Need of Modern Data Architecture

Background:

Data Architecture, one of the pillars in enterprise architecture, is the most important part for any organization. Data Architecture consists of data models, set of rules; set of policies to govern data; set of standards to store and access data etc. [1] From a conservative IT perspective, data architecture , describes the data structures used by the business and its corresponding software. However, from an holistic perspective, data architecture provides appropriate methods to design , develop and implement a complete business driven data architecture , which not only includes set of standards , policies or rules but also include real world object mapping with underlying environments in the organization. In overall enterprise architecture, data architecture provides a blue print to guide the implementation of a physical database. The Data architecture describes the way the data will be processed, stored and used by the organization[2]. .In Zachmann framework, one of the most popular enterprise architecture frameworks,  the data column , what column , describes data aspect for an organization[3].

View	Data (WHAT)	Stakeholder
Scope / Contextual	Material List	Planner
Business / Conceptual	Entity Relationship Model	Owner
System / logical	Data Model Diagram	Designer
Technology / physical	Data Entity specification	Builder
Detailed	Data Details	Subcontractor

Figure 1

As we can see that Data architecture consists of following major steps[4]:

• Creating list of things and architectural standards important to the business.
• Creating Semantic model or conceptual enterprise data model.
• Creating enterprise logical data model.
• Creating Physical data model.
• Creating Actual database.

At High level, Data architecture is process of breaking down a given subject to its lowest level and then building the architecture backwards. At each level , data architecture is viewed from one of following aspects[5] :

• Conceptual – it includes all business entities.
• Logical – It provides logical relationship between entities
• Physical – Implementing the conceptual and logical view in to actual database.

Following are important data elements that must be considered while engineering the data architecture for an organization[6]:

• Administrative structure to manage the data
• Methodology to describe the data
• Description of database technology
• Description of the process to process data.
• Interfaces to other systems
• Standards for common data operations.

Modeling the “as-is” data architecture is extremely useful in terms of getting insights of current situation however in order to continuously improve the data architecture it is very important to have  a data strategy in place , which can provide enough guidance to realize the “to-be” architecture. This is very important task which should include business managers and data architect to define[7]:

• How the data is collected, managed and used.
• Data models both “as-is” and “to-be” models
• Data governance and change control processes.
• Policies for data management such as how data is collected , managed , stored; how long it should be stored; access rights ; appropriate security measures etc.

 It is important to note that within each organization, various constraints will have an impact on overall data architecture. These constraints include any specific enterprise requirements,  laws , data processing needs and business policies etc[8].

From its theoretical definition so far, data architecture may sound simple but the reality is quite different. We are living in information era, the exponential growth of data through advancement in technology has increased the importance as well as complexity of managing such huge data. Moreover, most of the data in an organization is either held in legacy systems without any descriptions or is dispersed among various non-standard tools or applications[9]; this can include personal worksheets, personal Microsoft access databases etc. Additionally, some of the key data resources may lie with vendor or other stakeholders, outside the organization boundaries. This dispersed data not only lacks in quality but may also have lot of duplicity.

As per Gartner, by 2020 the number of new devices connected to internet would cross count of 26 billion[10]. This exponential growth in internet driven devices would bring unprecedented challenges and complexities to manage data, generated from such devices.  Industry has already started to journey towards Internet of things.[11] Traditional data ware housing solutions are being complemented by data lake solutions and advanced analytics solutions are used to drive insights of data. Therefore,  the task of data architecture becomes one of the most complicated parts of entire architecture. Given the situation that industry has come long way, it is very important to understand that the traditional data solutions only cannot solve the current business problems. Industry cannot sustain only on traditional data architectural frameworks or solutions anymore, it is very important to devise new solutions and address the new challenges to have efficient data architecture of modern information era. For instance : data warehousing solutions was one of the most important part for the organization in an effort to centralized the data in the organization and use the data through unified processes. However, with increase in data , data ware houses needs to be coupled with by the data lakes[12] , where data can be stored in its Raw format and the responsibility to clean and use the data is left to the end user. New database technologies and frameworks such as Big data , Hadoop , hive , Mongo DB etc. are changing the way data is used traditionally.[13]

 In the next section, we would discuss some of the issues that exist in tradition data architecture.

