reporting. Investors should evaluate the range of various data providers against their investable universe to ensure the key data elements they are looking for are met. After the organization has evaluated several aspects of the ESG data providers, the selection can be made based on factors like cost, contractual terms, and operational assistance.  Read more: The Future of Corporate Sustainability: 2023 Predictions  ESG Data Sources  ESG data can be accumulated from different sources in many forms. It can be categorized into - inside-out and outside-in. Inside-out data refers to ESG data that is reported by corporations. It is used by analysts to make judgments and identify ESG ratings and scores. Inside-out can lag by 6-12 months due to the additional research and dependence on company reporting on ESG-related disclosure. On the contrary, outside-in data is updated more frequently, in real-time.  Inside-out ESG data include:  Data gathered from environmental management systems (EMS) of organizations and environmental performance;  Data selected from governance or financial performance;  Information concerning labor relations, such as the quality of working conditions and work practices;  Data related to corporate leadership.  Outside-in ESG data include:  Data from external sources like smart meters, sensors;  Data from sources that are not physically sourced, including weather forecasts and traffic reports;  Data from review websites;  Data collected from social media such as LinkedIn, Twitter, and Facebook;  Processed data from News, Media, and Press Releases   Role of ESG Data in Market Analysis  ESG data is being used more effectively by investors, along with asset managers and institutional investors, to build sustainable portfolios and improve corporate governance practices. ESG data is also being used by organizations to report on their social and environmental performance. The role of ESG data is growing as it enables investors and organizations alike to conduct market analysis, support asset allocation and risk management, and offer insights for long-term sustainability.  Key themes in ESG analysis that have an increasing impact on how organizations are valued:  ESG data is mainstream and set to become even more important in the future.  ESG data is becoming easily available to investors.  Investors are utilizing ESG data to analyze the corporate governance and performance of an organization.  ESG incentives are being used to align the interests of asset owners with asset managers.  Asset owners are utilizing ESG data to engage with companies.  Read more: Anatomy of Consumer Data: How is it driving Changes in Decisions for Businesses?  Taking Control of the ESG Data  Organizations are taking control of their ESG data narrative by tracking data and presenting voluntary disclosures. They are working on setting up and integrating a reasonable and standardized ESG metric to achieve comparability. By devising a high-quality process, organizations can onboard data providers that can assist in tracking accurate data and delivering it in a clear and timely manner. Investors, companies, and stakeholders are all working together to enhance the accessible and reliable ESG data for sustainable investing to grow.  By anchoring corporate sustainability disclosures with sound governance and performance systems, organizations can establish a transparent and clear communication channel that will help ensure the exchange of quality data.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.       A leader in ESG Services, SG Analytics offers bespoke sustainability consulting services and research support for informed decision-making. Contact us today if you are in search of an efficient ESG (Environmental, Social, and Governance) integration and management solution provider to boost your sustainable performance.   

Read more
Understanding the Benefits of Generative AI

Accelerating Growth: Understanding the Benefits of Generative AI

The global market is evolving faster than ever, and businesses are feeling the need to adapt quickly to emerging trends to stay relevant and competitive. One of the most significant challenges they are facing is the need to develop their employees continuously.  A subset of artificial intelligence, Generative AI, is being employed to create new content in the form of text, voice, images, videos, and processes. Generative AI is powering an easily accessible chat interface, empowering businesses to witness a breakthrough moment. Businesses and research organizations are experimenting with Generative AI and paying close attention to exploring its potential capabilities and implications.  Let's uncover the potential of generative AI for enterprises.  Read more: The Role of Artificial Intelligence in Achieving Sustainability and Profitability  Leveraging the Generative AI-Conversational Nexus across Operations  Generative AI is enabling organizations to create flexible and adaptable conversational systems that understand and learn from interactions with users and enhance over time.  Conversational AI systems use language models such as BERT, GPT-2, GPT-3, and GPT-4 to understand conversations and assist enterprises with advanced capabilities and outcomes. The recent developments in large language models (LLMs) have also portrayed that these systems are equally capable of generating human-like text.  The enterprise adoption of generative Ai is expected to grow in the near future. However, to integrate generative AI in conversational operations, developers need to fine-tune the existing framework by employing proprietary enterprise as well as domain data. This is helping to significantly reduce the cost of generating interactive text, authorizing enterprises to dynamically create multiple versions of the text to convey the same information.  By leveraging learning, businesses can optimize these variants and enhance their conversion rates. This approach is further equipping them to create dynamic content for a comprehensive experience. Some prominent enterprise use cases where generative AI is assisting in enhancing the capabilities of conversational systems are as follows:  Consumer Support: Customer support teams can integrate conversational AI systems to identify as well as understand customer issues faster. With ticket summarization powered by generative AI, they can resolve them as well. The systems will also help in gaining context and the status of a query without the customer having to explain the problem multiple times. It will also further help frame the response tone that suits the context of the query. Organizations can address the submitted queries in languages unknown to them by using generative AI’s power to translate in real time.  