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The Role of Data Science in Real Estate

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The Role of Data Science in Real Estate

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Are you a Data Scientist working in the Real Estate industry? Are you trying to build a Data Science team with expertise in spatial?

We bring the London Real Estate Data Science community together to discuss use cases such as whitespace analysis, twin area analysis & indoor analytics - sharing best practices and experiences from both residential and commercial.
Jaime Sanchez walked through a specific case of Spatial Data Science applied to Shared Workspace investment analysis, with an interactive component, before we break out into a discussion about the challenges and opportunities of building Data Science teams in the Real Estate sector.

Geolytix joined the conversation to speak about location planning. As trusted advisors, they help their customers decide how many stores, who to acquire, where to open, which format and how to
optimize home delivery and click & collect operations.

Visit our website for more information: https://carto.com/

Are you a Data Scientist working in the Real Estate industry? Are you trying to build a Data Science team with expertise in spatial?

We bring the London Real Estate Data Science community together to discuss use cases such as whitespace analysis, twin area analysis & indoor analytics - sharing best practices and experiences from both residential and commercial.
Jaime Sanchez walked through a specific case of Spatial Data Science applied to Shared Workspace investment analysis, with an interactive component, before we break out into a discussion about the challenges and opportunities of building Data Science teams in the Real Estate sector.

Geolytix joined the conversation to speak about location planning. As trusted advisors, they help their customers decide how many stores, who to acquire, where to open, which format and how to
optimize home delivery and click & collect operations.

Visit our website for more information: https://carto.com/

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The Role of Data Science in Real Estate

