Have you ever noticed that as soon as you search for a pair of shoes or download a white paper, you’re seeing ads for them on almost every page? Do you use Apple’s Siri or Amazon’s Echo? Welcome to the world of big data and artificial intelligence (AI).

Every click, word and image online generates potentially valuable data. It can come from almost anywhere: your laptop, smartphone or Fitbit; the checkout counter, satellites or security cameras; insurance companies, utilities providers or healthcare organizations; or even your home or vehicle. The sheer amount produced can make the term “big data” seem like an understatement.

What about AI? In short, it’s about teaching computers to see trends, correlations and patterns in information. For example, computers can learn what cancer biomarkers, MRIs or scans looks like in order to identify them in millions of data points.

Part one of this series explores how to collect the best data and process it effectively. Part two will discuss how to use it strategically to drive specific business objectives.

Big data and artificial intelligence: the opportunity

“Every time we do everything, we leave data exhaust, and it has value,” said Allison Sagraves, chief data officer of a major U.S. financial institution told me.

The opportunity to collect this enormous quantity of data on every aspect of our economy, coupled with the opportunity to understand it via AI is transforming everything. Think autonomous vehicles, robots for disaster relief, refrigerators that order milk for you and even correlations between climate change and public health.

Not to mention business strategy. As Randy Bean writes in the MIT Sloan Management Review, “The convergence of big data with AI has emerged as the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities.”

Garbage in, garbage out: collecting data

Having data doesn’t mean you have the right data — or that you know what you have or that you’re seeing what you need to see. Data is only as good as the methodologies used to collect and analyze it. It’s crucial to ask the right questions in analysis and adapt them to the market. “The key to getting actionable insights that can improve your operations is to combine the right data with the right questions, continuously,” said Peter Kelly-Detwiler, an energy and power markets expert.

That’s where the convergence of big data and AI is most powerful: AI can help you figure out what your data is telling you, enabling you to identify its use as a strategic asset.

Here are six tips for doing so:

  1. Ask, “What do I need to know?” and “What am I missing?” Data may offer opportunities to find connections and correlations that drive innovation.
  2. Challenge your assumptions. In the 2016 U.S. presidential election, almost all the polling models were completely wrong. Their algorithms were based on wrong assumptions. Sagraves suggests actively bringing in new perspectives, even philosophers and citizen data scientists.
  3. Consider what you’re not asking, Sagraves emphasizes. “The value is going to come from the things we never knew were possible.”
  4. Be aware of biases. Our biases tend to be unconscious, so as you develop question phrasing and analysis models, include people who can help point them out.
  5. Check your weights. Developing predictive analytics requires setting how much each data point will be weighted or valued in the model, so do it carefully.
  6. Have a “data evangelist,” as Sagraves calls it. Effective big data and AI coupling requires that employees actually use it. You may need someone from the top to incentivize employees and reinforce it consistently.

“It requires some creativity to know what to ask and what to do with the answers,” Sagraves said. That’s where the humans come in. “It’s an art and a science.”

A data collection and analysis system can be expensive to build and maintain, but it will likely pay off in savings revealed, information you can leverage to drive business growth and new revenue streams.

Stay tuned for part two of this series to learn specific how-to ways.

To learn more about Big Data, listen to Allison Sagraves interview.

This post originally was originally published at http://blog.investis.com/how-can-big-data-and-artificial-intelligence-drive-your-business-strategies-part-one.