Conversational Operational Flows: Generative AI is assisting organizations in creating responses automatically. The responses require human intervention only to review and streamline the output. While designing a conversation, a conversational AI system offers suggestions depending on industry-specific use cases. It also makes suggestions based on the next probable flow and alerts. A generative AI system can automatically suggest use cases. It can also auto-suggest the inputs required from the customer.  Goal-Based Conversational Marketing: The conversational AI systems can be leveraged to design omnichannel as well as goal-oriented marketing campaigns. With generative AI, organizations can define nuanced goals like special promotions to re-engaging with inactive customers. They can also provide specific instructions for customers to convince them to make a purchase or suggest the customer try a product that is targeted and relevant offers. This will further help enterprises to design a dynamic and hyper-personalized marketing campaign strategy.  Read more: Artificial Intelligence (AI): The Next Frontier for Product Innovation  Generating ROI with Generative AI  Today conversational AI has come a long way as users are easing up to the idea of interacting with a chatbot for their queries.  With the technology further adding new and highly intuitive features, dynamic AI agents are becoming more human-like and accurate in their responses. These are being integrated across a wide range of applications - from customer operations to marketing and personal assistants.  However, with these significant advancements, there is still room for innovation and enhancement. This helps establish the convergence between generative AI and conversational AI. The systems are helping to pave the way for the development of highly advanced and dynamic AI agents.  Businesses are integrating Generative AI for further benefit. However, it can be conceived along two distinct approaches.  The first one involves using available models with a simple interface to provide near-direct access to the underlying framework in the form of a text or an image generation tool.  The second approach includes integrating Generative AI with other tech innovations to automate operational processes. With Generative AI, businesses can undertake human-level expressive interactions while controlling the flow and ensuring factual accuracy.   Generative AI is competent to generate new data by identifying patterns in existing data. Conversational AI involves using machine learning algorithms to create natural language responses to user queries. The use of generative AI in conversational AI systems is further opening new avenues for improving customer experiences, thereby increasing productivity and driving actionable results.  Read more: Artificial Intelligence and the Downsides of AI Data Collection  Tips for Enterprises to Incorporate Generative AI   Conversational and generative AI is transforming operational development by presenting innovative ways to create personalized training and feedback framework. By providing organizations with real-time feedback, personalized content, and engaging learning experience, the systems are helping in building and fostering a skilled and motivated workforce. It also equips them to navigate the challenges of the modern workplace.   As the market landscape continues to evolve, businesses are adapting to the changing conditions, and conversational and generative AI is playing an increasingly vital role in shaping the future of work.  Enterprises scrutinizing the implementation of Generative AI into their conversational systems across different areas like customer support, customer engagement, and marketing should consider the following broad aspects:  It is vital for enterprises to determine the specific problem they want to resolve with generative AI, which involves identifying and understanding the business objectives along with the end-user needs.  Enterprises should partner with an automation partner that offers them guidance on the right technology and team expertise on this journey. It is equally important for them to ask vendors about their expertise along with the set goals outlined.  Enterprises need to design a plan to integrate ethical as well as responsible AI for operations like regular auditing, testing, and validation of generative models, thus ensuring transparency.  By developing a strong framework, enterprises can integrate generative AI and elevate their customer experiences by offering them personalized interactions. They can also employ potential use cases within their operations to follow emerging trends. With generative models continuing to evolve, organizations are becoming better at understanding the nuances of customer conversation by gaining relevant and useful insights.  Read more: Designing an Effective Data Storage and Management Culture to Simplify Technology Mix  Final Thoughts  The AI field is taking a turn with the release of powerful generative artificial intelligence (AI) models, and the world is witnessing the automation of some skills, including creativity and imagination. For some organizations, Generative AI holds the potential for higher-order opportunities, including the introduction of new services and business frameworks. Generative AI is also capturing the attention of global media and the public square, thus provoking questions and discussions around this technology.  Generative AI is rapidly advancing on the path to becoming an industry norm due to its vast versatility in accomplishing different tasks. Organizations are further diving deep to discover the potential of generative AI that is driving enterprise-wide use cases in real-time.  However, it is important to significantly pinpoint how effectively generative AI is supporting enterprises with service management. With enterprises widely hinging upon their IT supremacy, generative AI is enabling them to deliver customer service and drive operational efficiency.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.        A leader in the Technology domain, SG Analytics partners with global technology enterprises across market research and scalable analytics. Contact us today if you are in search of combining market research, analytics, and technology capabilities to design compelling business outcomes driven by technology.                      

