What’s Wrong With My Enterprise Architecture? – a response

Recently, a fellow Enterprise Architect reached out and asked my opinion on his article.  Below is my response:

• Enterprise Architecture has many definitions. Here is one that I tried to create in 160 characters. “EA bridges business and IT via enterprise integration/standardization resulting in people becoming more efficient and effective in achieving their objectives.”

• While there are many reasons behind failures of EA within organizations but as I see it, they essentially boil down to only one thing (i.e., lack of communication in understanding the true value of what EA brings to the organization). It takes effort from everyone (EA, Business and IT) in the organization to use EA for business transformation. Before anything else organizations need to decide:

  • Why they need/want EA? Here is a good video that alludes to this.
  • What quantitative and qualitative values does EA bring to the table?

• Unfortunately, EA has turned into merely an information collection activity and moved away from why this information is being collected in the first place. What is the strategic intent? In my observation, most EA is not strategic (e.g., the Federal Government’s use of EA)

• My biggest issue with EA these days is where it resides within the organization. These days EA reports to or is a part of IT and suffers the same fate as IT (e.g., reduced budgets, no executive representation, etc.). Ideally, EA should report into Chief Strategy Officer (CSO) or Chief Executive Officer (CEO) but not to the Chief Information Officer (CIO) or Chief Technology Officer (CTO).

• EA is a conceptual mindset. In my view, it is not about frameworks, modeling or programming languages. EA is about a business transformation that may or may not require IT to accomplish the transformation. Blasphemy! I know ☺

• True EA is difficult to do and it takes a long-term commitment from the organization to pursue it.

In today’s business world quickness and agility are often used as a pretext/excuse for a lot of things mostly because the people using these terms just want additional lines added to their resume before they move on. To put in an analogy, what kind of car would you like to drive? One that goes really fast but has bare minimum safety or one that has optimum safety but you might get it a month late? The short answer is, it depends. Mainly it depends on what is the end goal the organization or person is trying to achieve. The same is true for EA. Without measurable end-goals, EA just becomes a complacent black hole.

Top 5 Articles of 2013

Thank you to the readers in 72 countries that read my articles in 2013. Following are the top 5 articles that you have been interested in:

  1. 5 Observations on Being Innovative (at an individual level)
  2. 5 Observations on Being Innovation (at an organizational level)
  3. Future Considerations for Kodak
  4. 5 Factors for Business Transformation
  5. Where is My Big Data Coming From and Who Can Handle It

Following are the top 20 countries where most readers have come from:

  1. United States
  2. Canada
  3. United Kingdom
  4. India
  5. Australia
  6. France
  7. Pakistan
  8. Germany
  9. Netherlands
  10. Philippines
  11. Finland
  12. Colombia
  13. New Zealand
  14. Brazil
  15. Switzerland
  16. Singapore
  17. Saudi Arabia
  18. Italy
  19. Ireland
  20. Greece

Zillow.com and the MLS CIO

Let’s suppose that you are Chief Information Officer (CIO) of a Multiple Listings Service (MLS) and a proposal has been put forth by Zillow.com to join Zillow’s Partnership Program (ZPP). For this scenario assume: (1) hiring, business processes, and technology infrastructure would remain unchanged and (2) a budget would only be provided to create data feeds used by Zillow.com. Here are some of the risks, disadvantages, and advantages of joining ZPP:

Risks to MLS:

  1. Brand recognition: The MLS brand recognition would be compromised if (1) current MLS users completely transition over and prefer to use Zillow and (2) future MLS users may not be aware of MLS’s existence.
  2. Zillow’s Zestimate: Zillow provides a property’s cost estimate to users based on a proprietary algorithm called the “Zestimate”. Research indicates that (1) in certain areas these estimates are wildly off and (2) Zillow has changed the algorithm in the past without prior notice. This would result in user confusion and the perception that it could be an MLS issue thus affecting the MLS’s credibility.
  3. Zillow’s acquisition strategy: Zillow has grown through acquisition and it is expected that this strategy would continue. Due to the complexity of management and systems integration during acquisitions, there is a possibility that not enough resources would be available from Zillow if there were issues with the MLS at the same time.
  4. Customer conversions: By joining ZPP, the MLS would exponentially increase the users who can view the MLS data through Zillow’s website and mobile applications. However, the increase in the number of views is not a guarantee that those users would become customers.

