The Cloudera Support Organization has always strived to not only provide solutions to our customers but to also deliver helpful knowledge. One of the primary sources of that knowledge comes from our Knowledge Articles. This content is created and curated by our knowledgeable Support Staff based on real-world experience coming from support cases. These Knowledge Articles have proven to be invaluable to our Support Staff over the years. While the content is also available to our customers to use in their own troubleshooting efforts, we want to do more to help bring the right Knowledge Articles to our customers at the right time.

Whether it's simply interacting with Siri or OKGoogle on our phones, or talking to Alexa on our countertops, we have come to rely on the convenience of being able to casually check the weather before heading out for the day. We can see another example of a question answering system in the form of chatbots, which have become ubiquitous for digitally transformed businesses as a way to streamline and improve customer service operations with more natural language interactions (though the jury is still out on whether these chatbots facilitate or frustrate the user experience). A third example that is becoming increasingly popular can be seen in augmented analytics tools that are enabling non-technical workers to become "information workers" thanks to the ease of access to data insights through plain natural language queries, rather than highly specialized database languages. These types of capabilities are predicted to increase dramatically in the next three to five years as natural language capabilities march towards maturity.

In 2019, organizations invested $28.5 billion into machine learning application development (Statistica). Yet, only 35% of organizations report having analytical models fully deployed in production (IDC). When you connect those two statistics, it's clear that there are a breadth of challenges that must be overcome to get your models deployed and running. The following paragraphs will give you deeper insight into these challenges and how you can overcome them. No matter what stage of machine learning development you're in, if you are working with point solutions or siloed toolsets you're creating vulnerabilities for your models and your business.

A secret to adopting machine learning that has nothing to do with the actual technology. Machine learning has the potential to transform your business. To automate processes, uncover new insights, make your products and services better, and customers happier. Integrating the capability into your organization requires operational transformation and lots (and lots) of experimentation. But, you know this already. What you may not know is one key secret to making your initiative successful: keep an open mind.

Wine quality is judged by an external group of fancy wine people who will determine your wine's future. If you get an Excellent quality rating, it's good for business. It's like Yelp reviews, mostly out of your hands but can make or break your business. So there is clearly a business benefit in trying to produce a wine that will get a good rating. While that seems self-evident – make good wine and people will like it – there is enough subjectivity in wine preferences that it makes this hard to do. I don't believe the producers of wine that are rated as a poor quality set out with that intent.

Machine learning (ML) has become one of the most critical capabilities for modern businesses to grow and stay competitive today. From automating internal processes to optimizing the design, creation and marketing processes behind virtually every product consumed, ML models have permeated almost every aspect of our work and personal lives -- and for businesses, the stakes have never been higher. Failing to adopt ML as a core competency will result in major competitive disadvantages that will define the next market leaders. Because of this, business and technology leaders need to implement ML models across their entire organization, spanning a large spectrum of use cases. However, this sense of urgency, combined with growing regulatory scrutiny, creates new and unique governance challenges that are currently difficult to manage.

Today's modern enterprises are collecting data at exponential rates, and it's no mystery that effectively making use of that data has become a top priority for many. According to a recent survey of 2000 global enterprises by McKinsey & Company, 47% of organizations have embedded at least one AI capability in their standard business processes. This is up from 20% in 2017 and it's clear that this growth has created a global race to enabling the next important evolution of business as we know it: The AI-first enterprise. But what does this actually mean? With investment in AI technologies poised to reach $9.5 billion over the next three years, the imminent opportunity involves embedding data and machine learning intelligence across the business at scale -- predicting the next best move for growth, making every product a data product, or creating entirely new data-driven revenue streams.

Drum roll… Starting from well over 100 nominations, we are excited to announce the finalists for this year's Data Impact Awards! Each year, nominees have raised the bar, and this year is no exception. The level of impact that organizations have shown and the variety of use cases are inspiring. From AI models that power retail customer decision engines to utility meter analysis that disables underperforming gas turbines, these finalists demonstrate how machine learning and analytics have become mission-critical to organizations around the world. Two weeks from today we will announce the winners at the Data Impact Awards Celebration on Tuesday, 11th September the week of Strata Data 2018, New York.