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The role of blockchain for environmental, social, and governance (ESG) reporting Introduction As more companies begin to transition toward sustainability reporting, they are looking for innovative solutions that provide greater transparency and accountability. Blockchain is one such technology that can help these organizations become more transparent in their reporting efforts. Blockchain is a digital ledger of transactions that is maintained and updated in real-time. Blockchain is a digital ledger of transactions that is maintained and updated in real-time. It is essentially a database of activity on the network, which is maintained by computers (called “nodes”) that all have copies of the same ledger. Each node verifies and validates all transactions before they are added to the blockchain, creating trust among all participants. The initial reason for creating a blockchain was to facilitate financial transactions between parties — mainly cryptocurrencies like Bitcoin — but its potential applications extend beyond just money transfers. Blockchain technology has been used to track products through supply chains, provide proof of ownership when using virtual assets like music or gaming accounts, and much more than one can imagine at this point in time (with more uses coming soon). Data transparency is critical for ESG reporting, and blockchain provides it. Blockchain is a distributed ledger that allows for the storage of data in a decentralized, immutable and transparent way. The technology has been recognized by many as an important innovation in finance and other areas, but it also holds great potential to help improve ESG reporting. It is already being used to store sustainability performance management (SPM) systems data. But how can blockchains be used to improve ESG reporting? Blockchain could be the answer to providing a holistic view of sustainability data. A blockchain is a distributed ledger where data transaction records are stored in blocks of information, which are then verified by multiple participants. This prevents data tampering and makes tracking changes made over time easier. Blockchain could be the answer to providing a holistic view of sustainability data. The technology can allow companies to track environmental impact in real-time, allowing them to take action when certain conditions are met—and, therefore, better manage risk for investors and stakeholders alike. Blockchain could open up new avenues for companies to improve their supply chains. Blockchain could provide a better way to track the movement of raw materials, parts, and finished products. The technology could help companies monitor the use of their products in the supply chain and allow them to identify when those products are being used for unintended purposes or in ways that may not meet their standards. Companies might also be able to use blockchain technology as part of their ESG reporting by using it as a means of tracking waste or other materials that were generated during production or consumption. The ability to see where these materials go after they leave a facility could give companies visibility into whether they're following through on their sustainability goals. Blockchain has provided the aviation industry with an innovative solution to sustainability reporting. Blockchain has provided the aviation industry with an innovative solution to sustainability reporting. By tracking flight data from the moment a plane takes off until it lands, blockchain can be used to track carbon emissions. Blockchain is a distributed ledger technology (DLT) that maintains a continuously growing list of records called blocks, where each block contains information about some or all recent transactions. Each block contains a hash pointer as a link to its predecessor and timestamp information, forming an unbroken chain. Because it's decentralized, there's no central server or database to hack into; instead, blockchain relies on networks of connected computers called nodes which verify transactions through cryptography and store them in their local copy of the ledger. When you purchase something using Bitcoin or another form of cryptocurrency - like Ether - you're using a public key (also known as your wallet address) that only allows you access to your account if someone else sends coins into it from theirs. Blockchain can help provide end-to-end traceability for products and services. The ability to track a product's entire life cycle is critical for consumer confidence and environmental protection. The blockchain offers a reliable way to offer complete traceability from the beginning of production until the product or service is sold in stores, online or in person. Blockchain technology can be used to create an end-to-end digital record of any kind of transaction. This includes information about each stage in the production process—from raw materials, through processing and assembly, right up until shipping—and who was involved at each stage. In addition to presenting comprehensive data on suppliers’ records and performance history, blockchain also allows companies that use it as part of their ESG reporting process greater transparency into all areas within their supply chains that are relevant for sustainability purposes. Blockchain is enabling improved ESG reporting by providing better data transparency and accountability. Blockchain is a decentralized database that creates an immutable and transparent digital record of transactions. It allows for the creation of a permanent and unmodifiable digital ledger, which can be shared within a network or copied to multiple locations. This technology provides data transparency, allowing all users to access the same information at any given time. Blockchain has been used in many industries, including healthcare, finance, supply chain management, and, more recently, sustainability reporting. This emerging technology can help provide a holistic view of sustainability data and open up new avenues for companies to improve their supply chains. Conclusion Blockchain offers a new way to share data and information in the world of corporate sustainability reporting. The technology offers businesses a chance to open up their ESG processes to the public and show how they are meeting these goals through an immutable digital ledger of transactions. This can empower customers and stakeholders, who will have access to more holistic information about how their products or services are produced or delivered.