  1. 1. Welcome! Real Estate Meetup
  2. 2. Introductions Account Executive at CARTO CEO & Data Scientist at Geolytix Solutions Engineer at CARTO
  3. 3. Which community are you here from?
  4. 4. Which use cases are you typically focused on? Investment Analysis Indoor Mapping Site Planning Market Analysis Trade Area Analysis Pricing Optimization
  5. 5. APAC 9.8% Industry Participants Seniority Region 43% individual contributors 45% Mid management 12% Senior management North America 47% EMEA 38.6% Latin America 4.5%
  6. 6. Data Science and GIS teams at organizations
  7. 7. Data Science and GIS teams at organizations
  8. 8. How many Data Scientists actually know about spatial?
  9. 9. Which technologies and languages are preferred in the industry?
  10. 10. Discovering data useful for their analysis Evaluating and purchasing data ETLing the data into common structures Analyzing, doing feature extraction and modeling 30% 30% 20% 20% Where do SDS spend time?
  11. 11. A Data Scientist needed demographics and zip code data for Portugal to perform a particular market analysis: An example:
  12. 12. 80% of participants believe that it is difficult or very difficult to hire Data Scientists with expertise in spatial analysis 1. Strong background in statistics 2. Extensive experience in coding skills relating to Data Science (Spark, SQL, Python, R, Tensorflow, Pytorch) 3. Experience developing production-quality data products using the results of quantitative research 4. Extensive experience in data visualization (in Python and R or other applications) 5. Effective application of Data Science workflows to business problems, and the ability to storytell around results 6. Familiarity with data pipelines and ETL practices (Airflow, scheduled notebooks, Google DataFlow, etc.) 7. Familiarity with neural networks and deep learning (e.g. Tensorflow, PyTorch) 8. Experience working with distributed computing systems like Spark or Google BigQuery 9. Experience working with GIS software such as CARTO, QGIS, or ArcGIS
  13. 13. 47% of participants do not find it challenging to identify the right software & data to support Spatial Data Science projects How difficult Is it to find the right software and data?
  14. 14. How will investment in Spatial Data Science initiatives expand? 68% of organizations are likely to increase their investment in Spatial Data Science in the next 2 years
  15. 15. New data & new use cases. Let’s discuss!
  16. 16. Use code: #SPATIAL15
  17. 17. Real Estate Meetup Jaime Sánchez, Solutions & Customer Success at CARTO
  18. 18. The Sum of Our Parts The Complete Journey As an organization, we have defined 5 steps that, together, create a holistic Location Intelligence approach. Our goal is to empower organizations as they traverse each of these 5 steps.
  19. 19. Spatial analysis in 5 key steps: Data Ingestion & Management Data Enrichment Analysis Solutions & Visualization Integration Clean, geocode and visualize your data. Clustering, outliers analysis, time series predictions, and geospatial weighted regression, change spatial support Using 3rd party datasets — ideally on standardized spatial aggregations to reduce your time to insight. WebGL for big datasets, dashboards, widgets, apps. Productize model into a web service API. Feed back into Data Warehouse, LoB systems, consumers, others.
  20. 20. 1. Data Ingestion & Management ● Spatial database with multiple ways to connect and manipulate your data ● Dynamic data in the cloud and multiple data sources: local and remote files, cloud storages, other databases, and more ● Fully managed database with automatic backups and regular upgrades ● Enterprise data sharing and access across CARTO Wide support for geospatial formats (inc. Shapefiles, KML, KMZ, GeoJSON, GPX, OSM, GeoPackage, GDB, CSV, Excel or OpenDocument). Plug ready database connectors (ArcGIS Server, DB Connectors via APIs (MySQL, PostgresSQL, Microsoft SQL Server, Hive on request)).
  21. 21. 2. Data Enrichment ● Save time in gathering spatial data, augmenting your existing data with new location data streams from across the globe ● Create locations from addresses and understand travel time all from within CARTO ● Develop robust ETL processes and update mechanisms so your data is always enriched ● Premium data to understand and analyze deeper trends and behavior
  22. 22. 3. Analysis ● Bring maps and data into your Data Science workflows and the Python data science ecosystem with CARTOframes ● Machine learning embedded in CARTO as simple SQL calls for clustering, outliers analysis, time series predictions, and geospatial weighted regression ● Use the power of PostGIS and our APIs to productionalize analysis workflows in your CARTO platform
  23. 23. 4. Solutions & Visualization ● Develop and build custom applications with a full suite of frontend libraries. ● Work with CARTO’s Professional Services and Support team as and when you need it. ● Create lightweight, intuitive dashboards for simple sharing of insights across your organization.
