Which Technologies Are Set To Make Data Analytics Easier?

We ask the experts


In a recent Teradata survey, 96% of business leaders who responded said they consider a big data analytics strategy vital for the future success of their enterprises. Despite this, just six in ten claimed to be satisfied with how they manage their information assets.

With technologies in the sector evolving rapidly, organizations are likely to find their data efforts improve vastly over the next year. We asked nine data professionals what technologies they believe will have the most impact on their ability to analyze the wealth of data now at their disposal.

Mario Trescone, Senior Director of Business Intelligence and Data Analytics at YMCA of the USA

Open source code, greater accessibility to external information (market and consumer data), and the availability of tools that allow you to capture data, connect it to operational information, and then apply predictive analytics and machine learning will have the biggest impact in how the Business Intelligence role operates moving forward. These new technologies and methods will allow for faster, richer insights, enhancing decision-making capabilities, and provide companies of all sizes a way to stay competitive, allowing them to tap into new opportunities than ever before in history.

Calvin Dudek, Head of Data Science Research at the Department for Work & Pensions

One of the real breakthroughs was the release of the open source big data platform, Apache Spark. It's a big step up from Hadoop and makes large scale machine learning and near real-time analytics doable for everyone. Databricks recently released a community edition which makes it easy for beginners to learn Spark without all the pain of setting up their own Spark cluster, which is ideal for students and people who want to get their hands on it.

Matt Kautz, VP of Business Intelligence at Machinima

All of the data engineering solutions which allow for real-time data processing – Redshift, Spark w/ Hadoop, etc – are transforming the way that we evaluate quantitative data sets to allow for immediate optimization. I think advances in cognitive analytics and natural language processing offer the same opportunities for the qualitative data sets that businesses rely on for creative/strategic decision making.

Gustavo Canton, Senior Director of Research at Walmart

Mobile has become the new canvas. Our associates and customers spend countless hours with their mobile devices so our team is putting more emphasis in developing self-service apps to answer basic reporting questions. The rise of the artificial intelligence type softwares will become a big trend as well not only for their ability to answer questions in a timely manner but it will allow companies to create an insights, data repository system so that the knowledge is always readily available. Imagine having a Google-like search engine that will answer 90% of the business questions without the assistance of an analyst and where you get answers in a matter of minutes.

Vishal Singh, Country Head of BI & Data Analytics at Moët Hennessy USA

Open source technologies in the data sciences space will play a significant role in the ever increasing role and relevance of BI for the business. The push is not only to inform the business on ‘how we are doing?' or ‘why we are where we are,’ business is now looking up to BI to partner with them on what the future looks like for the company and if there are any corrective measures or options they could be presented with.

Saurabh Bhatnagar, Senior Data Scientist at Rent The Runway

Deep Learning will have a major impact in how we think about learning patterns in big data in the far future. But that will first happen in a few companies and slowly trickle down to others.

In the near future, common statistical techniques will be more automated. For example, to see if two trends are related, correlation coefficient can be computed and a fit for each trend computed automatically. Things like that will get easier with smarter tools.

George Sadler, Senior Director of Marketing Analytics at eBay

Machine Learning will become a game changer. ML will replace much of the decisions that marketers make today (manually), so that they can be made at scale and in real-time. This will lead to increased personalization and ultimately customer-centric marketing at scale. It will also relegate marketers to campaign and copy design (until the machines take that over too).

Eric Farng, Technical Lead Data Scientist at YP

We're all excited about how Deep Learning created huge gains in speech recognition, natural language processing, and image recognition. There are lots of people trying to find the right place for it in marketing, and I think it can have great success in marketing analytics, possibly click fraud detection or behavioral targeting.

Kuntal Goradia, Customer Experience & Digital Analytics at PayPal

IoT, connected devices, and connected cars will enable consumers to consume information at an unprecedented speed. Consumers are also expecting more relevant content - they want to spend less time finding and more time-consuming. Tools that provide agility in testing new features/ideas, personalization, and testing products that integrate deeply with content and application platforms, as well as tools that can collect information in-context from within the product vs. randomly surveys, are just some of the technologies set to influence the field of customer analytics.


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