Major enterprise architectural issues:

• ·        Inefficient in keeping pace with increasing data demands
• ·        Inefficient in defining strict security principals
• ·        No guidance for real time analytics
• ·        Change management
• ·        Incapability to offer data as Service
• ·        No Guidance to promote Self Service Environments
• ·        Data Redundancy
• ·        Complex, lengthy and inflexible process

1.     Inefficient in keeping pace with data growth:          

In 21^st century, Data is one of the longest tails being produced and consumed every day through our day to day activity. Any online shopping , internet browsing , credit card usage , smart phones , smart watches , activity trackers etc is generating huge amount of data[14] . Data does not only vary in variety but also in volume. Industry is expecting even more growth in data with the realization of Internet of Things. It is estimated that by 2020, 26 billion devices would be connected to internet and each[15]. These devices would not only be able to connect to internet only but also would be able to communicate with each other in order to accomplish certain tasks. This is going to add a new dimension to the data growth. Businesses are working towards having effective data storage and usage strategy to deal with the data challenges of information era.Of course, there is no point in saving the data if business can’t use it to mine useful information. Businesses are continuously investing in new data analytics initiatives to reap the true potential of this data.[16]

With increasing data, the importance of having efficient data architecture has increased even more. This exponential growth of data requires that business need to devise new framework and architectures. Traditional data architecture was not meant to handle such huge data. It does not address the issue of voluminous and versatility of data. Current(Traditional) data architecture provides enough ground work to devise strategy, policies and procedures to manage the data within the organization but the boundaries of the data are mostly limited within an organization. In today’s world, organization data is being shared and used quite extensively outside the organization as well. The need for having efficient data architecture, geared towards dealing with this situation, is not limited to volume of the data but also includes complexity and variety of the data. Data is no longer being generated from limited set of applications[17]; it is generated through varying sources and is different in structure. Moreover, current data architecture is geared more towards storing and managing the data and not towards mining or analyzing the data. It focuses on Entity Relationship models, Data relationships and devising data models. However, with the latest trends and technology frameworks, data is being stored in its raw form and not in a well-defined clean form.  

 Also , It is very important to understand that the value of the data lies in the information it contains and there should be enough consideration given to how to mine and analyze the data, right from the conceptual phase of the data architecture[18]. Below is an example of new data architecture with one of the leading frameworks in Big data, hadoop. As evident from the figure , the new data architecture with hadoop has enough focus on  Statistical analysis and business intelligence.[19] . Current data architecture is limited in terms of dealing with 21^st century’s volume and variety of data[20].

Figure 2

2.     Inefficient in defining strict security principals:

Increasing number of data breaches in last one decade are evident that industry has yet not become successful in incorporating enough security principals in data architecture. Data architecture does an outstanding job in terms of providing guidance for data governance and data access policies[21]. However, data is increasing beyond leaps and bounds; it is no longer limited to the premises of an organization so that enough principals and policies could be enforced to secure the data. Data is travelling outside the organization across different networks; this situation adds much more complexity and importance in defining security principals for the data[22]. Recent Target breach, in holiday season of 2013, was targeted from point of sales; it was evident from the incident that the data architect needs to devise security from all possible point of communications[23].

Even though, Current data architecture provides some level of guidance in terms of authorization and access rights but it does not address the issue of security to the required detail. The increasing importance of securing the data has led to whole new Security architecture[24], which should be used along with data architecture process.  Current Data architecture fails to provide enough details on what all policies and procedure put in place in underlying situation to ensure required security. There is not enough guidance in categorizing the organization’s data and defining security principals to secure the access to the data. Data security covers vast variety of tools and techniques for security such as tools to ensure data compliance; data security solutions such as firewalls; data encryption; data encryption; physical security and data analytics solutions for security[25].

Big data brings whole new complexity to the security aspect of the data[26]. It has proved out to be extremely difficult to secure this huge volume of data, if proper consideration is not given to the security aspect of data right from the inception. Industry has been facing new security challenges every day. Increasing popularity of service oriented architecture or cloud services add whole new another perspective to data security in the data architecture. Security of data has gone beyond the hands of the organization to the cloud service provider and it is very important to consider the required security level in Service level agreement with the cloud vendor. The importance of security cannot be ignored in this information era. Current data architecture not only fails to provide enough guidance for data security with changing market needs but also fails to address the concept of data recovery, in a situation of failure.

Even though data recovery is a separate issue all together but it forms an integral part of any data architecture while devising data security principals and policies. Due to Increasing dependence on data , business can’t afford to lose any data. Current data architecture does not provide enough guidance to recover data in an event of security mishap.

