Big data has grown to immense proportions and triggered a similarly huge move by businesses to integrate machine learning (ML) — an engine that drives artificial intelligence (AI) — across their enterprises to manage the collection, analysis and distribution of digital information.
Some estimates predict aggregate business investment in ML will exceed $200 billion by 2029 — a 38% year-over-year increase from 2023 until then — as organizations race to adopt or expand the leading-edge digital innovations. But technology is only as good as the people who use it, so businesses are placing a premium on non-technical managers and executives who can leverage ML analytic outputs to make smarter decisions faster and with greater degrees of confidence.
Commenting on the democratization of data, Harvard Business Review (HBR) notes that the ML’s potential for transforming commerce across the economic spectrum is in its early stages. Leaders in the adoption of ML and AI are taking steps “to ensure that as many stakeholders as possible have the skills and resources they need to employ advanced digital approaches, rather than keeping this expertise in the preserve of specialists,” according to HBR.
How Do Business Professionals Gain Data Expertise to Compete Successfully in the ML Era?
A comprehensive business background backed by fluency in fundamental data management is rapidly becoming a baseline requirement as organizations adopt ML technologies to support operations ranging from recruiting, retaining and training talent to optimizing customer experience and cybersecurity.
A Master of Business Administration (MBA) with a concentration in Data Analytics online program, such as the online program offered by Florida Gulf Coast University (FGCU), equips graduates with the knowledge and insights to lead data-driven business operations.
Its AACSB-accredited curriculum includes explorations of database operations that are vital in collecting and preparing digital information as well as analyzing large, complex data sets and strategies to interpret data and accelerate decision-making.
What Is ML’s Role in Managing and Analyzing the “Three Vs” of Big Data?
Data professionals describe the three main data challenges that are driving the rapid adoption of ML capacities to manage complicated datasets: volume, velocity and variety. Amphy defines the Three Vs of big data as:
- Volume refers to the tsunami of data that businesses must manage. Collectively, the volume created, captured and stored was about 64.2 zettabytes (1 ZB equals 1 trillion gigabytes) at the beginning of the pandemic. ML rewrites its complex mathematical equations without human invention as data is received to identify key patterns, trends and anomalies in near-real time.
- Velocity not only describes the speed at which data is received but, critically, how fast it can be analyzed and distributed to decision-makers. Data has a shelf life, and ML automation ensures that “firehose” volume is sanitized, analyzed and distributed efficiently to optimize operations across the enterprise.
- Variety is acknowledged as the most complicated of the Three Vs. ML breaks data out of the silos where it previously was stored according to format (among them, text, images and natural language), source (everything from autonomous vehicles to social media) and type (qualitative vs. quantitative, for instance). “Variety is messy,” Tamr notes, and “it’s only getting messier.”
How Does ML Enhance Decision-Making to Improve Customer Engagement?
ML and AI data processes can automate decision-making to enrich the customer experience and drive sales. Amazon, for instance, reports that 35% of its sales come from recommendations based on ML analytics of customer browsing and buying histories. The social media giant Instagram uses ML to track and analyze members’ views to recommend images that are relevant to individuals, keeping the experience fresh, personalized and engaging.
Additionally, ML and natural language processing drive AI in customer service. Chatbots, for instance, mimic human engagement with customers. McKinsey & Company describes ML as the new “frontier in customer engagement” that will “unlock significant value for the business…better service, higher satisfaction, and increasing customer engagement.”
How Does ML Support Decision Making That Improves Operations and Efficiency?
ML processes that immediately spot trends and patterns from underperforming benchmarked operations provide data-fluent managers and executives with business intelligence insights. Those insights enable decisions that can result “in the evolution of the business itself,” TechTarget predicts.
“Machine learning is the backbone of today’s business, turning data into insights and insights into action and predictability,” TechTarget concludes. For instance, ML can improve operations and efficiencies by:
- Automating analytics that detect anomalies in historical data trends, making for a powerful tool in cybersecurity
- Running simulations based on ML predictive analytics enables business leaders to bench test solutions and leverage optimization opportunities across the enterprise
- Evaluating new information and multiple business scenarios at scale to provide decision-makers with the costs and benefits of each
Graduates of FGCU’s MBA with a Concentration in Data Analytics online program are equipped to use data and ML to make business decisions. Through the program’s three elective courses — Data Management; Big Data Analytics and Data Mining; and BI and Visualization Tools — students gain foundational knowledge in the theories, practices, tools and techniques for using data, text and web mining.