'Democratization of Data' Pushes Big Data Trends in 2014
What does 2014 have in store for 'big
'? Current Analysis is predicting a picture is worth 1,000 words in a new report entitled, “Data Visualization and Discovery Market Trend and Requirements.”
Brad Shimmin, a principal analyst at Current Analysis, penned the report, which offers four takeaways: (1) data visualization and discovery tools seek to speed human thought in making business decisions using a wide array of data sources; (2) current tools are evolving to augment and replace traditional business intelligence (BI) and data warehouse analytical tools; (3) leading vendors are pushing into the cloud and focusing on mobility as a means of making big data more digestible; and (4) buyers must approach these tools with caution, as access to more and broader data points only heightens existing concerns over trust and validity.
Shimmin’s bottom line: a picture is worth 1,000 words, and well-designed data visualization is worth 1,000 spreadsheets filled with both words and numbers. Shimmin’s report looks at big data in the palm of your hand and discusses the current requirements of enterprise customers exploring the advantages of big data, a well as future opportunities technology providers are exploring within this rapidly growing marketplace.
Fueling Enterprise Big Data Investments
We caught up with Shimmin to get some insights from the report. He told us the democratization of data -- putting data into the hands of more people in the enterprise -- is fueling enterprise big data and BI investments.
“This trend is going to play out pretty big this year,” Shimmin said. “It’s also going to drive a lot of opportunity for data management and security vendors to step in and offer some controls to make that democratization of data a little bit more trustworthy, secure and safe than it might be otherwise.”
As Shimmin sees it, the general technology trends that are impacting most markets are also influencing big data and analytics. Those trends, specifically, are cloud-based delivery models and mobile centricity for the user experience.
“This is a huge trend because as you look at a BI solution, historically they have been pretty heavyweight server solutions that are usually tied to a data warehouse that itself is a pretty major undertaking for any enterprise,” Shimmin said. “That data warehouse may also be tied to other line of business applications and complex solutions that extract and make use of that data.”
Shimmin pointed to vendors like Tableau Software and QlikTech that are rolling out what he calls “lightweight” cloud-based solutions for data visualization and discovery. These solutions, he said, mean users are no longer tied to heavyweight solutions in the enterprise.
“Business users don’t have to wait for their company to invest in a huge BI program. A sales manager, for example, can simply register for one of these cloud-based tools, open up their iPad and ask how they can best align their sales department with the customers and these tools will help you understand that,” Shimmin said.
“These tools will even help you understand what questions you should ask about your data. That’s how good they are getting. It’s all about the democratization of data this year. That’s the leading trend.”
Posted: 2014-01-26 @ 8:11am PT
The comment about a sales manager registering for one of the cloud tools and getting answers related to alignment of department to customers, is highly simplified statement of reality. Subscribing to one of the cloud tools will alleviate many of the problems related to infrastructure. The applications required to answer those business questions are to be built either by corporate IT or groups part of the business groups. The light-weight solutions referred in the article are based on the assumption that reporting/visualization tools such as Tableau can be used on the raw data with no or little transformation; this very attractive proposition since major portion of BI budget is spent on making the data in “transformed” format easily consumable by business groups. The question is how much of big data and BI solutions need only such “light transformations"?