Introduction Data is everywhere. It's in our smartphones, on the streets, and even in our bodies. As an enterprise, you're dealing with a lot of data—from your supply chain to your customer service department. But how do you make sense of all this information? And how can it be used for better decision-making? In this article, We'll cover four key concepts that will help any enterprise leverage their data: Data as a service, artificial intelligence/machine learning (AI/ML), data democratization, and data-driven decisions. By implementing these concepts into your strategy and business processes today, you'll be able to keep up with the ever-changing landscape of big data analytics! Data as a service Data as a service. Data as a commodity. Data as a utility. The last one is the most interesting because it changes the way we do business in the future. Artificial intelligence/machine learning AI is the future of business. It’s a way to make smarter decisions and can help you make better, more informed, faster and more accurate decisions than ever before. AI is more than just a buzzword; it’s an actual technology that will allow businesses around the world to operate at new levels of efficiency and profitability. Data democratization Data democratization is the idea that data isn't just for the IT department. All business units should be able to make decisions based on data and analytics. This means that all departments, not just IT, should have access to their own sources of data. It's also important to note that while there are many different types of users in an organization (IT staff, C-level executives, marketing teams), they all should have access to their own sources of information as well. It is not enough for only one department—such as IT—to be responsible for the collection and distribution of data; everyone needs access so that they can do their jobs effectively without having too much red tape getting in their way. Data-driven decisions Data-driven decisions are more accurate, efficient and effective than those made without data-driven insight. They're also more impactful. The next time you're considering a decision, ask yourself if it's going to be driven at least in part by data. If it isn't, you might want to reconsider your approach—and even change the way you think about business altogether! Data-driven enterprises will have a better chance of reaching their goals and staying relevant in their industries as they increasingly face competition from other companies that know how important it is to use data effectively (and are willing to put the work into doing so). Enterprises know that data is important. They need to be able to access and share their data seamlessly and use it to make better decisions. Enterprises know that data is important. They need to be able to access and share their data seamlessly and use it to make better decisions. However, many organizations struggle with this because they're unsure how to work with multiple data sources. How do you organize all the information? What's the best way to share it? How do you analyze it in a way that makes sense for your team? These are questions we hear all the time at Derisk360 (and we'd love the chance to help).

What is Single Risk View (SRV)? To understand what is SRV, it will help to understand the history of Single View of things in the business world and how it has evolved. Single View of things range from assets, data, customer etc. and the list goes on. In the last decade, Single Customer View (SCV) has been the most commonly tackled the challenge in data-centric organisations. Large organisations that have inorganically have different systems with customer data and many with the same customer records. This scenario has led to poor customer service, operational overheads, inability to improve the bottom line. An SCV is an aggregated, consistent and holistic representation of the data known by an organisation about its customers. An SCV provides companies with the ability to monitor customers and their communications across every channel. The apparent benefits of this include much-improved client service levels, better customer retention, higher conversion rates and an improved overall customer lifetime value. Organisationally this will also lead to better communication between Traditionally separate teams and a more concerted approach to customer service. * Risk functions can draw parallels from SCV because of the nature of the challenge and impact to the business. Risk functions struggle to have a single view of the risks (CREDIT RISK, MARKET RISK, LIQUIDITY RISK, and OPERATIONAL RISK) that impact the business as a whole. The Chief Risk Officers and risk functions biggest challenge is the constant fire fighting to understand holistic risk profile. This is mainly due to the siloed and manual nature of risk capture mechanism, storage of data in disparate systems, very tedious and old school manual mechanism to identify and create risks. Single Risk View (SRV) - is a solution to enable Chief Risk Officers to understand the risk profile of the entire company at any given point in time and take impactful, timely decisions. We tested several hypotheses with Chief Risk Officers in large banks: 