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Livro: Financial Data Engineering: Design and Build Data-Driven Financial Products
Cultures built on advanced analytics are increasingly synonymous with high-performance organizations.
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Why is that? Consider the intersection of these powerful forces. Culture is about how organizations evaluate performance, allocate resources and encourage people to act. And big data potentially transforms all of those areas. That’s why cultures built on big data and advanced analytics are increasingly synonymous with high-performance organizations.
After all, everyone has loads of data. But the winners are those who have the expertise, motivation and capacity to use it effectively and to drive bottom-line results across the whole enterprise. At such firms, data is not just an asset, but rather a way of life.
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Moving away from risky, “gut-feel” management styles to data-driven and analytics-enabled models.
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Is Your Culture Data-Driven? Key Questions to Ask
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How often do senior executives review key operational or performance metrics? Do they use dashboards for on-demand reporting?
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Are legacy, “gut-feel” decision making models still in place?
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What is the turnaround time for ad-hoc reporting from the marketing organization? Supply chain? Finance?
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The most senior executive with responsibility for data and analytics is …?
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Which business unit or function has the best reporting capacity? How broadly are their reports shared or emulated?
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Are standard reports easy to read and understand?
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How many different data sources are used to make significant strategic decisions? Which data are most persuasive?
Data-Driven Finance: Above and Beyond the Bottom Line
CFOs seizing the opportunity to engage the business as a strategic advisor providing insights that promote operational excellence and foster innovation, while eliminating waste and reducing risk.
A Principled Approach to Data Analytics
Data-driven finance organizations can become analytics champions by embracing five key principles – agility, sustainability, extensibility, predictability and accountability.
Data-driven finance is not just about running the numbers more efficiently through standardization, simplification and rationalization. It’s about optimizing business performance through broader and deeper visibility into operations, more insightful analysis and more rigorous, fact-based decision making.
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the benefits for companies with data-driven finance organizations:
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Lean cost structures, with more resources committed to value-adding services
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Fewer metrics overall and more effective prioritization based on what really matters to the business
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More time and resources focused on analysis and commentary – and less time and fewer resources working to gather data
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Stronger predictive and early-warning capabilities
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Higher rates of automation and use of advanced technology to improve efficiency and flexibility and generate insights
In summary, data-driven finance is not a single tool or technology or a commitment to gathering every piece of available data. Think of it as a way of life and an evidence-based approach to setting priorities, evaluating performance and making decisions.
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How to Make Data-Driven Finance a Reality
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So where do finance leaders start to leverage the transformative potential of Big Data and Big Data analytics? Much depends on how quickly their organizations set themselves up with data management systems that foster financial transparency, as well as related organizational structures and decision-making practices. It will be a multi-dimensional journey for most organizations. That means CFOs and other leaders seeking to make Data-Driven Finance a reality must ask the right questions to:
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Set the Strategy: How specifically will Big Data and analytics improve performance? What’s the business case in finance?
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Understand How Big Data Works: What are the right technology components (including data sources, warehouse and discovery platforms) and the optimal environment design to become data-driven in finance?
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Build the Right Teams with the Right People: What are the most important skills to have and how should they be structured?
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Instill and Sustain a Data-Driven Culture: Is the organization ready to make decisions in new ways? Can analytics facilitate a move beyond management by “gut feel” and seniority?