Data is everything in today’s business environment. The problem is there is just so much of it. Every application we use, every interaction we have, and every system we access generates and consumes data.
The information held within this data can be used to drive business decisions, improve performance, and increase efficiency, but before we can extract value from the data, we first have to understand what it is telling us.
In today’s era of Big Data, it’s almost impossible to draw out the relevant insights using traditional data analytics. In order to make the data useful and actionable, many organizations are turning to artificial intelligence (AI) routines and machine learning technology to power their analytics.
The Role of Artificial Intelligence and Machine Learning in Data Analytics
Traditional data analytics involves a group of very specialized data scientists spending days, weeks, or even months examining and manipulating data, looking for patterns and relationships that can be used for business intelligence.
The process is tedious and time-consuming, and often not very effective. With the introduction of artificial intelligence (AI), organizations can analyze exponentially larger volumes of data in a fraction of the time, with far less intervention from skilled data experts required.
Machine learning (ML), a subcategory of AI, applies algorithms to structured and unstructured data looking for interrelationships, anomalies, and trends. AI routines and multi-dimensional visualizations are then used to render the results in a format that is highly understandable by anyone.
This “AI understandability” and visual modeling is crucial in today’s enterprises. Many of the key stakeholders and decision-makers who rely on the data insights are non-technical professionals, such as C-level executives, marketing directors, facility operators, and fleet maintenance managers.
Common Challenges of Adopting Artificial Intelligence and Machine Learning
Although there are many benefits to implementing AI and machine learning solutions to assist with data analytics, there are also inherent challenges. Here are four common roadblocks organizations encounter when taking on AI and ML adoption:
1. Legacy Systems
Data analytics has become a business imperative in essentially every industry. However, not all of these industries have kept pace with technological advances.
Organizations that operate with a significant number of legacy systems in place will encounter integration and compatibility issues with current AI platforms. Older technologies use different programming languages, frameworks, and configurations that simply don’t play well with today’s flexible cloud- and IoT-driven solutions.
To alleviate some of these issues, consider modernizing legacy systems prior to investing in AI and ML analytics tools, to help ensure all systems are compatible and integrate easily.
2. Data Quality
We’ve all heard the adage, “Garbage in, garbage out.” Machine learning models don’t know the difference between good and bad data. Machine learning precedent is set using whatever data you tell it to use. If that data is inaccurate, out of date, or otherwise poor quality, your analytics will not yield good results.
To increase the quality of your machine learning training dataset, employ a human to do a thorough review of the training data to ensure it is clean, complete, and consistent.
3. Knowledge Gaps
Although some AI-driven data analytics solutions are designed for a broader audience, every organization needs access to knowledgeable data scientists and analysts. However, as with most technical roles today, there is a significant lack of trained machine learning professionals actively looking for work. In fact, the shortage is so severe that a recent RELX survey found that 39 percent of respondents aren’t using AI because they don’t have the technical expertise to do so.
Organizations that are unable to staff needed analytics and data professional positions can partner with a managed services provider on projects that require a higher level of technical expertise.
4. Siloed Operational Knowledge
In manufacturing and utilities, facilities data is often siloed by department, which makes identifying interrelationships among data sources difficult. Without access to all datasets, both structured and unstructured, AI and machine learning capabilities are inefficient and won’t generate actionable insights.
Creating a “single pane of glass” for data analytics enables AI routines to provide full visibility into which variables are having the biggest impact on the target data. In turn, this allows data, operations, and maintenance teams to work together on a holistic solution to performance issues.
Tactics to Overcome Obstacles to Machine Learning and AI Adoption
Take the next step in supercharging your data analytics by implementing machine learning and AI solutions. Watch this on-demand webinar, ML Model Explainability Using 3D Visualizations, to:
Gain a deeper understanding of your machine learning models
Discover use cases for business analysts who need to utilize 3D visualizations to explore complex models
See how using Virtualitics API enables data scientists to fit our 3D visualizations into their existing process when it comes to creating and refining models