Machine Learning allows organizations to make better data-driven decisions. It also helps solve machine learning mistakes that were previously beyond the reach of traditional analytical methods. Machine learning presents many of the same challenges as other analytics methods. We will discuss some common machine learning mistakes organizations make when incorporating machine learning into their analytics strategy. A shortage of deep analytics talent is a constant problem.

Data is a guiding light. Staring directly into it can blind us, but with it, we can see everything else. The facts and figures present in any given dataset tell stories, identify trends, and chart courses of action. But none of these come to fruition by the mere presence of information. Empirical evidence usually has this intended effect when those in possession of it commit to data analytics -- a critical foundational need in every company that wants to analyze data and use it to make decisions.

It is almost inevitable to introduce analytics in some format or other in the products you own. However, before you open the gates to this world of magic, there are three questions you should try answering. These three basic questions shall help in better planning for your analytics strategy and would act as a compass in times of uncertainty. Great that you have decided to embark on the journey - could be because of fear of missing the bandwagon. Nevertheless, without answering this question, your team would always be involved in directionless busy work.

Dr. Velkoski serves as Director, Data Science at the National Association of REALTORS and Adjunct Professor at DePaul University. It has been nearly eight years since data scientist was declared the sexiest job of the 21st century. As senior professionals demonstrating the talent and creativity necessary to transform raw data into deep, intuitive knowledge, data scientists were poised to revolutionize decision-making and strengthen organizational performance. According to PwC's 22nd Annual Global CEO Survey, organizations continue to struggle to extract actionable intelligence from data. Those that participated in the survey highlighted a lack of analytical talent, data silos and poor data reliability as the main causes for the absence of progress.

As a long-time part of this industry, I have spent a good part of the last decade talking about the digital transformation. Call it the digital transformation or the data-led transformation, but this transformation has been the talk of the town for a long time now. Not only are organizations geared toward achieving a better analytics setup, but they are also on the lookout for a more efficient data policy in the process. With many cloud service providers around us offering AI as a service, companies can tap into these ready-made options and enjoy a certain extent of customization options. Creating an AI setup of your own can be a tiresome task, as you have to feed different algorithms into the setup, and wait for it to deliver the right analysis on the data fed into it.

As data analytics becomes a more pervasive business tool, many leaders are being sold on the idea that all you need to diagnose any perplexing problem is more data. While there's no doubt that quantitative analysis can play a powerful role in telling you what happens, even the most robust, granular data won't tell you why something happens. Get the latest from Kellogg Insight delivered to your inbox. Instead, employing a combination of qualitative and quantitative methods to identify both the what and the why, according to two Kellogg School professors, is what makes an analytics strategy a useful tool for change. "Each has something powerful to offer," says Joel Shapiro, a clinical associate professor of data analytics at Kellogg.

As technology advances, the amount of data that is being created is growing at a rapid pace. Where throughout the 1970s, '80s and '90s most data that was being collected was structured and placed in databases, emerging technologies like the internet, mobile phones and smart devices, are generating more data than ever before. In 2013, IBM wrote that each day 2.5 quintillion bytes are created, and it is estimated that 40 Zettabytes of data will have been created by 2020. But just as the Volume of Data is rapidly increasing, so is the Variety of Data. Everything from emails, tweets, images, video and music, are now digital and being stored not just on local devices but more and more in the Cloud.

Data has evolved to become the lifeblood of every organization and analytics has grown and expanded enough that almost every organization today, recognizes the business value that analytics offers.Significantly improvedcomputational power, combined with low-cost storage and increasingly sophisticated algorithms mean that the next two-three years could possibly usher in the most exciting phase for analytics. Let's take a look at some of the trends that could dominate the near future. For the last couple of years, the trendwas to label everything that does something remotely clever or unexpected as Artificial Intelligence. While AI is certainly worthy of attention,2018 promises to be the year that separates the reality from the hype.Analytically mature organizations have already embarked on small scale experiments to embed greater smartness in their systems in areas of Chat Bots, Fraud detection, and so on. Those who have applied AI in a practical and clearly defined manner will see success.

Data is invaluable to all companies, from budding startups to global enterprises. This growing commodity is triggering organizations to deploy BI solutions that will elevate and accelerate data-driven decisions. Successful organizations are prioritizing a modern BI approach, and in turn, priming their workforce to be the most analytically savvy generation ever seen. For a competitive edge in 2018, organizations must recognize the strategies, technologies, and business roles that can enhance their approach to business intelligence. Here are some of the most critical trends to bear in mind looking ahead to a new year, and even beyond.

"You can't get good insights from bad data, and AI is playing an instrumental role in the data preparation renaissance."–Narendra I have interviewed Narendra Mulani, chief analytics officer, Accenture Analytics. What is the role of Artificial Intelligence in analytics? Narendra Mulani: Artificial Intelligence will be the single greatest change driver of our age. Combined with analytics, it's redefining what's possible by unlocking new value from data, changing the way we interact with each other and technology, and improving the way we make decisions.