Everyone agrees that data analytics can bring benefits. The devil is in the details for the rest of the year, with vendors commenting on how, what, and why the field will evolve in 2016:
JY Pook, Vice President, APAC, Tableau Software, said visual analytics is becoming the common language to collaboration and reaching insights quickly. "We have seen a variety of users incorporating visual analytics tools in their daily lives, from business users, to students and homemakers. For example, Ngee Ann Polytechnic, one of Singapore’s leading institutions of higher learning, has embedded data analytics into its curriculum for both teachers and students. In fact, lecturers have also started using visual analytics tools in their classrooms, encouraging students to be more comfortable and familiar with working with data," he said.
The provision of more data sets is also boosting the field of big data analytics. "One of the main focus of Singapore’s Smart Nation initiative is to provide more open data to the public. With this, an increasing number of organisations will establish a Centre Excellence to foster adoption of self-service analytics. These centers play a critical role in implementing a data-driven culture. Through enablement programmes like online forums and one-on-one training, the centres empower even non-experts to incorporate data into their decision-making." Pook said.
Pook predicts the rise of self-service analytics. "Millennial workers will expect to have easy access to data no matter in the office or when they are on the road. They will want to explore the data themselves to make their own discoveries on the fly. That is why the demand for self-service data preparation tools and even self-service data warehouses will grow as a natural extension of self-service analytics. This democratisation will allow people to respond quickly to shifting priorities." he said.
John Schroeder, CEO and Co-founder of MapR Technologies, which provides a converged data platform that leverages Hadoop and Spark, has a related observation. He notes that traditional best practice has been to keep operational and analytic systems separate, in order to prevent analytic workloads from disrupting operational processing. “In 2016, we will see converged approaches become mainstream as leading companies reap the benefits of combining production workloads with analytics to adjust quickly to changing customer preferences, competitive pressures, and business conditions. This convergence speeds the “data to action” cycle for organisations and removes the time lag between analytics and business impact,” Schroeder said.
Schroeder also observed that tech cycles have swung back from centralised to distributed workloads. “Big data solutions initially focused on centralised data lakes that reduced data duplication, simplified management and supported a variety of applications including customer 360 analysis. However, in 2016, large organisations will increasingly move to distributed processing for big data to address the challenges of managing multiple devices, multiple data centres, multiple global use cases and changing overseas data security rules (safe harbour). The continued growth of Internet of Things (IoT), cheap IoT sensors, fast networks, and edge processing will further dictate the deployment of distributed processing frameworks,” he said.
Platforms will evolve to allow more access and analytics to non-analysts, Pook added. "Organisations will adopt platforms that let users apply statistics, ask a series of questions, and stay in the flow of their analysis," he said.
"New technologies designed for the business intelligence ecosystem are constantly emerging. As these go to market, we will see gaps that need to be filled. Hadoop accelerators, NoSQL data integration, IoT data integration, improved social media, each of these present an opportunity for businesses.
"In 2016, we will see the rise of the gap fillers, leading to a market consolidation. Organisations will continue to shift away from single solutions and embrace an open and flexible stack that includes these new technologies."
Kenneth Arredondo, President & General Manager, Asia Pacific & Japan, CA Technologies, says that analytics has evolved from business intelligence to transactional and big data. "Real-time analytics that enhance the customer experience by linking predictive insights to
prescriptive actions will become the new normal," the company said.
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| Source: Tableau Software. Pook. |
The provision of more data sets is also boosting the field of big data analytics. "One of the main focus of Singapore’s Smart Nation initiative is to provide more open data to the public. With this, an increasing number of organisations will establish a Centre Excellence to foster adoption of self-service analytics. These centers play a critical role in implementing a data-driven culture. Through enablement programmes like online forums and one-on-one training, the centres empower even non-experts to incorporate data into their decision-making." Pook said.
Pook predicts the rise of self-service analytics. "Millennial workers will expect to have easy access to data no matter in the office or when they are on the road. They will want to explore the data themselves to make their own discoveries on the fly. That is why the demand for self-service data preparation tools and even self-service data warehouses will grow as a natural extension of self-service analytics. This democratisation will allow people to respond quickly to shifting priorities." he said.
John Schroeder, CEO and Co-founder of MapR Technologies, which provides a converged data platform that leverages Hadoop and Spark, has a related observation. He notes that traditional best practice has been to keep operational and analytic systems separate, in order to prevent analytic workloads from disrupting operational processing. “In 2016, we will see converged approaches become mainstream as leading companies reap the benefits of combining production workloads with analytics to adjust quickly to changing customer preferences, competitive pressures, and business conditions. This convergence speeds the “data to action” cycle for organisations and removes the time lag between analytics and business impact,” Schroeder said.