Read more
How Tokenization is Helping to Unleash the Power of Blockchain

The Future of Finance: How Tokenization is Helping to Unleash the Power of Blockchain

The conversion of financial instruments into digital tokens on a blockchain network or tokenization has been garnering significant attention. With blockchain technology operating as the foundation for this groundbreaking concept, it is enabling financial institutions with a decentralized platform to create, exchange, and trade digital tokens.   Today tokenization is continuing to gain traction and becoming an integral component for investors and regulators. To adapt to this emerging trend, financial institutions are embracing the potential of tokenization and functioning collaboratively to address the challenges that follow.  Tokenization enables them to unlock new economic opportunities as well as drive sustainable growth in the global economy. The future of the finance industry lies in the integration of tokenization and blockchain, and institutions that are preparing themselves to navigate this new landscape will stand at the forefront of this financial revolution.  The convergence of blockchain technology across financial markets is highlighting the significance of tokenization and its impact on the economy. Tokenization holds the possibility to transform financial markets and investment opportunities, thus revolutionizing the global economy.  Read more: How are Technological Advancements helping to Reshape Global Economic Growth?  The Pillar of Tokenization: Blockchain   Blockchain technology offers institutions a secure and transparent framework for tokenization. By incorporating a decentralized ledger, blockchain networks provide the creation and management of digital tokens symbolizing financial assets. These tokens can further be traded, exchanged, or utilized across different transactions without the need for intermediaries.   The tokenization of blockchain technology presents the following benefits:  Security: The decentralized nature of blockchain networks enables an easy and secure exchange of information, thereby mitigating the risk of fraud.  Cost Efficiency: Tokenization helps eliminate intermediaries, thus decreasing transaction costs and expediting settlement times.  Transparency: Blockchain technology facilitates real-time tracking and auditing of transactions, thus facilitating trust among participants and reducing the risk of disputes.  Accessibility: Tokenization authorizes financial institutions with fractional ownership, allowing investors to engage in asset classes that were previously limited to high-net-worth institutional investors.  The global financial markets are waking up to blockchain technology. Even amid the turmoil set off by inflation, interest hikes, and quantitative tightening, the global financial market is yet off the radar for retail investors. This transition toward on-chain assets is being considered a big leap in the bond markets—and capital markets, in general. Issuance of assets on-chain significantly reduces the settlement time. But the situation appears to be changing as they are slowly earmarking 2023 to integrate blockchain technology across operations.  Read more: Future Of Tokenization: A Comprehensive Look at Tokenization and its Impact  The Future of Finance: Blockchain  The changing economic landscape is putting financial institutions on a path that is moving fast, enabling them to decisively take the lead in shaping cutting-edge technologies. Global economies are exploring and leveraging new avenues like DeFi, as well as adopting technologies to fortify existing financial plumbing.  The distributed ledger technology found significant prominence as the backbone of Bitcoin, the pioneering cryptocurrency. The technology offers several key attributes, making it uniquely suited to enable direct interaction between business trading parties. Blockchain holds the potential as a groundbreaking technology for business, equipping businesses with the capability to slice through layers of inefficiency, and is gaining momentum.    The characteristics include the following:  Cryptographic security empowers institutions to write or read any private blockchain requirements. This further leads the user to where the data is stored, permitting access to digital assets.  Blockchains offer both a public and permission incarnation. But on a private blockchain, they can be used for financial applications, enabling companies must be invited to participate.  Blockchain’s built-in redundancy across decentralized structures across the network harbors a copy of the database.   In addition to having the correct credentials, process integrity enables users to update the blockchain once a consensus of participants has validated the data.  Business blockchains are now operating as standalone solutions as they realize the increasing significance combined with other technologies like automation or artificial intelligence. With the financial landscape evolving and increasingly automated and digitized, blockchain is taking center stage, and financial institutions are understanding the increase in strategic importance.   The fueling interest and growing awareness of the value blockchain are driving financial firms to integrate operational processes like supply chain, customer operations, and service with finance processes. In addition, it is further driving significant efficiencies and cycle-time reductions. Blockchain offers full end-to-end transparency across operations and finance, facilitating predictive operational insights and opportunities to optimize capital.  Read more: The Changing Fintech Landscape: Top Trends for 2023 and Beyond  Influence of Tokenization in the Financial Markets  Tokenization of assets like bonds and securities, along with designing a smart contract, assists institutions in executing their issuance, sale, and market functions. Tokenization implies creating a representation of the real-world asset on a blockchain in the form of a virtual token.   Tokenization is further paving the way for the fractionalization of the instrument, making the market accessible to investors. This is proving beneficial as it fosters greater participation and strengthens the integrity of the market while also supporting healthy investing habits.  Tokenization is reshaping the financial markets by creating new, accessible, and tradable financial assets. This has resulted in several substantial shifts in the financial landscape. They are as follows:  Democratization of New Opportunities: Tokenization is facilitating financial institutions with fractional ownership, enabling investors to buy as well as sell portions of assets, including real estate, art, or private equity. It is further presenting new investment opportunities for a broader audience, thereby facilitating financial inclusion and wealth generation.  Improved Market Liquidity: Tokenization is leading to the release of liquidity in previously illiquid asset classes. This assists institutions with more efficient trading and price discovery, leading to reduced transaction costs and enhanced market efficiency.  The Emergence of New Markets: Tokenization is facilitating the establishment of new markets and investment opportunities. Tokenized assets help to aggregate into diverse portfolios and investment products with unique risk-return profiles.  Regulatory Challenges: With tokenization becoming popular, regulators are facing the task of adapting existing frameworks to accommodate this new form of financial assets. This results in financial institutions integrating new regulations with existing ones.  Read more: Last Mile Delivery Network for Better Route Optimization is One of the Priorities in Managing the Entire Supply Chain  Implications of Tokenization in the Financial Markets  The widespread adoption of tokenization is enabling financial institutions to yield significant economic advantages. They are as follows:  Increased Efficiency: Tokenization holds the potential to boost overall economic efficiency for the industry by lowering transaction costs as well as streamlining asset management, and improving market liquidity.  New Job Creation: The expansion of tokenization is leading to generating jobs across different sectors, such as technology, finance, and legal services.  Financial Inclusion: By democratizing access across new investment opportunities, tokenization is also contributing to enhanced financial inclusion and wealth distribution, thereby leading financial institutions on the path of economic stability and growth.  Entrepreneurship: Tokenization is assisting with capital raising for startups and small businesses, thus fostering an environment for innovation and entrepreneurship.  Final Thoughts  Tokenization is revolutionizing the way institutions perceive assets in the financial markets. By integrating blockchain technology, financial institutions are capitalizing on security, transparency, and efficiency. Tokenization is also enabling them, providing them the potential to transform and democratize investment opportunities.   The economic impact of tokenization is far-reaching. It has the potential not only to bolster economic efficiency but also to create new jobs, promote financial inclusion, and foster innovation. But the emergence of tokenization has also introduced regulatory challenges for policymakers as they need to adapt to this new landscape while ensuring sufficient regulations are in place for investors and the economy.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.        A leader in Market research, SG Analytics enables organizations to achieve actionable insights into products, technology, customers, competition, and the marketplace to make insight-driven decisions. Contact us today if you are an enterprise looking to make critical data-driven decisions to prompt accelerated growth and breakthrough performance. 

Read more
What Does Cloud Security Consolidation Mean for Enterprises

What Does Cloud Security Consolidation Mean for Enterprises?