3.     No guidance for real time analytics:

The value of the data lies in the information it contains. In today’s world, data is not worth if it is not being used to drive useful information. Data analytics has become an integral part for an organization. With the changing dimensions of data, the importance of data analytics cannot be ignored in the data architecture. Increasing popularity of data analytics tools and the potential of data science has led the industry to invest heavily in data analytics. However, in order to reap the true potential of the data, it is necessary to give due consideration to data analytics right from the conceptual phase of data architecture. Data analytics is not only limited to inferential analytics but also provides tools for predictive data modeling to help businesses make useful decisions[27]. The exponential growth in data, in recent years, has led to whole new perspective of storing, managing, mining and using the data to draw conclusions. Traditional database systems are neither able to support this huge volume of data nor they provide right analytical tools to deal with this huge data. Future data architecture needs to consider the data analytics as an integral part of data architecture.  Below is a pictorial representation of current data architecture[28]. As we can see the current data architecture does not provide direct support for emerging new data types and the volume of the data, which is expected to grow 40 ZB by 2020[29]. This huge data with wide variety cannot be used to drive analytics if required consideration is not given right from the conceptual phase of data architecture. Even though, current data architecture provides some guidance to envision data analytics and business applications but the traditional solutions does not fit in the modern situation.

Figure 3

 Below is one example of new data architecture with focus on varying data types and volume of data.[30]

                                                Figure 4

Current data architecture fails to address this new dimension of the changing market. It not only fails to provide enough consideration to the huge volume and variety of the data but also fails to address how to process data when data is being generated at very high speed[31]. There is no guidance for real time data processing.

Current data architecture does not provide enough guidance for data analytics and it becomes very difficult to incorporate the data analytics initiative in existing architecture. Therefore this issue must be addressed during the design of data architecture.

4.     Change management:

Current data architecture does not provide enough guidance for managing the changing data needs with an organization[32]. Current data architecture is focused more toward selecting a given database technology or solution and using that to devise the data architecture [33] however with changing market and data needs it has become very important that data architecture is flexible enough to accommodate new technologies and solutions . It should be adaptable to future changes with respect to technology and solutions. Current data architecture is neither flexible nor adaptable to accommodate change in the system. The whole baseline of choosing any given data solution or technology needs to be replaced with an architecture that can support varying data structure and data types and can provide plug and play solutions in the architecture without worrying about the underlying environment. For instance, when NOSQL arose in the market – the data architecture should have been able to accommodate it in the current architecture. (8-Steps-to-Building-a-Modern-Data-Architecture-101417.aspx)

Current data architecture is not only inflexible at high level in accommodating any new data solution but is also inflexible at low level in accommodating new data types and data structures. In today’s world, data is being generated from varying number of resources and the structure of data varies a lot. Each day, more and more such devices or resources are coming to market; under this situation, business can’t afford to work with any single data type or data structure. The success of a good architecture lies behind the collaboration of business and technology department to work for a common purpose. Information has value only when it is able to meet the business needs. It is very important to understand the underlying data needs of business to devise data architecture. In current data architecture, there is no focus on data integration architecture[34]. Efficient data integration architecture makes change management a simple task and also adds flexibility in the overall architecture, improve reusability and consistency and reduce number of interface to reduce complexity.[35]

Change management is not limited to only incorporating a change in the system but at broad level it includes other important aspect of data management such as defining the lifetime of the data, defining the volatility of the data, defining the reusability of the data and defining the CRUD (Create, Read, update and Delete) cycle of the data[36]. The Data architecture , from it foundation , needs to consider this fundamental issue of data variety and needs to provide sufficient tools to accommodate these data types in the data architecture with minimized impact on the overall architecture. There is only one solution possible to meet this future need of the market and that is making the data architecture flexible to change.

5.     Incapability to offer data as Service:

Current data architecture provides no guidance to access data from existing range of databases within an organization. Data is often spread across different database and legacy systems within an organization and it is often challenging to pull data from these dispersed systems (McKendrick, 2015). With the increasing dependence on data within any organization makes it very important data is treated as a service and required tools are provided to access the data as easily as possible. Increasing importance of data in business decision making and increasing popularity of data analytics platforms makes it essential that required design considerations are taken in to account while designing data architecture for an organization[37].

