Business intelligence (BI) has come a long way; it also has a long way to go.
Data processing and visualisation has evolved from producing handwritten numbers, tables and graphs, to manipulating data and outputs in spreadsheets, to using supercharged digital platforms to analyse data and offer insights, all in the space of a couple of generations. Artificial intelligence (AI) is the next frontier.
Scale is the driver. Demands on data, and the complexity of those demands, are growing exponentially. Meanwhile, the volume of data being produced by organisations is skyrocketing. Humans can’t keep up. AI, however, has the power to process this data goldmine at lightning pace and deliver real-time analytics.
But this transition won’t happen overnight. Julie Sweet, the global boss of consulting firm Accenture, and other leaders, say it will be five years or more before organisations truly start to transform their businesses with AI. Why the delay? Quite simply, most organisations’ data platforms are not fit for purpose: they're either built on cumbersome legacy technology or they're not adequately maintained.
They may be unable to produce actionable insights to keep up with the business need; they may even struggle to produce actionable insights at all. These organisations cannot keep up with BI, let alone AI.
We’re seeing these same challenges here in New Zealand. Local organisations are being held back by legacy platforms that are not designed to scale. In response, some try to scale with people and end up with a large unwieldy data team. If data teams rely on people to do everything, they are never going to achieve the scale needed by the business.
To progress, organisations first need the desire to win through data and insights. Second, they need to commit to the solution, establishing the right core platform and scaling with automation. It starts by thinking beyond the scale that we can even imagine today.
Baked-in success
Data, in its many shapes and forms, is the raw ‘ingredient’ to actionable insights, and these data platforms can support masses of it. But as with any form of cooking, it’s what you do with those ingredients that is decisive.
Importantly, these complex ingredients need to be delivered in the right form and be of the right quality to yield a good product. In baking, using the wrong flour could produce a cake that fails to rise; you have to throw it out and start again. With a data platform, it isn't a few dollars worth of ingredients and a couple of hours lost; this is potentially millions of dollars and tens of thousands of hours effort gone. You can’t win over stakeholders and customers with a product and platform done wrong; a mistake of this magnitude can be unrecoverable.
If you have the right ingredients and the right plan, effectively processing the sheer volume of data and generating valuable outputs requires the right tools. The move to AI is fundamentally a scale challenge. Best-of-breed industrial tools are necessary to be successful; DIY fundamentally isn’t up to that challenge.
Tools and automation are critical success factors, as without them we have to rely on people for scale; this is not a long-term strategy for success. People are the costliest and most limiting part of the whole process of producing actionable insights. Why build a team of 100 to achieve what 30 can accomplish with the right tools? That’s the power of automation.
The value of fully leveraging data is two-fold: to help organisations become better and more efficient, and to pursue new avenues for growth. But when a data team is working at full tilt in a bid to deliver quality and satisfy existing demand, there is simply no room for innovation. To understand where your customers’ tastes are heading, you cannot afford to be struggling with the business-as-usual problems of people and platforms; you will miss the business shift that a capability like AI enables.
If you want to enable your highly skilled team to drive innovation with data and prepare your organisation for an AI future, partner with like-minded technology companies to help you scale up with automation. Seek experienced partners that understand the common pain points organisations encounter, and that invest in developing solutions and automation. This way, you will benefit from their products, accelerators and lessons learned through hard-won experience.
If you want to be successful at creating actionable insights and AI, you need to:
Fundamentally, transitioning from BI to AI demands a strategic approach. Organisations must invest in the right technology and infrastructure to support AI capabilities while ensuring scalability and flexibility through automation. Nurturing a data-centric culture, which fosters collaboration across teams and encourages data-driven decision-making, is crucial. Partnerships with technology experts on this journey are invaluable. A successful approach to scaling up for data and AI will unlock new possibilities for efficiency, growth and innovation.