New technologies are enabling companies to perform increasingly sophisticated data analytics on very large and very diverse data sets, an upcoming report from The Data Warehousing Institute (TDWI) shows.
The report is based on responses from 325 IT managers, business users and consultants at small, medium and large companies.
Slightly more than a third of the respondents said they are currently running some form of advanced analytics on big data — mostly for business intelligence, predictive analytics, data mining and statistical analysis tasks.
Close to 45% of those surveyed expect that big data analytics will enable more accurate business insights while 38% are looking to use the technology to better recognize sales and market opportunities better. More than 60% are hoping that big data analytics can boost their company’s social media marketing capabilities.
The fastest growing use case for big data analytics is advanced data visualization, according to the TDWI survey. A growing number of companies are running sophisticated analytics tools on big data sets in order to build highly complex visual representations of their data.
“Big data used to be a technical problem when companies were struggling to deal with the management of large volumes of data,” said Philip Russom, a TDWI analyst and author of the report. “Now, if you apply analytics to it, there is a lot that can be gained from big data, that you could not get” from traditional BI and data warehousing technologies.
The term “big data” refers to very large data sets, often hundreds of terabytes or petabytes in scale. Increasingly, the term is used to describe not just large volumes of structured data but also unstructured data such as weblogs, clickstream data, machine and sensor data and social media data.
In many cases, companies have long been hoarding large amounts of data compiled by call centers, RFID chips, supply chain applications and logistics tools, but had no good means of tapping it, Russom said,
Now, advances in technology combined with plummeting storage and hardware costs are allowing companies to store, manage and analyze massive amounts of very diverse data, quickly and effectively he said. A growing number of enterprises are sifting through huge volumes of detailed and intricate data for facts and patterns they didn’t know about or just weren’t able to recognize in the past, he said.
Helping them in these tasks are specialized database and data analytics technologies from companies such as Aster Data, GreenPlum, Teradata, Netezza, ParAccel , Vertica and SAP.
Products from such vendors feature new technologies and innovations that address some of the limitations in older database technologies. The new technologies include in-memory databases, columnar, massively parallel processing analytic technologies and tightly bundled appliances that allow people to store, manage and query big data in a way that was simply not possible previously, Russom said.
Open source tools like Hadoop and MapReduce are also giving companies new ways to attacks big data.
“Analytic tools and databases can now handle big data. They can also execute big queries and parse tables in record time,” Russom said in his report. “Recent generations of vendor tools and platforms have lifted us onto a new plateau of performance that is very compelling for applications involving big data.”
Implementing an advanced analytics capability with big data is not without its challenges, the TDWI report noted.
More than 45% of respondents said one of the biggest roadblocks to big data analytics was a serious shortage of skilled professionals. Exacerbating the situation is the fact that the skill sets required for new analytics applications are somewhat different from those needed for traditional business intelligence and data warehousing, the report noted.
A lack of business support and the overall costs associated with implementing big data analytics are other major roadblocks, the report noted.