10 big data trends for 2017
January 26, 2017 6:30 AM
Top 10 Big Data Trends Well See in 2017
Last year was the year of big data. This will be the year of data intelligence, as organizations look for actionable insights from all that data. Here are 10 trends to expect.
Demand for Big Data Gives Way to Data Intelligence
2016 was a big year for big data, notes Joe Caserta, founder and president of Caserta Concepts. Last year, more companies worked to become analytics-driven. As we look ahead to this year, not only will we see an even greater focus on data intelligence, well see very specific examples of the power of this new paradigm involving many proven and many new technologies.
Blockchain Grows Beyond Finance
Invented in 2008 and introduced to the world by Bitcoin, Blockchain has proven itself to be a viable solution on a global scale, Caserta says. Its reach will soon spread beyond finance. This new technology can revolutionize how business is conducted in healthcare, retail, law, etc. The music industry is already taking advantage of Blockchain, as reported by Venture Beat. Look for rapid investment and advancement in Ethereum and Blockchain technologies in 2017.
Apache Spark Becomes Standard For Data Handling
Open-source has proven itself as the way of the present; and Spark is one of the most proficient open-source frameworks in the industry today, Caserta says. Regardless of whether the data platform is on the cloud or on-premises, Spark has become the common tool to prepare, blend, and analyze data across all areas of business in an efficient, scalable way. Established enterprises will turn to Spark for its flexibility and portability across Python, Scala, and SQL to analyze data within structured, unstructured, and graph environments. Start-ups will rely on Spark for speed to value within Agile programs to catapult them into the future.
Big Corporations Move to the Cloud for the Enterprise
Most start-ups today are built from the ground up on the cloud, Caserta says. While Amazon may have embraced it early on, many tech giants (i.e. IBM, Oracle, and Microsoft) werent as quick to jump on the cloud bandwagon. In 2017, they will make up for lost time. Innovations (i.e. IBMs Watson Data Platform) and wider adoption of Googles Cloud platform prove that big tech companies will help enterprise-level clients get acclimated to todays cloud landscape. 2017 will introduce fierce competition for IAAS and PAAS solutions.
The Data Lake Becomes the New Norm
The adoption of the data lake during 2015 and 2016 has proven its advantages to existing and new data warehouse analytics environments, Caserta says. However, theres still a growing need for more companies to leverage data lake concepts, architectures, and technologies to share and manipulate data across data sources. The data warehouse alone is not the most effective way anymore. In 2017 and beyond, well see a tremendous growth in data lake implementations mostly built on the cloud. I wrote in depth on this subject in a recent article on how the data lake fits into the existing data ecosystem.
The Data Warehouse Remains Relevant
Even with the onslaught of emerging concepts and technologies, the data warehouse still has a place in todays data engineering world, notes Caserta. When it comes to presenting fully-governed, trusted data in an environment that can support arbitrary queries by non-technical users, theres simply no rival to the traditional data warehouse and business intelligence paradigm. Data warehouses will still be built in 2017. However, the need for the relational database will dwindle as emerging NoSQL technologies mature.
More Businesses Create APIs for Data
APIs have been common protocol in the software application world for years, Caserta says. Soon, they will become the standard method to build analytics platforms in the data world as well. In 2017, we will not only use APIs to get data from source systems, but also to provide the data assets from within our internal analytics platform. The growth of Apigee (now owned by Google), an API management platform, is an example of the vision on this front.
Integration Between Analytics and Apps
Businesses that intend to competitively survive are abandoning traditional business intelligence methods to follow the new paradigm of integrating real-time analytics with their business transaction applications, Caserta explains. Amazon, the cloud-computing giant, has used this technology to spoil consumers with their recommendations of similar products based on machine learning algorithms as they add items to their shopping cart. This year, as big data ecosystems become more commonplace, integrating analytics and applications will become the standard way of doing business.
Cognitive Goes Mainstream
In the simplest terms, cognitive computing simulates human thought through artificial intelligence, Caserta explains. Until recently, cognitive was limited to a small subset of industries, i.e. medical advancements and call center automation. However, self-teaching robots and chatbots have become regular news topics. This year, these technologies will become an integral part of enterprise data analytics by influencing and enhancing the customer experience. In 2016, IBM and Google did huge marketing pushes for their Watson and Brain offerings respectively. Well see more vendors offering Cognitive solutions and wide adoption of these solutions in 2017.
VR & AR Continue to Boom
Sure, weve all had fun putting on Oculus Rift goggles and exploring virtual worlds created for our enjoyment, Caserta recalls. But starting this year, well see progress towards using virtual reality as an application development tool, and augmented reality applications will be used to combine the virtual world with reality. Data collected by these applications will generate unprecedented volumes of valuable user data.
Search-Based BI Surges in Popularity
The business intelligence community has suffered dropdown menus and dragging and dropping for decades, Caserta says. We need our BI interfaces minimized to a simple search bar. By the end of 2017, search bars and facets will become the BI norm. And before long, companies will be able to interact with their internal corporate data the same way consumers interact with Google Search. Companies like ClearGraph and ThoughtSpot are on the right path. I predict more vendors like these will come to light over the next few years.
So, What Does it All Mean?
The common thread of all the years emerging trends is the need to make data and insights more accessible to the business world, Caserta says. Data analytics has already become necessary for business survival, so it only makes sense that decision makers be able to access it without having a PhD in SQL. Data intelligence is the amalgamation of business intelligence and data science, and 2017 is the dawn of its era.(About the author: Joe Caserta is the Founder & President of Caserta Concepts, a specialized data intelligence firm. Along with renowned data guru, Ralph Kimball, he is the co-author of the continually bestselling book, The Data Warehouse ETL Toolkit. For all things data, follow Joe on Twitter: @joe_caserta.)
