Horizontal integration was initially a term used in the realm of business. According to Investopedia, it refers to a merger between multiple companies that offer the same level on the value chain, a measure of where in the scheme of production a particular business exists. Horizontal integration has found its way into the realm of data analytics and stands at the cutting edge of data analytics strategies that want to encapsulate all the data that they can gather together. For analytics, knowing all of the known values is the aim because with that data we can produce more precise results within that data set.
A Case Study in Horizontal Integration
The United States army is currently the largest army in the world, based on reports by Military.com . Where better to test the return on investment in horizontal integration than in a field where up-to-date data can mean the difference between life and death? In recent years, the US military has invested heavily in horizontal integration of its IT systems. By combining multiple intelligence sources such as on-the-ground human covert-ops intelligence with things like remote sensing data via image intelligence and signals intelligence through the interception of radio communications, the military has managed to leverage the usability of horizontal integration to increase their efficiency on the ground.
The Power of Interoperability
When integrating different disparate systems that exist at the same value level, the idea should be to ensure that the systems are interoperable – whatever data one system has, all other systems on the network have access to as well. This has been employed in the medical device field by services such as Express MRI. This ensures that the data that is collected between these distinct systems is pooled into a common data store which can then be used to generate our analytics by taking this raw data and putting it through our analysis algorithms. What this ease of access allows for is the setting up of a platform that can return information from data in a very short space of time, further enabling quick decision making – something the US Military has a great need for in their everyday exercises.
Communication Across Systems
Horizontal integration allows for a system to be even more modular that it was initially designed. Thus, a system can take any number of inputs in a wide array of data types and then use that data to generate our analyzed results. In data analytics, including this sort of integration between systems allows for us to use data that was collected by a completely different system in order to expand our source data. As with any sort of analysis tool, the more data we have to work with, the more realistic our predictions will become. By using horizontal integration to expand the data source we also allow for parallel processing on similar systems, distributing the load and cutting down the time of processing. Getting things done faster is always better when it comes to time-sensitive data processing.
Raising Efficiency through Horizontal Integration
As more and more systems are interconnected, the data source swells, but so does the efficiency of the connected systems. by having access to all of this data as well as a means of processing this data, the system becomes much cheaper to maintain over the long term. Data duplication, as you see with Google analytics GDPR integration, ensures that a backup of all data is always available and results can be double checked to add a layer of verification to the results. All of this translates to a much more efficient system overall, leading to a primary benefit of a speedier processing system. When we combine the interconnection between systems, the interoperability that reaches all the nodes of the network, and add in the redundancy and efficiency inherent in such a system, we realize that this interoperable, integrated system can produce accurate results at a much faster speed than one of those systems working on its own, allowing us to cut down our processing time and get to our analytical predictions so much faster.
How Data Analytics Can Leverage Horizontal Integration
At its heart, horizontal integration offers a unique advantage to data analytics. Statista notes that Big Data is likely to generate as much as US$150.8 billion worldwide in 2017. This market is among the reasons why horizontal integration should be an interesting premise for data analytics. With improved speed and efficiency through horizontal integration, it’s well within the realm of possibility to look into a larger total number of metrics and discover new insights about the data that flows through a site each day. Combining the acquired information with the lower maintenance costs of collecting and analyzing this data and horizontal integration almost pays for itself. The returns in terms of insights, however, are priceless.
