Dr. Ralph Hünermann (@RHuenermann) is the founder and CEO of odoscope, the state-of-the-art Operational Intelligence platform (SaaS) for optimizing your digital touch-points.

We recently asked Ralph for his insight on the future of data management through operational intelligence. Here’s what he shared:

Can you tell us about the mission behind odoscope? How are you hoping to impact the world of digital marketing?

Imagine the ability to appropriately address each individual user in each individual situation. At odoscope, this is our vision: Revolutionizing online communication by aligning digital touchpoints on the current situation.

We empower brands to base user approaches on their existing data treasures – and take both individual and situation-aware criteria into account. We believe that every digital touchpoint should be designed like this: data-driven and situation-aware. The results will be perfectly relevant customer experiences, optimized for each individual user and situation in the digital world.

What are the biggest challenges you’re observing in companies in how they manage and leverage data today? What are the most common mistakes you observe brands making?

Most companies still struggle with making their data actionable. It’s not that it would be a lack of skills or technology that kills Advanced Analytics and thus intelligent decision-making – it’s plain old access to the data.

First, their data is siloed between a variety of different departments and apps. Second, they lack the ownership over their (raw) data. A common enterprise uses about 500 apps simultaneously to collect, manage and analyze its data. Most of the apps’ vendors claim the ownership over the (raw) data. See Google Analytics – its users only get access to the analytical results. Thus, it is impossible for common brands to connect their data, correlate it and gain actionable insights. Instead, they analyze their siloed data – an unbelievable waste of potential.

What should companies be doing with their data today in order to make it work for them? What should they be doing to prepare for the future of data technology?

In order to completely leverage the potential of (big) data, brands must ensure their ability to combine any existing (raw) data. This means to choose an analysis vendor who leaves the data ownership in their own hands (Google Analytics doesn’t!). This means to break down data silos. This means to reinvent itself as data-driven through progressively understanding and benefiting from the data in an organization-wide manner. And this means to choosing the right technology, which may combine data from any source into one platform.

What is Operational Intelligence? Why should organizations care about it?

Operational Intelligence (OI) is the state-of-the-art response to those tough requirements. This disrupting technology basically works with a combination of in-memory computing and data-parallel analyses. Thus, it enables the continuous storing, updating and analysis of live, fast-changing data sets. These real-time data may be enriched with historical data from all possible sources.

The result: An unsiloed data lake that enables a 360°-view on a brand and its operation as a whole. Ground-breaking Prescriptive Analyses scrutinize this data lake on hidden correlations. As a result, they determine the specific actions required for achieving a certain goal – e.g. the most relevant user approach according to his/her certain situation. Because Operational Intelligence includes a self-learning system, the analyses’ findings are included in the data lake. By this, the decisions are constantly being refined.

What does Operational Intelligence look like in action?

Currently, especially e-commerce vendors leverage Operational Intelligence for individually tailored user approaches:

The Operational intelligence system in action: 1. Tracking, 2. Self-learning, 3. Correlation-based real-time clustering and 4. Prescriptive analytics

The OI-system firstly tracks user interactions with the displayed shop contents and integrates them into the data lake (step 1: tracking). Secondly, it connects data from all possible sources (e.g. web analytics, CRM, CMS, stock, returns,…). This provides the historical, un-siloed data lake for upcoming analyses (step 2: self-learning). When a user enters the shop, the system records his profile and situational properties. It then detects historical situations most similar to the current one for each site element (step 3: correlation-based real-time clustering). Lastly, prescriptive analyses find the most relevant elements for the current users’ peer groups. Those with the highest conversion probability are displayed automatically “per click” (step 4: prescriptive analytics). This is how an online shop may be adapted on any individual user in his whole diversity. The result: a perfectly relevant shopping-experience.

In tomorrow’s Part 2, Ralph builds on today’s discussion, diving deeper into why Operational Intelligence is important.

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