Machine Learning Success Starts with Healthy Data
This article was originally published on the InterSystems global website (link), written by Scott Gnau, VP of Data Platforms.
Machine learning (ML) and artificial intelligence (AI) are becoming core functions of successful organizations, with data playing an increasingly important role in making or breaking these applications.
The required fuel for effective and precise AI and ML is healthy, normalized, and comprehensive data. In today’s competitive business landscape, organizations can garner valuable insights from analytics to better inform the critical business decisions impacting their bottom line. Basing these decisions off of healthy data is often the difference between success and failure.
Poor or unhealthy data has been estimated to cost the US more than $3 trillion per year. Data science teams that don’t have access to enough data lack the necessary resources to build successful ML architectures and models, which can lead to potentially flawed systems and less-than-desirable outcomes.
The Foundation of Healthy Data: Organization
Healthy data is without duplicate records, missing information, formatting errors, incorrect information, or mismatched terminology. As the amount of data being generated and made available to organizations increases exponentially, collecting large amounts of data in an organized way can be a significant challenge. Failing to keep pace and properly collect this flood of data often results in missed insights and faulty outputs.
Organizations looking to optimize healthy data must think beyond business objectives and take an integrated approach, identifying what data will be collected – including from where and how – and cleaned, and how its use maps back to the desired business goals.
Healthy Data Benefits
Healthy data leads the way to better insights into all areas of the business. But even with the most cutting-edge models, a small piece of unhealthy data can significantly warp results. Healthy data has the ability to boost the effectiveness of an entire organization by providing:
- Trusted Data: Organizations must be able to trust that their data is clean so that it can be relied on to make quick and accurate business decisions before an opportunity is lost or a threat is missed.
- Real-Time Decisions and Actions: Working with healthy data empowers organizations to immediately use the data to guide decisions, and for intelligent programmatic actions to maintain a competitive edge.
- Better AI: Using healthy data enables data scientists to focus more on conducting analyses that improve the business, instead of on data munging and wrangling.
Organizations require access to healthy data if they want to more efficiently leverage AI and ML, improve business outcomes, and increase operational efficiencies. It all begins with healthy, clean data – from there, the opportunities for business value are endless.
Learn more about Healthy Data.
Read the latest blog posts on Data Excellence.
Listen to the InterSystems Healthy Data podcast series.
About Scott Gnau:
Scott Gnau joined InterSystems in 2019 as Vice President of Data Platforms, overseeing the development, management, and sales of the InterSystems IRIS™ family of data platforms.
Gnau brings more than 20 years of experience in the data management space helping lead technology and data architecture initiatives for enterprise-level organizations. He joins InterSystems from HortonWorks, where he served as chief technology officer. Prior to Hortonworks, Gnau spent two decades at Teradata in increasingly senior roles, including serving as president of Teradata Labs.
Gnau holds a Bachelor’s degree in electrical engineering from Drexel University. Please follow Scott on LinkedIn by clicking here.
