However, not everyone sees the potential of cloud BI. “Analytics in the cloud is in its infancy and will take some time to evolve,” said Tiwari. “Unless big transactional systems move to the cloud environment, I don’t believe that analytics alone will be able to support that market.”
Cloud BI adoption “has been slower than I would have predicted a few years ago,” Saunders agreed. However, he continued, “the momentum is unstoppable. The days of the ubiquitous corporate data center are numbered.” Cloud BI will increasingly be an attractive—or perhaps the only—option. “The weight of data will continue to decrease. Cloud analytics will not only be more cost-effective, but seamlessly integrate with collaboration, messaging, CRM, and line-of-business applications,” Saunders added.
Cloud BI is likely to only keep gaining converts as well. “If your traditional cloud-based system can one day make BI functionality available for you without the need of setting it up yourself, that’s obviously good news,” said Alexey Utkin, financial services practice leader at DataArt. “Moreover, certain cloud systems may seamlessly integrate with one another, thus enabling the linking of your data for BI.”
Big Data Complexity
Is big data opening things up, or making things more complicated? The experts are divided on this question. “Like any other innovative technology in the past, big data analytics still present a challenge for many enterprises,” said Dragan Rakovich, CTO of analytics and data management for HP Enterprise Services.
New-generation BI and analytics systems are needed to handle the scale of big data that will be part of corporate decision making. “BI and analytics are being transformed by the onslaught of new data sources,” said Smalltree. “However, collecting and analyzing data like this is a major challenge: Consider millions of vehicles sending sensor readings every minute, or billions of video game players moving through an application. The data is not just massive in size. It’s also created very rapidly, in new formats and often remotely, far outside corporate firewalls. This has created a challenge for traditional data warehouses and BI tools, which were not built for data of this format, velocity, and scale.”
New tools are coming on the market, but many early offerings required new investments in skills, resources, and training, Smalltree added. As with any transformative technology, big data analytics will require investments and disruption. “Big data doesn’t magically get transformed into information,” said Crupi. “Money needs to be spent on professionals who wrangle, cleanse, and write the code to analyze the data quickly.”
Big data “is definitely opening things up and the market has seen a slew of products cater to this demand,” said Tiwari. “For example, SAP has bet on HANA to address this space. But big data can also be overwhelming if you do not understand your business well. There has to be a very competent business and data science team in place in order to take the right data inputs, in the right context, and generate relevant insights. If this is not in place, irrecoverable mistakes can be made.”
Ultimately, the ability to manage big analytics will directly impact business success. “Companies that are able to unlock the value of their data will win and ride the next wave of innovation because they are data-driven and agile,” said Rakovich. He outlined the questions he is now seeing being asked: How do I use data to enable employees to create new innovations? How do I start today without entering in technology obsolescence risks? How do I analyze 100% of relevant data from new sources—whether human, machine, or transactional? How do I secure talent and expertise? How could I start today without making massive investments?
The new analytics means a behavioral shift for enterprises. “Companies will have to get more comfortable in making data-driven decisions,” said Amladi. “With the widespread availability of data, they will have to get better at distilling the signal from the noise. The way companies will do this is through new technology capabilities. They will need the right talent and processes to slice and dice the information to get answers to the right questions that impact their entire value chain. The past has shown us we can answer faster with data analysis, but now we need to get smarter.”