            Gone are the days, when data used to be accessed from limited number of devices. Now a days data is being accessed from variety of devices such as mobile phone, tablets, laptops , smart watches , computers and virtualized networks etc. Therefore, it is very important to provide access through virtualized data access layer[38]. Providing a data access layer would not only help in providing a unified access method to data but would also help in standardizing data management tools, platforms and application across the organization. It is also equally important to provide data services as reusable components that can be integrated with applications. Data as a service (DaaS) provides following benefits in terms of data management (http://www.dataversity.net/data-as-a-service-101-the-basics-and-why-they-matter/, 2013):

·         Agility

·         Quality of data

·         Cost effectiveness

According to Gartner, DaaS is going to act as Launchpad for business intelligence and big data analytics. The market for BI and big data analytics is expected to reach $17.1 billion by 2016. (Gartner, 2013). 

The high importance of offering data as a service makes is very important that this aspect is taken in to account right from conceptual phase of data architecture. Current data architecture fails to address this fundamental issue to cope up with changing market dynamics. The incapability of current architecture to consider Data as a service not only makes it difficult to manage the overall data but also proves incapable in providing a unified method across different business intelligence tools and application within an organization.

6.     No Guidance to promote Self Service Environments

In today’s world,  data is ubiquitous, so is the number of methods to process and refine the information from the data. Same data set can contain numerous sets of information and it depends on the user how to process the data set to fetch the required information. For example:   Business user and a software developer can have different requirements from the same data set. It essentially depends on the type of underlying problem and task to decide what information is required. With the huge amount of data being generated each second with varying types, it is not a good idea to build the foundation of data architecture on limited data structures and data models. Data architecture needs to consider the raw form of the data and the end users should be responsible to process and mine the data, the way they want[39]. Concept of Data Lake in big data is based on raw data, whereas data ware house is cleaner but limited and abstract way of storing the data in today’s world. Traditional data ware house only cannot fulfill the changing business needs.

 Traditional data architecture solutions are not designed to provide guidance for self-service environments (8-Steps-to-Building-a-Modern-Data-Architecture-101417.aspx, 2015). With the increasing demand of real time data analysis it is required that data interoperable interfaces are developed and deployed for self service by different department inside as well as outside the organization for easy access by all stakeholders. Gartner defines Self Service business Intelligence as  “End users designing and deploying their own reports and analyses within an approved and supported architecture and tools portfolio.” [40] .  Self service environments not only augments the existing BI environments but also help business users , often called as power user, to become producer of the information ; this information can then be consumed by different departments, if required. Below is an example of Self service environment. (COATES, 2013). Current data architecture fails to address the changing business needs in terms of self-service environments and solutions. It is based on the assumption that one-size-fits-all . This approach can lead to various business problems in today’s world.[41] Below is an example of self service environment.

Figure 5

In order to develop the right solution, it is essential that due importance is given to ensure data is as easily accessible as possible and there is minimal dependency in terms of accessing the data from different stakeholders.   (Recipe-for-self-service-BI-calls-for-flexibility-governance-user-aid)

7.     Data Redundancy

Data redundancy, as the name suggests, is the issue of duplicity of the data within an organization (redundant-data.html, 2009). It means same data set is stored at multiple systems, resources or applications. This data may have different structure depending upon where it is being used and processed but at low level it is essentially the same data . It not only costs storage space on the systems but also leads to redundant efforts in order to align the information produced through these different systems[42]; it also leads to the issue of data synchronization. Even though, by using the current data architecture ,data redundancy can be avoided to an extent. Data model and Entity Relationship diagrams helps in having non-redundant data architecture but it provides no guidance how redundancy can be avoided with varying data types and structures, the problem of modern era. Current data architecture does not provide enough guidance to deal with three basic characteristics of growing data (BIG DATA,BIG DEMANDS)

·         It is voluminous

·         It is highly unstructured

·         It’s constantly changing

With the increasing volume of data and variety of data, it has become very important to be able to categorize the data to avoid redundancy. It is also important to note that the issue of redundancy and data variety needs to be handled simultaneously without confusing one for the other. It is essential to devise unified method to deals with varying data sources to avoid data redundancy but it does not mean that varying data sources would essentially produce redundant data[43]. The data strategy should be able to identify and deal with such situation. Current data architecture does not provide any direct guidance to deal with the situation of varying data sources and volume of data. With changing data demands, the issue of data redundancy needs to be scrutinized in much more detail than provided by the current data architecture.

8.     Complex, lengthy and inflexible process:

As per Zachmann framework the implementation to data architecture consists of six levels : Scope , business , system , technology and detailed level [44]. The overall data architecture require long term data strategy be defined at early stages , which become extremely difficult for mid- level and small companies. Infact , even for big organization it becomes increasingly difficult to define long term goals and objectives. Additionally, the incapability of data architecture towards change makes it a complex, lengthy and inflexible process. Any change in the systems is coupled with length reiteration of the documentation and remodeling of the various artifacts. With the often changes in the system, at times , this become a never ending documentation oriented process .

