How do you talk about risk in data analysis?

???? "Risk" and "data analysis" are inseparable companions. As data professionals, it's our responsibility to anticipate uncertainties and navigate through them strategically. By factoring in risk, we not only enhance the quality of our analyses but also empower decision-makers with a comprehensive view of potential outcomes. Let's keep sharing insights on managing risk for more impactful data-driven strategies! ???? #DataStrategy #RiskNavigation #DataProfessionals

In the context of quantitative finance, there is a risk that we choose a mathematical model that does not incorporate all the relevant factors driving the problem. In addition, there is uncertainty about the model parameters, which are fitted to current and/or historical data. The data source should be reliable, hence the importance of data analysis and governance around it. In practice, the risk and uncertainty are mitigated through additional valuation adjustments. The takeaway from this note is that appropriate governance, monitoring, and mitigation around model risk, parameter uncertainty, and data quality are of paramount importance. Failing to have such a framework might result in significant losses and reputational risk.

Navigating Risk in E-commerce Analytics ???? In the dynamic world of e-commerce, 'risk' and 'uncertainty' are terms that professionals grapple with daily. Every online campaign highlights the importance of differentiating 'risk' (the potential of not achieving set targets) from 'uncertainty' (inherent fluctuations in sales forecasts). As brands evolve and marketplaces change, it's vital to have a strategy that's both resilient to potential setbacks and adaptive to unpredictable outcomes. The fusion of e-commerce strategies with sharp data insights paves the way for businesses to tackle risks and uncertainties with confidence. ???? #EcommerceTrends #RiskManagement #DataDrivenDecisions

Digital business transformation brings both opportunities and risks. To succeed, foster a risk-engaged culture where teams embrace risk rather than avoid it. As organizations advance digital transformation, they heighten information risk. Data and analytics (D&A) teams must prioritize managing this risk to minimize business risk exposure. However, D&A teams often lack a risk-engaged culture, treating risk as something to avoid. This mindset can lead to missed opportunities. To enhance risk engagement: Analyze how information risk from digital initiatives affects business outcomes. Evaluate D&A team's awareness of information risk and its impact on goals. Develop a plan within existing governance to improve risk culture.

In my experience, it is more optimal to effectively communicate the possible risks to stakeholders beforehand. As data analytics business intelligence professionals, we all need to make sure that stakeholders understand the risks/ uncertainties so the data story can be appropriately made use of to make business decisions.

In the world of data, acknowledging and effectively communicating risk is paramount. It equips stakeholders with a clearer picture of the landscape, enabling them to make more informed choices. Looking forward to more insightful discussions on navigating risk while harnessing the power of data! ???? #DataAnalytics #RiskManagement #InformedDecisions

Always try to use either a memorable image that will help clarify a point or associate a new idea by translating to an accepted idea or process to reduce confusion. Great storytellers take complex topics and make them simple for most audiences.

In data analysis, combining visuals and narratives enhances communication of risk. Visuals elucidate data distribution and trends, while narratives contextualize findings, resonating with audiences. Example: In pharma research, graphs portray market dynamics, while a story weaves the drug's path amidst regulatory shifts and patient needs. This fusion bridges analytical insights with emotional connection, fostering informed decision-making.

First and foremost, visualising risk should be aligned to the levels of risk defined within an organisations risk appetite statement. Depending on the risk metric being visualised, different visualisations of data may be more appropriate. To generalise however, the visual representation should be aligned to monitoring the business taking the appropriate level of risk, and not averting risk completely. This can be descriptive in nature i.e. describing our past behaviour, or forward looking and predictive, to manage our risk outlook.

Visualising a risk has its own merits, the right set of tools with optimum level of charts, graphs make it appealing for the audience and make them engaged. However data clutter and congested visualisation May sometime make viewers disinterested and then how you tell story with May be using personalisation will get them back to the presentation.

???? Clear communication is the cornerstone of successful data analytics projects. Your emphasis on avoiding jargon and ambiguity is spot on, [Name]! By fostering transparency and using plain language, you're ensuring that insights are accessible to everyone involved. This not only drives collaboration but also empowers informed decision-making. Kudos to your commitment to clarity in the complex world of data analytics! ???? #DataAnalytics #ClearCommunication #ProjectSuccess

In my view, the use of jargon and ambiguity is a "double-edged sword". Three scenarios: (1) if you are explaining the job to new joiners, you should use and explain the jargon, (2) not everything is crystal clear on some policies items. This might turn in your favor to use your creativity to solve a particular situation, (3) since historical data and particular time-frames can not predict future data behavior 100% accurately (uncertainty), it is good to have some room for interpretations, i.e., a "reasonable" level of ambiguities. As a closing remarks, jargon has its utilities in Academia. Although it is also used in the industry, it tends to be more demystified, in particular when your audience is Senior management.

Make it simple and you will drive home more success! Understand the audience and Pitch accordingly. If it’s presented to higher leadership, start with how is it worth their time and address the right KPIs. Don’t expect them to understand crammed codes and analysis paralysis!

