Presented by Wizeline
Many enterprises are facing barriers to leveraging their data, and making AI a company-wide reality. In this VB On-Demand event, industry experts dig into how enterprises can unlock all the potential of data to tackle complex business problems and more.
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Across industries and regions, realizing the promise of AI can mean very different things for every enterprise — but for every business, it starts with exploding the potential of the wealth of data they’re sitting on. But according to Hayde Martinez, data technology program lead at Wizeline, the obstacles to unlocking data have less to do with actually implementing AI, and more with the AI culture inside a company. That means companies are stalled at step zero — defining objectives and goals.
For a company just beginning to realize the benefits of data, AI efforts are usually an isolated undertaking, managed by an isolated team, with goals that aren’t aligned with the overall company vision. Larger companies further down the data and AI road also have to break down silos, so that all departments and teams are aligned and efforts aren’t duplicated or at cross purposes.
“In order to be aligned, you need to define that strategy, define priorities, define the needs of the business,” Martinez says. “Some of the biggest obstacles right now are just being sure of what you’re going to do and how you’re going to do it, rather than the implementation itself, as well as bringing everyone on board with AI efforts.”
The steps in the data process
Data has to go through a number of steps in order to be leveraged: data extraction, cleansing, data processing, creating predictive models, creating new experiments and then finally, creating data visualization. But step zero is still always defining the goals and objectives, which is what drives the whole process.
One of the first considerations is to start with a discovery workshop — soliciting input from all stakeholders that will use this information or are asking for predictive models, or anyone that has a weighted opinion on the business. To ensure that the project goes smoothly, don’t prioritize hard skills over soft skills. Stakeholders are often not data scientists or machine learning engineers; they might not even have a technical background.
“You have to be able, as a team or as an individual, to make others trust your data and your predictions,” she explains. “Even though your model was amazing and you used a state-of-the-art algorithm, if you’re not able to demonstrate that, your stakeholders will not see the benefit of the data, and that work can be thrown in the trash.”
Making sure that you clearly understand the objectives and goals is key here, as well as ongoing communication. Keep stakeholders in the loop and go back to them to reaffirm your direction, and ask questions to continue to adjust and refine. That helps ensure that when you deliver your predictive model or your AI promise, it will be strongly aligned to what they’re expecting.
Another consideration in the data process is iteration, trying new things and building from there, or taking a new tack if something doesn’t work, but never taking too long to decide what you’ll do next.
“It’s called data science because it’s a science, and follows the scientific method,” Martinez says. “The scientific method is building hypotheses and proving them. If your hypothesis was not proven, then try another way to prove it. If then that’s not possible, then create another hypothesis. Just iterate.”
Common step zero mistakes
Often companies stepping into AI waters look first at similar companies to mimic their efforts, but that can actually slow down or even stop an AI project. Business problems are as unique as fingerprints, and there are myriad ways to tackle any one issue with machine learning.
Another common issue is going immediately to hiring a data scientist with the expectation that it’s one and done — that they’ll be able to not only handle the entire process from extracting data and cleaning data to defining objectives, graphic visualization, predictive models, and so on, but can immediately jump into making AI happen. That’s just not realistic.
First a centralized data repository needs to be created to not only make it easier to build predictive models, but to also break down silos so that any team can access the data it needs.
Data scientists and data engineers also cannot work alone, separately from the rest of the company — the best way to take advantage of data is to be familiar with its business context, and the business itself.
“If you understand the business, then every decision, every change, every process, every modification of your data will be aligned,” she says. “This is a multidisciplinary work. You need to involve strong business understanding along with UI/UX, legal, ethics and other disciplines. The more diverse, the more multidisciplinary the team is, the better the predictive model can be.”
To learn more about how enterprises can fully leverage their data to launch AI with real ROI, how to choose the right tools for every step of the data process and more, don’t miss this VB On Demand event.
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- How enterprises are leveraging AI and machine learning, NLP, RPA and more
- Defining and implementing an enterprise data strategy
- Breaking down silos, assembling the right teams and increasing collaboration
- Identifying data and AI efforts across the company
- The implications of relying on legacy stacks and how to get buy-in for change
- Paula Martinez, CEO and Co-Founder, Marvik
- Hayde Martinez, Data Technology Program Lead, Wizeline
- Victor Dey, Tech Editor, VentureBeat (moderator)