Schroeder also observed that tech cycles have swung back from centralised to distributed workloads. “Big data solutions initially focused on centralised data lakes that reduced data duplication, simplified management and supported a variety of applications including customer 360 analysis. However, in 2016, large organisations will increasingly move to distributed processing for big data to address the challenges of managing multiple devices, multiple data centres, multiple global use cases and changing overseas data security rules (safe harbour). The continued growth of Internet of Things (IoT), cheap IoT sensors, fast networks, and edge processing will further dictate the deployment of distributed processing frameworks,” he said.
Platforms will evolve to allow more access and analytics to non-analysts, Pook added. "Organisations will adopt platforms that let users apply statistics, ask a series of questions, and stay in the flow of their analysis," he said.
"New technologies designed for the business intelligence ecosystem are constantly emerging. As these go to market, we will see gaps that need to be filled. Hadoop accelerators, NoSQL data integration, IoT data integration, improved social media, each of these present an opportunity for businesses.
"In 2016, we will see the rise of the gap fillers, leading to a market consolidation. Organisations will continue to shift away from single solutions and embrace an open and flexible stack that includes these new technologies."
 |
| Source: CA Technologies. Arredondo. |
"In 2016, an era where the 'demographic of one' will
emerge and enable organisations to personalise services, pricing, sales
and products in real-time for the individual versus larger segments.
This will partly be driven by the increased
prevalence of analytical tools and techniques to derive deeper and more
meaningful insights.
Pook agreed, predicting more activity in the data integration space. "These days many companies want agile analytics. They want to get the right data to the right people, and quickly. This is no small challenge, because that data normally lives in many different places," he said.
"In 2016, we will see a lot of new players in the data integration space. Companies will stop trying to gather every byte of data to have them stored in the same place as more sophisticated tools for data integration emerge. Data explorers will connect to each data set where it lives and combine, blend, or join with more agile tools and methods."
EMC envisions a two-tier model for big data analytics. "Tier one will comprise ‘traditional’ big data analytics: large scale data analysed in non-real-time. The new second tier on the other hand will comprise relatively large data being analysed in real-time, courtesy of in-memory analytics. In this new phase of big data, technologies such as DSSD*, Apache Spark and GemFire will be every bit as important as Hadoop. This second tier represents a new and exciting way of using data lakes – for on-the-fly analysis to influence events as they happen. It has the power to give businesses a level of control and agility simply not seen before," said John Roese, Senior Vice President and Chief Technology Officer, EMC.
In-memory analytics is still nascent, however. Roese noted that there must be enough memory and space to house large scale data sets within memory. "Some thought will also need to be given as to how data can be efficiently moved between big object stores and the in-memory capability. The two operate at radically different performance curves and IT teams will need to be able to manage the demarcation point to ensure data can move back and forth at speed transparently. Work is currently underway with new object stores, rack-scale flash storage, and technology to make them work together as a system. Open source initiatives will play an important role in meeting this challenge," he noted.
Data persistency will also be essential for large-scale in-memory environments. "The issue here is that if you ‘persist’ data within the in-memory environment, any flaws in the data will also persist. As a result, in 2016 we will see the rollout of storage-style data services to the in-memory environment. These services will include deduplication, snapshots, tiering, caching, replication and the ability to determine the last-known state where the data was valid or the system was operating correctly. These capabilities will be incredibly important in the move to real-time analytics as more non-volatile memory technologies become commercial in 2016," Roese said.
CA Technologies and MapR Technologies both say that analytics will become more targeted in future, while Veeam Software says its definition has changed. Big data will shrink as it is redefined in the face of cheaper storage, explains Julian Quinn, Vice President of Asia & Japan at Veeam Software. "Big data will drop its 'big' label and instead come to be viewed simply as data to be harnessed effectively for customers, partners and staff. Businesses will be able to increasingly focus on interfaces and connecting end users to data, further elevating the importance of availability of service delivery," he said.
"Analytics is most useful when applied to specific problems – not general ones. Big analytics packages that can crunch data endlessly looking for that needle in the haystack will be the past. The way forward is to think of analytics as not the solution to anything, but that it’s going to be part of the solution to everything," Arredondo from CA Technologies added.
Interested?
Read the TechTrade Asia blog posts about 2016 trends:
Citrix outlines cloud, IoT and security trends for 2016
What will happen to cloud computing
Vodafone M2M Barometer shows increased adoption for M2M
New challenges for the Internet of things in 2016
Security in 2016
New software architectures and technology frameworks
*An EMC acquisition focused on 'rack scale flash storage'.
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