The Lag in AI Adoption in Analytics
Knowing what’s good for us and taking action to reap the benefits are very different things. It’s the classic “theory vs. practice” dilemma.
In the age of Big Data, enormous opportunities lurk within the raw information enterprises collect, primarily accessible via various subsets of artificial intelligence. However, businesses are experiencing a disconnect when it comes to merging theory and practice to actually extract these valuable insights.
Business executives know that emerging data analytics technologies are vital to the organization’s long-term prosperity, but many are unsure of how and where to deploy them.
Let’s discuss the lag in the adoption of next-generation analytics technologies and how companies can address roadblocks.
The Skills Gap
For companies to realize ROI on analytics investments, they need to build their culture around data. After all, findings could be accessible to users, but they still need to be interpreted and communicated to relevant team members. This requires everyone in the business to adopt a shared language around data.
Developing a data lexicon, instructing users on how to analyze data sets, and practicing communication scenarios will give non-technical employees a foundation to base their data interactions. It’ll also add to their confidence as they increasingly look to data to inform decisions. Establishing data literacy is the first step to succeeding in analytics, but it’s not a one-time initiative. IT and analytics employees should offer resources for continuing education, hold regular Q/As to clear up any confusion and distribute monthly surveys to highlight areas where employees need improvement.
Lack of Leadership & Poor Communication of Analytics Benefits
Technical skills and people skills don’t always mix. Thus, the people who understand the data AI needs, and the potential it can provide in clarifying various aspects of business, often do a poor job relaying those things to the rest of the company.
This is one reason we’re seeing a rise in data-specific C-suite roles like the Chief Data Officer and Data Evangelist. These hybrid characters possess both technical and social skills that help companies align their overall business goals with their analytics initiatives. As organizations work on addressing their data literacy, leadership roles will get employees to trust AI-generated insights by clarifying the business’s vision and how data informs it.
Not Investing in the Right Infrastructure & Tech
Proper communication and data skills are the biggest hurdles companies must get over before fully integrating artificial intelligence into their analytics efforts. However, technical infrastructure ensures that knowledge moves fluidly across an organization.
In the age of AI, the data team serves as the designers of the system. They decide how raw information will be stored, which data AI learns from, and how to best deliver insights to decision-makers across the company. This is an exciting development compared to legacy BI systems, in which data teams would find themselves bogged down with the task of creating reports for other employees.
To keep pace with a fast-moving business world, extra tools can be leveraged to free experts from creating reports. Machine learning analytics company ThoughtSpot recommends incorporating a search-based BI tool that uses natural language processing and deep learning so that every employee has AI as a data sidekick whenever they need it. This provider also recommends employing an intelligent bot to handle tedious tasks and using neural networks to provide instant answers about historical data to gauge future outcomes.
Artificial intelligence may still be in its infancy, but that doesn’t mean it can’t provide tangible business outcomes to businesses right now. To get there, though, they’ll need to level up their employees’ data skills, employ the right leadership that can communicate the merits of using data effectively, and implement tech that ties the analytics program together.
