The buzz around artificial intelligence is everywhere – anybody who’s anything in the world of technology wants to voice his or her opinion on how AI is going to change everything in the world of business. Though the potential of artificial intelligence hardly needs any underscoring, it’s a stark reality that most enterprises are not yet ready to harness AI’s potential by deploying artificial intelligence-based systems in production environments.
AI is not plug and play
Even technology powerhouses are stumbling in their quest to harness AI’s potential. The road to stable and profitable AI-powered solutions is littered with a lot of mistakes. (Case in point – Google Photos identified black people as gorillas, causing uproar that forced the global tech giant to apologize.) Whereas tech powerhouses can afford experimenting with AI, most enterprises need more disciplined analysis of use cases, payback periods, ROIs, and long-term impact before they can ask for lofty budgets. That’s natural, because AI is not something you can plug and play. It must rest on strong pillars, such as disciplined data management practices, strong analytics capabilities, business use cases, and a commitment to AI.
In this guide, we’ll explore the critical role of data and analytics in helping enterprises advance to the stage where they can harness AI’s potential.
Defining problems instead of purchasing solutions
With AI, the biggest mistake you will ever make is to look for technologies without clearly understanding your business requirements. New technologies aren’t necessarily beneficial for every enterprise. For instance, if you’re only dealing with a few TBs of data, you don’t need to implement Hadoop architecture as yet. Then, if your business has more value to gain from static data analysis and doesn’t need a lot of real-time predictions and reporting, you don’t have to spend on Spark.
Increasingly, enterprises are finding out that an approach of creating an intelligent ensemble of traditional statistical methods, basic machine learning algorithms, along with well thought out neural network programming-based tools is what they need to do.
The core idea: clearly mention business goals and key metrics, and follow it by making the most of existing data analytics methods along with specific new approaches to fulfill gaps. This helps you avoid technology lock-in, particularly when the world of AI is still evolving.
Data quality drives machine learning success
The thing with AI, or any sophisticated machine learning system is – you feed it bad data, you get bad results. Note that we mentioned “bad data” and not “less data.” This takes us to what’s probably the biggest misconception about AI – that it needs massive data to deliver good results. Here’s the truth: The biggest limitation faced by AI systems is centered on low quality of data, and not low volume of data.
Whereas massive data is always good to have, it won’t make your enterprise’s AI adventures successful. To harness AI’s potential to get focused business results, you need to provide it good data. Creating processes that capture relevant data, store it in a structured manner, and keep it organized over time takes a lot of time, expertise, and experience in data management.
The big takeaway here is that when you have specific business goals, and have mechanisms that capture the most relevant data from the related business processes, your machine learning systems can become smarter quickly. This approach is much better than capturing a lot of data, without focus on quality and relevance.
Implement company-wide data standards
Enterprises that implement consistent and stringent data cleanliness methodologies stand to gain a lot from AI. However, most organizations’ data is scattered, incomplete, inconsistent, and distributed across disparate systems. This is where you as an IT leader need to implement standards of data creation, archiving, and management in general, across organizational departments. This is essential for streamlining the operations of your enterprise’s analytical engines, and enabling machine learning.
Application of consistent meta data rules, labeling, and documentation helps keep your organization’s knowledge recorded and accessible for quick use, anytime, apart from being a faithful feed for the machine learning platforms to create self-learning algorithms. The kind of delays caused in AI system implementations because of lack of critical data are unimaginably huge. Enterprises that have been able to encapsulate core business functionalities into well-documented interfaces make machine learning more potent.
Connect business and IT
This is a sin that most CIOs and CDOs are guilty of; only a surprisingly few enterprises implement practices that promote collaboration between stakeholders from business and IT, in terms of data management. This has grave implications for the company’s AI readiness.
When vendors talk to business leaders who are not aware of the data management practices of the organization, they sign AI contracts based on weak assumptions about the company’s data maturity. On the other hand, when technical experts decide on AI projects, they do so with a myopic view of the business applications where AI could deliver immediate benefits.
In conjugation with ensuring data quality and implementing data harnessing methods, IT and business leaders need to collaborate to advance the enterprise’s understanding of how its data and analytics capabilities can pave the way for AI to do its magic.
Promote a culture where decisions are made on data, not opinions
This is a problem persistent in almost all aspects of business work – too many decisions are being made on pure opinions, often in stark contrast to what the data has to say. Highly paid executives, project managers, and even IT leads tend to take the easy way out by devising plans based on pure heuristics, if not entirely opinions. Ignoring data inputs not only makes decisions highly subjective, but it also equates to a lost opportunity of validating the decision maker or planner’s approach to problem solving. By adopting and promoting a “data-driven decision-making” approach, you can lay the foundation for sustainable and successful AI systems.
It’s up to you to realize AI’s potential
AI will soon be huge. However, enterprises can’t just wait for the waves to soak them. They need to climb on their surfboards and ensure that they use data management and analytics to make AI a big success in their organizations.
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