1. All data-driven enterprise risks are not documented in a structured and consolidated ”single” view 2. Our risk function prefers to have the ability to AUTO-DISCOVER and AUTO-CREATE the risks 3. Risk functions will need to look into multiple sources for data supporting the risks and fixing a material cost 4. Risk functions make the risk-mitigating decision typically with few or no decision options 5. Risk functions make decisions based on experience, intuition but by gathering supporting data for a decision 6. Risk functions will prefer to have a dynamic dashboard of Risks with parameter showing risks volatility over multiple factors 7. Risk functions will prefer to have AI predicted the best action based on options for the risks identified 8. Risk functions will prefer to have steps linked to a workflow with other relevant company teams to work on risk mitigation 9. The company has to balance prioritising automation of (IT) development over the quantum of risk and value 10. Priority IT development projects within the risk management/valuation area take time to live, and until then the firm carries the risk and cost of managing the risk 11. No tool can be pointed/configured out of the box to manage complex instruments or portfolio items 12. Ability to assess the interconnectivity of the risks related to a customer and their customers (direct or indirect). Example: If a bank holds a AAA bond by Google and at the same time, they offer revolving credit facility (RCF) to 5 different companies that work with Google. If there is an adverse judgement where by Google has to stop a service these companies benefit from potentially the security of the RCF could decrease from AAA to CCC or even underperforming. How to implement a Single Risk View? Single Risk View (SRV) is difficult to implement by using the same methods as Single Customer View (SCV) because of the different data types involved. Single Customer View involves only customer data which is static data that does not change over time, but Single Risk View involves static data (Customers, Product etc), transactional data (Credit transactions, Thresholds), and reference data (Credit ratings, FX etc). Single Risk View (SRV) requires a blend of automation, AI on the enterprise data to successfully deliver the value and impact. Critical Components of a Single Risk View are: 1. Automated Data Integration 2. Auto-risk discovery and auto-generation 3. Rules management 4. AI to identify the probabilities of risk and robo-adviser for the mitigation action Below is an illustrative example of a Single Risk View (SRV) solution: 1. Single Risk View of a bank 2. Auto-discovery and auto-generation of risks based on thresholds 3. Heat map of auto-discovered and auto-generated risks 4. Auto-created risk detail with probability (AI-driven), criticality, implication and robo-adviser (AI-driven) 5. Rules management (1/3) 5. Rules management (2/3) 5. Rules management (3/3) 6. Data management (1/3) 6. Data management (2/3) 6. Data management (3/3) What is the need for a Single Risk View now? The answer simply is that opportunities outweighs the threats and overheads.** Key benefits of a Single Risk View (SRV) are: 1. Improve operational efficiencies 2. Proactively address regulatory compliance such as Basel, Solvency etc 3. Turn the risk function from a cost centre into revenue realisation unit by de-risking non-performing assets 4. Integrated data is treated as an asset for BAU and can be used for other use-cases 5. SRV AI models can trained and re-used in other use-cases for a different data sets Single Risk View (SRV) solution and the term "Single Risk View" are proprietary to Derisk360. To discuss how to implement Proof-of-Concept of Single Risk View, please get in touch. *https://econsultancy.com/ ** McKinsey Global Institute #singleriskview #srv #riskmanagement

Zero-Based Data (ZBD) is an efficient method of how data is captured, managed, provisioned, processed, and consumed within an organisation. Please read earlier post to understand the concept of ZBD (2 minute read). This is first of a series of posts on best practices to implement ZBD in large organisations. To understand the approach to implement ZBD, it is necessary to identify areas in an enterprise data architecture that will have profound impact on the business. Reference Enterprise Data Architecture components can broadly be grouped based on the type of activity that impacts the business i.e.; Data collection/capture (Internal and External data stores) Storage and management (Data Acquisition and Integration) Provisioning for internal and external consumption (Data Propagation and Distribution) Operational and business processing (BI and Analytics) Moonshot projects (Artificial Intelligence) In this article, let's explore the concept of ZBD for 1. Data collection/capture (Internal and External data stores) using a business scenario. Scenario An insurance company leveraging blockchain and smart contracts to offer the insurance products. Impact Data can be collected in a business at various stages depending on the type of business. It is evident from the the value chain the data is a crucial factor in executing a policy sell, underwriting, payments, claims, back-office