Embrace AI with Data Governance

Embrace AI with Data Governance

Organizations around the world are preparing for a future powered by artificial intelligence (AI) that will add business value and bring them closer to their goals. In a bid to position Australia as a global leader in AI technology, the Australian government plans to build capacity in the country with the investment of $124.1 million Artificial Intelligence Action Plan. Given the value of AI to business, PwC recently predicted that 45% of total economic gains by 2030 will come from product improvements, envisioning that AI will drive greater product variety, increased customization and affordability.

There is no doubt that the next wave of technology, driven by greater automation, will rely on data more than any previous era. To take full advantage of these advances, data must be well understood, continuously analyzed for relevance, strategically located where it can add value, and tightly regulated for compliance.

If organizations downplay any of these considerations, they are potentially building their AI future on pillars of sand.

What businesses can do to get the most out of AI

1. See data as the cornerstone of AI

Data, and how it is managed, is ultimately the “cornerstone” of AI-enabled initiatives. Thus, organizations should strive to ensure that data quality, governance, and a modern data platform are the basic requirements for extracting the maximum value from data. While many organizations are investing heavily in big data and exploring the nuances of AI, machine learning (ML) and deep learning, only a few have the expertise to take full advantage of it. these tools.

Software development, which was once exclusively human work, is increasingly becoming the work of ML. ML meticulously selects, consumes and analyzes data in a recurring cycle. Human programmers became “data producers”, maintaining and monitoring data produced by machines, ensuring consistent quality of input.

This would be a simple task were it not for the fact that in the digital age there has been an explosion of data – collected and stored everywhere with little planning or structure – much of it poorly governed.

The challenge of inefficient data management is common. McKinsey research shows that 72% of leading Australian organizations identified data management as one of the biggest barriers to achieving key data and analytics goals.

2. Cultivate a data-driven mindset

The data lakes accumulated when organizations have been preoccupied with “infrastructure-first transformation” initiatives need to be carefully assessed. While digitizing business processes, easing the burden of siled multi-generational IT, and moving to the cloud first, it will only take organizations this far on the transformation continuum.

To prepare for an AI-led future, organizations must mobilize their operations around data-centric value creation. Fostering a data-driven mindset when approaching technology and investing decisions can be an important step forward. It can help build customer loyalty with hyper-personalized digital solutions and improve predictive capabilities to future-proof their business.

Data-first organizations are poised to fully embrace the digital “now” and prepare to capitalize on the AI-powered digital “next.”

3. Take the challenge

There is evidence to suggest a blind spot when it comes to data in the context of AI. Many organizations focus too much on fine-tuning their IT models in their quest for quick wins. However, AI success is not about tweaking and recalibrating models; it’s about continually refining the data.

As data-centric AI evolves, so do relevant data management disciplines, techniques, and skills. These include data quality, data integration, and data governance, which are fundamental capabilities for scaling AI. Data management activities do not end with the development of the AI ​​model. They must be followed to combat the triple threat of bias, mislabeling, and poor selection of data.

One way to support this is to adopt cloud-agnostic digital service platforms, which give data producers and curators more control as they build intelligent systems. By addressing the compliance considerations that exist for critical data sets, we can gain frictionless access to the data we need, which drives better integration and governance.

4. Plan ahead

To ensure the success of AI programs from the start, it is important to define the appropriate formats and tools for AI-centric data as early as possible, thereby avoiding the need to reconcile multiple data approaches as AI is evolving.

This will put organizations in a strong position to take advantage of an ecosystem of AI-centric data management tools that combine both traditional and new capabilities to prepare the business for success in the age of decision-making intelligence.

With the right technology, businesses can maximize the potential of their data and prepare for an AI-powered future.

Image credit: iStock.com/bymuratdeniz