With traditional analytics marketers anticipate behaviors, engage customers, reward loyalty, and retain loyal customers. Traditional analytics provide visibility into what customers buy, how frequently and in what combinations.
In-database ad hoc analytics does all the above in seconds, instead of hours.
In-Database Ad Hoc Analytics — Benefits
Speed. And the ability to accurately repeat analytics anytime — in seconds.
With in-database analytics, data prep plunges from hours to seconds. Gone are data transfers across networks and data crunching on workstations. Instead, the data warehouse handles data management. Analytical data sets are created at the source; and analytics complete in seconds, not hours. In-database analytics empowers marketers to use their data as strategic, competitive weapons; and provide instant visibility into shopping behaviors, market baskets, and responses to offers.
Data has become marketing’s most important asset. It enables better decisions about what customers want next. But acquiring data and ensuring in-time access to all can be a huge challenge.” — Fierce CMO, Dec 2015
In-Database Ad Hoc Analytics — Challenges
In-database analytics require exhaustive in-database processing – drilling deep into transactions, and easily include several hundred million data rows. In-house reporting tools fail at deep-dive analytics, especially with complex data. As a result, reporting tools create incomplete reports, and marketers lose confidence in the reporting.
the vast majority of retailers have spent huge sums of money implementing systems that ensure they sweep up every conceivable piece of data into a big data store. It is happening so fast that they have tons of data, a little information, and even less insight. — Chain Store Age, Nov 2015
Another challenge is that business analysts might be wizards in Excel or R, but aren’t trained in in-database processing; they can’t manage complex data structures. So they create error-filled analytics. And again marketers lose confidence in both the analytics and the supporting data.
Retailers will have to face up to the reality that every piece of data they hold about customers is not going to be valuable. Interesting, maybe. But not valuable. Probably useless. Forbes, Oct 2015
In-Database Ad Hoc Analytics — IDP
IDP encourages marketers to control their data, their analytics and their marketing technology. We believe that accurate, reliable supporting data is critical to marketable analytics. We also know that your data will be challenging, and that you have limited resources.
We remove the requirement that business analysts manage large data volumes or complex data structures — bridging the gap between data management and analytics — so analysts focus, not on data, but on the statistics.
It’s an absolute myth that you can send an algorithm over raw data and have insights pop up… — New York Times, Aug 2014
We manage large data volumes with complex structures and integrate disparate data sources. We build rock-solid analytical data sets that generate high-speed, marketable results.