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Several years ago, I was working as the CTO of a major tech company with significant operations around the world. One morning, while reviewing our daily collected figures, I noticed something odd. I determined that we were facing a significant sales drop in one of our most important target regions. We immediately scrambled all the relevant people in our department and began combing through our data to try to identify what the problem was and how to fix it. In a matter of days, we had identified the source and created an effective way to counter the problem, yet the scars of the experience were lasting and deep.

Today’s workforce and automation of key functions is moving us toward an unprecedented level of innovation. One recent study from KPMG found that about three out of every four U.S. tech industry CEOs believe automation and machine learning are likely to replace at least 5 percent of their manufacturing, technology, sales, and marketing workforce by 2019. Yet the same study found that over half of the executives expect their organization’s headcount to grow by at least 6 percent.

Data discoveries are changing the conversation — in the boardrooms of every organization and in the media. Whether you are using data to find new planets or to better understand user behavior, data is the foundation of every new insight.

There is systemic install fraud in the app economy, according to business intelligence platform Adjust.

In the future, we may look back on 2015 as the year that machine data analytics emerged as a true market disruptor, emanating from the mega market trends of Big Data, Cloud, DevOps, and the Internet of Things. Ironically, these mega trends have played a dual role in this market disruption: both as the creator of the problem — the volume, velocity, and variety of data — and source of the solution — a cloud-native, service platform to provide the scale, security, and rich analytics to transform real-time log streams into powerful operational and customer insights for business growth and success.

Visualizing data has led to world-changing triumphs — from tracking epidemics to pinpointing weather impacts. Today, every company has adopted some form of data visualization to better understand their customers, service, and market. Yet, while both companies and users tend to emphasize data visualizations, they fail to recognize that visualizing data is an end result. The means to this end is to properly prepare the data for analysis — or more specifically, to prepare data that’s increasingly complex due to its size, disparity, or structure.

It’s been 15 years since the bubble heyday, and things still aren’t quite the same. For one thing, U.S. tech companies on the whole are raising less money. They raised a whopping $71 billion in funding in 1999, compared to only $48 billion in 2014. Even more dramatic has been the drop in the number of IPOs. 1999 saw 371 — one for every day of the year with a few left over — while 2014 saw only 53, an 85 percent drop.

Are we in the midst of a tech bubble, one that’s headed for a dot-bomb-type crash?

When it comes to changes in the business-intelligence (BI) technology market, old giants of the enterprise software industry like SAP and Oracle aren’t leading the charge as you might expect. Instead, younger companies are the ones to watch when it comes to the most exciting business-intelligence software products that we’re seeing hit the market today.

The Internet of Things (IoT) is guaranteed to have an enormous impact on business that will bring productivity and competitiveness to levels never previously seen. What is not commonly discussed, however, are plans for how to get there and the consequences that will meet us upon the IoT's arrival.