Incapability of current data architecture to deal with varying data sources and structures is one of the underlying issues related to change[45]. Given that the current architecture does not directly support varying data sources and structures, the change become inevitable. The provided set of principles and guidelines are not sufficient to meet the changing market needs and increasing dependence on the data. In order to align with the future goals of the enterprise, it is very important that the issue of complexity and inflexibly is scrutinized right from the most fundamental level of the overall architecture[46]. Adoption of enterprise architecture or data architecture, in particular, cannot be increased if this fundamental issue of complexity is not addressed from its core.

Conclusion:

Data architecture is most important pillar in overall enterprise architecture. It has many uses such as:

·         It acts as a key artifact to devise data governance strategy.

·         It guides through cross systems developments such data ware housing solutions etc.

·         It helps in providing insights and end to end view of organization data.

Enterprise data architecture is essentially a collection of blue prints to align IT initiative and information resources with overall business strategy with in an organization.

Increasing dependency on IT functions , few years ago , led enterprise adopt to large scale systems, the enterprise Data ware house, to manage and process data. Today, each organization has one EDW to serve various data needs across the enterprise. However, in recent years , the introduction of various new data types and volume of data has put enormous pressure on data ware house. It has become very important to efficiently store and process this growing data with modern data architecture. Even though, current data architecture does an outstanding job in providing a solid framework for any organization to deal with its data needs but it presents some serious implications, given the current market conditions, and fails to directly support growing data needs. The basic idea behind the traditional data architecture is based on relational database systems and the steps to develop data models and entity relation diagrams are centered towards relational database systems however, relational database systems are not scalable enough to support the exponential growth of the data and organizations are choosing to shift to new database technologies and frameworks such as HADOOP , NOSQL , MONGODB , HIVE etc. New database technologies and frameworks are not based on relational model but are based on storing data in its raw format of the data in order to store the growing volume and data types. Traditional data architecture has been able to provide a firm foundation to any enterprise in the past years but with changing data needs it has become important to consider the required changes in the traditional data architecture and provide new capabilities to deals with modern situation. Therefore, data architects are facing new challenges of data quality, data governance, end to end view of an organization and big data challenges.

The issue of change needs to be addressed from its core. With current data architecture, changes in the systems leads to rework of developing the artifacts, data models and entity relationships models. The current data architecture needs to be flexible enough to support change without involving lot of rework and efforts. This fundamental issue of change needs to be considered right from the conceptual phase of the data architecture. In order to be adaptable to future, data architecture needs to emphasis on data value chain – discovery of data , processing of data , analysis of data , integration and access to data. Data value chain in an organization needs to be re analyzed in order to provide the required flexibility in the architecture. Data value chain not only helps in providing the insights for future capabilities but also help in driving the technology options. For example, the requirement of real time data analytics not only provides certain performance requirements related to data processing but also dictate the technology solutions for presentation of the results and any considerations related to service agreements. From its nature , enterprise data architecture is an iterative and continuous process but the issue of change management needs to be addressed explicitly so that architecture is adaptive to changes with minimal efforts and impact.

Additionally, in the current era, it is not sufficient to only support the business processes but is has become utmost important to innovate and iterate to provide future capabilities. Efficient future data architecture needs to:

• ·         Support current Systems.
• ·         Provide capabilities for easy access and use of data.
• ·         Support Future changes, technologies and frameworks.
• ·         Provide a flexible implementation plan to migrate from current data architecture.
• ·         Provide guidance to select required technology.

Also, Issue of data management, data security, self-service environments, data as a service , real time data analytics, change management and complexity needs to be worked upon in order to develop a modern data architecture. Traditional data architecture lay down a good foundation for modern architecture so there is no need to re-invent the wheel; current data architecture needs to be improved to support the growing data needs and changing market dynamics.

            On a separate note, the increasing popularity of new database technologies and framework has added a new dimension for required skill set in the market. The future data architecture would require set of new skills and capabilities to manage this new architecture. People in the industry would have to either enhance their skill set to be able to sustain in this changing market or develop new skills by learning Pig , Hive , Python , R , SAS etc. to be able to help business reach their objectives. With increasing dependence on data , a new Job Title “Data scientist” has emerged in recent years. Even though, a data scientist does not play any significant role in data architecture process but is responsible to reap the benefits of successful data architecture by providing insights on organization data. Data scientist , needs to have appropriate knowledge of underlying business domain , statistical concepts ,  forecasting models and programming languages , which means data scientist must possess a blend of computer science , statistics and mathematics . The importance of data related initiatives and new trend of data architecture can be estimated from the fact that Data Scientist is anticipated to be the most demanding Job Title by the year 2020.

