Open thinking can be vastly improved by applying the agile analytics architecture.
Open thinking is one of the most commonly used building blocks when companies begin to build platforms nowadays. Companies like Amazon rely on open thinking to distribute information across their operations, marketing, finance, and technical teams. It is built for rapid expansion and scalability, allowing its proponents to constantly be in a growth state without any need to slow down.
However, like the prior building blocks, agile analytics will be vastly improved by applying the agile analytics architecture.
What is open thinking?
Open thinking relies on one core tenet: all of your data is stored in one place. Sound familiar? It should. We call this repository a DMP (data management platform), and it is one of the required pieces to a successful agile analytics architecture.
How do open thinking and agile analytics differ?
You might be tempted to say that open thinking and agile analytics sounds very familiar. You’d be right. They are familiar but differ in one vastly important aspect.
Agile analytics works across all available data: 1st, 2nd, and 3rd party data. Open thinking was developed to optimize the workflow internally within a company, giving all parties access to all of your 1st party data.
How does agile analytics help open thinking?
Agile analytics makes open thinking behave in a much larger way by bringing in further data sets to bolster the initial data. The 2nd and 3rd party data will allow us to broaden the capabilities of the 1st party data and ensure accuracy.
Let’s take the Amazon example and apply it to a product they already have going called anticipatory shipping. Anticipatory shipping uses an algorithm powered by Amazon’s 1st party data to ensure that they always have products in areas where they are going to be needed.
In open thinking, Amazon is connecting to their DMP and using past sales data combined with user and geographic information to make sure their local warehouses always have an appropriate supply of products in demand. So when you need toothpaste, Amazon already has toothpaste nearby and waiting.
Let’s apply agile analytics to the same algorithm and see how it improves. Let’s add in Scarborough 3rd party local demographic data combined with Experian segmenting and BlueKai web usage. We’ll also throw in some 2nd party social data from Facebook, Twitter, Instagram, and Pinterest.
We now have information that will not only help us figure out what items their customer might need a second time, but also what items which will be purchased in the near future. How? We’ll know what items they are loving on social media, articles they’ve read about specific items, what their local demographics are like, and what others in their demographic profile can afford.
Using the above information, we can now not only ensure that the items are in stock in local warehouses, but we can use the same data to inform on platform campaigns. We can show consumers the products we know they are going to be interested in and ensure the consumer can afford them.
The above example is one of many but as you see, bringing in more data can only expand the capabilities of your company, no matter what your process is. Data is something we should embrace and use across every facet of our companies to ensure greater success.