Big data isn’t really about the data – it’s about the questions we ask, and how we ask them. The era of big data is evolving rapidly. Today it’s moving beyond the complexities of data analytics, and, like all things tech, it’s moving from the domain of a back-end priesthood of specialists to the front line of business users.
The prevailing myth about big data is that it’s the size of the data sets that make it “big.” And to an extent, that’s true. By collecting and analyzing exceedingly large amounts of data from multiple sources, it is possible to derive intelligence and spot trends that would not otherwise be visible.
But volume doesn’t automatically lead to insight. This perception leads many enterprises to start the process of data accumulation without much regard for the types of insights they might want to find. The hope seems to be that insights will magically fall out of the data by default.
The reality is that for those insights to appear, we need to know what data to look at, what to ask, and how to ask for it.
Bringing big data out of the back room
Deriving true insight from constantly increasing bodies of corporate information is a process that requires an army of data analysts in a back room, with insights derived through the resource-intensive filter of analyst teams.
This model, however, defeats the entire point of business intelligence and analytics, which is to provide line-of-business users with better, easier and more direct access to business insights that can be used to make better decisions. Data analytics software from Sisense, integrated with an Amazon Echo device to make use of its voice recognition features, is enabling this paradigm shift by allowing business management users to query big data with a natural language interface.
In the 1960s, Star Trek’s Captain Kirk amazed us when he first spoke to the ship computer in a natural voice, but today that type of interface is no longer the stuff of science fiction. Desktops and smartphones have voice-activated semantically aware interfaces with artificial voices like Cortana and Siri that can answer your questions and even tell you jokes. But the biggest indicator of a given technology’s success is when it crosses over from being a consumer curiosity to a business essential.
Such is the case with the emergence of natural language interface with corporate big data. By integrating with Amazon Echo, originally conceived for the consumer electronics market, Sisense is able to make those big data insights more accessible to business users.
The Star Trek analogy is not lost to Amazon, whose senior vice president for devices David Limp said in an interview, “The longer-term vision is a little bit like the ‘Star Trek’ computer.
Captain Kirk or Picard could sit on the bridge and ask anything and get the right answer.” According to Sisense CEO Amir Orad, the system, when used with Amazon Echo, uses Amazon AI and natural language processing to allow, for example, a department manager to make a verbal query on the fly during a meeting, and get an intelligent answer.
“Simplifying business intelligence means streamlining the entire process of preparing data for analysis, running complex queries and returning the results in a way that business users can easily understand and act on,” he said. “Technologies like In-Chip analytics can reduce the complexities of data prep, while introducing speech recognition and smart devices into the process makes the insights generated by data analysis immediately accessible and actionable, allowing users to replace traditional reports with a much more intelligent - and intelligible - interaction with their data.”
Orad highlights the natural language capabilities of the system, noting that, for example, a manager could say, “What was our total revenue last year?” and get an answer. What’s even more natural about it is that the system understands complex grammar and context. After asking the first question, a user could simply ask, “What was it in Europe?” and the system would understand that the pronoun “it” refers to the annual revenue figure mentioned the previous question.
Access, not volume, will guide the future of big data
The Internet of Things has created an environment in which a single manufacturer could have tens of thousands of sensors, each collecting millions of data points in real time. According to Ken Edwards, an independent consultant for channel data management vendor Zyme, estimates that there will be 28 billion “things” connected to the Internet by 2020.
By the same token, the as-a-service model has contributed to an even greater exponential explosion of information, and new integration technologies and middleware allow what was once an informal and widely dispersed – and inefficient – sales channel to move from out-of-date anecdotal data provided by sales staff, to a more rigorous and accurate body of customer information that adds even more to the corporate body of intelligence.
This increased volume of data, increased sources of data, and increased variety in types of data – together with the complexity inherent in business intelligence, brings up inherent challenges in actually getting insights into the hands of people who need it.
According to Aberdeen, 93 percent of organizations have seen significant growth in data volume over the past year, with respondents using an average of over 30 unique data sources regularly. Also, to make things even more confusing, 40 percent of respondents also use unstructured data from both internal and external sources.
According to the report, “larger datasets can offer deeper and more impactful insights, but also create strain on analytical tools and user skillsets.” The sheer volume, and technical sophistication required to deal with this massive volume of data, can be overwhelming. Aberdeen notes that the most successful vendors of analytics will incorporate user-friendly tools, like drag-and-drop interfaces, to make data engagement more possible for all users. Insight-driven organizations rule the day
Consulting firm Deloitte’s Analytics Trends 2016 report makes six predictions about what will shape businesses, noting the rise of the “insight-driven organization” which goes beyond isolated use of insights to drive decision-making, to instead deliver insights at every point of action, throughout the entire organization.
This transformation can only take place when complex analytics technology is accessible to all. With big data analytics solutions making their way out of the server rooms and into the board rooms, this transformation may finally be taking place.