As AI expands access to clean, rich data, governance, privacy, and protocol issues become more urgent, especially around privacy, bias, and fairness. To learn more about the kind of actionable insights AI delivers and how to avoid the pitfalls when launching AI strategies, don’t miss this VB Live event!

Register here for free.

Big data and AI is a booming business — with over 97 percent of companies interested in investing in AI initiatives, the compound annual growth rate of spending on big data and analytics has climbed to 11.9 percent, and revenues will rack up to more than $210 billion by 2020.

But while 78 percent of companies are pretty confident about their data maturity, confidently rating themselves as “medium” or “high” when it comes to their in-house skills, tech, and initiatives, only 12 percent actually qualify as mature. The advantage of AI-powered enterprise analytics are huge, from increased agility and better decision-making to reduced costs after the initial investment as well as improved fraud detection.

The unstoppable push to AI analytics

A study found that across the globe, 60 percent of enterprises use AI to drive greater process and cost efficiencies, 57 percent leverage AI analytics to support strategy and change, and 50 percent are  improving their financial performance. More than half have integrated data and analytics into their strategy to improve insights into current products and services, to manage risk, and to boost customer growth and retention. And AI dashboards are making it far easier for senior level execs to access essential data to inform critical business decisions.

But perhaps the most important reason to get on board the AI train: As more companies seize the AI advantage, it’s increasingly clear that businesses that don’t are going to get left behind. That said, companies taking the AI-powered analytics leap are wrestling with challenges.

Finding talent

AI analytics at enterprise level means that your staffing strategy is a key element in the success of your initiative, but finding that talent is rough in a world where data science specialists are rare and precious highly paid unicorns. It’s a buyer’s market out there for IT workers with the right skill set, and between the search and the wooing, it is one of the biggest challenges to hurdle, right out of the gate, and one of the largest layouts of cash you’ll have to make — though the infrastructure you have to put in place when you get into the big data game isn’t peanuts, either.

Cleaning up the data

With AI, huge strides have been made in raw data clean-up, taking it from a horrifically painful and unending task to an only very painful and unending task. Enterprises generate extraordinary amounts of data, in a wild array of formats and a wide range of quality. Machine learning makes it far easier to wrestle that data into shape, at scale — the more data it works on, the better it gets at wrangling it into something usable. But big data cleanup efforts will always feel like trying to take a mop to the ocean, and it’s one of the most prickly pain points for enterprises. If your data is dirty, your insights aren’t just unhelpful –they could be downright dangerous to your organization, when you try to act on them.

Rules and security

One of the reasons your data is so valuable is because it encompasses sensitive or personal information. So it’s a great source for important insights and business intelligence, but it’s also a big source of pain when it comes to government compliance. From GDPR to Sarbanes-Oxley to HIPAA to PCI DSS, the regulatory hurdles keep multiplying, and your responsibilities increase.

Your data is also a great and shining beacon to hackers and other malcontents, and keeping it safe and secure is an increasing challenge in a world where the cybercriminals are getting smarter and the tools more sophisticated. Though AI-powered cybersecurity is giving enterprises a fighting chance, the more data you have, the more important it is to assess your risk of a breach or other catastrophe and ensure you have mitigation plans in place to plug the leak and manage the fallout.

To learn more about how AI-powered analytics is transforming enterprises, how to launch your analytics strategy, and how to hurdle the challenges and reap the rewards of AI-powered analytics, don’t miss this VB Live event.

Don’t miss out!

Register for free here.

Attend this webinar and learn about:

  • The importance of clean, rich data and how AI is helping this
  • Why AI dashboards will replace KPI dashboards
  • How AI and analytics approaches differ across devices — with mobile being so much more meshed within the Internet of Things
  • How AI and analytics quickly surface problem areas in customer strategies and allow actionable insights in real time
  • The importance of clearly-defined questions in creating AI analytics dashboards

Speakers announced soon!