ops, and re-insurance. The issue in the current insurance landscape is non-streamlined data movement through the value chain added to poor quality that is inadequate and irrelevant. This issue becomes a show stopper especially to implement new technologies such as blockchain and smart contracts since inadequate and irrelevant data will result in automated inaccurate execution of policies, claims and payments at scale. Solution Apply ZBD principles (relevance and adequacy) during initial data collection combined with a strict data quality rules and validation before the data persisted in the data store. Where to apply the ZBD principles in the insurance value chain? As described in the above diagram, ZBD principles for data relevance and adequacy can be applied at different value chain components of an insurance business. Only relevant customer, product, and location must be collected to underwrite a policy Only relevant customer, product, and location must be collected to verify a customer or to a create a new customer Only adequate customer, product, and location must be exposed to re-insurers or authorised 3rd parties It is crucial to implement a strict "Data Governance" to reap the benefit of ZBD which will be explored in separate article. In the next article, we will explore applying ZBD for storage and management 2. (Data Acquisition and Integration). To assess data capability and implement ZBD in your organisation, contact us at http://bit.ly/2ETLOpX

Just as Zero-Based Budgeting (ZBB) is re-emerging as the efficient process for allocating and provisioning funding, Zero-Based Data (ZBD) will be the future of how data is captured, managed, provisioned, processed, and consumed within an organisation. 'Zero-Based Data (ZBD)' is a term coined by Shen Pandi (Founder of Derisk360) in 2018. ZBD is a term for data capture, management, provisioning, processing, consumption to be adequate, relevant, and limited to what is necessary within an organisation. A brief history of ZBB ZBB first rose to prominence in the government during the 1970s financial crisis. Faced with mounting public pressure, U.S. President Jimmy Carter promised to balance the federal budget and reform the federal budgeting system using ZBB, which he had used while he was governor of Georgia. Though it was initially well received, ZBB proved not only complicated and time consuming, but also ineffective, as it was Congress and the executive branch that were ultimately responsible for deciding whether to keep or eliminate a program. Additionally, the president’s budget office used a variant of ZBB as agencies were asked to rank their programs within funding limits. This forced the agencies to assign priorities and identify possible reductions. * Rising popularity of ZBB ZBB has recently experienced a resurgence of interest in both the public and private sectors. In the public sector, this stems largely from contemporary fiscal constraints precipitated by the 2008 recession. Facing budget cuts and due to an increase in public scrutiny, government agencies have started using alternative budgeting methods such as ZBB, instead of more traditional budgeting methods such as incremental budgeting.* Adequacy Sufficient data to fulfil the intended purposes Limitation Relevance Review the data held, and delete Collect only the necessary data anything unnecessary for the specified purposes Mapping of ZBB principles to ZBD Budget is not connected to previous year spending ---------------------- Adequacy Spending increases or cuts are not simply spread even across budgets ------ Relevance Budgets are tied very specific activities and levels of service --------------- Relevance Funding is targeted more to activities that align with the strategy ----------- Limitation Key issues with current data provisioning and processing approach Not asking right questions to the users about data provisioning Start with data that already exist that may be more than what is required Duplication and data redundancy due to above stated reason Lack of diverse data sampling Non-existent or inefficient archival and audit mechanism Best practice to implement ZBD Establish a robust governance reporting to the Chief Data Officer Map all data lineage to the consents for data subjects Establish a global metadata and business glossary linked to data lineage Report audit of the ZBD 'Key Performance Indicators' (KPIs) to the senior management Benefits of ZBD Resulting data usage is well justified and aligned to data privacy and GDPR strategy Accelerate broader collaboration across the organisation Supports cost reduction by avoiding automatic under-utilised data volume increase Improves operational efficiency by rigorous challenging of assumptions To assess data capability and implement ZBD in your organisation, check out our GDPR Assessment or contact us. http://bit.ly/2voT3iC * Source: Deloitte #data #dataprotection #datacollection #dataprivacy #gdpr #riskassessment #datasecurity #Compliance #derisk #360 #informationsecurity #hacking #privacy

