The road to AI (the less traveled one)

Organizations have apprehended the importance of data analytics in their businesses and are looking deeper into data to gain a competitive advantage, implementing machine learning and artificial intelligence to achieve new business objectives and to move ahead of competitors in the industry.

However, the adoption of AI and machine learning is critically impaired by the necessity of high-quality data, and changes must be made organization wide to identify and reduce pouches of bad data and create mechanisms that allow the organization to adapt quickly to the data needs and embrace the full potential of these technologies.

The business benefits of AI are obvious, with larger and larger volumes of raw data available and increasing computing power and real-time processing speeds, any organization can easily start its AI strategy.

However, from starting to being able to deliver a successful AI strategy goes a distance. The capability to build a secure, centralized, and scalable data repository, being able to combine large volumes of disparate data from multiples data sources, is the first challenge to overcome.

Data is the biggest challenge when employing new technologies and the impact of data-driven decisions is larger than ever before. The availability of high-quality data is paramount for technology to have the right impact.

It’s never enough to emphasize the critical roles data governance and quality play in these processes, and prior to any investment on AI data governance and quality strategies must be in place to support and assure the return on this investment.

Owning and trusting data are two completely different things, the amounts of data available are increasingly bigger however that data is not suitable or trustable enough to make business-critical decisions with. This lack of trust in the data has a twofold impact, it either inhibits decision making, or even worse, it induces decisions based on false assumptions.

This can only be solved by developing strategies to address existing and ongoing issues and implementing effective methodologies to execute on that strategy.

A data governance program and structure covering the people, processes and technologies needed to manage and protect the organization’s data assets to ensure understandable, correct, complete, reliable, secure, and discoverable across the organization.

A data quality framework encompassing data quality discovery, remediation, and monitoring can help organizations approach the data quality issues that hinder the success of analytics or machine learning initiatives.

This approach allows data quality processes within the organization to be performed in a more agile and scalable way, reducing the siloed approach and manual interventions. Resulting in an overall improvement of the value returned from analytics or AI.

Considering that data is one of the organizations most critical assets, making data governance and data quality the basis of all analytics and AI initiatives, leverages the value of data and unlocks new insights critical to create the necessary edge on an increasingly competitive business ecosystem.

Never loosing sigh that none of these practices is an end, they all contribute to one larger goal, to provide value to business, feeding the organizations decision processes with reliable data that allow accurate and timely insights and decisions.

Overlooking this fact can be catastrophic and cost millions.