DATA has been dazzling businesses for almost a decade now, ever since leaders realized how it can help them create exciting insights and give them an edge in the marketplace.
Initially, it was digital-first companies such as Netflix and Airbnb that used data to create beautiful experiences for customers — especially with the help of artificial intelligence (AI) and machine learning (ML).
In the last two to three years, banks, insurance companies, telcos, retailers, and almost every other organization has started collecting, storing, and leveraging data, often in real-time, to delight customers.
Analyzing this data helps them develop new products, improve existing offerings, and better engage with customers, aside from optimizing the business and its processes of course.
With new self-serve analysis and visualization tools, powered by AI and ML at our disposal, the biggest challenge for organizations is breaking down silos to help data flow seamlessly through the organization while ensuring data governance best practices are observed.
Analysts at Gartner’s recent Data and Analytics Summit in Sydney aimed to help delegates overcome that challenge by showcasing the success stories of private sector organizations such as insurance leader Bupa and government sector agencies such as the Queensland Office of State Revenue.
The event welcomed more than 1,000 delegates who had the opportunity to attend over 60 research-driven sessions and workshops hosted by around 25 analysts and 35 hand-picked exhibitors.
Strong data governance is the need of the hour
Trust and Governance: The Evolving Challenge was amongst the most powerful tracks at the Summit, with well-known members of Gartner’s team such as VP Analyst Saul Judah, Senior Director Analysts Ehtisham Zaidi, Melody Chien, and Lydia Lydia Clougherty Jones, and Distinguished VP Analyst Mark Beyer speaking at length.
The key messages were simple:
- Data is being produced at a significant pace, and hence, it must be cataloged correctly
- When AI and ML are used to catalog data quickly, they break down silos, but without well thought out structures, monitoring and controlling data flow is difficult
- Data governance, therefore, must be baked into new-age data management, analytics, and data science use cases right from the start.
Delegates also heard from Toyota Head of Advanced Analytics and Data Transformation Lead Travis Lewis who presented a case study of successful data transformation within the automaker’s Australia operations.
The company’s efforts have allowed it to get to grips with the explosion of data, walk the tightrope data protection and ethics tightrope, and deliver radical improvements across the value chain while building capabilities and technologies that will be required for the future world.
Of course, Lewis did emphasize the fact that at Toyota — and probably at any organization — ‘data transformation’ is an ongoing process and requires continuous focus and improvements. Ultimately, that’s how organizations can optimize processes and delight customers on an ongoing basis.
In the coming months, as organizations accelerate their journey to digital and harness real-time data with intelligent tools, via the cloud, on a multitude of devices, those that are able to enforce strong governance will find themselves able to break down silos and provide more contextual insights compared to those that don’t.
Governance, in many ways, will be a key differentiator, between different kinds of companies adopting different data and analytics strategies.