Like mobile and cloud, big data and advanced analytics have been reshaping organizations and business processes. In 2016, organizations increasingly moved data analytics projects into production as they sought the capability to better interrogate internal and external data to better understand their customers and drive efficiencies.
Here are our picks for the most significant big data and advanced analytics trends in 2016, as illustrated in 15 stories from the past year.
The market for data analytics is growing
Once regarded as hype, big data and advanced analytics are now busy transforming the enterprise. Organizations, determined to gain a competitive edge — or simply remain competitive — invested heavily in services, technology and people in 2016. The trend doesn’t show any signs of abating soon.
The market for big data and business analytics, already large, is continuing to expand at a rapid clip. Research firm International Data Corporation estimated worldwide revenues at nearly $122 billion in 2015 and has forecast they will hit $187 billion in 2019, an increase of more than 50 percent over IDC’s five-year forecast period.
2. Careers, staffing and training remains top-of-mind
Concerns about the analytics skills gap have existed for years. In 2016, it became increasingly clear that the shortage isn’t just in data scientists, but also data engineers, data analysts and even the executives required to manage data initiatives. Organizations and institutions in 2016 expanded their efforts to train, hire and retain data professionals.
Data scientist has become one of the most in-demand, high-profile careers in IT as companies seek the capability to make predictions based on data. There’s still no one-size-fits-all way to become one.
As the demand for data analytics professionals explodes, universities are rushing to fill the gap with undergraduate degrees in big data.
Experienced data scientists, and the people seeking to hire them, are running into difficulties because organizations often don’t understand the data scientist role and what their responsibilities should be.
Data scientists may be in short supply, but so too are managers that understand data science and machine learning enough to spot the opportunities for using these disciplines to optimize their businesses.
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.
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.
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.
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.
NoSQL databases provide the agility, scalability, performance and availability to support many applications today, but implementing them is not always easy.
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.
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.
IBM’s Watson Analytics cloud-based data discovery service is helping customers put advanced analytics into production without the complexity of going it alone.
Toyota Financial Services’ Collection Treatment Optimization (CTO) program uses optimization concepts developed by credit card companies and applies them to auto financing. The program seeks to help Toyota’s collections agents optimize which borrowers to call to help reduce delinquencies and keep customers in their vehicles.
Starwood Hotels & Resorts Worldwide uses an analytics engine to alter hotel pricing rates on the fly, improving demand forecasting by 20 percent.
Echo Global Logistics’ human resources function uses an analytics service from HighGround to monitor employee engagement as part of an effort to improve retention and customer satisfaction.