Sheppard Narkier

In my last blog, I covered the Mythos of IT Transformations; mapping the iconic figures of Greek Mythology: Sisyphus, Tantalus and Heracles to the never ending drama scenarios we experience in our daily work lives when trying to affect change. I ended with mentioning the role of Prometheus as the game changer supported by a comprehensive multi-stakeholder decision support platform for the entire IT lifecycle. As we remember from Greek mythology Prometheus was chained to a rock while having his liver eaten as a punishment for giving mortals fire. Now in our slightly more enlightened corporate political realities, that action would be deemed illegal, but any aspiring, “modern day Prometheus” will often see the key vision they promote have the same fate as Icarus, who flew too close to the sun. Why does that happen? Too often the introduction of a new technology is viewed as the magic potion to solve all ills but in reality the hopeful Prometheus is often faced with introducing that technology in an environment similar to the cartoon Prometheus and Bob. Please view the cartoon before proceeding and after you stop howling with laughter.

Does anyone have to deal with a host of Bob’s out there when planning for change? Technology is one of three types of capabilities that affect change in an organization. All three types of capabilities (technology, skills and process) must be advanced for an organization to have sustainable change that matters to that organization’s success. Technology is often a game changer, but the introduction of a technical capability without the appropriate supporting skill and processes often means limited impact, limiting the ROI and hampering key objectives. A comprehensive approach must be taken, and that means multiple stakeholders across different reporting lines and P&L centers need to cooperate to advance the capability maturity of an organization. This is the essence of sustainable IT maturity. The diagram below is a simple example. Notice how basic technology capabilities are combined with skills and process capabilities to provide higher value, higher impact compound capabilities. The big question in most people’s mind is how is that accomplished? Previously I addressed this dilemma focusing on what must be done, starting with culture.

But even with a strong cultural line up, timely, fact based decisions still need to be across groups that don’t have the same semantics, or thought domains. We need a way to think about how we as humans interact with data, information and knowledge to make decisions that matter.

Data, Information and Knowledge- Overcoming Decision Inertia

In a previous blog I examined the killer app. The one I primarily focused on was the spread sheet for its simple modeling style, the resulting ubiquity of use and the significant unintended consequences of its massive proliferation. The spreadsheet, more than the PC itself, changed the world of IT by changing IT’s priorities.

• The Fire Ignites then spreads– Anyone, with little to no programming background could gather data (e.g. point of sale data for a region), create some information from it (e.g. monthly sales trends by region) and then start the process of building knowledge (e.g. in certain states we are likely to see spikes in purchases of beer and diapers from the same store on Friday night). Business Intelligence greatly expanded the spread sheet apps capabilities to achieve that kind of analysis, and Data Science Analytics have greatly expanded that reach, with all of the expected questions.
• Spreadsheets are intuitive– The structure of the data is clear based upon what is placed in cells. The relationships between data are expressed by the intersection of the cells and therefore all calculations are traceable. Meaning is easy to express and the derived knowledge can be understood.

But as we have seen spreadsheets are hard to maintain, harder to share and as the data sets grow, and don’t scale in performance very well as they become unwieldy 10 megabyte monsters with dozens of sheets. Tracing a calculation in such a sheet can be a nightmare, debugging even worse.

Too often the answer is to convert this treasure trove of knowledge to a programing paradigm where the programmers have no understanding of the problem domain.

What if the decision making we need to make better IT designs requires knowledge from 6 different domains, such as business analysis, application portfolio analysis, enterprise architecture, application forensics, engineering compatibility, operational feasibility? What if the knowledge we need to leverage is not just bottom up data (e.g. forensic performance analysis)? What if that knowledge is locked up in configuration guides, engineering checklists, and the heads of our consultants? In data science that knowledge is considered a priori, hard won knowledge that we already have assimilated and use and is of vital interest when applied to machine learning.

These questions remind of a 500 year old quote I saw posted in an EMC office building.

“ He who has not first laid down his foundations, may with great ability lay them afterwards, but they will be laid with trouble to the architect and a danger to the building” from the Prince by Niccolo Machiavelli 1515.

These knowledge sets need to be maintained by experts who will take care of their relevance cycle. But even more important than that is how the knowledge from different experts can be used systematically together to make informative decisions about application portfolio investment, application rationalization, cloud deployment, and infrastructure design. How do they enter knowledge that can be leveraged by other experts in any sequence they deem fit? How do we make knowledge trapped in configuration sheets and best practices documents come to life? How do we make well thought out business recovery policies into actionable rules that can be used to configure a platform for an adequate disaster scenario? And how does this all happen with simultaneous decision making threads across multiple stakeholders?

One vision of such a concept might look like this:

Enter Prometheus – the evolution of knowledge modeling

Prometheus is the evolution of Adaptivity’s decision support platform, and in 2016, much will be heard about it. The mission is ambitious:

• Create Sustainable Relevance for IT decision making by delivering workload-driven analytics that merges top-down and bottoms-up IT portfolio data across multiple dimensions of IT knowledge

The value

This platform enables experts to add knowledge and create new analytic processes that are repeatable and traceable in order to render decisions based upon facts and observed behaviors/patterns.

The results we expect:

This empowers IT to become a strategic enabler of the business and contribute to market relevant moves that are instrumental to the business’s strategic vitality.

We will change the trajectory of IT decision making by combining machine learning, crowd sourcing, inventive worflow collaboraiton, all while enabling the evolution of knowledge. Sysiphus, Tantalus and Hercles can finally get some rest.

Prometheus Unbound – Knowledge Unleashed
Sheppard Narkier