Experts answer your top data science and machine learning questions
There’s no doubt data science and machine learning are main areas of focus for enterprises to better their business. However, talking about data science and machine learning isn’t the same as making it a reality. This is why we’ve pulled together a few prominent data technology experts to answer how enterprises can make these illusive concepts a reality by operationalizing data science and machine learning and implementing it throughout their business.
Let’s meet the experts:
- Colin Sumter is the founder of CrowdMole. He's an analyst, consultant and programmer specializing in IBM Bluemix.
- Adam Gabriel is a tech consultant & analyst with more than 25 years of experience.
- Steve Ardire, also known as the Merchant of Light, advises AI startups by interrogating reality to shape serendipity and connect and illuminate the dots that matter with clarity, conviction and passion.
What do organizations need to do make machine learning accessible and easy to implement across their businesses?
Steve Ardire: “Machines can attend to vastly more information and more complex processes than human beings. Because of this companies are reimagining business processes with algorithms and machine-reengineering to create new models for thinking about work and processes. It has the potential to augment our thinking beyond cause and effect and allow us to understand, and then improve, operations that are too complex for the human mind to manage, in some ways making the previously invisible visible.”
Colin Sumter: “The easiest starting point is with a simple question about your data: 'How's your data?' using Watson Analytics. Because machine learning is like credit. Your enterprise data is either great, or something else! The easiest way to implement machine learning into your business is to bring your great data into Watson Machine Learning & IBM Data Science Experience.”
Adam Gabriel: “The most important thing is to maximize awareness of machine learning amongst employees. Despite the slew of clever IBM Watson ads running on TV, very few people that I talk to actually know what Watson is, and what it's really capable of.”
In what ways can enterprise-ready deep learning frameworks simplify the intersection of business intelligence and artificial intelligence?;
Steve Ardire: “Natural language processing and machine intelligence are obvious basic elements. But what’s crucial is a dynamic hyper-relational graph knowledge base to proactively manage the five Vs of big data (volume, velocity, variety, variability and value). Another crucial ingredient is automated reasoning, which continues to learn even as conditions change. Knowledge workers will become more productive, fostering augmented intelligence where machines and humans work together.”
Adam Gabriel: “With the overwhelming amount of data currently generated by an enterprise, it's impossible to garner any useful business intelligence (BI) out of it without using artificial intelligence (AI). It’s simply beyond the capability of any human to wrap his or her head around it.”
How can we use data science and machine learning to re-imagine the customer experience?
Steve Ardire: “Effective data science means optimizing business outcomes for improved customer service, sales, ad conversions, productivity, employee performance and revenue growth — and most of all, where user experience is driven by AI. The future of audience development will be shaped by real-time personalization that will become emotionally aware, letting designers create the best experience for each user that’s hyper-aligned to the individuals’ interests. Emotional intelligence is the future because you cannot have ‘human-like’ intelligence without personality or emotions. People don't change behavior based on information, they change it based on emotion.”
To continue this discussion with fellow data enthusiasts, join us Thursday, September 7 at 1:00 PM (EDT) for the Fast Track Your Data — Live From New York webcast. This event will help you understand how to make the most of your data by learning to deploy data where it’s needed, adapting it to your changing needs and allowing for integration of multiple platforms, languages and workloads. Click here to register!