Gartner D&A Summit Bake-Offs in London 2023: Flooding Impact And Reasons for Optimism!

Is the age old debate in the UK around a classic British tea time favorite finally resolved? Is it pronounced 'Scone' or 'Scon'? More importantly, extreme weather events and their devastating impacts are more frequent and severe, but which populations are most affected? Are there mitigation strategies that show reasons for optimism? We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in London with 3,700 of our closest D&A friends and family. The answer to the first question is: I knew it!! We did two Bake-Offs at the Gartner Data and Analytics Summit in London last week. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. The Bake-Offs are fast-paced, fun and informative sessions that let you see leading vendors side-by-side using scripted demos and a common data set in a controlled setting.   And this year's Bake-Offs did not disappoint! To  extend the Bake-Off experience to a broader range of vendors, including smaller innovators, we also did a series of Show Floor Showdowns for analytics and BI, data science and machine learning and for data management. The Show Floor Showdowns were held on the Exhibit Hall show floor and were based on and abridged version of the same demo script and data set used in the Bake-Offs. Any vendor that Gartner has covered in any of its research and all vendors that had a booth in the Data and Analytics Summit, London Exhibition Hall could apply to participate in Show Floor Showdowns. From there, participants were randomly selected and invited to present live at the Show Floor Showdown sessions. We also gave the demo script and data set to all vendors in the Exhibit Hall to create demos for their booths and to submit for this blog. For the vendors that participate in the Bake-Off and Show Floor Showdowns, it is in equal measure fun and extremely stressful. It’s a high stakes session where they put everything on the line in front of their arch competitors, revealing what’s coming and coveted sales tactics all while being judged by attendees. It takes a fair bit of preparation, extreme commitment, and an ability to shine under pressure. What Data Did We Use? We use the Bake-Offs and the Show Floor Showdowns as a platform for data for good. This year's scenario was  to understand the likelihood of more frequent and anomalous flooding and the resulting devastating human and economic impact because of unpredictable weather conditions. Are there mitigation strategies that can be implemented successfully that could provide policy guidance and reasons for optimism in the face of ever increasing frequency of extreme weather events? We gave the vendors data from Organization for Economic Co-operation and Development (OECD) data and National Oceanic and Atmospheric Administration (NOAA) from the National Centers for Environmental Information. They could supplement this data with any other relevant data sets. Last year’s topic was the UN’s Sustainable Development Goals.  In 2021, we explored vaccine effectiveness and inequity using global vaccine data. In 2020, we analyzed population health data. In past Bake-Offs, we explored loneliness and happiness data, opioid epidemic data, traffic fatalities, college costs, and homelessness. The results showing the  impact of flooding, predictions for the future and reasons for optimism were fascinating.  This blog highlights some notable findings and the videos from participating vendors. BI Bake-Off Vendors’ Key Findings and Videos Microsoft Here is the link to Microsoft's BI Bake-Off video. Qlik Key Findings: In the US alone, there’s $367 billion in agricultural commodities at risk to flooding in the US alone. There have been 338 weather events that have created over a 1 billion dollars (USD) plus of damage, totaling to $24 trillion in damages altogether and averaging $71 billion per event. For most coastal countries, climate change and flooding are critical topics of policy and development strategies.  A large part of under-developed Asian countries ranging from Bangladesh to Vietnam are at high risk of flooding events. Here is the link to Qlik’s BI Bake-Off video. Salesforce (Tableau) Key Findings: Unlike other European countries, the Netherlands has the most square meters of land at risk of flooding, as well as the greatest population at risk of flooding (70%). While there are many factors that can contribute to flood risk such as changes in land-use from urbanization and deforestation, there are also natural geographic features that can put certain populations at increased flood risk. These factors include % of population at risk, average elevation, and % of inland waterways throughout a region. A big factor as to why Netherlands faces extreme flooding risk is that 15% of the region is comprised of inland waterways. As population density increases in major metropolitan areas pre-disposed to flooding, risk to life and livelihood is expected to continue to increase. In 2000, the Netherlands had 8.5 million people at risk of catastrophic, flooding. In 2040 that number is projected to grow to 10.2 million. Here is the link  to Tableau’s BI Bake-Off video. Data Science and Machine Learning Bake-Off Vendors’ Key Findings and Videos Alteryx Alteryx focused on flood risk in the UK by region and year and the resulting impact on infrastructure and preventive strategies, which led to some critical findings and recommendations. With £5.2 Billion allocated in the capital budget for Flood Defences, Alteryx investigated the most at-risk regions of flooding to prioritize budget allocation for flood mitigation and defense strategies. Here are some of the key findings: Increased flooding significantly impacted Yorkshire and the Humber region, suggesting a higher frequency of flooding and subsequent damage. Yorkshire and the Humber region have a considerably higher flood