Standard Chartered Bank - Data Informed Space Planning to Support Hybrid Working & Optimized Capital

svk87 Registered Posts: 1

Name: Shouvik Nandy

Title: Senior Manager


  • Andrew Alldis
  • Greg Bartos
  • Mark Cunningham
  • Chris Ma

Country: Singapore

Organization: Standard Chartered Bank

Standard Chartered Bank is a leading international banking group, with a presence in more than 70 countries and territories across Asia, Africa, the Middle East, Europe, and the Americas. It is one of the world’s most international banks, with a network of over 1,200 branches and outlets and a customer base of more than 87 million. Standard Chartered is committed to responsible banking and is a signatory to the United Nations Global Compact, a voluntary initiative that encourages businesses to adopt sustainable and socially responsible practices.

Awards Categories:

  • Best Acceleration Use Case
  • Best Moonshot Use Case
  • Best Data Democratization Program
  • Best ROI Story

Business Challenge:


With the emergence of hybrid, the frequency of how often people go to the office has dramatically changed and our global office attendance decline from 72% all the way down to 42%. As a direct impact a large majority of our offices were underutilised.

In addition, the bank’s focus on improving profitability as measured by Return on Tangible Equity (RoTE) has become a top priority. With property being one of the biggest expenses the challenge to the SC property team was to find a for reducing cost to help achieve the desired RoTE while at the same time providing a great employee experience and a sustainable hybrid model.

In summary the business challenges we faced were:

1. Cost

  • How do we reduce the overall property cost being too high and decreasing the profitability of the bank?
  • How do we reduce capital wasted on underutilised/empty space?

2. Sustainability

  • How do we reduce unnecessary carbon emissions generated by having empty spaces fully operational?

3. Employee experience

  • How do we reduce space without negatively impacting the employee experience and enabling a sustainable hybrid model?

4. Capacity

  • How do we automate very manual and time-consuming processes to bring the different data sets together to make informed decision?

From an execution point of view the challenge was to:

  • Identify underutilised spaces/buildings,
  • Determine the ideal size of the new space or optimisation of existing space,
  • At the same time making sure we have enough space to accommodate the current occupancy peaks.

Due to inelastic nature of physical Real Estate sector, we also needed the capability to predict future peaks in order make better informed decision on whether we need to look for additional or more efficient space layout and plan months/years in advance.

What we needed was a dynamic platform that brings together data from various systems, quickly adapt to changes and seamlessly transition from an ideation phase to execution.

Business Solution:


Space Planning and Optimisation Tool (SPOT) #beingspoton

The purpose of the solution was to associate data from different systems(#simpler), provide single-source of truth for analysing data for space planning (#faster), and utilize analytical techniques(#better) to determine the right space in offices in support of a Sustainable Hybrid approach.

Dataiku was the key platform to solve the problems related to data sourcing and data preparation. It enabled association, modelling, and transformation of data to develop a purpose-built data store for performing Space Planning analysis which includes:

  • Attendance data sourced from the building entry system
  • Office configuration (desks, workpoints) sourced from the Workplace system
  • Headcount information sourced from the HR system
  • Reference data sets for Master Property Data, Organisation & Team structure.



Dataiku also helped us to solve data availability challenges by easily forecasting data that was incomplete. Once raw data from respective systems was available in data lake, System specific data processing performed in Dataiku to standardize data per System. This enabled reusability of same data for multiple purposes.


Dataiku’s capabilities of sharing datasets across multiple workflows was a core feature leveraged to implement the Unified Start Schema model in the Property Data Lake. This further provided us the flexibility and agility to model the data from different source systems as we added more integrations, without the necessity to design the data model before starting the development of the data pipelines.


Once the data was prepared, statistical techniques were employed in Tableau dashboards to right size space and identify opportunities to optimize our global portfolio. As we progress our journey to actively use data for decision support, we have developed a successful PoC to forecast (Timeseries analysis) attendance in various offices based on past data using Dataiku’s Forecasting capabilities. Now not only can we understand the right space required to enable Sustainable Hybrid based on past attendance we can also understand future attendance trends to shape Space in our portfolio.

Day-to-day Change:

SPOT has enabled us to make better informed decisions for our overall portfolio. It has allowed us to validate at what level of attendance we can support to achieve our long-term strategy and enabled us to evolve our global planning principles accordingly.



  • Instead of using multiple systems/data sources we now have one fully integrated tool to access all the information needed to make informed decisions.


  • Automated generation of insights enabled us to have meaningful conversations (data-informed) with stakeholders instead of spending hours munching through manual data sources
  • Ability to simulate scenarios on the fly enabled faster turnaround to business questions


  • Using SPOT has already resulted in optimised strategies for our office in Paris, South Africa, Poland, and Hong Kong

Business Area Enhanced: Internal Operations

Use Case Stage: In Production

Value Generated:

Using SPOT, we will be able to achieve:


  • ~4,000 hours of productivity saved (equates to USD 340,000) annually due to the automated calculation/simulation we can perform with SPOT.


  • 3-month increased lead time to add space should there be a notable change in return to office behaviour.


  • Reduce our property portfolio by 700’000 SQFT by 2028 and achieve a 34-million-dollar reduction in our annual property cost helping the bank to improve its overall profitability as measured by RoTE.
  • Change in mindset to adopt more data informed decisions.

Value Brought by Dataiku:

Dataiku gave wings to our analytics mantra – #simpler#faster#better.


Firstly, Dataiku simplified the process of associating data together from various sources in a way that supports solution of a business problem. Dataiku Academy simplified the learning journey for new data analysts in the team. Moreover, its low-code intuitive graphical interface enables non-coders (data analysts and power users in Business team) to employ emerging data science and AI capabilities for solving real world business problems.


Furthermore, the ability to seamlessly adapt the data preparation process with incremental features, reuse existing recipes and datasets in Dataiku’s ecosystems and rich collaboration capabilities supercharged the path from ideation to production. In our case, we went from a simple report to understand attendance(descriptive), to defining a principle and employing the principle to right size space(prescriptive), to forecast attendance(predictive) in our offices – all within a period of 3 months.


Finally, Dataiku enables Business and Analytics teams to take charge of the last-mile delivery of data products. This often is the make-or-break phase of any analytics initiative. One can have all the data, unless the process to access data and build a solution to solve real world business problem is simple and agile, data would not be used for strategic decision making. With Dataiku we were able to utilize our existing data assets to support a key business strategy. This also enables our objective to dial-up digital practices in business processes by democratizing data and lowering the barriers of entry to self-service data analytics. We believe that the right path towards better business outcomes starts with trusted data and insights that results in action.

Value Type:

  • Improve customer/employee satisfaction
  • Reduce cost
  • Save time
  • Increase trust

Value Range: Millions of $


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