Industry Leading Content

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What key trends are influencing customer behaviour in the retail industry? 

Are you already implementing AI? What does the future look like for data leaders?

An exclusive interview with Sean Gustafson, Director of Engineering- Data Platform, exploring Delivery Hero's use of data mesh, the benefits and results they have seen, the challenges of scaling it and the foundational necessities of maintaining data quality.

It is no secret that it is a job seeker's market for data professionals, with many organisations viewing data as a vehicle to navigate them into the future. This creates a need for the right individuals and teams to drive this vehicle of change, a need that is not being met because of a shortage of talent. How do you hire and retain the right talent in data when competition is so high? IQPC Exchange interviewed three Data Leaders for their advice on how to meet one of the most significant challenges facing CDOs currently. 

An interview with Martin French, Head of Data Engineering at Sanne Group

How Public-Private Partnerships Are Creating New Pathways to Economic Mobility

Two years ago, employers and policymakers alike were grappling with the tightest labor market in fifty years. Conversations about the shrinking shelf-life of skills and the accelerating pace of technological change dominated the headlines.

As we continue to navigate these challenges and opportunities, it’s clear that demand for tech skills will continue to skyrocket. New investment, both from businesses and from the government, will continue to enter the system. As these programs continue to grow, the goal of this white paper is to apply the best of what we’ve learned through these efforts to help local and regional leaders across the country navigate their own community reskilling efforts.

The role of the Chief Data Officer presents an interesting paradox. Despite being one of the most sought-after positions in an increasingly data-led world, the tenures of CDOs are seemingly short-lived. Why is this? 

The explosion of data being generated, curated, and shared within organisations in recent years has brought with it unprecedented risks. As consumers and regulators alike are becoming more concerned with the misuse of data, the question of ethics and inherent societal bias in data has been thrust into public consciousness. High-profile data breaches have proliferated over the past few years, so it is crucial that organisations create ethical data practices in order to avoid the astronomical consequences of a breach, not just from a financial point of view, but also from a reputational perspective.

This panel discussed how to navigate the complexities of data ethics in order to foster a trust-based, transparent approach to data and analytics.

• What are the key risks that organisations need to be aware of regarding the ethics of data and AI?
• What metrics/procedures can be put in place to ensure that you can both understand and mitigate your risks?
• How can you ensure that customers have a transparent view of how their data is being used?
• How can you navigate the risk of unconscious societal biases?

Speakers on the panel:

• Sudip Trivedi, Head of Data and Analytics, London Borough of Camden
• Mahesh Bharadhwaj, Head of Europe Analytics, Funding Circle
• Dan Gilbert, Director of Data, News UK
• Nina Monckton, Head of Data Strategy, Advanced Analytics and Data Science, AXA Health
• Moderated by Robin Sutara, CDO, Microsoft
  

As data increasingly emerges as the currency for innovation and competitive advantage, it is essential to build a modern and agile data ecosystem that will support and augment business-decision making. A robust strategy that treats data as a strategic corporate asset is crucial to maximising its value across the business. But as digitalisation proflerates and the world becomes more connected than ever, businesses are grappling with an overwhelming volume and variety of data.

This begs the question: where should you begin in formulating a vision and strategy that connects the disparate parts of the data puzzle to unlock enhanced decision-making capabilities? In this session, Rob McKendrick shared how The Co-Op is building a strategy that places data at the core of the business to both drive internal operational efficiencies and foster improved customer outcomes.

• Moving from federated to centralised operating model to break down functional and data silos
• Aligning your data vision to the creation of customer value
• Embedding trust and ethics into your approach to data usage in order to build transparency

Rob McKendrick, Head of Data Governance and Management, The Co-Op

Tableau has developed the Data Culture Playbook to guide executives and data leaders in how to mature data usage across the organization in a way that ensures value is being created where and when it is needed most. The playbook lays out a phased approached to build, run, and expand your Data Culture.

Data virtualisation is the fastest and most cost-effective way to integrate all your data, making it easily accessible from one single abstraction layer

Together, Snowflake and HVR enable organizations to become data-driven. With its Log-Based Change Data Capture (CDC) feature, HVR can replicate high volumes of data from a variety of source systems into Snowflake. HVR is one of the only solutions on the market that can move data from SAP HANA into Snowflake using Log-Based CDC, with the ability to decode cluster and pool tables on the fly.

Most organisations are storing sensitive data, but often do not know where it is or which data it is. Moreover, some companies are storing data that they do not realise is sensitive, which may be either directly or indirectly sensitive.

Assuring the quality and provenance of your data is vital for both operational and analytic purpose. As we use data more and more, and especially as we increasingly automate the processing of that data, we need the assurance that the data is both accurate, timely and complete. 

• Data lake origins and challenges including hybrid data integration, multiple data source platforms, and lakes both on premises and in the cloud.
• Real-time integration, with change data capture technology that integrates live transactions with the data lake.
• Rethinking the data lake with multi-stage methodology, continuous data ingestion and merging processes that assembles a historical data store.
  Leveraging a scalable and autonomous streaming data pipeline to deliver analytics ready data sets.