impact, around 11.86 times compared to the average of other regions, leading to substantial environmental and infrastructural damages. The year 2020 saw a surge in flood impact, 4.52 times higher than the average of other years, indicating an alarming increase in flooding incidents. Our analysis suggests prioritizing allocating more resources to the Yorkshire and the Humber region to mitigate flood risks, especially considering the significant impact in 2020. Here is the link to Alteryx's Data Science and Machine Learning Bake-Off video. Dataiku Dataiku chose to focus on flood risk by geography and flooding’s economic impact on commercial insurance policies and claims. With a business use case of defining a location and insurance strategy for a proposed warehouse in southeast Florida, here are some of the key findings: Extreme rainfall and rising sea levels are driving up both flood frequency and insurance costs, especially in high-risk Florida areas. Southern Florida's Atlantic coast has increased flood risk, leading to more and costlier flood insurance claims in areas like Homestead and Miami compared to West Palm Beach and Port Saint Lucie. Our analysis suggests prioritizing Hobe Sound or Stuart, FL for real estate investment due to no past floods or claims within a 60-minute driving radius, reducing risks to warehouse, contents, and distribution routes. Interpretation of our machine learning model suggests buying $50k in building coverage and $100k in warehouse content coverage, as the latter significantly boosts predicted flood claim payouts. Here is the link to Dataiku's Data Science and Machine Learning Bake-Off video. SAS SAS took the view that mitigating and anticipating flooding events should be focused on the goal of helping people. To achieve this, they helped policymakers to understand, predict and mitigate floodings. They combined different data sources about the OECD countries, including economical policies, weather data, and historical floodings events. This enabled them to use advanced AI techniques, such as: Machine learning models to predict the likelihood of floodings that are served to policymakers in what-if scenario dashboards. The results indicated that taxation policies for CO2 and diesel have a negating effect on the risk of floodings. Time series regression on spatial data to estimate future flooding locations within hurricanes/cyclones paths. Policymakers can then consider mitigating actions in advance. Optimization algorithms to optimally help displaced people reach shelters and safer areas. This way, SAS helps policymakers make trustworthy decisions to reduce the impact of floodings on human lives. Here is the link to SAS's Data Science and Machine Learning Bake-Off video. Analytics and BI Show Floor Showdown Vendors’ Key Findings and Videos iGenius Key insights from iGenius include: After checking the correlation between the number of high-magnitude floods and the number of deaths in different countries, we found that North America has a better mitigation strategy in terms of insurance policies and infrastructure than Asian countries. We also discovered that countries with the highest number of floods in Asia should focus on insurance policies, infrastructure improvement, and allocation of additional financial measures to reduce impact during flood times. We decided to dive deep into North America to extract insights from their mitigation strategy. NA had 303 total floods from 1990 until 2020. Heavy rain and tropical cyclones are North America's leading causes of floods. After that, we checked that the average annual temperatures rise over time in North America directly correlates to flood severity. The number of days above average temperature and flood severity are directly proportional. After checking the correlation between the number of high-magnitude floods and the number of deaths in different countries, we found that North America has a better mitigation strategy in terms of insurance policies and infrastructure than Asian countries. We also discovered that countries with the highest number of floods in Asia should focus on insurance policies, infrastructure improvement, and allocation of additional financial measures to reduce impact during flood times. Here is the link to iGenius’s Show Floor Showdown video. Oracle Key Findings: Enriching the dataset with contextual information (for map layering for example) is critical to make sense of flooding data. The economic impact on cities like Boston is clear in the model we built, down to the individual property level, showing that flooding will have a personal impact on families, especially with the data showing that those in low-lying coastal regions will be worst hit economically by rising water levels. This combination of physical and economic data is key in bringing potential impact home to people, and the use of a 3D rendering of spatial data is a powerful tool to convey this information. Societies do know how to deal with being below sea level and if forewarned through analysis can take action (the Thames Flood Barrier in London is a great example). Final thought – if you’re in the USA and want to escape the economic impact of flooding, move to Montana! Here is the link to Oracle’s Show Floor Showdown video. ThoughtSpot Key Findings: When we analyzed the % of the population at risk of flooding, many of the countries with a high % have comparatively low people, like the Netherlands, Suriname, and Turkmenistan. However, if we focussed at the overall population impacted, South Asian countries like Bangladesh, Pakistan and India really stand out. The South Asian countries are no strangers to heavy rains and flooding, but the overall number of people being displaced is on the rise due to adverse weather and in recent years. Looking at the average rainfall across the years in India for example, it is not easy to conclude why so