While there are several security vendors available for businesses today, the driving force behind them is the never-ending need to innovate. The ultimate purpose is to safeguard organizations from ever-evolving cyber threats and attackers, pushing vendors to remain up to date with the trends as well as organizations to stay secure. This, in turn, brings a proliferation of solutions, leaving organizations to take a more fragmented approach to cyber security by incorporating different tools to solve every risk.  With this approach, organizations are simply trying to protect themselves against as many cyber threats as possible. While this approach seems straightforward, it forces organizations to maintain a surplus of tools that requires dedicated resources. And the time, resources, effort, and teams required to maintain them are simply unmanageable.  This is the reason why consolidation of security tools is so common. The network security space is undergoing a significant convergence with network security solutions assisting in separating good traffic from malicious ones. Today there is no longer the best breed of VPN as niche cyber security players are converging their operations into next-generation firewalls.  But what factors drove these organizations to converge?  Organizations are onboarding new vendors and tools every time a new technology is introduced, which is likely to burden them with the high cost of maintaining, supporting, and configuring the integrated tools. At the same time, new cyber threats are constantly emerging and evolving, compelling organizations to establish new frameworks to protect themselves and innovate quickly to establish a cyber defense.  Read more: How are Technological Advancements helping to Reshape Global Economic Growth?  Cloud business practices have been on the radar for many years, and current situations are being fueled by market consolidation among the top public cloud infrastructure providers, including Google Cloud, AWS, and Microsoft Azure. The top providers own nearly two-thirds of the global cloud infrastructure services market and three-quarters of spending on public cloud. Along with flagging competitive practices, organizations are integrating cloud practices to compete on their ability to provide secure storage for customer data.  A Gartner report predicted that by 2025, 80% of organizations would adopt a strategy to unify web, cloud services, and private applications that can be accessed from a single vendor’s security service edge (SSE) platform. With many security and risk management leaders continuing to face more demand for service, they are trying to navigate their path through the fast-changing threat landscapes as well as insufficient technical talent.   For niche players, it is, therefore, vital to create new features to remain relevant. However, with the addition of more features, the previous ones each become less important and thus less valuable. With the growing need to stay relevant, security vendors are creating converged platforms to innovate quicker and create important features.     Cloud Security: A Brief Overview  A collection of procedures and technology, cloud security is designed to address external as well as internal threats to business security. Organizations are integrating cloud security to move ahead on their digital transformation journey and strategically use cloud-based services as a part of their infrastructure.  While digital transformation and cloud migration have been used over the years in enterprise settings, both hold different meanings to different organizations; each is driven by the need for change.  Read more: Designing an Effective Data Storage and Management Culture to Simplify Technology Mix  Today many organizations are embracing these concepts and moving ahead to optimize their operational approach and address new challenges that arise when balancing productivity levels and security.   Modern technologies are assisting organizations in advancing their capabilities outside the realms of on-premises infrastructure. They are now transitioning to cloud-based environments and integrating several implications of security.  To strike the right balance, organizations are understanding the framework of modern-day enterprises and benefiting from the use of interconnected cloud technologies for best cloud security practices.  Cloud Security: Why is Context Important?    Many organizations believe that they will not see the cloud cybersecurity space converge as cloud tools do not require the deployment of agents. It is easier to manage different cloud security tools at once. Due to this reason, there is a growing need for key factors behind cloud security convergence for context. Organizations are now being drawn to converged cloud security solutions with a promise to gain a holistic understanding of their environments with niche tools. Often times it is impossible for organizations to ensure everything in their environment is protected at all times. With a good security practitioner, organizations can fix different cyber alerts. This enables security teams to correlate the alerts across silos and identify more critical issues in the business.  Read more: Artificial Intelligence and the Downsides of AI Data Collection  Approaching Cloud Security with a Growth Mindset  Meaningful consolidation begins with identifying and measuring how the team’s effectiveness is likely to increase. Consolidation shows the best when there is a real end goal versus when the process the undertaken just for procurement reasons. If an organization decides to work with a single vendor and makes a purchase for just one tool for mobile device security and one for IoT security, there will be no relationship between the two. In such cases, buying two solutions from the same vendor helps with procurement.  Consolidation is not only about reducing the number of vendors in the purchasing department; it is also about enhancing the day-to-day operations of practitioners.  Organizations are slowly becoming aware of consolidated cloud systems. When transitioning from fragmented tools to converged players, organizations are identifying certain functionalities that are not available with the converged platforms. And gaining a precise understanding of the functionalities that should not be incorporated is the first important step of consolidation.  Further, consolidation also offers better collaboration between teams - a positive step for any organization. However, they need to be prepared for this cultural shift in order to manage their operations. With a fragmented approach, different teams across the organization can get accustomed to the flexibility and choose tools that meet their specific needs.  Read more: Establishing a Data-Driven Cybersecurity Strategy for Business Growth  Key Highlights  Cloud infrastructure is under the scrutiny of many as there lies the threat of information theft to seek public data on the cloud.   To overcome the potential security risks, organizations are integrating measures to determine the extent to which certain industries, like healthcare, finance, transportation, e-commerce, and defense, depend on the cloud for infrastructure.  Today, large parts of the economy rely on cloud computing solutions for a range of services. And therefore, the aim is to understand better the impact of its reliance on establishing broader competitive dynamics in cloud computing and potential security risks.    Time to Face the Inevitable  It isn’t a doomsday call for niche cloud security companies but rather a new possibility in space. Organizations that depend on cloud security systems are incorporating new cloud frameworks to ensure greater security. By learning from the errors of other security markets, cloud security companies are offering the most meaningful approaches to security in the future.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.      A leader in the Technology domain, SG Analytics partners with global technology enterprises across market research and scalable analytics. Contact us today if you are in search of combining market research, analytics, and technology capabilities to design compelling business outcomes driven by technology.                         