Risks to MLS Information Systems:

  1. Technology infrastructure: The MLS could encounter an exponential increase in the number of users who can view MLS data that could overwhelm the servers. This could be an issue if MLS is currently (1) running at full capacity and (2) does not have an updated technology infrastructure.
  2. Data security incidents: Due to sharing data with Zillow, the MLS could anticipate an increase in security incidents either from (1) data in transit from the MLS systems to Zillow and/or (2) data compromised at Zillow

Disadvantages by not joining ZPP:

  1. Users: 55.7 million mobile and web visitors access Zillow compared to the entire population of a major metropolitan area. The MLS would not have access to such a large user base if ZPP were not joined.
  2. Adoption: If MLS does not adopt in a timely manner then it would be perceived by the industry in general and the MLS community in particular as behind the times and could erode MLS’s ability to hire top talent for projects.
  3. Information relevance: Since (1) Zestimate pulls information from previous years’ tax assessments and (2) users have the ability to edit data, careful consideration should be made about the relevance of the information since the accurate reflection of the up-to-date fair market value could be an issue for the user.

Advantages by joining ZPP:

  1. Users: Access to 55.7 million mobile and web visitors that access Zillow monthly.
  2. Account Executive: A dedicated account executive would be assigned to the MLS. This could help in coordination and quickly resolving issues between Zillow and MLS.
  3. Metrics and traffic statistics: Zillow would be sharing user metrics and traffic statistics. This information (1) could be used by MLS to prepare for peak times and enhance maintenance schedules and (2) could be used by brokers and agents to improve their business through trends and predictive analytics.

Recommendations:

Based on the above, the disadvantages and advantages of joining ZPP, the MLS would not be ready however joining would be significantly beneficial. Thus the CIO should recommend:

  1. Budget increase needed to develop data feeds and updates the technology infrastructure to make it robust, resilient, scalable and highly reliable that could handle exponential user growth.
  2. New policies, procedures, processes and governance models need to be developed to address optimal firewall settings, data integrity issues, security, escalation, prioritization, communication channels between the MLS and Zillow.
  3. Recruit an experienced account executive that has taken their MLS through the same ZPP process.
Risks, disadvantages and advantages of joining ZPP
Risks, disadvantages and advantages of joining ZPP

What should NASDAQ OMX, SEC and Congress do?

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:

  1. 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
  2. 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
  3. Role of Congress
    • Increase the budget of the SEC to create systems that monitor markets and exchanges

Where is My Big Data Coming From and Who Can Handle It

Recently, a reader asked my insights on the article (Data Scientists are the New Rock Stars as Big Data Demands Big Talent).  Here is my response.

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:

  1. How will data be managed?
  2. 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:

  1. Establishment of standards, governance, guidelines. (E.g., open architectures)
  2. Creation of industry specific data exchanges. (E.g., healthcare data exchanges, environment data exchanges, etc.)
  3. 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:

  1. Why do they need a Data Scientist? (E.g., have strategic intent, jumping on the bandwagon, etc.)
  2. Who will the Data Scientist report to? (E.g., Board, CEO, CFO, COO, CIO, etc.)
  3. Does the organization have the ability to enhance/change its business model? (E.g., making customers happy, leading employees, etc.)
  4. 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?
  5. How often will you measure the relevancy of the data? (E.g., key data indicators)
3 Phases of Big Data Harmonization
3 Phases of Big Data Harmonization