Welcome to what will be the first in a series of blog posts covering the art and science of decision support as it applies to internet properties, including content websites, e-commerce sites, and attendant applications for fixed and mobile devices.
Although this covers the same domain space as Web Analytics (WA), Digital Analytics (DA), Business Intelligence (BI), Data Warehousing (DW), and even Big Data (BfD); I find that these terms are more useful when selling technology and consulting than to describe real business problems and proposed solutions. For example, I’ve heard it said that the definition of a Web Analytics application is simply a BI application that Marketing will pay for these days.
What is clear is that the WA, DA, and BI/DW spaces are rapidly converging out of necessity. Ask marketing decision makers what they like least about the way their data is delivered to them, and most will tell you that having to use bespoke siloed applications for clickstreams, search, ad performance, attribution models, CRM, etc. is not only inefficient, but ineffective as they often produce conflicting information.
After purchasing these tools on their own, business leaders realize they require integration of this data and a coherent access path. This is mandatory since customer acquisition, conversion, and retention decisions are not (or at least should not) be made entirely independently of each other. When attention is turned to profitability, additional information is needed around revenues, cost of goods owned, transported and sold, inventory levels, labor rates, etc. This level of integration is only practical using a data warehouse or one of the new cloud based integration and presentation platforms. More on those in a future post.
Of course, looking at history is also insufficient since a full decision loop includes analysis, projection, action and reporting the results of those actions against expectations. Actions in this case are not confined to changes in strategy, tactics or resource allocations. Digital properties afford us the ability to change what we present to the customer frequently, even continuously in the form of personalized experiences and marketing experiments. These tools need to be integrated as well but are often separated both technologically and organizationally.
When this reality becomes apparent, the business leadership often looks to IT to provide an architected solution. This approach was feasible, often after some fits and starts, with Financial, Supply Chain, and CRM data which was mostly structured and static. Even the protracted ERP/CRM/DW efforts that lasted years eventually bore some fruit in many cases. In the digital world, however, we operate on Internet time. The data we must integrate and use is often neither structured (customer textual feedback, video content) or static as key data like prices can change many times a day. We are expected to acquire, analyze, and present useful information with very low latency, sometimes in real or near-real time.
Historically, the software industry was content to lag behind the decision support needs of its customers and react with half-baked and often re-purposed solutions; allowing more nimble startups grab market share by innovating and selling directly to business-side buyers who were much more willing to go with less established vendors than their risk-averse IT counterparts. That was then. These days, the established players in software are gobbling up the analytics startups quickly as they recognize the need for integrated solutions with robust service and support capabilities and facing a market where CMOs are spending more on technology than CIOs.
This shift in buying power has specific implications. In my experience, and that of my industry contacts, it is rare in larger enterprises that one person in the organization who has both the expertise and the authority to make the architectural and purchasing decisions with regard to the decision support technology stack. In other words, it is common to see Finance, Marketing (traditional), Marketing (Online), HR, IT, etc. all making purchase decisions of BI/Analytics technology without any real collaboration or architectural vision. The availability of these capabilities as cloud based services has drastically lowered the barriers to purchase, further exacerbating the problem. This is great for the vendors, but IT often loses any real control or even awareness of all the enterprise data assets that are living in the cloud. Eventually, I predict this situation will prove unsustainable for most firms as they realize that the whole of their decision support capability is drastically less than the sum of its parts.
What do you think? How do we regain the ability to architect our decision support toolsets?