IDP delivers data-driven marketing with unmatched expertise implementing in-database processing. It’s a fact, the closer the analytics are to the data source, the faster and more accurate the results. That’s why we focus on ‘invasive’ data management & deep-dive data mining. And why we build rock-solid ‘institutionalized’ analytical data sets that are available 24/7.
It’s easy to drown in data. The key is to take one specific process, make changes based on that specific purpose, and do it in a way that’s repeatable. You don’t need an army of analysts to be successful. The key is without a process you won’t succeed. — TechRepublic May 2015
When analytic demands exceed reporting tool capabilities, business analysts get in trouble. It’s not statistics causing problems, it’s data. Data volumes are too large; data structures are too complex; or there are too many disparate sources.
Far too much handcrafted work — what data scientists call ‘data wrangling’, ‘data munging’ and ‘data janitor work’ — is required. Data scientists spend from 50% to 80% of their time mired in this mundane labor of preparing unruly data. – New York Times Aug 2014.
Data Management is step one in analytics. And, along with data mining, absorbs at least 75% of the analytics process. It’s the heavy lifting – joins, sorts, aggregations – that can easily include several hundred million data rows.
At IDP we manage, prospect and probe large data volumes with complex structures and from multiple sources. We gather and prepare your unruly data; we discover new data patterns and relationships; and we build rock-solid analytical data sets — the foundation to marketable analytics.
It’s an absolute myth that you can send an algorithm over raw data and have insights pop up. — New York Times Aug 2014
Data Mining is step two in ad hoc analytics — finessing the data. At the heart of data mining is identifying relationships and interactions that support (or not) business assumptions or explain buying and viewing behaviors. Many business analysts don’t engage in data mining because they lack the skills. Or they’re less thorough in data mining because they lack training or because they believe their data is “good enough”. Instead, they depend on reporting tools to lump and dump data with no investigative dive — a primary cause of errors in analytics. These errors are buried in undocumented desktop code or Excel spreadsheets. And searching the reports for these errors is like searching the tip of an iceberg for navigational danger.
Unfortunately, few data scientists seem to be extensively trained in exploratory and devil’s advocate analysis of datasets. All too often they will download a dataset and proceed with the analysis based purely on what documentation says the data should look like. — Forbes, Jun 2016
At IDP, we mine data to measure data interactions against marketing directives and business rules. And we test data transformations for validity and significance. And with data mining we develop the best analytical approaches to answering business questions.
Ad Hoc Analytics
When it comes to analyzing data, in many ways it is far more important to have an understanding of the data one is looking at than it is to have a PhD in statistics. — Forbes, Jun 2016
Step three crowns the data management and data mining iceberg. Fact, data management & mining are meaningless without analytics and analytics are dangerous without data management & mining. Fact, the statistics for data-driven marketing analytics are not rocket science.
Today, more than ever, marketers depend on ad hoc analytics – drilling deep into customer transactions. Methods like segmentation, or recency, frequency, monetary (RFM) uncover powerful behavior patterns and define customer lifecycle profiles. But, reporting tools can’t deliver the deep-dive. And most business analysts, although Excel gurus, aren’t trained to manage complex data. As a result, analysts create error-filled reports and marketers lose confidence in both the analytics and the data.
It takes more than data scientists crunching data to be successful. Having the business perspective to develop actionable insights is essential. — GulfBusiness.com, July 2016
IDP solves your ad hoc analytics challenges, while understanding that you have limited resources. We dissect customer data. We identify, develop and validate data interactions and relationships to build accurate pictures of customer preferences and behaviors — and transform these insights into marketable strategies. IDP removes the requirement that business analysts manage unruly or fragmented data — bridging the gap between data management and analytics. And if you plan to repeat the analytics, we ‘institutionalize’ the processes; that is, we migrate the data management, mining and analytics inside your data warehouse so they’re available 24/7.
Today, data warehouses using in-database analytics can quickly process big data. Best of all, these solutions generally cost around 20% less to build than traditional platforms and perform more than ten times faster. Not only does this keep costs down, this time saving means the data being analysed is, by definition, more accurate. — ITProPortal, April 2016
Once you’ve developed great analytics sitting atop rock-solid analytical data sets then ‘institutionalize’ the analytics and take full advantage of in-database processing. Especially if you plan to re-run the analytics.
Stop moving data volumes across networks and (re-)creating analytics on workstations. Use the power of in-database processing to generate operational analytics that anticipate customers and launch behavioral triggers.
Automated, in-database analytics run side-by-side with your business processes and can launch real-time decisions – decisions generated from your marketing directives. For example, call centers use in-database processing to cross-sell new products. By analyzing customer history and behaviors with similar histories, an in-database engine recommends actions that reduce churn or suggest upgraded products.
At At IDP, we collaborate with Marketing and Technology to translate marketing directives into in-database processes. We engineer the intricate processes from marketing directive to analytics to in-database processes.
Processing data at the source, where it resides, is much more efficient than on a desktop or workstation. Deep-dive analytics require a scalable platform that can meet growing computing requirements. And exporting data limits users from running deep-dive analytics.
An analytic sandbox is physical space inside the data warehouse where business analysts build, test and run deep-dive analytics. Business analysts create and modify analytical data sets inside the sandbox.
The initial and most notable gains from an analytic sandbox are vastly improved speeds. Business analysts no longer depend on massive data dumps to workstations. Through in-database processing hours of effort literally drop to seconds. You also gain consistency in reporting across all departments when the sandbox is the single source of analytics.
IDP implements analytic sandboxes for marketing analytics as well as data quality management and business rules management.