Monetizing Data to Produce Data as an Asset

A topic we followed and experimented with for years.   What has been added now is also the risk of holding data.   Consider risk analysis.   Good piece:

Monetizing Data: Follow the Yellow Brick Road   written by Mark Katz in FinancialTechnologyToday

Once Dorothy and her colleagues made the journey to OZ, they quickly found out that there was no there, there (every now and then, one must throw in some Gertrude Stein).  The Wizard simply told her what she really should have known all along.   That classic is about self-reliance, and the ability to differentiate, understanding the real value of what is already there–and the real range of possibilities.  Dorothy and her companions just had to figure out how to reframe their own perceived shortcomings and recast them as strengths to achieve real transformation.

Firms can undergo the same kind of journey, only to find out that there is indeed no “magic” to solving data monetization challenges.  While the tools have vastly improved, and the power of BI buttressed by AI and Machine Learning has helped greatly with incorporating challenges like unstructured and disparate data (internal and external), that Yellow Brick Road journey still requires cultural and operational steps including empowerment of associated teams.  There is not a lot of room for autocracy in achieving the best results.   Foundational elements work best, and collaboration is a must.   But ultimately, is it worth this kind of upheaval? ..... "

Monetizing Data, Considering Risk

Data as an asset is a continued exploration, mentioned many times here.  Our original view also looked at the risk of capturing and holding assets.  Negative valued assets.  Another view,   In SiliconAngle.

Our Brain's Biases, as seen by Watson

Have long been involved with R&D, Laboratory and Product development data and tests.  So always interested in how bias can creep into data.   Often 75% of it is confirmation bias!    Early work in astrophysics, where much early bias research was founded, from observational data, made me alert to different kinds of bias.  So this piece in Engadget was confirming (?) and enlightening:  What IBM Watson can teach us about our Brains Biases.    Do we explicitly think of the risk of bias?  So can a Cog be unbiased?  Should it be,  to be humanly intelligent?  What is the relationship to creativity? See my text tab below for more on this.

Value and Risk of Data in Context. Open Group on Risk

Open Group on Data Risk.  An notes on data asset/risk architecture.

The broader task has been to look at both the VALUE and the RISK of data in CONTEXT Knowing that accurately would be very valuable. But further, it came to mind, and prompted me to post this discussion, that the architecture of the data would also be useful to evaluate this way. Alternatively you could consider the architecture a key part of the CONTEXT. And further, is the metadata also part of the CONTEXT?

Network Economics of Cybercrime and Data Assets

In Analytics Magazine:  This connects with some of my recent work looking at data asset and risk value.  Technical details at the link, reviewing. 

" ... A new computer-based model examining cybercrimes adds an important way of examining the perishable value of stolen data so policymakers can plan against future hacks such as the recent Anthem data breach, according to a study authored by Anna Nagurney and published by the INFORMS journal Service Science. The model captures the network economics of cybercrime activity and permits the policy evaluation of interventions. ...   A novel feature of the model is its inclusion of the critical time element and perishability of stolen cyber financial products whose value, as in the case of fresh produce, decreases over time, which in turn impacts their price on the black market. The model also identifies different demand prices for different financial products, with certain credit cards being more valuable because of credit limit, expiration date and continent of origin. ... " 

Managing Mission Critical Knowledge

There are all kinds of levels of knowledge.  Also, knowledge has direct value, and risk.  In the HBR:

" ... When executives talk about “knowledge management” today, the conversation usually turns very quickly to the challenge of big data and analytics. That’s hardly surprising: Extraordinary amounts of rich, complicated data about customers, operations, and employees are now available to most managers, but that data is proving difficult to translate into useful knowledge. Surely, the thinking goes, if the right experts and the right tools are set loose on those megabytes, brilliant strategic insights will emerge.

Tantalizing as the promise of big data is, an undue focus on it may cause companies to neglect something even more important—the proper management of all their strategic knowledge assets: core competencies, areas of expertise, intellectual property, and deep pools of talent. We contend that in the absence of a clear understanding of the knowledge drivers of an organization’s success, the real value of big data will never materialize.   ..." 

Placing a Price on Data Assets

Am in the midst of addressing just such a task.  What is data worth?  What is its risk?  " .... We know in our gut that data has value. No company can run without it. But what is it really worth? As CEOs realize that data is an asset that can be exploited as a new source of revenue, they will start to ask CIOs about its financial potential. Responding with a shrug and a shot in the dark won't exactly enhance your own value in the CEO's eyes. ....  "