  24. 24. 5. Integration ● Using CARTO’s APIs and SDKs, connect your analysis into the places that matter most for you and your team. ● Bring CARTO to other data destinations, such as desktop GIS and BI tools. ● Embed CARTO inside other tools, such as Salesforce Einstein Analytics or Qlik Sense. ● Work with our Professional Services team for custom configurations or developments.
  25. 25. Let’s apply the journey to a real world business question!
  26. 26. How can we analyze and understand real estate sales in Los Angeles?
  27. 27. Pains 1. “Disconnected experiences to consume data - it is broken into separate tools, teams DBs, excels.” 2. “Limited developer time in our team.” 3. “Current data science workflow doesn’t have a geo focus. and Spatial modeling is cumbersome because I have to export results to XYZ tool in order to visualize and test my model effectively.” 4. “Having trouble handling and visualizing big datasets.“
  28. 28. Outline the Process 1. Integrate spatial data of past home sales and property locations in Los Angeles county 2. Enrich the data with a spatial context using a variety of relevant resources (demographics, mastercard transactions, OSM) 3. Clean and analyze the data, and create a predictive model for homes that have not sold 4. Present the results in a Location Intelligence solution for users 5. Integrate and deploy the model into current workflows for day to day use
  29. 29. 1. Data
  30. 30. Integrate LA Housing Data The Los Angeles County Assessor's office provides two different datasets which we can use for this analysis: ● All Property Parcels in Los Angeles County for Record Year 2018 ● All Property Sales from 2017 to Present
  31. 31. 2018 Parcel Data
  32. 32. 2018 Parcel Data
  33. 33. Past Sales Data
  34. 34. Past Sales Data
  35. 35. CREATE TABLE la_join AS SELECT s.*, p.zipcode as zipcode_p, p.taxratearea_city, p.ain as ain_p, p.rollyear, p.taxratearea, p.assessorid, p.propertylocation, p.propertytype, p.propertyusecode, p.generalusetype, p.specificusetype, p.specificusedetail1, p.specificusedetail2, p.totbuildingdatalines, p.yearbuilt as yearbuilt_p, p.effectiveyearbuilt, p.sqftmain, p.bedrooms as bedrooms_p, p.bathrooms as bathrooms_p, p.units, p.recordingdate, p.landvalue, p.landbaseyear, p.improvementvalue, p.impbaseyear, p.the_geom as centroid FROM sales_parcels s LEFT JOIN assessor_parcels_data_2018 p ON s.ain::numeric = p.ain Clean and join the data on unique identifier using SQL
  36. 36. 2. Enrichment
  37. 37. Integrate LA Housing Data Next we want to add spatial context to our housing data to understand more about the areas around: ● Demographics ● Mastercard (Scores and Merchants) (Nearest 5 Areas) ● Nearby Grocery Stores and Restaurants ● Proximity to Roads
  38. 38. Demographics Add total population and median income from the US Census
  39. 39. Mastercard Find the merchants and sales/growth scores in the five nearest block groups to the home via Mastercard Retail Location Insights data
  40. 40. ( SELECT AVG(sales_metro_score) FROM ( SELECT sales_metro_score FROM mc_blocks ORDER BY la_eval_clean.the_geom <-> mc_blocks.the_geom LIMIT 5 ) a ) as sale_metro_score_knn, ( SELECT AVG(growth_metro_score) FROM ( SELECT growth_metro_score FROM mc_blocks ORDER BY la_eval_clean.the_geom <-> mc_blocks.the_geom LIMIT 5 ) a ) as growth_metro_score_knn
  41. 41. Grocery Stores/Restaurants Find the number of grocery stores and restaurants using OpenStreetMap Data and the SQL API.
  42. 42. ( SELECT count(restaurants_la.*) FROM restaurants_la WHERE ST_DWithin( ST_Centroid(la_eval_clean.the_geom_webmercator), restaurants_la.the_geom_webmercator, 1609 / cos(radians(ST_y(ST_Centroid(la_eval_clean.the_geom))))) ) as restaurants, ( SELECT count(grocery_la.*) FROM grocery_la WHERE ST_DWithin( ST_Centroid(la_eval_clean.the_geom_webmercator), grocery_la.the_geom_webmercator, 1609 / cos(radians(ST_y(ST_Centroid(la_eval_clean.the_geom))))) ) as grocery_stores
  43. 43. Roads See if a home is within one mile of a major highway or trunk highway using the SQL API and major roads from OpenStreetMap.
  44. 44. ( SELECT CASE WHEN COUNT(la_roads.*) > 0 THEN 1 ELSE 0 END FROM la_roads WHERE ST_DWithin( la_eval_clean.the_geom_webmercator, la_roads.the_geom_webmercator, 1609 / cos(radians(ST_y(ST_Centroid(la_eval_clean.the_geom))))) AND highway in ('motorway', 'trunk') ) as highways_in_1mile
  45. 45. 3. Analysis
  46. 46. Analysis The analysis for this project followed the following steps: ● Moran’s I Clusters & Outliers (Exploratory Data Analysis) ● Neighbor Homes Analysis (Spatial Feature Engineering) ● Predictive Modeling & Hyperparameter Tuning (using XGBoost)
  47. 47. Moran’s I Using Moran’s I to evaluate spatial clusters and outliers via the PySAL package, we can see these groupings and visualize them in CARTOframes.
  48. 48. The Sum of Our PartsThe Sum of Our Parts Moran’s I
  49. 49. The Sum of Our Parts Neighbor Analysis Evaluate the attributes of neighbor properties using k-nearest neighbor spatial weights in PySAL to perform spatial feature engineering.