In data business, market forces are more brutal than regulators... There is no question that GDPR has paved the way for a massive increase in both the number of jobs in the corporate world, as well as the number of startups, promising to provide solutions to the various issues the new regulations bring. Since the approval of GDPR in the European Parliament in April 2016, masses of online articles have appeared discussing personal data protection, data privacy, security etc. However, more significantly, many of these articles have incited fears - no company wants to be penalised by regulators for non-compliance. Yet what many have not realised is that market forces are much more brutal than any regulator (a truth that holds more importance especially for publicly listed companies). Some of today's most powerful companies (Facebook, Apple, Alphabet, Microsoft etc) are backed by their financial muscle and a powerful lobby. However, history has shown that big companies, having made their money in a certain way, are often reluctant to alter their business model, due to complacency. For example, DEC, Kodak, Netscape and Palm were market leaders in their respective fields, but eventually all of them failed catastrophically, unable to sustain themselves for more than a couple of decades due to failure to innovate and a reluctancy to disrupt themselves. More recent examples of this are MySpace and Nokia. Recently, Facebook experienced a colossal disaster, losing US$120 billion in one day - an event so absurd, simply due to its sheer scale. To make it easier to comprehend the scale of this loss, Adobe's market cap in 2018 is US$119 billion, GE's market cap is US$126 billion and Pepsico's market cap is US$ 138 billion. Some of these iconic brands took decades to build their tangible assets and each with a market capital of over US$100 billion. But, it is important to realise that Facebook's business model is completely different to a traditional business in that it is completely built upon intangible assets (i.e. their real asset is user data). And still, Facebook knowingly ignored the blatant necessity to safeguard their user data, which eventually took its toll. There is a common misconception that the reason for this huge loss was due to missing the quarterly revenue target. But, in actual fact, the underlying reasons were the company's decision to overlook the issues of fake news and the exploitation of user personal data. And thus, big issue now is a lack of trust in the company, whether with it's users, clients or the markets (Amazon has been missing its quarterly target for most of existence but yet is one of the most trusted brands). Facebook's 2017 revenue was approx. US$40 billion.* As per GDPR law, the maximum penalty from the regulator would have been only US$1.6 billion (based on 4% of the total revenue), which is significantly lesser loss from US$120 billion. But as suggested, market forces are much more brutal than the regulators. The lesson to be learnt here is that when your business is fuelled by user data, you must win the user's trust by genuinely giving the power back to the user over their data. To achieve this, having strong foundational principles is key (e.g. "Privacy by Design"). Essentially, you must design your entire business with user privacy at the core. This will enable you to gain user trust right from the start and then, allow you to focus on offering better services. To assess how well you are doing in "Privacy by Design", check out our free 5-minute GDPR Assessment. http://bit.do/esG4i *Source: Statista

If AI and automation are the engine, then data is the fuel... This decade has been the most conducive time ever to start a business, with easily available relevant tools and channels enabling businesses to interact directly with the customers, collecting data at an unprecedented speed and volume. And due to the minimal cost of starting a business, in the UK alone, 589,000 startups were launched in the 2017.* The data collected from various channels act as fuel for these startups, training their AI systems to predict the future. Moreover, in terms of automation, the simplest form is collecting customer data in subscription lists and sending automated emails. But, even this simple process is subject to data protection during data collection, storage, analysis, usage for marketing, decision making etc. Customer data is collected by organisations of all sizes, from all industries, and in all geographic locations. However, despite customer personal data's great value to any business, in recent history, it has been exploited unscrupulously for commercial and political reasons, leading to the formation of General Data Protection Regulation (GDPR). The information governing bodies in the EU have formulated guidelines and checklists on the best practises to safeguard personal data and give users control of it. derisk360 leverages these guidelines and offers GDPR compliance certification, by using our proprietary "weighted risk ranking" methodology. Some of the benefits of GDPR compliance certification are: Providing transparency and accountability Creating effective safeguards in order to mitigate risk around data processing Improving standards through establishing the best practise Mitigating against enforcement action/penalty Having a competitive advantage Try our free 5-minute GDPR assessment. http://bit.do/esG4i * Source: companies house