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Top 4 big data and analytics trends of 2016

Thor Olavsrud | Dec. 22, 2016
Big data and advanced analytics continued to make inroads in the enterprise in 2016 as organizations learned how to interrogate data to better understand their customers and drive efficiencies.

3. Data analytics going into production

2016 saw analytics initiatives increasingly moving from proof of concept and test to production. That process was not without growing pains.

IT wants (but struggles) to operationalize big data

In 2016, most companies — especially larger enterprises — have begun to seek ways to leverage the data at their disposal. While big data leaders at large companies are confident their big data strategies are headed in the right direction, most also feel that they're struggling to operationalize them effectively.

Executives still mistrust insights from data and analytics

Part of the difficulty in effectively operationalizing data analytics initiatives may stem from business leaders, who are accustomed to making decisions based on gut-instincts and have trouble trusting insights from data and analytics.

It should come as no surprise, then, that organizations that are moving their data analytics initiatives into production are learning which questions they need to ask to make sure they're using the right technologies, tools and platforms.

10 questions to ask analytics vendors (before you buy)

Choosing the right analytics platform is essential. After all, the product you choose will be helping leaders make critical business decisions for years to come — if you get it right.

10 tips for integrating NoSQL databases in your business

NoSQL databases provide the agility, scalability, performance and availability to support many applications today, but implementing them is not always easy.

8 tips to get more bang for your big data convergence bucks

CIOs and other IT decision-makers are used to having to do more with less. In the world of big data, they may be able to achieve orders-of-magnitude cost savings and productivity gains due to the convergence of development, IT ops and business intelligence (BI) strategy, exploiting advancements in open source software, distributed computing, cloud economics and microservices development.

5 key requirements of successful big data projects

Successful big data projects have five key requirements that can make or break them: buy-in, urgency, involvement of non-data science subject matter experts (SMEs) and psychological safety.

4. Organizations share their successes

As more and more analytics projects went into production in 2016, and organizations began realizing value from those projects, they began sharing their success stories and lessons learned.

8 ways IBM Watson Analytics is transforming business


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