By Flavio Villanustre, CISO & VP, Technology at LexisNexis® Risk Solutions, HPCC Systems
Today’s organizations are drowning in Big Data. Their IT architectures connect to customers, partners, and other third parties as well as to mobile apps and devices, social media networks, and the IoT – all of which produce massive amounts of data. In fact, IDC expects the sheer volume of global data to grow from 33 zettabytes (ZB) in 2018 to 175 ZB in 2025 (which is 175 trillion gigabytes).
Enterprises are under tremendous pressure to become data-driven, from the digital natives who are disrupting entire industries to the competitors who are using data in more sophisticated ways. According to a recent survey of 1,000 U.S. executives by global research and consulting firm Ipsos on behalf of RELX Group, 74% of organizations currently utilize Big Data, 56% use machine learning, and 48% use AI. The respondent organizations are using those technologies to increase efficiencies/productivity, inform business decisions and streamline processes.
How Machine Learning and AI Are Transforming Businesses
Organizations across industries are discovering they can accomplish tasks with machine learning and AI that they were unable to do previously. Some examples that apply to nearly every type of business include:
- Improving the responsiveness and ROI of customer service and technical support using chatbots and virtual assistants
- Improving task and workflow efficiency with AI
- Discovering new markets, customer segments, product and service opportunities using machine learning
- Understanding customer journeys and individual customer behavior using AI and machine learning
Different industries and the individual organizations within them are at different stages of data maturity, however. For example, the Ipsos/RELX survey indicated that more companies in the insurance, science/medical and banking sectors are using Big Data, machine learning, and AI than those in the legal, healthcare and government sectors.
Why Analytics, Machine Learning and AI Outcomes Differ
Central to the efficacy of analytics, machine learning and AI is the underlying data. The quality of insights, whether from analytics, machine learning, or AI depend on access to the data and data quality.
Some organizations still struggle to liberate data from disparate systems. However, others have solved the problem by building a data lake that centralizes their data. Add to that tools that provide a unified, view of the data and businesses begin to see how data could be combined in new ways to solve more business problems and drive more value.
The other important piece is data quality because the dependability of analytics, machine learning and AI outcomes depend on it. If you have good quality (accurate) data, you’re in a better place to rely on the results whether it’s a recommendation or an autonomous action.
Bridging the Talent Gap
The rapid growth of analytics, machine learning, and AI has not been matched by an equal proportion of data scientists, so there is a trend toward easier-to-use tools that analysts can use. The newer tools make it easier to connect to data sources and prepare data for analysis and machine learning. Some options, such as the HPCC Systems open source project, include machine learning capabilities and the flexibility to integrate with Apache Spark, Hadoop and R which helps organizations accomplish more with the talent they have.
