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Generating Value from Your Artificial Intelligence Analytics Architecture


Being data-driven is an oft-stated goal of enterprise leaders. Companies want more than to just have and use data. They want to use it to gain a competitive edge. Having business intelligence and analytics tools available is just the beginning for these organizations. True data leaders are deploying immersive end-to-end data solutions with the objective of bolstering business outcomes.

Artificial intelligence-powered analytics reign supreme in this landscape. Again tough, having the tools handy is just the start. Generating value from your AI analytics architecture depends on other factors. These include data literacy among users, alignment with core business strategies and conducive platforms.

Drive Better Decision-Making with AI Data Analytics

The very nature of decision-making in the workplace has changed — and continues to do so. Gone are the days of the highest paid person in the room making decisions based on intuition, or making choices based on the limitations of static reports.

With artificial intelligence architecture powering search analytics and automated AI analytics engines, decision-makers now have more direct access to insights. However, as we mentioned before, deploying these solutions are merely a first step toward generating value.

Besides empowering various stakeholders to use self-service analytics, there’s the matter of helping them “learn to ask good questions.” As one expert writes for Towards Data Science, data products can deliver insights, but they are not a substitute for knowing what to look for within data. As a result, increasing data literacy is also critical to deriving value from AI analytics — especially in terms of which questions to ask and how to analyze the insights they receive.

Another component of maximizing adoption of AI analytics is company culture. It literally pays to ask: How well is our organizational culture supporting/encouraging data-driven decision making or limiting it? As research firm Gartner advises, establishing a data-driven culture hinges on “influencing mindset and behaviors” rather than trying to control employees.

Here are a few ways leaders can work toward forging a company culture that’ll help employees get the most value from analytics:

  • Talk about the concrete business value of data.
  • Help business users understand what types of data are available to them.
  • Outline the cultural changes needed to support data-driven decision-making.
  • Positively motivate users to want to embrace data.
  • Set clear, realistic expectations as to how data can be utilized.
  • Ensure data is handled ethically and securely to boost user trust.

To recap: The actual AI analytics architecture implemented addresses the “how” of generating value from data. Data literacy helps get the “who” on board. Efforts to successfully transition to a data-driven culture address the “why.”

Integrate AI Analytics into Core Business Strategy

Another key component of getting value from your analytics architecture is tying its usage to your core business strategy.

As one expert writes for Forbes, failing to align data efforts with strategic goals can lead to bad analytics habits that only make it look like your org is data-driven, such as: 

  • Getting bogged down in extraneous metrics that do not drive goals.
  • Cherry-picking whichever metrics look the best.
  • Becoming overwhelmed with the number of reports generated.
  • Missing out on opportunities to improve and innovate.  

Before all else, start with business goals — then use these to inform everything from your choice of analytics technology to your company’s culture, literacy training and decisions. Generating true value from AI-powered analytics architecture requires getting the tools, culture and priorities right.