  50. 50. The Sum of Our Parts how the attributes of your neighbors influence the price of your home and spatial context…
  51. 51. The Sum of Our Parts
  52. 52. The Sum of Our Parts Predictive Modeling Using XGBoost we can use this data to create a regression model to predict housing prices and push that data back to CARTO using CARTOframes, never leaving the notebook environment.
  53. 53. The Sum of Our PartsThe Sum of Our Parts Sale Price Past Sales Spatial Data Enrichment Spatial Modeling Analyze the values of nearest neighbor sales, clusters of high Mastercard areas, proximity to features Train & Test Model Predictions Spatial Feature Engineering
  54. 54. The Sum of Our Parts Predictive Modeling After hyperparameter tuning the model, we can reduce the Mean Average Error down to $58,179.78.
  55. 55. The Sum of Our Parts Feature Importance
  56. 56. 4. Solutions
  57. 57. The Sum of Our Parts Solutions To present the data and predictive analysis, both on data from the model that has a sales price and for homes that have not sold, we can develop a location intelligence application to showcase these results.
  58. 58. Los-Angeles Prediction Explorer
  59. 59. 5. Integration
  60. 60. Application Development Deploy the model via a Python based API and sync to data to perform on the fly predictions for specific properties.
  61. 61. The Sum of Our Parts Other Use Cases ● Predicting revenue from different physical retail locations ● Identify clusters and groups of specific patterns to optimize activities such as sales outreach or site selection ● Classify property types or buying patterns in a city ● Review spatial feature importance for site performance, and modify models using different spatial components ● find areas with similar behavioral patterns
  62. 62. Similarity Analysis We built a model to identify areas with similar behavior patterns based on footfall, socio economic and financial data and more. The similarity score is modeled based on: ● Distance between cells is calculated with a L2 norm on a Principal Component space. ● Uncertainty due to missing values and dimension of PC space is tackled following an ensemble probabilistic approach. ● Similarity Score = Continuous Rank Probability Skill Score. By enriching the data with other sources this model can be used for Site Planning, Investment Analysis, etc.
  63. 63. POPULATION HOUSEHOLD INCOME VISITORS TRANSACTIONS 109 S 5th Street Brooklyn NY 11249
  64. 64. DEMOGRAPHICS ● Population ● Household spending ● Household income
  65. 65. DEMOGRAPHICS ● Number of visitors HUMAN MOBILITY
  66. 66. DEMOGRAPHICS ROAD TRAFFIC ● Number of vehicles HUMAN MOBILITY
  67. 67. DEMOGRAPHICS ROAD TRAFFIC FINANCIAL HUMAN MOBILITY ● Ticket size ● Number of Transactions
  68. 68. ● Offices ● Shops ● Transport POIs DEMOGRAPHICS ROAD TRAFFIC FINANCIAL HUMAN MOBILITY
  69. 69. Let’s see the Python notebook….
  70. 70. The Role of Data Science in Real Estate
  71. 71. Network strategy Location planning Omnichannel analysis Spatial modelling Our whole business is about location planning. As trusted advisors we help our customers decide how many stores, who to acquire, where to open, which format and how to optimise home delivery and click & collect operations. Team of 36 location specialists to work collaboratively with your business Led in-house location planning for major global retailers. Experts in spatial modelling, forecasting, web development and systems. Create innovative new datasets for local markets. Growing to a global company Offices in London, Leeds, Warsaw, Dortmund, Shanghai, Tokyo and Melbourne INTRODUCTION
  72. 72. 2. MODEL1. DATA 3. TOOL OUR OFFER
  73. 73. HISTORY Clients Key Events Team 2012 Sainsbury’s Whole Foods Foundation 1 2013 ASDA, Boots Waitrose ASDA project transformative, enables growth. Build key datasets. 4 2014 Post Office, Camelot, Barclays New multi-year deals giving confidence. Take office space. Evolve data offer. 6 2015 Amazon, Swinton, Savills Growth in ‘adjacent’ spaces. Invest in capacity and recurring revenue growth. 10 2016 M&S, TRG, EE, On the market Growing & diversifying the client list. Exploring innovative global DAAS solutions. 15 2017 Adidas, Rightmove Dominos Growth in international markets, Shanghai & Tokyo office open. Leeds office opens in the UK. 18 2018 Costa, Dr Martens Warsaw office opens. Launched MAPP, our online map based analytical tool 24 2019 Lego, Starbucks Melbourne office opens Large multi-country, multi-brand advise 35
  74. 74. OUR COVERAGE
  75. 75. PARTNERSHIPS 2013
  76. 76. WHERE PEOPLE ARE
  77. 77. HOW RICH ARE THEY
  78. 78. WHERE CAN THEY SHOP
  79. 79. DATA SCIENCE
  80. 80. REAL ESTATE FOUNDATIONAL FEATURES • Decisions are complex and outcomes only become clear over years • Choices are multi-faceted and driven by dynamic competing interests • Key information is tightly held • The amounts of money involved are vast • Decisions are hard to undo • “Retailers make few decisions that are as permanent and unforgiving as selecting store locations.”
