Nexus 2

Decisions as a Service sits at the center of another web, see the diagram below.  It is where data comes together from numerous data sources, including:

• Third party data - one of the benefits of this type of approach is that you can pick and choose the data you need and get it when you need it.  This also ensures that the data you have is always fresh.
• Internal data - obviously internal data is always valuable when making decisions.  Particularly proprietary data can give competitive advantages.
• Internal systems - data may be generated real-time and might be pushed to the decisioning system, or requested by the decisioning system.

Results from the decisioning system can be pushed back to the internal systems or databases of the end user, or they may be pushed to third party service providers such as:

• Call centers
• Printers
• Content providers
• Suppliers

A complete Decisions-as-a-Service system allows the end user to tie all of this together and create tremendous value.

Beneficiaries

I see two groups of companies deriving benefit from Decisions-as-a-Service.

1.  Direct users.  These are folks who wish to implement automated decisioning logic themselves.  Examples are far and wide, including in marketing, membership management, credit policy, fraud policy, and so forth. This normally requires some level of sophistication from the users as they have to get down into the nuts and bolts of formulating decision flows.  Depending on the platform used this may require significant IT resources or, for more business friendly applications, a decent business analyst.
2. End-solution providers.  For example, many smaller analytics shops do not have the facilities to host their models of solutions themselves.  So, what often happens is that they "partner" with large servicing providers to do the hosting for them.  The issue with this is that these "partners" in the final analysis really couldn't care less about the smaller shops, so time and again the smaller shops get thrown under the bus.  The smaller shops can benefit tremendously from an independent provider of hosting services specifically designed for the delivery of analytical decisioning solutions.

Nexus 1

It seems to me that Decisions as a Service comes together at the intersection as a set of exciting technologies that are about to hit center stage:

• Data as a Service.  New data sources are popping up all over the place, and access is becoming automated.  Gone are the days of loading your data warehouse with every morsel of data you can get your hands on.  In the new world, you reach out and get just the data that you need and when you need it.
• Web delivery of applications.  The presentation of full featured applications through the web is all but mature.  There is almost no reason that any application couldn't be delivered through a browser interface.
• Cloud computing.  I suppose it is fitting that the term cloud has been used for this nebulous concept.  I will take it to mean the execution of applications on a distributed platform the interface to which is essentially opaque to the user.  Amongst the benefits of this, in this context, are that resources become shared (i.e. cheap) and the distributed nature virtually guarantees universal accessibility.

Combining these technologies with a powerful decisioning system will results in a platform that can deliver superior automated decisioning capabilities to businesses.  This platform has the following features:

1. Decisioning power on demand, tailored to each business' cycle,
2. Instant and transparent access to data that is critical to making informed and competitive decisions, and
3. Very low entry barrier to sophisticated decisioning.

Nirvana.

Use case: real-time talk-track optimization

Today I will discuss my first use case example.  The objective is the creation of a dynamic talk-track for out-bound calling agents.  In this particular case, the company I'm talking to has a call center where they call their prospects and walk them through a list of offers.  The current state has the agent essentially walk through all the offers sequentially, hoping that we get to an offer that the prospect is interested in before the prospect calls it quits.

So, what we want to do is create a system that in real-time evaluates what we know about the prospect up to that point (including information gathered during the call) and what the best possible offer is given that information.  The proposed system is diagrammed in the figure below.

This system - which has been set up in pilot form - allows business analysts to implement and modify the business logic to select the next optimal offer entirely independent of the calling agents.  Additionally and extra level of independence is achieved by making the web content, in this case the offers, entirely database driven. This is a significant benefit, because it means that the content can be modified by simply changing fields in a database, rather than having to engage in web-page coding.

Because the decisioning can use any of the data that is stored in the prospect database in real-time fashion, and because data is saved into the database during the conversation with the prospect, the system becomes a dynamic optimizer of agent talk tracks.  It is anticipated that this solution will at least double the revenue generated from this particular endeavor. 

Stored procedures

I was having lunch with a good friend of mine the other day.  He recently took a new position as analytical big wig for a marketing company.  The company has a great deal of data, but he lamented that all the current business logic was coded as stored procedures in their databases.

To be sure, there are benefits to stored procedures.  A lot of the processing requirements get off-loaded to the database server which typically has plenty of power, and it reduces the volume of data transfer that may be required.  Additionally, in some cases it allows you to call business logic directly within SQL queries.

These benefits come at a steep price, however.  Stored procedures are typically very difficult to modify and any modification requires involvement of IT personnel.  This is a death knell in environments where competitive advantage relies on continuous learning and updating of business logic and strategies.  And really, show me an industry vertical where continuous learning and improvement is not essential to remain ahead of the game.  A decisioning platform that allows business analysts to control decision flows and strategies can take you to the next level if you're still doing your automated decisioning in stored procedures.

Benefits

In my view, the main benefit of Decisions-as-a-Service is that it levels the playing field between large and smaller players in terms of ability to implement sophisticated business logic.  No longer is it necessary to shell out millions of dollars in capital expenses to get access to a decent decisioning platform.  Instead, users can start designing and implementing automated decisioning through subscription or per-click models, essentially providing instant ROI.

A number of features are critical to make this model work:

• Shallow learning-curve business logic design interface.  The creation and deployment of decision flows needs to be intuitive and easy to learn, so that the users can truly be business analysts, instead of IT personnel. 
• Superior data connectivity.  Distributed data is essentially inherent in the hosted services model.  The platform must be able to access data easily wherever it is, given appropriate credentials.
• Superior system connectivity.  Decision platform do not just interface with data sources.  In many cases decisions need to be delivered to live systems that are likely to be remote.
• Shared resources.  While sharing of resources is not absolutely required, it does help to keep costs down.  This is important if small to medium-sized entities are to be able to afford access.

There are likely others, if you can think of them, please let me know.

Introduction

Hello everyone, and welcome to my blog.  The purpose of this blog is to share my thoughts on the Decisions-as-a-Service concept with whomever might be interested in them.

I have worked for a number of companies that fundamentally provide decisioning solutions, and have come to the conclusion that the conventional, heavy (i.e., expensive), in-house installation of decisioning platforms is the way of the past.  The advent of Data-as-a-Service, distributed computing, and web-based application delivery opens the way for sophisticated automated decisioning and analytics to become available to small and mid-tier B2C players that are spurned by aforementioned vendors.

The applications for Decisions-as-a-Service are far and wide, and as I come across use-cases, I will make mention of them in this blog.  In general, I intend to discuss the various aspects of DaaS in future posts, so stay tuned.