Read more
ESG Strategy for growth

ESG Strategy for Growth: Embedding ESG in the Business Model

With climate and sustainability taking center stage in every operational framework, organizations are paying more attention to environmental, social, and governance (ESG). However, only a few organizations have been able to align their strategies with it.  Today organizations need to plug a comprehensive ESG vision into their corporate strategy in order to deliver immense financial and non-financial value. The financial value can be in the form of increased productivity, cost reduction, fostering the usage of sustainable products & solutions, and better access to finance. Further, ESG-focused companies need to establish better operational efficiency to lower employee turnover rates and support innovation with strong governance and better risk management.  Organizations focusing on ESG initiatives tend to attract more investments as the demand for sustainable investing is advancing rapidly. And ESG investing is picking up a steady pace and emerging as a mandatory factory for companies looking to make sustainability and ESG an integral component of their business model.   Stakeholders - investors, policymakers, regulators, and customers - today expect organizations to disclose their sustainability plans and how the set business model will be compatible with driving a net-zero economy. But this presents a challenge for them as it may require decades of effort to explore as well as implement opportunities within the existing operational framework.  Read more: The Role of Artificial Intelligence in Achieving Sustainability and Profitability  Embedded ESG Strategy: A Structured Framework  Many organizations are embedding ESG principles into their core corporate strategy to establish a more resilient and sustainable business model as the focus on the ESG framework continues to sharpen.  Tailored Solutions: The embedded ESG framework mandates a tailored strategic solution. The strategy requires structured frameworks for a business that meets its specific needs and goals. The solutions can vary based on the type of business, sector, investor base, supply chains, and workforce.  A critical starting point for a corporate ESG strategy is to determine the top opportunities as well as the risks. This is important in the early stages of designing a strategy that assists in addressing that every ESG issue requires the same level of attention. Engagement within the organization, along with key stakeholders, further helps in determining the top opportunities for growth. A tailored ESG strategy will lead to influential interactions with employees, partners, and other stakeholders, thereby improving the organization's ESG performance.  Sustainable Futureproofing: Embedded ESG strategies help businesses in designing a sustainable future-proofing plan for the long term. Different businesses have different areas that are the most important to them. With climate risk dominating the ESG dialogue across sectors, workplace issues related to conservation, diversity, equity, and inclusion, along with employee well-being, are becoming more prominent.  Businesses are now taking further steps to future-proof their ESG issue across supply chains and other domains. Companies are making ESG policies a priority when selecting their associates. There has been a shift to adopt more resilient and sustainable.  Top-down and bottom-up approach: Embedded ESG strategy mandates both top-down as well as bottom-up approaches. With the development of the ESG platform, priorities and communications are emerging from the top and integrating throughout the organizational operations. Through a defined ESG lens, organizations are applying decision-making frameworks that help establish meaningful engagement with employees, partners, and other stakeholders. Businesses are adopting a holistic approach to embed ESG into their mission, vision, and values, thereby making it part of their culture.  Read more: The Future of Corporate Sustainability: 2023 Predictions  The Underlying Concerns and Solutions   Organizations often fail to recognize ESG plans as they do not get prioritized. This has led to undefined goals, along with unclear ownership and a lack of a defined approach to achieve the set vision.  Setting up a defined framework: By defining the organizational ESG priorities and associated objectives, organizations can work towards achieving the time-bound targets and drive key performance indicators for performance. Many organizations often make sense of the leveraged ESG data to drive business objectives. And if the data is siloed across extended business relationships and service providers, it becomes difficult to gain a unified view of the initiatives. To overcome this, organizations are now embedding their ESG priorities within the corporate framework to stay on track.  Assembling the data right: Gathering the right insights for ESG strategies can assist businesses to sail smoothly on this journey. Many organizations are deriving more value by collaborating with diverse business ecosystems to simplify their data collection and reporting. Consumer-facing organizations are also enhancing the maturity of their sustainability programs to strategize their ESG policies and have a tangible impact.  Choosing the right companions wisely: Staying ESG-focused needs to be one of the driving factors when selecting third parties. Organizations can easily monitor and evaluate the new as well as existing third-party relationships and their impacts on ESG priorities. With consumers demanding more transparency on ESG matters, organizations have to disclose their environmental footprint as well as display their ethical and responsible sourcing to demonstrate their responsibilities to their people and community.   Read more: Sustainability in Tech: 3 Ways for Companies to Become More Sustainable  Communicate the ESG growth narrative: By telling the ESG vision clearly, businesses can win consumer support. However, they need to develop a method to monitor their organizational ESG matters and align them with a standard reporting framework to follow on the sustainability journey. Organizations are now aiming to reach net zero by the year 2050 in order to avoid the most catastrophic impacts of climate change. They should also strengthen their operations to lower carbon emissions and adopt new sustainable practices to move toward the goal.  Today consumers are searching for truly environmentally responsible organizations. Organizations are therefore making fundamental changes to their DNA - corporate strategy - and aligning it with principles to facilitate improvements to environmental, social, and governance (ESG).  Bringing Corporate ESG into the Mainstream  There has been a shift in management thinking with regard to incorporating the benefits of ESG to gain ground. There are several ways corporates are integrating ESG into their decision-making strategies to lead sustainable development:  Clear accountability and oversight across the Board level are enabling organizations to achieve Board-level ESG committees or individual positions like Chief Sustainability Officer (CSO) are being formulated to foster sustainable decisions further. At the management level, senior executives are taking responsibility to ensure that future investments are ESG-centric. They are cultivating a sustainability culture that is embedded at the bottom of the organizational pyramid.   Organizations are setting ambitious ESG targets for their business and preparing implementation roadmaps to achieve the defined goals. While conducting the analysis, it is important for an organization to consider the inward impact as well as the outward impact of their decisions on the environment and society. This further helps in making operations resilient to ESG-related risks.  Read more: Sustainability Outlook: Top Emerging Trends in 2023 and Beyond  A critical component for any business, the supply chain issue, is equally significant. Organizations are imposing measures to identify the emission hot spots and address the underlying risks from a climate risk perspective. Businesses are taking stewardship responsibilities by instilling eco-design principles that consider product sustainability.  The journey toward sustainability also includes multiple stakeholders within the organization. It is, therefore, important for organizations to upskill their employees with ESG skills and enable them to appreciate their role by contributing towards the sustainability journey.  While adopting ESG is an active challenge, organizational leaders are playing a vital role in promoting ESG investing among corporates through obligatory regulatory frameworks on sustainability. With increasing focus on ESG investing and reporting, businesses are becoming more tolerant of their actions to establish transparency and gain stakeholder trust.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.         A leader in ESG Services, SG Analytics offers bespoke sustainability consulting services and research support for informed decision-making. Contact us today if you are in search of an efficient ESG (Environmental, Social, and Governance) integration and management solution provider to boost your sustainable performance.

Read more
How is Advanced Analytics Presenting Businesses with Next-Level Insights

The Future of Business: How is Advanced Analytics Presenting Businesses with Next-Level Insights

