As we continue to connect processes and people with the help of the Internet of Things (IoT), the time is not far, when every object will have data transmission capabilities. Consequently, data governance is fast becoming an important aspect; especially around individual access to the collected data. Besides, considering the wide range of different AI services businesses opt for, from a multitude of cloud service providers, they will require to manage the multi-cloud environment in a consistent operating model. Further, as the applied fields of AI (like voice, text, and visual recognition) expand, it makes way for more complex data analytics services. Therefore, it becomes yet another challenge to ascertain service orchestrations around these new data analytics, and how businesses use those data internally.
The Immediate Challenges
While there is a significant need for businesses to remedy these challenges, there are a lot of groundwork that need to be done before. In this context, there is an increased emphasis on data quality, and data quality is directly related to data standardisation. In particular, for legacy businesses, we still witness low-quality data and handwritten information that are not machine-readable. Unless these organisations set up established data standards, they will need to comply with a variety of different classifications of data. To this extent, the deluge of data also increases the risk of data fraud. Thus, another key challenge now is to implement strong data privacy and security protocol.
Additionally, there has been a misunderstanding about how AI is used in general. When we are talking about enhancing data analytics or machine learning with AI capabilities, they are not the only application of AI. The technology has gained particular attention in its application in chatbot and as human-to-machine interfaces. Especially, with capabilities like text-to-speech recognition, visual and voice recognition, AI has found its latest application in virtual assistance like popular voice smart speakers Alexa and Google Home. So, as all these data become available to us, we need to streamline these interactions and put them in context before applying them for a decision-making logic.
Best Practices for CIOs
In our journey to capture customer data, we also need to remember the key principle is transparency. Gone are the days when businesses would operate behind the smokescreen of fine prints. If they want to strike a close and harmonious relationship with their customers, businesses must understand that customers are the owners of their personal information. So customers must know how and where their data will be used, and if they are to trust companies more, they should be able to decide not to share their personal information. To achieve this level of transparency, companies need to set up strict internal guidance and procedures around how they use customer data and who they further authorise to use the data.
My Advice to CIOs
Many companies today look at AI to speed up their processes. But the focus should be on AI’s ability to offer precise information at the right time at the right place. From the aspect of contextual data, precision to process the information is more important than speed to mine the data. If the quality of your data is weak, then using AI, machine learning, or analytics-driven processes might create more harm than good. Key decision-makers need to have a good understanding of AI and its broader applications, and they should be able to educate others when required. We need to understand, AI is not a magic technology that we integrate with a process, and everything starts working. Instead, it is a journey to demystifying the realm of AI and put that to our best of uses.