Big Data and Business Analytics Strategy PDF

Stuvia - Koop en Verkoop de Beste Samenvattingen W3: Big Data and Business Analytics DalleMule, L. & Davenport, T.H. 2017. What’s your data strategy. Harvard Business Review, 95(3): 112- 121. In brief: o The challenge: “To remain competitive, companies must wisely manage quantities of data. B...

Stuvia - Koop en Verkoop de Beste Samenvattingen W3: Big Data and Business Analytics DalleMule, L. & Davenport, T.H. 2017. What’s your data strategy. Harvard Business Review, 95(3): 112- 121. In brief: o The challenge: “To remain competitive, companies must wisely manage quantities of data. But data theft is common, flawed or duplicate data sets exist within organizations, and IT is often behind the curve.” o The solution: “Companies need a coherent strategy that strikes the proper balance between two types of data management: defensive, such as security and governance, and offensive, such as predictive analytics.” o The execution: “Regardless of its industry, a company’s data strategy is rarely static; typically, a chief data officer is in charge of ensuring that it dynamically adjusts as competitive pressures and overall corporate strategy shift.” Data strategy: Defensive vs. Offensive The developed framework balances "defensive" and "offensive" data uses, urging companies to make strategic choices between these two approaches: Defensive: data management aims at minimizing downside risks: ensuring compliance with regulations, maintaining data privacy and integrity, fraud detection, and preventing theft. Offensive: data management aims at supporting business objectives, like increasing revenue, profitability, and customer satisfaction through activities like data analysis, modeling, and integrating customer and market data. Offensive data management is often more real-time and is critical for customer-focused business functions like sales and marketing. Some company or environmental factors may influence the direction of data strategy: Strong regulation in an industry (e.g. financial services or health care) would move the organization toward defense; strong competition for customers would shift it toward offense. Decisions about these trade- offs are rooted in the fundamental dichotomy between standardizing data and keeping it more flexible. Single Source of Truth (SSOT) vs. Multiple Versions of the Truth (MVOTs): SSOT: This is a logical, often virtual, and cloud-based repository containing one authoritative copy of crucial data, such as customer, supplier, and product details. It must have robust data provenance and governance controls. SSOT is essential for both defensive and offensive activities and uses a common language for key data elements. MVOTs: These are developed from the business-specific transformation of SSOT data into information — data imbued with “relevance and purpose”. MVOTs allow different groups within an organization to transform, label, and report data, creating distinct, controlled versions of the truth that respond to their specific requirements. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Data architecture: SSOT & MVOTs: The implementation of a data architecture that supports both SSOT and MVOTs is crucial. SSOT focuses on the data level, while MVOTs manage the transformation of this data into useful information. The article emphasizes that not having an SSOT can lead to chaos within an organization. Regardless of what industry a company is in, its position on the offense-defense spectrum is rarely static. Good governance, good data: A sound data strategy requires that the data contained in a company’s single source of truth (SSOT) is of high quality, granular, and standardized and that multiple versions of the truth (MVOTs) are carefully controlled and derived from the same SSOT. This necessitates good governance for both data and technology. In the absence of proper governance, some common problems arise: o Data definitions may be ambiguous and mutable: With no concrete definition at the outset of what constitutes the “truth” (whether an SSOT or MVOTs), stakeholders will squander time and resources as they try to manage non-standardized data. o Data rules are vague or inconsistently applied: If rules for aggregating, integrating, and transforming data are unclear, misunderstood, or simply not followed—particularly when data transformation involves multiple poorly defined steps — it’s difficult to reliably replicate transformations and leverage information across the organization. o Feedback loops for improving data transformation are absent: If rules for aggregating, integrating, and transforming data are unclear, misunderstood, or simply not followed - particularly when data transformation involves multiple poorly defined steps - it's difficult to reliably replicate transformations and leverage information across the organization. Organizing data management The article also touches on the organization of data management, which can be either centralized or decentralized. The optimal design depends on the company's position on the offense-defense spectrum: Centralized data functions are suitable for businesses focusing on data defense: o Single CDO who is accountable across the entire organization. o Consistent application of data policies, governance, and standards ensures that data is consistently applied across the organization, which is crucial for data defense. o It follows a more uniform policy enforcement and standardization of data practices. Decentralized management is better for offensive strategies: o CDO in each business unit allows for agility/customization of data reporting/analytics. o Prevent development of data silos, which cause redundant systems/duplicate work. o Decentralized budgets are mostly focused on offensive investments, closer to business users, and have more tangible ROIs, in contrast to centralized budgets, which are typically more focused on minimizing risk and reducing costs. