Businesses today face a real paradox when it comes to understanding visitor behavior.
One on hand, there is more and more data available about who visitors are and how they behave – both in-store and online. On the surface, this should be a benefit, as more data should be able to provide more insights, at least in theory.
But at the same time, all this data only makes the process of understanding visitor behavior more complex. From smartphones, to social media, artificial intelligence, and “Internet of Things” devices (just to name a few), there are far more factors that play a role in measuring and influencing visitor behavior than ever before. This trend is only growing.
Up to 85% of Big Data Initiatives Fail
To deal with this challenge, some businesses have embarked on a “big data” approach. In the process, they’ve often made massive technology investments to log as much data as possible. But unfortunately, that approach hasn’t been particularly effective, as up to 85% of big data projects typically result in failure, according to Gartner.
Meanwhile, businesses without the means for a big data initiative have often been left without the data they need to draw any useful insights.
The result? Despite a plethora of visitor behavior data now available, most businesses have yet to find a way to get value from it. In fact, many marketers and operators are more confused than ever when it comes to finding out who visitors are and what influences their behavior.
That’s why some businesses are starting to adopt a new approach using data, called omnidata intelligence. In this post, we’ll explain what omnidata intelligence is, how it differs from older methods like “big data”, and how it’s being used to address specific business challenges.
What is Omnidata Intelligence?
Omnidata intelligence is a business methodology for efficiently extracting value from multiple data sources. Unlike IT-led approaches to gaining insights from data, omnidata intelligence starts with a business outcome in mind and ends with a clearly defined strategy to deliver it.
The practice of omnidata intelligence is based on three core components: the right data, experienced people, and intelligent technology. By combining these components, omnidata intelligence practitioners, such as marketers, retail operators, and research teams can improve the quality, depth, relevancy of the insights they gather.
Here’s how the three components of omnidata intelligence fit together:
Omnidata Intelligence Component #1: The Right Data
Previous methodologies for data analysis often began with a “data first” mindset. That is, any and all data were collected, with little or no thought given to the desired outcome. This approach tended to generate high costs, both in terms of data infrastructure and IT staff.
Omnidata intelligence, however, takes a “solution” first approach. That is, the desired solution is specified first. This end result then informs what data sets are used in a particular project.
Whereas other fields of data analysis may involve dozens of data sources, an omnidata intelligence approach may simply require two or three data sources in order to generate an acceptable outcome. The result is less overhead from not having to deal with irrelevant data sets.
Omnidata Intelligence Component #2: Experienced People
Omnidata intelligence recognizes that data alone usually isn’t enough to provide reliable and consistent insights. Much like business professionals need a CPA to help them fully understand financial data, omnidata intelligence practitioners rely on experts to help them gain value from business data.
These experts typically fall into three groups:
Vertical domain experts. Every industry vertical has its own unique characteristics. That’s why involving experts who have spent time in a particular industry is key. Whether an omnidata intelligence project involves understanding insights in retail, transportation, entertainment, hospitality, or another industry vertical, industry vertical experts can help design a successful project and provide context to the results.
Data science experts. Omnidata intelligence wouldn’t be possible without data scientists. When data projects involve high volumes of information, data scientists are equipped to process it relatively quickly. At the same time, when limited data is available, data scientists are often able to extract insights where others would fail.
Engineering experts. Omnidata intelligence may involve acquiring new data sources, and engineers make this possible. They’ll integrate software APIs and may even build custom software and hardware integrations. Furthermore, engineers may also need to create new software to help business users access and visualize data appropriately.
Omnidata Intelligence Component #3: Intelligent Technology
Just like experienced people help improve the outcome of a data project, intelligent technology is also needed to deliver a successful initiative.
First and foremost, omnidata intelligence projects require the right software to help business users interpret data. Unlike open-ended business intelligence software, where users are usually required to construct data visualizations themselves, omnidata intelligence software may be built for the needs of a specific type of industry vertical or set of problems.
Secondly, intelligent technology helps users access the right data. For example, data from IoT (Internet of Things) devices can provide a wealth of information from physical objects that was never before possible, but without the right technology to tap into these data sources, they don’t provide much value.
Finally, intelligent technology refers to new methods of working with data, such as machine learning, artificial intelligence, and predictive analytics. These advancements help data scientists create faster outcomes and even predict the likelihood of future events.
Omnidata Intelligence vs Big Data
Big data is just one phase in the evolution of how technologists and researchers use data to inform business decisions. Omnidata intelligence is not a replacement for big data, it’s an evolution that attempts to address some of its shortcomings.
Here are a few of the key differences:
- Data first vs solution first. Big data initiatives often collect data with little or no discretion as to what will be useful. This can lead to massive costs of running data centers and the resulting IT staff. Omnidata intelligence, on the other hand, starts with a solution in mind first and only deals with the data needed to address it.
- IT-led vs business unit-led. Typically, big data initiatives are cost centers led by IT departments. Business applications aren’t always considered ahead of time. Since omnidata intelligence is a “solution-first” methodology, it’s typically led (and often paid for) by marketing, operations, or finance.
- Technology exclusive vs holistic. Big data can tend to emphasize technology and de-emphasize the need for relevant human expertise. Omnidata intelligence places equal emphasis on both.
Examples of Omnidata Intelligence
Omnidata intelligence can be applied in nearly any industry and applied to nearly any purpose. Here are a few examples of common omnidata intelligence uses cases today:
- Customer experience. An airport pairs Net Promoter Score® and passenger journey data to understand which terminals are negatively impacting the travel experience.
- Revenue analysis. A retailer combines data from people counters with POS data to understand the impact that visits to physical showrooms have on revenue.
- Venue performance. A shopping center group uses people counters and tenant data to obtain location-based analytics and understand how a new retail mix impacts visitor traffic.
- Operations Improvement. A sports venue uses data from people counters and live weather data to optimize the operations of its HVAC system.
- Marketing optimization. A restaurant uses Guest WiFi data, anonymized device ID tracking, and SMS response tracking to see which campaigns were most successful in driving return guests.
Getting Started With Omnidata Intelligence
As a new approach to understanding and getting value from data, omnidata intelligence is continuing to evolve. What’s most promising about this new methodology, though, is that it can support the needs of businesses that already have sophisticated data strategies as well as those who are just getting started.
Businesses can practice omnidata intelligence on their own, or they can choose an omnidata intelligence partner to help them in the journey. In either case, though, omnidata intelligence is helping businesses get accurate insights from their data more efficiently and more reliably than ever before.
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