Semantic layers can be the key to a powerful, widely used business intelligence solution and an analytical waste of money--but what does a good semantic layer for self-service BI look like today? Tune into this webinar to learn how you can architect your systems and train your employees to encourage data sharing, improve data quality, and enhance your ability to produce average, powerful analytics across your organization.

The volume of data businesses produce today carries much significance in terms of overall growth. Foresighted companies know that if they want to vie in a highly-competitive market, they must deploy advanced analytics to ever-growing data sets. Using business intelligence allows them to look into their historical and current data sets, and it provides them predictive views of their business operations. Augmented by artificial intelligence and machine learning, business intelligence provides enterprises with decision-making context and recommendations. This significantly drives a move towards decision intelligence, the creative blend of technology into enterprise decision-making strategies and workflows.

Data visualization tools, desktop data discovery tools, and visual analytics are examples of traditional self-service BI tools that business analysts embrace because they provide a user-friendly way of quickly turning data into insights. These tools are geared toward business analysts that have the skills and knowledge to acquire the right data sets, perform the analysis, and present the insights needed to solve a business problem. Often, these data sets acquired by business analysts are not governed or managed by IT, but this is acceptable because business analysts have enough business knowledge to evaluate whether insights are reasonably accurate to address the business problem. Business analysts also have the skills to best present analysis in the form of beautiful charts and reports to make it easy for others in the business to interpret insights quickly for decision making. Machine-generated insights can remove business analysts entirely from the analytic process.

Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large data sets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots?

By 2020, 85% of customer interactions will be managed without a human. In the years ahead, businesses will probably still be run by human beings, but in order to compete and succeed, they'll have to leverage the growing and ever-more-prevalent intelligence of machines.Forget for a moment about about geeking out on toys like hover boards and flying cars -- we're talking about leveraging real AI for hard business purposes. Business intelligence (BI) enables businesses to know more about their wider markets, internal process performance and progress over time. AI makes it possible to get this critical information faster and cheaper. Businesses using AI for customer-facing or employee-facing tasks can often free up resources that would have gone to paying a human for analysis tasks -- and reinvest those resources in humans executing on the insights produced by AI.

By 2020, 85% of customer interactions will be managed without a human. In the years ahead, businesses will probably still be run by human beings, but in order to compete and succeed, they'll have to leverage the growing and ever-more-prevalent intelligence of machines.Forget for a moment about about geeking out on toys like hover boards and flying cars -- we're talking about leveraging real AI for hard business purposes. Business intelligence (BI) enables businesses to know more about their wider markets, internal process performance and progress over time. AI makes it possible to get this critical information faster and cheaper. Businesses using AI for customer-facing or employee-facing tasks can often free up resources that would have gone to paying a human for analysis tasks -- and reinvest those resources in humans executing on the insights produced by AI.

Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large datasets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots? What are the most visible 2017 Machine Learning trends?

Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large datasets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots? What are the most visible 2017 Machine Learning trends?