  81. 81. SOME HISTORY SPATIAL DATA SCIENCE • William Playfair – 1780s • Charles Minard – 1830s • John Snow – 1850s • Charles Booth – 1890s • Roger Tomlinson – 1960s • Arthur Samuel – 1950s • David Huff – 1970s
  82. 82. SOME ISSUES UNDERPINNING STATISTICAL ISSUES Samples are not randomly drawn from the variable space Items from within the sample influence each other Hardly any variables are normally distributed These three features fatally wound pretty much every standard statistical approach WHAT DATA SCIENCE CAN DO Describe things Classify stuff Predict responses What we really want to do is to predict the future
  83. 83. THE GP ANALOGUE Diagnose the business problem Bring in the specialists if you need them (e.g. algorithm/model creation) Communicate to business stakeholders Support decision making
  84. 84. Machine Learning
  85. 85. WHAT IS MACHINE LEARNING? ● “Machine Learning” refers to the field of study that gives computers the ability to learn without being explicitly programmed (Samuel, 1959) ● In practical terms, a series of different algorithms can be applied to detect patterns in data (including big data), which can lead to actionable insights ● Common machine learning applications include (not extensively): ● Regression Forecasting (e.g. sales forecasts) ● Classification or Clustering (e.g. segmentation or image classification) ● Association Rule Learning (interesting relations; e.g. which other products are you likely to buy based on your other purchases?) ● Reinforcement Learning (e.g. chess AI)
  86. 86. IT’S NOT NEW ● Machine learning is not a new concept… In 1952 Arthur Samuel wrote the first computer program which learned as it ran ● First neural network to solve a real world problem was designed in 1959 (an adaptive filter to remove echoes from phone lines) ● So if ML isn’t new, why is it becoming so popular now?
  87. 87. WHY NOW? - MOORE’S LAW
  88. 88. COMMON TYPES OF MACHINE LEARNING ALGORITHMS ● Supervised Learning: ● The user (human) teaches the algorithm by providing it with input data and a sample of result data (e.g. x = input features, y = actual sales) ● The algorithm then attempts to learn from the input data how best to predict a result (e.g. predict sales) ● Unsupervised Learning: ● The computer is trained with unlabelled data; there is no teacher ● This family of machine learning algorithms is useful for pattern recognition and rule detection ● Semi-supervised Learning: ● A combination of supervised and unsupervised methods ● Reinforcement Learning: ● Maximises reward and minimises risk, iteratively learning from the environment ● Determines ideal behaviour within specific contexts
  89. 89. USE CASE FOR WITHIN LOCATION PLANNING ● So what’s the catch!? ● How can we use this in location planning/real estate!? • • • • • • •
  90. 90. Case Studies
  91. 91. EXAMPLES OF USE CASES ● Using K-nearest neighbour to create demographic segmentations, based on known customer data ● Learning about key drivers of success by examining feature importance ● Building forecasting models to predict sales based on property location ● Using NLP for categorising customer comments ● Etc. One of the more interesting solutions we’ve used recently combines traditional methods with machine learning…
  92. 92. GROCERY GRAVITY MODEL ● Gravity models are common practice within the grocery retail location planning/real estate place ● It is important for grocers to understand which locations would be ideal for a new supermarket, but also to understand the impact this might have on existing locations and competitors… Gravity Model in a nutshell: ● Based on theory of gravity ● More attractive destinations have a greater ‘pull’ ● Attraction is linked to distance ● Using customer data we know how far people actually travel to their chosen stores ● Fundamental concept is logical, and simple to understand
  93. 93. GROCERY GRAVITY MODEL ● Gravity models are often very accurate at estimating customer patterns and interactions at close range… ● However, this accuracy usually wanes as you try to model sales from further afield: ● Consumers decisions are much harder to understand ● Consumers have more choice ● Are they workers or residents? ● Decision is not as simple as “I’ll just pop into my nearest, most attractive supermarket”…
  94. 94. GROCERY GRAVITY MODEL ● The solution… To use machine learning to create an estimate for ‘Sales beyond 30mins’ ● Created a datamart for each property in the portfolio (see opposite) and tested various machine learning algorithms to see if we could more accurately predict sales than previously ● Eventually settled on a neural network It’s not as easily interpretable, but gives better results on interactions which are inherently difficult to understand anyway! √ +20% of store sales beyond 30 minutes drivetime were more accurately predicted √ R² increased by 0.25 for beyond sales
  95. 95. CASE STUDIES
  96. 96. SOME OF OUR CUSTOMERS