In today’s digital world, businesses are bombarded with data. Generating the right insights from this large pool of data enables businesses with informed decision-making, thereby driving growth. With the rise of business intelligence, data, and analytics technologies surfacing, businesses are feeling the need to adopt and gain competitive advantage.  With exponential data disruption, more sophisticated algorithms are being designed and developed to steadily improve computational power and storage. The convergence of these trends is further fueling rapid technological advances and business disruptions.   Industry Revolution 1.0 to 5.0 - Here's What You Need to Know   The world is opening up to the endless possibilities of artificial intelligence (AI) as AI-driven technologies are slowly becoming a reality, as well as facilitating whole-business functions and processes. AI analytics is increasingly being integrated for important data-crunching work and for business reinvention altogether.  While experts have been stating that “Industry 5.0” is now underway, with AI-powered tools and platforms taking on the repetitive tasks for organizations, let's uncover what the journey from Industry 1.0 to Industry 5.0 looks like and how it drove change.  Industry 1.0, the original Industrial Revolution, started in the late 18th century in Britain. It was the very first-time mass production was made possible by using water and steam power instead of manual physical labor. Manufacturing blossomed with the power of machines, thus revolutionizing society altogether.  Read more: Trends in Big Data Analytics: Forecast for 2023  Then followed Industry 2.0, which took place a century later. Assembly lines and the use of oil and gas, along with electric power, stimulated the manufacturing industry to level up. With automation entering the manufacturing process, industries were able to mass produce, thereby catapulting them on the path of growth.   The advent of computers in the mid-20th century kickstarted Industry 3.0, where digitization, telecommunications, and data analysis impacted manufacturing. Automation advancements, along with the use of programmable logic controllers, helped in automating more processes.  This further led organizations on the path of Industry 4.0 or the Fourth Industrial Revolution. Industry 4.0 officially started between 2011 and 2016, as the manufacturing sector was hit with the wave of technological advancement brought by data and the creation of smart machines. The world has been encountering the Fourth Industrial Revolution or Industry 4.0, which can be defined by a mix of artificial intelligence (AI), advanced robotics, as well as the Internet of Things (IoT). Machine learning is now set to bring a new phase to this ongoing industrial revolution.  Industries are today experiencing a radical shift that many are already calling Industry 5.0. The advent of the COVID-19 pandemic accelerated this shift. The Fifth Industrial Revolution is on the path of maturing and transitioning into a digital experience, one where humans are back to steering change. However, irrespective of the human-centric approach, experts believe that the fifth industrial revolution is set to further fuel technology. Machine learning tools are also presenting innovative solutions to undertake repetitive tasks. AI-powered tools equip organizations with the potential to minimize their employee stress while maximizing productivity.   This rising convergence of human cognition, along with artificial intelligence and machine learning, is set to present a slew of new use cases for industries in the near future. These new possibilities will further equip businesses to offer immersive experiences to their consumers in ways that were unimaginable before Covid-19. With AR and VR machine learning tools, AI-powered bots will also play a significant role in Industry 5.0, empowering manufacturing to optimize outcomes, thus minimizing the need for human intervention.  Read more: Top Data Analytics Certification Programs to Explore in 2023  Advanced Analytics  A term for leading-edge techniques, advanced analytics uses AI and machine learning. While data analytics offers insights drawn from raw data, advanced analytics presents unique insights collated from untapped or unstructured data.   Advanced analytics goes beyond traditional business intelligence (BI) and data analytics strategies. It encompasses mature approaches, including predictive modeling, machine learning (ML), and process automation. It makes use of mathematical algorithms to identify patterns, predict outcomes and associated probabilities. Advanced analytics include data mining, pattern matching, forecasting, visualization, semantic analysis, and sentiment analysis, as well as complex event processing and neural networks. With advanced analytics, organizations can predict future trends along with the likelihood of certain events.   Gartner stated that advanced analytics is the autonomous analysis of data or content by employing sophisticated techniques and tools that are beyond traditional business intelligence (BI) in order to discover deeper insights and make new predictions. With AI models continuing to evolve, the amount of data being fed to the system to help them learn is also increasing. As a result, algorithms are getting better, and companies are able to use even more data to create greater business value.  While classic descriptive analytics concerns characterizing components the way they are, advanced analytics identifies and presents future outcomes and generates recommendations for the best courses of action.   Benefits Of Advanced Analytics  Today's complex business decisions are supported by a comprehensive understanding of operations, workforce capabilities, product performance, as well as consumer behavior. With investments in advanced analytics, businesses are experiencing substantial enhancements within their organizational operations. It presents businesses with capabilities to consolidate the accumulated data and create different business strategies to increase visibility and transparency across operations as well as to facilitate day-to-day decisions in real time. Organizations are better able to tailor consumer experiences while also mitigating the risk of customer churn.   Some of the high-level samplings benefits of how organizations are benefitting from advanced analytics are as follows:  Enhanced capacity for end-to-end supply chain visibility  Delivery of more refined and accurate insights that enhance day-to-day business decision making  Elimination of duplicate data modeling  Better identification of threats like security breaches  Improving the efficiency of data science-related processes  Fostering the identification of data monetization schemes  Accelerating agility by enabling organizations to predict and respond to future events   Access to crucial, evidence-driven guidance  Read more: Establishing a Data-Driven Cybersecurity Strategy for Business Growth  Integrating Advanced Analytics into Company Operations in the Post-Pandemic Business Scenario  The COVID-19 pandemic, followed by geopolitical tensions due to the Ukrainian invasion by Russia, proved challenging for global business. The crises resulted in notable declines in value chain productions, and waves of layoffs started gripping multiple industries.  The global economy experienced severe blows due to the Russia- Ukraine war, with significant disruptions in trade, food, and fuel price shocks. All factors contributed to high inflation along with subsequent tightening in global financing conditions. Higher interest rates, falling stock prices, supply chain crises, and a delicate state of geopolitics were perceived as vital indicators.  The overlapping war crises, the ongoing pandemic, and the surge in food and fuel prices served as painful reminders for governments globally, compelling them to take effective measures in the face of shocks and long-term challenges. In such unpredictable situations, making sound decisions for businesses was emerging as a major challenge, and consumer data was considered vital to making it to the other side of the crisis.  While large enterprises were equipped with significant resources to handle the rising business risks, enterprises with smaller teams and minimal resources took unexpected hits to their environments. These factors made it vital for organizations to gather data related to their customers and analyze it for critical business decisions.   For businesses to emerge strongly in the market in this post-recession scenario, advanced analytics tools offered them surplus resources to monitor their operations. Advanced analytics tools equipped them with a comprehensive outline of their consumer demographics, behaviors, and other parameters for consumer engagement and retention, even in times of economic crises.  Read more: Artificial Intelligence and the Downsides of AI Data Collection  Roadmap to Becoming an Intelligent Enterprise  The transition to advanced analytics across industries is set to grow in scope and magnitude, and it is happening at an ever-quickening pace. Organizations with higher overall growth in revenue and earnings are likely to receive a significant proportion of that boost due to data analytics.  Organizations are set to transform themselves into truly intelligent enterprises with the integration of advanced analytics and smart machines in the cognitive sense for data-driven decision-making. To further advance on this digital transformation journey, it is imperative for organizations to strategize a road map for growth.  Transforming organizational capabilities: Organizations often face a disconnect between existing technology capabilities and evolving digital culture. To deploy advanced analytics successfully, they are integrating digital approaches to align their company culture with digital tools in order to design and adopt modern management changes that work with vendors and consultants as well.  Selecting the right data: With the volume of organizational data growing on a day-to-day basis, it is imperative for organizations to gain deeper insights and accelerate innovations. With advanced analytics, they are identifying the useable data to enhance their operations, customer experiences, and business strategies.  Building a predictive model to improve business outcomes: Company data is critical for improving business performance as well as gaining competitive advantage. Advanced analytics models allow businesses to anticipate and optimize the desired outcomes as well as improve their business performance.  Companies are further tempering their expectations about AI-backed technologies to move forward aggressively, and advanced analytics is presenting them with a series of small evolutions that are likely to transform into an analytical revolution.  The rapid advancements in artificial intelligence (AI) technology in today's market have compelled employers to think of automated processes to make work faster and more efficient. However, on the other hand, employees probably have the looming fear of losing their jobs and being replaced by a machine. AI is designed to make manual labor more effective and quick, and it cannot override the need for human inputs. And with AI changing how companies function, many believe that employees who do this work are also likely to change.  An essential element to take into consideration to integrate a truly intelligent type of future of work is to develop a workforce where both humans and machines will have equal roles to play, thereby leading to the more efficient execution of jobs. Regardless of how AI systems are programmed, it is unlikely that humans will develop a strong emotional connection with these machines. Hence, it is safe to state that AI cannot replace humans, particularly as connecting with others is vital for business growth.  We at SGA are exploring new horizons to create augmented AI that enables efficient and faster output. With a commitment to businesses and society, we are driven to create a world of AI that functions in unions with human existence.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.         SG Analytics - Global Insights & Analytics Company focuses on leveraging data management & analytics, and data science to help businesses discover new insights and build strategies for business growth. Contact us today if you are looking to make critical data-driven decisions to prompt accelerated growth and breakthrough performance.    