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Conclusion: The article provides a comprehensive framework for developing a robust data strategy, emphasizing the balance between defensive and offensive approaches, and the critical role of SSOT and MVOTs in this context. The framework is applicable across industries and levels of data maturity, supporting managerial decision-making and enhancing financial performance. A crucial aspect of the framework is distinguishing between information and data and differentiating information architecture from data architecture. While data architecture involves the collection, storage, and processing of data, information architecture is concerned with converting data into actionable information. The authors argue against centralized control-oriented approaches to data management, advocating for a combination of SSOT and MVOTs, to balance data control and flexibility. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen McAfee Andrew, & Brynjolfsson Erik. 2012. Big data: The management revolution. Harvard Business Review, 90(10): 60–66. Idea in brief: “Data-driven decisions are better decisions” —It’s as simple as that. Using big data enables managers to decide based on evidence rather than intuition. Therefore, it has the potential to revolutionize management. What's new here: Volume: Enormous amounts of data generated daily, highlighting the significant increase in data availability for business analysis. Velocity: The rapid speed at which data is created and processed, underscoring the need for real-time analytics and decision-making. Variety: The variety of data sources, including social networks, sensor readings, and GPS signals, is stressed, indicating the expanded scope of data types available for business insights. The authors found a broad spectrum of attitudes and approaches in every industry. One relationship of the conducted analyses stood out: “The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results”: o Companies in the top third of their industry in data-driven decision-making were found to be 5% more productive and 6% more profitable. o A major U.S. airline utilized big data to improve the accuracy of flight arrival times, leading to operational efficiencies. Sears Holdings used big data technologies to significantly reduce the time required to generate personalized promotions, enhancing their quality and relevance. Expertise from Surprising Sources Often someone coming from outside an industry can spot a better way to use big data than an insider, just because so many new, unexpected sources of data are available. New culture of decision making: Muting the HiPPOs: the shift from high-paid person’s opinions (HiPPOs) to data-driven decision-making, emphasizing the importance of data over hierarchy or intuition. New roles: It highlights the emerging role of data scientists and the need for traditional domain experts to focus on identifying the right questions to ask in the era of big data. They will be valued not for their HiPPO-style answers but because they know what questions to ask. Getting started: you don’t need to make enormous up-front investments in IT to use big data (unlike earlier generations of IT-enabled change). One approach to building a capability from the ground up: Step 1: Pick a business unit to be the testing ground. it should have a quant- friendly leader backed up by a team of data scientists. Step 2: Challenge each key function to identify five business opportunities based on big data, each of which could be proto typed within five weeks by a team of no more than five people. Step 3: Implement a process for innovation that includes four steps: experimentation, measurement, sharing, and replication. Step 4: Keep in mind Joy’s law: “Most of the smartest people work for someone else.” Open up some of your data sets and analytic challenges to interested parties across the internet and around the world. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Five management challenges: companies won’t reap the full benefits of a transition to using big data unless they’re able to manage change effectively. 1. Leadership: The need for leaders to embrace and promote a data-driven decision-making culture: set clear goals, define what success looks like, ask the right questions, and spot a great opportunity. 2. Talent management: Managing and acquiring talent proficient in data science and analytics. 3. Technology: The tools available to handle the volume, velocity, and variety of big data. Implementing and maintaining the technological infrastructure required for big data analytics. 