many people are being impacted as the annual rainfall figures show no obvious explanation. However, drilling into the granular data — earlier seasonal rains, and more rainfall in shorter periods is happening more often across the country, in particular the North East. One such example is the Meghalaya region, which in June 2022 saw 3x its average rainfall in just the first three weeks. Here is the link to ThoughtSpot’s Show Floor Showdown video. Zoho Dam construction has proven to be an effective strategy worldwide for mitigating river-based floods. Coastal floods have become increasingly prominent in recent years, particularly in Southeast Asian countries. Rapid urbanization, climate change, and inadequate investment in town planning are the primary factors contributing to the impact of coastal floods. The following measures can effectively mitigate coastal floods: Desilting waterways/rivers: Accumulated sediment and silt in waterways and rivers reduce their capacity to carry water, increasing the risk of flooding during heavy rainfall. Desilting helps restore their capacity by removing excess sediment and improving water flow. Stormwater management: Upgrading and maintaining stormwater drainage systems, including drains, culverts, and canals, ensures proper flow and prevents blockages. Implementing retention ponds and detention basins: These water bodies temporarily store excess stormwater during heavy rainfall events. Addressing encroachments: Proactive measures to remove encroachments along waterways are crucial town planning strategies. Here is the link to Zoho’s Show Floor Showdown video. Other Vendor Aible - Analytics and BI Aible also submitted a demo for the analytics and BI script because the platform can address many of the requirements in the demo script, but was not selected for the Show Floor Showdown for analytics and BI. Here is the link to Aible’s Analytics and BI Show Floor Showdown video. Data Science and Machine Learning Show Floor Showdown Vendors’ Key Findings and Videos Aible - Data Science and Machine Learning Key Findings: Predicting floods is not as actionable as focusing our resources on trying to address floods that affect at least 100 people. Such a specific goal enabled a far more useful analysis. We need to balance multiple KPI such as ‘lives affected’ vs. ‘economic impact of the floods’ as well as stay within relevant budgets. We need to consider fairness. For example, if we stayed within current budgets and only focused on the economic impact of floods, we would under-invest in under-developed regions. This would be contrary to the goals of a hypothetical UN agency. By trying out different definitions of fairness we can balance our fairness goals against economic ones. A manual process cannot possibly consider so many different constraints, but an AI-first business-outcome-driven approach conducts such analysis in minutes. Here is the link to Aible’s Show Floor Showdown video. Altair Key Findings: Higher temperature increases water evaporation from the surface, which consequently causes the soil to dry out, that in turn increases the risk of floods when heavy rain occurs. Plant genetic resources can improve root systems, which reduces soil erosion and consequently reduces flood risk. Economic factors are also important for food prevention. For example, increased research and development can reduce the risk of floods by developing effective measures and implementing new technologies and strategies. Forests have a crucial role in regulating the hydrological system They absorb and store rainwater, release it at a slow rate into the river system, hence reducing the volume and speed of water into the rivers, and consequently reducing the risk of foods. Urbanization can worsen the impacts of flooding because the natural landscapes are converted to buildings and urban infrastructure, meaning that the land is no longer able to absorb and channel water in the same way. Rain caused the most floods however monsoons had a much higher human impact displacing the most people. Here is the link to Altair’s Show Floor Showdown video. DataRobot Key Findings: Impacted Populations - DataRobot analyzed the drivers impacting at-risk populations for river flooding within a 100-year period over 1260 cities in 27 European countries. Indicative Drivers - After considering features across multiple diverse categories, features belonging to air pollution and climate categories proved to be the most explanatory. The top 3 drivers were heating degree days, population PM2 exposure, and days of strong heat stress. Predictive Insights - Second-order effects proved to be highly predictive. For heating degree days, the lower the rate of change in recent years vs. previous years, the lower the risk. By ensuring that heating degree days do not increase in the future, we may be able to reduce the risk of river flooding. Here is the link to DataRobot’s Show Floor Showdown video. Data Management Show Floor Showdown Vendors’  Videos These vendors provided demos according to the script provided to them for managing the data in their platforms. Cognite Here is the link to Cognite’s Show Floor Showdown video. Denodo Here is the link to Denodo’s Show Floor Showdown video. Prodago Here is the link to Prodago’s Show Floor Showdown video. Thank you to all our amazing Bake-Off and Show Floor Showdown panelists!!   Thank you to my 'SCONE' monster and script compliance judge at the BI Bake-Off, Georgia O'Callahan, my co host for the Data Science and Machine Learning Bake-Off, Aura Popa and to the rest of the team, Mark Beyer, Prasad Pore, Zain Khan  and JC Martel! A special thank you to our project managers without whom these sessions would not be possible, Sharlynn Sarmiento, Leah Ciavardini and Nellie Taratorin!! Happy Baking! Regards, Rita Sallam, Georgia O'Callahan, Aura Popa, Mark Beyer, Prasad Pore, Zain Khan  and JC Martel