Read more
How will Data Analytics Transform the Insurance Industry in 2023

The Ultimate Guide: How will Data Analytics Transform the Insurance Industry in 2023

With the insurance value chain becoming more connected, insurers are able to gain a better understanding of consumer persona as well as gain a competitive advantage in the market. A recent Gartner report found that enhancing the customer experience and operational excellence is the new driving force for a majority of insurance digitalization initiatives in 2023. Data enables carriers to collaborate with their customers and generate real-time feedback for value-added business solutions.  To effectively execute business strategies, insurers are taking actionable data insights into consideration. And the answer lies in maturing analytics and getting actionable insight faster and more accurately. Let's explore the ways data analytics will positively shape the insurance industry in 2023.   Introduction to Data Analytics in Insurance   The technological landscape changes across industries are also impacting the insurance sector.  It is still challenging for the industry to make their clients understand through which insurance company they could start their insurance journey. Many questions come into consumers' minds, including:   Is the selected company safe or not?   Will they provide the best offer or not?   What is the reputation of this firm in the market?   Similarly, insurers are not able to understand customer behavior, policy risk, thefts, and claim surety.  But with the integration of prefiltration data methods and the use of advanced analytics, the sector is now harnessing the vast amounts of data available and employing the right solutions for conscious decision-making. Data analytics is further assisting the insurance industry in every facet of its business operations and in determining the next step.  Read more: Establishing a Data-Driven Cybersecurity Strategy for Business Growth  Leveraging Data Analytics to Transform the Insurance Industry  The rise of digital transformation in the insurance sector has been an ongoing process. It has increased speed, efficiency, as well as accuracy across every domain of insurance companies. Data analytics is presenting the industry with capabilities that enable them to optimize every operation in the insurance value chain. It helps in analyzing the customer’s risk and identifying which client is trustworthy. Even customers today are using data analytics to determine which insurance company offers a minimum price with suitable offers.   Advanced data and predictive analytics systems are assisting the insurance industry in making data-driven business decisions. Artificial intelligence (AI) is further empowering them with high-level data that can be leveraged for improved insurance processes and identifying new opportunities for growth.     The Next Frontier for Auto Insurance: Telematics  Telematics involves the use of tech to monitor, collect and transmit data. Within insurance, it is seen in the auto industry, facilitating data exchange. The use of telematics across industries is becoming increasingly popular. The National Association of Mutual Insurance Companies highlighted in their report that the use of telematics is set to grow by 20%.  Telematics assists insurers in gaining an accurate understanding of the risk. Smart IoT devices or sensors like Advanced Driver-Assistance Systems (ADAS) and in-vehicle navigation solutions help in generating invaluable data on vehicle speed, braking patterns, as well as driving habits. This helps in gaining a comprehensive picture of driver behavior, thereby enabling insurers to identify and offer specialized coverage options.    Telematics also assists insurers in accurately predicting and mitigating risk. Predicting risk directly coincides with offering competitive pricing and fostering insurance enrollment for safer driving. The use of data and telematics is further creating a structure for insurers to offer a better customer experience.  Read more: Rising Significance of Big Data Analytics for Exponential Growth  Embedded Insurance  The traditional coverage type or insurance options requires customers to engage with a specialized insurance agent. However, with the acceleration of digital transformation, consumers are moving from the traditional ways of interacting with their insurance agents and increasingly integrating digital procedures into their experiences.  Consumers are welcoming new opportunities to search and shop for their insurance products on different digital platforms. From websites to chatbots and voice assistants, the rise in AI-powered solutions is enabling insurance firms to use different data collection avenues and gauge consumers purchasing behaviors to make strategic decisions.  Embedded insurance includes offering customers the choice to opt for travel insurance coverage when booking a flight or as part of an existing policy when making a purchase. Data accumulated from digital channels further assists in offering tailored coverage based on the customer's purchasing journey. It helps in understanding the policies that are purchased and identifying what embedded insurance offerings are more suitable. This further aids in enhancing the overall customer experience and increasing customer satisfaction.   Transforming Risk Assessment with Imagery and Geospatial Intelligence   Insurance carriers are used to undertaking surveys along with conducting on-site risk management surveys to understand the potential risks and design strategies for process claims or policies. With the integration of imagery and geospatial intelligence technologies such as satellites, drones, and sensors, insurers can gain a comprehensive understanding of the underlying risks that a policyholder is likely to face.  The imagery data collected from the systems allow insurers to assess the likelihood of disasters. Geospatial data further assesses the risks associated with a location, like traffic patterns and environmental hazards. Together, the collected insights data presents the insurers with the underwriting process, thereby facilitating them to price policies accurately and offer targeted coverage to the customer.  With imagery and geospatial intelligence systems, insurers can also be proactive with their policy coverage and protection. It facilitates valuable data to generate new risk insights.  Read more: Trends in Big Data Analytics: Forecast for 2023  Application of Data Analytics in the Insurance Sector    Advancements in data and technology are emerging as the chief drivers for insurance companies to scale and speed up their operations. Businesses are using data analytics to their advantage. Let's explore some of the key uses of data analytics that are likely to shape the insurance industry:   Fraud/Theft prediction  Insurance companies are integrating data analytics to detect insurance fraud. They are driving the use of predictive modeling and artificial intelligence (AI) to prevent fraud before it happens.  CRM technology  To manage leads, segment customers, automate routine tasks, and analyze information, insurance companies are incorporating CRM into their everyday operations.  Risk assessment  By integrating the ability to combine behavioral data generated by IoT with other factors, insurance companies are using the accumulated data to study and understand driving behaviors and neighborhood safety statistics. They are also adopting predictive analytics to assess risk based on behavioral data.  Product optimization   It has been challenging for the insurance sector to customize its policies at the consumer level. However, with access to vast amounts of data and advanced automation, the scenario is changing. By integrating machine learning, insurance companies are focusing on using predictive analytics to understand buying preferences and pricing sensitivity and provide consumers with relevant insurance products.   Personalized user experience  With access to increasing sources of third-party data from IoT devices and machine learning, insurers are advancing their ways to turn the customer experience into a seamless automated journey where customers can experience personalized products and pricing.  Read more: The Changing Fintech Landscape: Top Trends for 2023 and Beyond  The Future: Unleashing the True Potential of Data Analytics  Data analytics is constantly evolving, but what's not changing is the value data analytics contribute to insurance sectors. It assists them in identifying their target customers, enhancing customer experiences, streamlining everyday operations, and improving efficiency.   Today policyholders are seeking digital solutions to purchase insurance products, and insurers are employing the accumulated data to understand user persona and work closely to develop value-added business solutions.  Insurance firms are employing artificial intelligence (AI) and machine learning (ML) tools and perceiving the evolving landscape through the lens of ethical application within the insurance space. Emerging technologies are playing an equally important role in understanding tech solutions providers to foster and deploy open platforms for insurers to conduct their core operations.   With telematics and insights into understanding consumer purchasing behavior, insurers are employing the accumulated data in real time and focusing on transparency. They are also fostering open collaboration with other ecosystem partners and customers and leveraging data to serve customers in innovative ways.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.         SG Analytics - Global Insights & Analytics Company focuses on leveraging data management & analytics, and data science to help businesses discover new insights and build strategies for business growth. Contact us today if you are looking to make critical data-driven decisions to prompt accelerated growth and breakthrough performance.                          

