It is estimated that by 2030, mankind will produce over a yottabyte of data annually. As digital experiences continue to gain more significance in all domains, more avenues will continue to emerge for technology like AI to penetrate deeper into every aspect of human life and work. From retail to banking, healthcare, and even power generation, enterprises of all sizes are already looking to fuel new AI initiatives that will leverage the vast treasure trove of data they have at their disposal. These enterprises are looking to build rewarding experiences for their customers that will foster engagement, loyalty, and advocacy. AI supremacy will eventually rise to become a competitive advantage for business. That being so, the real action is likely to take place behind the scenes at the data layer.
Enterprises that wish to dominate the AI age must adopt a seamless data-driven culture of operations. Only then would they be able to support intelligent interactions between machines and customers that are governed by highly complex data workflows.
So, what does it mean to be data-driven in the AI age?
The characteristics of a data-driven business
If your enterprise is aiming to catapult into an age of AI-driven success, then you need a clear understanding of the traits that a data-driven business must exhibit in its operations.
Let us have a close look at the top 4 characteristics that a data-driven business must possess in the AI age:
Every decision is data-driven
Any decision taken by leaders across the spectrum of operations must be based on a strong data foundation. Traditional approaches to problem-solving and methods that involve a lot of gut feel and intuition may no longer be required or effective in the long term. In effect, organizations must aspire to replace legacy problem-solving techniques with innovative data-driven approaches.
Identifying problematic patterns in data streams will help in formulating automated solutions for most problems. Eventually, the process of generating insights to enable data-driven decision-making capabilities can be automated. As that happens, think tanks can be freed to focus on more core business aspects like innovation, culture, and customer service.
A data architecture that accommodates real-time data processing
Data-driven businesses need to analyse and process data in real-time. A key enabler of that capability is the power and potency of the enabling data infrastructure and fabric. Obviously, these capabilities demand complex and expensive infrastructure. Enterprises must adopt more flexible and powerful computational assets for their data ecosystem to ensure that the right balance between real-time use cases and costs is achieved. The data architecture behind the scenes will need to be capable of handling insights from not just transactional software systems, but also data streams coming in from across the business.
Data could stream in from physical machines like IoT sensors, edge nodes, wearable smart devices, as well as other generic as well as special-purpose devices. Every single data stream will be connected to analytical engines deployed using a range of technologies that support real-time processing.
Have a productized model for data management
When data becomes a central pillar of enterprise decision-making, it needs to be accessible to all the relevant stakeholders and business drivers. That means that enterprises will have to organize their data functions in the most accessible formats with data of the highest quality and with configurable rules. Traditional approaches to data management will not be able to provide the growth foundation needed for a data-driven enterprise. Instead, such enterprises may have to focus on treating data management as a product. That will create an ownership model for data, dedicated teams focused on quality, organization, security, accessibility, and innovative evolution of all data functions, as would happen with any other product.
The productized roadmap will hold immense significance as it can be replicated in functionality in scenarios where data holds the key to several major operational challenges.
Automation will be an integral trait
Adding to the paradigm of productizing data management, data-driven enterprises will foster a culture of automation in almost every aspect of their data ecosystem. From security and privacy to compliance enforcement all the way to recovery, backup, and preventive threat aversion, all areas of data operations will require a high degree of automated activities in the core functions. The high level of automation will ensure that the data ecosystem is agile, responsive, resilient, and free from bias.
This will be a major requirement when adopting new AI innovations at scale. Automation will also ensure there are proper checklists in place before massive data operations are undertaken thereby ensuring the sanctity of the data. Automation can also help in fostering a major environment of trust for customers to part with their data without fearing misuse.
How can an organization be data-driven in the AI age?
Now that we understand what the enabling data infrastructure of a data-driven enterprise looks like, the next challenge is finding out how a business can become data-driven. The key objective to be kept in mind is that every major future initiative that a business undertakes with technology like AI will depend on the resilience of the data foundation. The tools you use, and the approaches to data processing, storage, and management will vary but the driving needs of all these must be to provide the best possible value from the organization’s data vaults.
To achieve maximum value from data ecosystems, organizations must build a sustainable data-driven roadmap that sets standards, offers guidance, and helps the business realize better profits. This is where an expert partner like Trinus can help make a huge difference. Get in touch with us to know more.