Data Literacy Has To Extend Beyond The Data Team

With the dramatic evolution of analytics technology and Artificial Intelligence (AI), much has been made of the potential for big data. From informing business or investment decisions to streamlining operations and personalising the customer experience, information management has become a key driver of economic success.

However, there remains a distinction between high quality data and actual knowledge. Unless you can understand and properly manipulate the data that you have, it cannot translate into actionable knowledge that’s capable of informing investment and business decisions.

Even when finance organisations provide their workers with the tools to effectively manage and integrate data, those same people may not have been given the skills necessary to understand it.

The Challenge Of Data Literacy

We often make the assumption that financial expertise equates to an automatic understanding of data. Information management tools can compound this error. While dashboards and reporting tools provide an accurate view of what has happened, they do little to encourage exploration into why things have occurred.

It’s also often the case that information management expertise remains restricted to the data team. This can leave workers in other departments stranded, if they encounter problems with interpreting and acting on information. What is lacking is an enterprise wide culture of data literacy, which empowers every worker to readily interpret information and act upon it. Such a culture must embrace tools and mechanisms for self-service analysis and reporting, such as AI, automation, and cloud technologies.

HSBC Is Looking To Cloud Technologies And AI To Improve Its Understanding Of Big Data

2021 marks the fifth year of a drive by HSBC to globally transform its IT operations and improve the way that it serves its customers. Cloud technology has been one of the foundation stones of this endeavour. Beginning in 2016, HSBC has been fostering partnerships with leading cloud service and technology providers. These collaborations have been enabling the organisation to establish new services and develop new engagement models which have facilitated HSBC’s large scale adoption of public cloud. The group’s cloud assets currently make up a sizable proportion of the almost 240 petabytes of data that the organisation manages in total.

Cloud technologies have allowed HSBC to embrace open source solutions, within the context of a diverse IT ecosystem. The Hadoop big data platform facilitates the processing of unstructured information. The platform also provides scale and speed, with the analytics capacity of data warehouses. Use of RDBMS guarantees data integrity and system performance.

To support data-driven decision making that is also in line with existing regulatory frameworks, HSBC is running a selection of Machine Learning and modern analytics modules. Applied to massive volumes of curated information, these analytics are supporting HSBC’s efforts to combat Risk, Fraud, and Financial Crimes. In its drive to prevent fraud and financial crime, HSBC deploys an industry-leading Anti-Money Laundering (AML) system, and an automated system for checking sanctions. The same systems also work in support of equity index products powered by Artificial Intelligence, and regulatory reporting in line with IFRS9.

Open source technology, data analytics, and Machine Learning are at the heart of the HSBC information management platform. One of the organisation’s recent projects is a mass migration of natively formatted data lakes into the cloud. This secure movement will hold these pools of unstructured data ready for future analysis.

Among the data engineering technologies that HSBC is employing to get a greater understanding of its information are cutting edge tools from the Google range of machine learning analytics. These include CLOUD SQL, BIGQUERY, and GCS.

In August 2019, HSBC launched AiPEX, the first stock index family in the world to be powered by Artificial Intelligence (AI). This Multi-Asset Index uses the IBM Watson Discovery data analytics processor. Employing a rules-based strategy for investment, AiMAX calls upon traditional sources of financial data, as well as less structured ones such as social media.

The system’s AI can develop “intuition” from its analysis of historical information, and fresh insights from the learning of new data. It uses this knowledge to build diversified growth portfolios. Multi-asset investment occurs across 15 asset classes and five asset types. These include emerging markets, developed bonds and equities, inflation, cash, and real assets.

With AI employed in conjunction with a well established diversification strategy, the platform enables investors to achieve a better balance of risk and returns. Since its inception, the AiPEX Total Return Index (AIPEXTR) has exceeded the performance of the S&P 500 Total Return Index by 4.79 percentage points.

Want to hear more from HSBC on Data Literacy?

Joining us at FIMA 2021 live and in person this November. We have;

Day One, 9.00am - 10.00am

CDO Case Study Interactive – How Can You More Effectively Drive Data Literacy in a Complex, Distributed Global Enterprise?

Kate Platonova, Chief Data and Architecture Officer, HSBC