Read more
Last mile delivery network

Last Mile Delivery Network for Better Route Optimization is One of the Priorities in Managing the Entire Supply Chain

Delivery in the last mile is a crucial but expensive aspect of logistics. Depending on the delivery method, it can make up as much as 35% of the overall delivery expense. So, it is essential to maximize last mile delivery to increase profitability and logistical efficiency. Also, a company's performance in the last mile has a significant impact on how satisfied customers are.  Last-mile delivery has become a vital consumer expectation in the food and grocery sector. For retailers, it is now more important than ever to guarantee a seamless and satisfying "last-mile" delivery—the final leg of the journey where a product lands in a consumer's hands. According to Emerald Publishing Limited, consumers are more likely to spend more if they have a great last-mile experience since they are engaged and kept around.  Read more: Future of Work: Top Hiring Trends to Look Out for in 2023 and Beyond  It is believed that such solutions are widely used by all kinds of enterprises as they ensure optimized delivery routes, enhanced shipment visibility, and improved customer engagement. As published by Technavio, the main driver of the expansion of the global last mile delivery business is the expanding e-commerce sector.  Both in China and the US, the e-commerce sector is expanding quickly. For instance, e-commerce retail sales in the US rose by 39% in the first quarter of 2021 over the first quarter of 2020, although total retail sales only grew by 16.4%. Similarly to this, China is home to a sizable number of online retailers, which has considerably aided the expansion of the B2B last-mile delivery market.  The cutting-edge last-mile delivery technology may provide organizations with real-time visibility over where their cars are, and how much time is spent idling, whether they are deviating from a scheduled route. This helps to reduce thefts and spoilage while also giving supply chain and logistics executives better control over their fleets. Predictive visibility is another option, where the technology informs the shipper of the location of the shipment at a specific time. Businesses may optimize delivery routes in real-time and hence save unneeded delays by using enterprise-grade last mile delivery software. The optimum path to take to ensure speedier deliveries can be updated frequently for drivers. This enables them to make more deliveries. Also, there are gasoline and other cost reductions. Supply chain owners can precisely determine who made an order when it was placed, who will deliver it, and when the delivery will be finished thanks to last-mile delivery software.  Read more: The Future of Mobility: Top Trends to Watch Out for in 2023  The e-commerce sector is steadily expanding into new online categories, such as food and home care, as a result of COVID-19's short-term disruption and long-term structural changes. The COVID-19 epidemic has caused an overall 30% spike in e-commerce purchases in the United States. Experts in the field predict that by 2021, 2.1 billion individuals will have made online purchases. This is due to rising urbanization, which will result in 60% of the world's population living in cities. Also, the local AIN pharmacy started autonomous drug delivery to patients' homes in March 2021. Starship Technologies began shipping food during the pandemic in 2020.  As published by Capgemini, consumers are leaning towards choices for speedier delivery as expectations rise. They discovered that more than half of customers (55%) stated they would switch to a rival company if the latter provided a speedier service. Businesses that offer superior last-mile experience will have an advantage over their rivals.  Read more: Trends 2023: Top Customer Experience (CX) Trends You Don't Want to Miss  Logistics and transportation companies across the world are fully aware of the last mile troubles when delivering shipments to customers and are getting increasingly inclined towards deploying modern last-mile solutions. The final mile is the most important part of the entire delivery process since it directly impacts end users and presents a constant challenge for logistics management.  Customers demand deliveries that are quick and dependable, and last-mile delivery might be able to meet those needs. Businesses can improve the overall customer experience by using last-mile delivery to offer more delivery alternatives, such as same-day or next-day delivery.  Read more: How are Technological Advancements helping to Reshape Global Economic Growth?  The last mile delivery sector is growing thanks to innovations and investments from major participants in the field. For instance, Amazon unveiled Scout, a delivery robot that can roll along sidewalks and leave products at customers' doorsteps. Scout is about the size of a small cooler. This inspired Amazon to develop a novel idea for autonomous last-mile delivery service. The market outlook for automobile last mile delivery is anticipated to see a number of improvements made by leading businesses, which will fuel industry expansion.  With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.         A leader in Market research, SG Analytics enables organizations to achieve actionable insights into products, technology, customers, competition, and the marketplace to make insight-driven decisions. Contact us today if you are an enterprise looking to make critical data-driven decisions to prompt accelerated growth and breakthrough performance. 

Read more