Last week, NASDAQ was closed for ~3 hours due to a software/computer glitch. Within 24 hours of this incident, the NASDAQ OMX CEO came on the news explaining what happened. Various news outlets criticized the company for not coming out sooner and informing the general public. On the surface, this incident seems like just a technical glitch and a communication breakdown but there might be deeper issues. Here are some recommendations to address this:
Role of NASDAQ OMX
Create backup hot-sites on a different electrical grid
Document and test offline scenarios so that markets and exchanges continue to function even if technology infrastructure is affected
Have communications SOPs to timely inform the public
Upgrade technology infrastructure
Role of SEC
Create policies and fines if something like this happens again
Create systems that provide real-time monitoring of markets and exchanges
Regulate the existence and maintenance of backup hot-sites
Regulate the technology infrastructure to check for obsoleteness
Role of Congress
Increase the budget of the SEC to create systems that monitor markets and exchanges
It seems like in today’s world people and organizations are somewhat struggling with this big data concept and do not know where to begin. Due to this reason, they are collecting everything they can think of in the hopes that one day they will be able to use this data in a meaningful way such as better customer experience, new products/services, better collaboration, increasing revenue, etc. This hope approach of “let’s collect data and later decide what we can use it for” on the surface might seem sound but last I checked hope is not a strategy. Perhaps this is one of the reasons that even now only <1% of the data collected is actually being analyzed. What good is more data when one cannot even make sense of the other 99%+ of data it already has? Are we chasing a ghost?
While it is true that vast amounts of data are and will be generated from financial transactions, medical records, mobile phones, and social media to the Internet of Things but there are questions that need to be asked to understand data’s meaningful use:
How will data be managed?
How will data be shared?
I believe that in order to come to a point where data becomes meaningful and useful it would require (broadly speaking) three phases:
Establishment of standards, governance, guidelines. (E.g., open architectures)
Creation of industry specific data exchanges. (E.g., healthcare data exchanges, environment data exchanges, etc.)
Creation of cross-industry data exchanges. (E.g., healthcare data exchanges seamlessly interacting with environmental data exchanges, etc.)
Additionally, let’s keep this in mind that the data we are talking about is data that can be captured by current tools and systems but the data which is perhaps the most difficult to capture is unstructured human data which within organizations is called Institutional Knowledge. This does not reside in a document or a system but in the minds of the people of an organization who understand what needs to be done in order to move things forward.
So, the question becomes, do we really need Data Scientists who have a mix of coding skills with PhDs in scientific disciplines and business sense or do we need someone who is able to connect the dots and have the ability to create the future. The answer is not a simple one. Perhaps you need both. The ability to code should not be the deciding factor but rather the ability to leverage technology and data should be. I agree that there is a shortage of people with diverse talent but there is also a shortage of people who actually know how to leverage this kind of talent.
Before organizations go on a hiring spree they should consider:
Why do they need a Data Scientist? (E.g., have strategic intent, jumping on the bandwagon, etc.)
Who will the Data Scientist report to? (E.g., Board, CEO, CFO, COO, CIO, etc.)
Does the organization have the ability to enhance/change its business model? (E.g., making customers happy, leading employees, etc.)
Is the Data Scientist really an IT person with advanced skills or does s/he have advanced skills and happens to know how to leverage technology and data?
How often will you measure the relevancy of the data? (E.g., key data indicators)
Business transformation entails assessing people, processes, and technologies of the organization in terms of the current state (where the organization is right now) and future state (where the organization wants to be). In these assessments people, processes and technologies are not standalone areas but are part of an integrated and holistic organization. If any of these areas are ignored or not given enough attention then true business transformation is just a pipe dream.
In order to have a holistic understanding of an organization and its broader role in society, there are 5 factors that need to be considered. These factors should have an inward focus and an outward focus. If the organization only has an inward focus then sooner or later it will be taken over by competitors and if the organization only has an outward focus then it will crumble under the weight of its own (mis)management. So, both are necessary. The 5 factors that will determine an organization’s success and longevity are Strategies, Politics, Innovation, Culture, and Execution or simply called The SPICE Factors. It is critical to remember that:
Strategies are to be used as blueprints. They are not shelf-ware.
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