How enterprises can get from siloed data to machine learning innovation
Presented by Wizeline
Any enterprise can unlock AI — but only if leaders know how to actually leverage their data, define problems and iterate. In this VB On-Demand event, join industry experts as they dig into how enterprises can turn data into company-wide AI and machine learning solutions.
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Companies are foundering in the quest to realize AI objectives, not to mention a return on their investment in AI, and it comes down to the right data and the right expertise, says Paula Martinez, CEO and co-founder of Marvik, a machine learning consultancy.
First, there’s the expense and effort of uncovering good quality data from the mountain that’s always growing, and labeling it properly to actually put analytics and machine learning developments into production. And then, going from proof of concept to a production-ready solution with quality standards that can be launched at scale is another enormous obstacle — and a large part of that is finding a team with the right skills to carry out the task successfully. Finally, some companies face problems just in narrowing down a business problem.
“Launching a proof of concept with cloud services or other developers can be fast and easy to do,” Martinez says. “But when the problems are complex and very specific to an organization or an industry, requiring very specific knowledge and experience, the solution must be fine-tuned for that particular case — and that takes a specialized skill set.”
The marriage of data and expertise
Getting to 95% percent accuracy might take days or weeks, but getting 99% accuracy can take months, as well as another degree of expertise and knowledge. Without that knowledge, collecting and organizing the right data is usually overlooked. And without the necessary resources allocated to find and prepare this data for machine learning models, that information stays siloed.
“Working with large organizations, we usually find ourselves working with information that is fragmented in different systems, in different business units of the company,” Martinez says.
Today, many companies are making efforts to consolidate and leverage that data with data lakes and warehouses, but it’s a complex and time-consuming task to reorganize and implement any of these solutions, and figure out how to take advantage of this information.
“It makes sense to invest money and time to improve how we handle data inside companies,” she says. “We need to plan for that, and decide on a road map for a successful AI investment overall.”
How companies can launch AI experiments
There’s no recipe for un-siloing and actually leveraging data — it’s a case-by-case situation. But in terms of methodology, Martinez recommends that clients start any AI project with a minimum viable product (MVP), to determine if this is the path toward adding business value to the company.
From the definition and design of the solution, to the creation of an action plan, and later the implementation of the AI solutions needed requires planning for the right technology stack, too. That must take into account the nature of the data, and how the company envisions implementing the solution. For example, will it run in the cloud, on edge devices, mobile or other applications? Does it need to run in real time, or in intervals? There are so many variables to take into account when defining the technology stack, and so many technologies coming into the market.
“An advantage we have is that we get to test new tools as they come to the market, and we can find out which ones work and which ones don’t,” she says. “We get access to so many organizations, and we get to see the pros and the cons of every cloud tool and technology.”
Her biggest piece of advice when planning and while implementing is to not be afraid to experiment — which just means forging ahead even if you’re not certain of the results. AI is about to explode, so companies need to start combining technology with business understanding and data right now, to ensure they’re prepared to face a far more competitive future.
“The good news, I think, is that external help is available to speed up the process,” she says. “We often see companies not innovating because they’re afraid, or they feel it’s outside of their reach, even though they have the correct data. Don’t be afraid to innovate. Just make sure you reach out to the right team to do it.”
To learn more about finding the data you need and tapping the right resources to experiment, iterate and innovate in a demanding market, 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)