Guerilla Analytics

As 2014 fades into memory, few would challenge the notion that we live today in an era of constant technology evolution and real-time information. Even for those early adopters who eagerly embrace the newest, trendiest technologies, it is a daunting challenge to stay current in the face of the seemingly constant onslaught of what is new and hip. Appification. Fitbit. 3D Printing. Vine. Personal Cloud. Digital Currencies. Mobile Biometrics. It is much the same in the Information space. The most committed information junkies who habitually and enthusiastically seek out all of the latest facts, figures and news on their businesses, industries and teams find themselves overwhelmed by the scale and velocity of available information.

Ironically, in contrast to this frenetic pace of growth and change, the Enterprise Information Management industry has not evolved nearly as rapidly or dynamically. One of the primarily limitations and complaints directed at Enterprise Information Management practices over the past number of years is that the Time to Market* for EIM solutions is simply too long. This is truer today than ever before. Business cannot afford to spend 6 to 12 months (or longer) to build a solution to meet today’s requirements. Often because of ever changing business needs, these solutions are obsolete before they are complete. Now there are a number of valid reasons why Time to Market for EIM solutions is so slow. Most notably, organizations have embraced overly formal development, process and governance models that conspire to make EIM projects take too long. While Adastra is at the forefront of more innovative, time efficient models and processes to improve Time to Market for traditional EIM solutions, this blog post is about an entirely non-traditional approach. Guerilla Analytics attacks the Time to Market problem from an entirely different angle, inspired by the disruptive, asymmetric and hyper-paced approach best exemplified by new business models such as Uber's.

By their nature, the EIM solutions referred to above are meant to address a relatively static set of requirements, and to be used on an ongoing basis for a period of time, typically not less than 2 years, and frequently for 5-10 years (or more). They are generally robust ‘enterprise grade’ solutions featuring high uptime, ability to handle multiple concurrent users, multiple tiers of support, etc. These solutions can and often do evolve to accommodate an increased and more diverse scope of data (and are even sometimes used in innovative ways that were not originally planned or intended). But even if new data and use cases can be accommodated, these solutions again face the Time to Market obstacle. The system that took 1 year to build may need an additional 3-6 months just to accommodate a new data source. This timescale - years and months - simply does not work when the opportunity to capitalize on new trends is measured in weeks and days. A radical new approach is clearly needed.

As alluded to earlier, excessive formality is a major contributor to solution delivery delays. So, in looking for a ‘revolutionary’ approach to massively reduce Time to Market, the focus needs to be on what is pragmatic, effective and fast. Similarly, because business needs/requirements may change during the course of the project (in fact discoveries made during the project will often be the driver of new/changed requirements), we need to be prepared to abruptly change course. We need a small, nimble team that will use whatever tools and methods are available (including unconventional ones) to quickly achieve the desired result. We need tactics that simply work. We need Guerilla Analytics. In fact Adastra has used this approach with tremendous success to help customers find remarkable new insights into their businesses, often in a matter of just a few weeks. Adastra’s extraordinary multi-disciplinary Analytics team utilizes leading edge tools and approaches combining Big Data, Data Science, Business Consulting, Statistics, Predictive Modeling, Advanced Analytics, and Visualization to address time critical business challenges. Now while we don’t actually call this group the ‘Guerilla’ Analytics team, they embody the creative, unconventional, and ultimately effective approach that the name suggests. Adastra’s Analytics team gets it done and gets it done fast.

The Guerilla Analytics approach really does dramatically improve Time to Market in many cases, yet there remains a need for the ‘old-school’ EIM methods, solutions and applications as well. In fact, the findings and discoveries from a short Guerilla Analytics engagement may justify and necessitate building a new (or extending a current) EIM solution. And some predictable and stable requirements do still exist, and new ones will continue to emerge (Regulatory Compliance, for example) that are well suited to the more formal ‘traditional’ EIM approach. Guerilla Analytics is not a one size fits all silver bullet for the Time to Market dilemma. But for an increasing number of business challenges and opportunities, it presents a low risk, high reward approach that traditional methods simply cannot compete with. If you want to know if this approach make sense for your organization, or how to engage with Adastra’s Analytics team, simply get in touch with me (or your favorite Adastran). Until then, good luck keeping up with the latest technology and information trends!

Time to Market’ is a fairly well accepted and understood term to describe how long it takes for a solution or application to go from inception until it is available for use by business users. I greatly prefer the term ‘Time to Value’, as it emphasizes that until the application is being used (or the insight/discovery has been made and is being used), the organization is not obtaining any value. A delay of 6 months in ‘Time to Market’ may not seem that significant, but in a competitive segment what if 90% of the value of the opportunity will be captured in the first year? A 6 month delay in ‘Time to Value’ could destroy the business case of the initiative