EA 874 Blog Topic 3 Data/Information Architecture

EA 874 Blog Topic 3 Data / Information Architecture Layer

Dominic Patruno

Data Breaches

Its difficult to discuss data and information without addressing the ever increasing data breaches. Some being very significant lie the recent Experian data breach. According to reports (WindowsIT) hackers were able to obtain ove 143 million records. This include names, social security numbers, driver license numbers and credit card numbers among other personally identifiable information.

Though the Experian data breach is an extreme case of data loss. Most companies, have some level of exposure when it comes to sensitive data. Most companies, are not even sure where all of the data lives and how it is transported around the company. Companies due implement control measures, such as data loss prevention and information rights management tools, but without proper classification of the data. How does one know when and were to apply the proper policies and controls.

The volume and velocity of data is only growing, and companies will need to have a proper data/information architecture in place to account for this and should start sooner rather than later. I can see where data architecture could be a differentiating factor. If these types of braches continue, government might get involved and require proper controls similarly to what happened with the Sarbanes Oxley act.

 

Data Architecture

Data architecture has become a very important practice to companies and is important to stay competitive if not relevant as companies become more digitally enabled. 70% of employees have access to data they shouldn’t while 80% of analysts’ time is spent discovering and preparing data (Dallemule & Davenport, 2017).

One the biggest hurdles to implementing data architecture, is where to start and trying to boil the ocean. If companies have not had any real data architecture in their enterprises, then they would want to start small with achievable milestones that deliver the best value for the effort.

Some experts agree with starting with “Single Source Of Truth” (SSOT) data. Usually this is customer, financial and supplier data. Most everyone can agree what system houses this type of data and how it is represented. Another element that will make data architecture successful is data governance.   This is very important, because there should be rules on how data is created, used, labeled and destroyed. This can also help with mitigating data leakage risks.

 

 

Another approach is to take either a defensive or offensive position with regards to data architecture and strategy. This could help set the scope and provide buy in and support from senior leaders and executives. The following table illustrates the difference in each position.

 

The Elements of Data Strategy

DEFENSE OFFENSE
KEY OBJECTIVES Ensure data security, privacy, integrity, quality, regulatory compliance, and governance Improve competitive position and profitability
CORE ACTIVITIES Optimize data extraction, standardization, storage, and access Optimize data analytics, modeling, visualization, transformation, and enrichment
DATA-MANAGEMENT ORIENTATION Control Flexibility
ENABLING ARCHITECTURE SSOT (Single source of truth) MVOTs (Multiple versions of the truth)

 

Data architecture is extremely important and companies that are not taking this data architecture seriously are in danger of being either bought or go out of business. Keep in mind data architecture is something that is iterative and needs to be designed with continuous improvement and agility going forward to be successful.

 

Data Is More Valuable Than Oil

If we look at some of the biggest tech companies today, Google, Amazon, Apple and Facebook, we can see a patter in how they use data to advance their business. Google with their search engine data, Amazon with their purchasing data, Apple and Facebook with their advertising data and it continues to grow. This leads to companies acquiring more data to make better decisions, which leads to capturing more data to make better decisions.   Along with tools such as AI and Machine Learning this only enhances their knowledge on how to better sell and market to the customers.

 

 

 

 

 

 

 

 

 

References

Retrieved September 20, 2017 from https://www.washingtonpost.com/business/technology/what-you-need-to-know-about-the-equifax-data-breach/2017/09/09/46d20dc4-957d-11e7-8482-8dc9a7af29f9_story.html?utm_term=.bcb7489a9cec

Dallemule, L. & Davenport, T. (2017, June). What’s Your Data Strategy. Retrieved from https://hbr.org/2017/05/whats-your-data-strategy

Hunt, T. (2017, September). The Trust Problem with Equifax. Retrieved from http://ezaccess.libraries.psu.edu/login?url=https://search-proquest-com.ezaccess.libraries.psu.edu/docview/1936769277?accountid=13158

 

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