  97. 97. EXAMPLE FASHION CLIENT OBJECTIVE With six stores operating in Hong Kong, Dr. Martens wanted to understand how high the achievable turnover is at each location. Additionally, an understanding of the best locations for new stores was required as part of a future store investment roadmap. RESULT We created new datasets and a bespoke model to calculate sales potentials for the existing store network. The model was then used in a future opportunity scan to identify the best locations for new stores in Hong Kong. STRATEGY MODEL DEMAND: Calculate how much people spend on footwear at the lowest possible geography MAP THE RETAIL LANDSCAPE: Understand the locations where retailers cluster in Hong Kong SALES POTENTIAL: Calculate how much turnover is achievable at a retail venue (e.g. Mall) and individual store level OPPORTUNITY SCAN: Use the developed model and data sets to find ideal locations for the next Dr. Martens stores in Hong Kong
  98. 98. STRATEGY • Understand the true drivers of store performance & the impact on nearby stores of opening new sites. • Predict new store sales and cannibalisation using a consistent, transparent fact base and model. • Improve the efficiency of the store forecasting process to allow more time for the value-add. • Deliver the ideal network blueprint and optimum network strategy. EXAMPLE F&B CLIENT "Our work with GEOLYTIX has enabled us to form a consistent approach to new site forecasting, step changing our understanding of customers catchments and improving our ability to understand regional and store performance. The collaborative approach has resulted in us being able to make decisions around our future location strategy and form ideal network blueprints with significantly increased confidence.” Craig Donnellan, Head of Location Planning Dominos Pizza. OBJECTIVE Support the Dominos strategy to be the number one pizza company in each neighbourhood with a focus on franchisee profitability. RESULT
  99. 99. EXAMPLE RETAIL CLIENT: FOOD, FASHION & HOME OBJECTIVE Support a step change in the roll-out of the Food estate, understand the drivers of performance for the Clothing & Home estate and recommend the optimum network blueprint. RESULT “GEOLYTIX have worked with us to create a bespoke toolset enabling us to proactively set our strategy and quickly answer any What if scenarios. Their analysis and recommendations have provided us with a consistent evidence base from which to make our network decisions." STRATEGY • Create an efficient selection & sales forecasting process, based on a rigorous, objective fact base and a consistent approach. • Understand the drivers and catchments of the Clothing & Home estate, in order to build optimal networks. • Integrate custom models with existing data and software to create the M&S modelling toolkit. • Bulk run multiple national and regional scenarios to guide network strategy and create future blueprints.
  100. 100. EXAMPLE REAL ESTATE ADVIOR PROJECT OBJECTIVE Data / analytical support in evaluating potential acquisition opportunities and ongoing asset management of retail assets. STRATEGY • Creation of town centre & grocery gravity models to asses: • Catchment profiles and fit to various potential new occupiers • Impacts of new greenfield developments and centre remodels • Existing retailer chain performance and potential ‘best next’ opportunities • Ad-hoc consultancy support • Assisting with major M&A and liquidity event support • Detailed asset reports including site visits to support redevelopment RESULT • We provide access to our data and models through a desktop GIS reporting tool which allows for: • Ad hoc area demographic reporting • Retail presence and chain list reports • Analogue tool to find similar locations • Drive time reporting • Bespoke ‘client ready’ site and area reports
  101. 101. WHY GEOLYTIX World Class Modelling. We have delivered optimisation models for many of the most successful organisations in the world, across multiple sectors. Innovation. The Queen’s Award for Innovation reflects our passion for being on the leading edge of new data, technology, and ideas. Practical Senior-Level Experience. We are practical operators, with Director-level experience in property teams of some of the UK’s largest companies. Technical Expertise. We are experienced data scientists, sales forecasting modellers and spatial web application developers, and will build a bespoke solution. Every element of our solution, from the analysis to the platform, will be specifically designed to meet your specific requirements. We are global. Accounting for the often vast differences in structure, maturity and data availability and quality we are able to apply a consistent approach across territories in order to support fact-based decisions. Proven Track Record. We have delivered similar solutions many times before. We will deliver to spec, to time, and to a fixed budget. Genuine Partnership. Our commitment is to work closely with you through to deployment, and maintain the support and relationship beyond.
  102. 102. Thank you! lrutherford@cartodb.com CEO & Data Scientist at Geolytix jsanchez@cartodb.com

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