Domain specific terminology may be unavoidable to collate, as they are intrinsically linked to understanding an organisation. Generally these are stored within a business glossary.

Simplify Technical Terms: Define Jargon: When using technical terms, provide clear definitions or explanations to ensure that your audience understands the terminology. Use Layman's Terms: Translate complex terms into simpler, everyday language to enhance accessibility. Provide Context and Examples: Contextualize Terms: Explain technical terms in the context of the specific analysis, illustrating how they relate to the data and the conclusions drawn. Use Examples: Offer real-world examples or scenarios to illustrate the meaning of technical terms. Clarify Degrees of Uncertainty: Avoid Ambiguous Terms: Minimize the use of vague terms like "likely," "unlikely," or "significant" without providing a clear context or definition.

Acknowledging limitations and assumptions in data analytics projects is a mark of true professionalism. By openly addressing these aspects, we build a foundation of transparency and credibility. It's through this honesty that we ensure our insights are not just robust, but also contextualized for decision-makers. Your commitment to holistic data analysis is truly inspiring. ???? #DataAnalytics #Transparency #InformedDecisions

Limitations and assumptions need to be identified (sometimes is a complex task) and presented in bold text (usually the less discussed). We tend to forget them when everything is running smoothly (goals are being achieved). They might (high probability) explain some unexpected and/or unrealistic models behavior.

Limitations and scope of our analysis help us definite boundaries within the scope of what we've looked at. Depending on the statistical approach, it may be important to note key trends within the data, noting for correlation, causality and independence. It can be difficult to allay bias within these scenarios as perspectives may be weighted towards an organisations interests. It is important to note any sources of bias which may occur.

Limitations & Assumptions in Data ?? Every data analysis walks a tightrope between 'limitations' and 'assumptions'. Limitations point to the constraints in our data or methods - whether that's missing values, biases, or inherent errors in tools. On the other side, assumptions are the foundational pillars we lean on, be it normal distribution, independence, or causality claims. To offer robust insights, it's imperative to transparently highlight these elements and discuss their influence on conclusions. Moreover, actively addressing potential biases, whether they stem from personal beliefs or external pressures, fortifies the integrity of the analysis. #TransparentDataTalks #LimitationsLandscape #AssumptionAwareness

It helps to encourage your organization to plan for pivots, NOT perfection. Show where there are limitations and how to pivot if they impact results. Acknowledge how past projects had limitations but still were overcome by getting ahead of them early.

By providing context, you frame the insights within a meaningful narrative, while recommendations offer actionable pathways forward. It's this holistic perspective that transforms data into actionable strategies. Your dedication to impactful analysis is truly commendable. ???? #DataAnalytics #ContextMatters #ActionableInsights

From Data to Decisions: Context & Recommendations ?? To make data insights resonate, we must delve into 'context' and offer actionable 'recommendations'. Context provides the backdrop—be it historical trends, industry benchmarks, or external events. Recommendations then become the roadmap, guiding decisions based on the data's story. For genuinely impactful insights, it's crucial to align data with an audience's goals, demonstrating how it can be a catalyst for success. Always anchor recommendations with solid evidence and address potential challenges transparently. #DataDrivenDecisions #ContextCounts #RecommendationRoadmap

Context in Data Analysis: Understand Audience Context: Know the background, goals, and interests of your audience. Tailor the context to make it relevant and meaningful to them. External Factors: Consider external events, market trends, or industry benchmarks that may impact the interpretation of your data. Highlight how these factors contribute to the overall context of your analysis. Historical Trends: Incorporate historical trends or patterns to provide a longitudinal perspective. Show how current data fits into the broader historical context. Comparisons: Compare your data to relevant benchmarks or industry standards. Use comparative analysis to highlight areas of strength or areas that need improvement.

In my role I analyse data to generate insight to support management decisions, identify data quality issues and assess impact of data pattern changes. The key to success is an end-to-end mindset. Things to consider include: - Where do I source the data? - What are the limitations and assumptions? - Have I done a proof of concept? - How do I validate the logic? - What controls do I have / can I rely on? - Is the output in line with expectations? - How do I standardise/productionise the analysis? Last but not least, a post implementation review is recommended. While scalable solutions are desirable, it won't hurt to start with the low hanging fruit for some quick wins to build the momentum!

Collaborative Planning: Include Stakeholders in Planning: Involve stakeholders in the planning phase to understand their objectives and expectations from the analysis. Define Key Questions Together: Collaboratively define the key questions that the analysis should address. Regular Updates and Feedback Sessions: Iterative Feedback Loop: Provide regular updates to stakeholders during the analysis process. Feedback Sessions: Organize feedback sessions to gather input, address concerns, and refine the analysis based on stakeholder input. Customized Reporting: Tailor Reports to Stakeholder Needs: Customize the reporting format to align with the preferences of different stakeholders.