4. Decision-making: Aligning decision-making processes with data analytics and ensuring that data-driven insights are effectively integrated into business decisions. A flexible organization minimizes the “not invented here” syndrome and maximizes cross-functional cooperation. 5. Company culture: Cultivating a corporate culture that values data and evidence-based decision-making over traditional intuition-based approaches. The evidence is clear: Data-driven decisions tend to be better decisions. Leaders will either embrace this fact or be replaced by others who do. In sector after sector, companies that figure out how to combine domain expertise with data science will pull away from their rivals. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Lecture 3: Digital governance of interorganizational relationships The blockchain revolution What is a blockchain? - A distributed database, a ledger - Creates a lot of transparency because each actor in the process owns the same data. They all need to agree on it. E.g. if a farmer says he delivered a 100 apples but the store says they only received 90, the blockchain needs to find consensus on the true value of the data Blockchain enables new forms of digital governance; new managerial challenges Interorganizational governance What is governance? Role of governance: governance functions as a mediator between competitive and cooperative dynamics On the one hand you have competition, which constrains value capture On the other hand you have cooperation, which enables value creation → there is tension between competition and cooperation → governance mechanisms mediate between competition and cooperation Interorganizational governance = how organizations plan, coordinate and safeguard transactions The governance tripod: Contingency planning = ‘’if, then’’ factor. If something happens, then what do we do? Traditionally, interorganizational relationships are governed by contracts and relational norms - The limits of contracts: contracts are (1) incomplete, (2) costly to write, (3) costly and time consuming to enforce. - Contracts and relational norms reach their limits if we move away from dyadic relationships to networks 7 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Modes of inter-firm collaboration: Digitalization enables ever larger collaboration networks, which substitute traditional governance instruments such as contracts. - From alliances to networks - The larger the collaboration network, the more complex the governance challenges Governance by blockchains Businesses face an increasing need to govern their many relationships with other organizations securely and efficiently. Blockchain technologies enable network-level governance by means of disintermediation: Advantage: no transaction costs. It is like using cash. Transaction fees are exactly what makes platforms rich Blockchain-based governance: take out intermediary, selling your data exactly to advertisers - Peer-to-peer network - In control of your own data distribution 8 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen - But: it is a challenge, because the incentives for the platform network are large. If you switch to a peer-to-peer network you make less money in contrast to platforms In simple terms; think of blockchain as a serverless whatsapp group in which new messages can be added but not deleted. Formally speaking, blockchains are distributed ledger technologies in which data duplicates are stored across a network of computers Blockchain technology and network base consensus: each transaction in a blockchain is stored in a block that is added to the chain if the network reaches consensus. The promise of blockchain Blockchains promise a novel solution to address central governance challenges 1. Reduces transaction costs: lower costs of reaching, formalizing, enforcing an agreement 2. Offers decentralized governance: money without banks, companies without managers, countries without politicians 3. Prevents opportunism: architecture for so-called trustless trust that allows us to trust the outputs of a system without trusting any actor within it 4. Creates radical transparency: transactional information are accessible to the participants Benefits of blockchain Blockchain technologies can improve the efficiency, transparency and security of transactions → Paper Hanisch et al. (2022) 9 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen 1. Coordination: - Automated execution - Increased transparency - Seamless scalability 2. Contingency planning: - Automated adaptation - Fast reactivity - Increased reliability 3. Safeguarding: - Automated enforcement - Automated verification - Technology-induced trust Governance of blockchains In the enterprise context, blockchains are often supported by administrative interfaces such as consortia Four generic blockchain governance modes: → In practice you see a lot of hybrid modes! Administrative interfaces such as consortia provide a forum to manage the co-opetitive dynamics that often arise in networks Strategic trade-off related to competitor involvement: - Risks if you do competitor involvement: 1. Risk of information misappropriation 2. Risk of collusion threats - Problems if you don’t do competitor involvement: 1. Problem of sufficient data coverage 2. Problem of weak network ties / antitrust conflicts 10 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?