To summarize, data architecture is going through evolution and change in order to keep pace with changing demands and technologies. The fact that the new volume of data being generated is huge and unstructured, the overall data architecture in the industry is going through a revolutionary stage. Companies have already started the journey to evolve the existing data environments to deals with high volume and unstructured data and to add analytical capabilities. A significant percentage of companies have already started the adoption of new data architecture and many are in process of starting the change. Current data architecture still provides a solid foundation to an organization to work upon and evolve from “as-is” state to a “to-be” state. Most of the early adopters of new data architecture are working to provide an abstraction layer to data so that the end user does not have to know lot of details to access the required piece of data. From the technology front, there is a clear shift towards open source tools, languages and frameworks; current proprietary technology products would continue to evolve but there would be an increase demand in open source tools and packages in new data architecture. Many of such tools, frameworks, languages and technologies such as Hadoop , Pig , Hive , R , Python , MongoDB etc. have already started  gaining a wide popularity and adoption in the market.

This is needless to say that new data architecture would also have some of its own disadvantages and shortcoming to meet demands of ever changing market but new data architecture would essentially provide a good foundation for the data needs for a long time.

Recommendations:

• ·         Data architecture needs to be capable of handling voluminous and versatile data by incorporating new frameworks and tools such as big data etc.
• ·         Data architecture should provide a separate prospective, a separate row if we talk in terms of existing enterprise frameworks, for security to deal with security issues of modern era.
• ·         Data architecture should incorporate data analytics and business intelligence right from the lowest fundamental block of the architecture.
• ·         Data architecture should provide flexibility and adaptability to varying data types and structures by incorporating and general and flexible, a plug and play kind of framework.
• ·         Data architecture needs to be more adaptive to change process without the need of long length processes and redevelopment of artifacts.
• ·         Data architecture should be able to identify the issue of data redundancy with a future vision of having varying data sources.
• ·         Data architecture should encourage self-service environments by offering data as a service across the organization.
• ·         Data architecture should be able to depict the future state of data architecture clearly.
• ·         The process of data architecture should consider bringing the business and technology people together to identify current and future types of data.
• ·         Data architecture needs to be design to encourage change.
• ·         Data architecture needs to consider the Real time processing needs of modern era.
• ·         Data architecture should support data as a service.

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End Notes:

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[1] https://en.wikipedia.org/wiki/Data_architecture

[2] http://www.learn.geekinterview.com/data-warehouse/data-architecture/what-is-data-architecture.html

[3] https://www.zachman.com/about-the-zachman-framework

[4] http://www.dataversity.net/the-steps-of-data-architecture/

[5] http://searchcio.techtarget.com/definition/Zachman-framework

[6] https://en.wikipedia.org/wiki/Data_architecture

[7] http://www.ibm.com/developerworks/rational/library/754.html

[8] https://msdn.microsoft.com/en-us/library/Bb945098.aspx

[9] http://www.ibm.com/developerworks/rational/library/754.html

[10] http://www.gartner.com/newsroom/id/2684616

[11] http://searchdatacenter.techtarget.com/feature/Plan-an-Internet-of-Things-architecture-in-the-data-center

[12] http://searchaws.techtarget.com/definition/data-lake

[13] http://www.computerworld.com/article/2690856/big-data/8-big-trends-in-big-data-analytics.html

[14] https://en.wikipedia.org/wiki/Big_data

[15] http://www.gartner.com/newsroom/id/2636073

[16] http://www.ey.com/Publication/vwLUAssets/EY-ready-for-takeoff/$FILE/EY-ready-for-takeoff.pdf

[17] http://www.datanami.com/2015/07/20/big-datas-small-lie-the-limitation-of-sampling-and-approximation-in-big-data-analysis/

[18] http://harvardmagazine.com/2014/03/why-big-data-is-a-big-deal

[19] http://info.hortonworks.com/rs/h2source/images/Hadoop-Data-Lake-white-paper.pdf

[20] http://hortonworks.com/hadoop-modern-data-architecture/

[21] http://www.informationweek.com/whitepaper/Security/Security-Administration/data-security-architecture-overview-wp1389200608

[22] http://searchsecurity.techtarget.com/answer/Securing-big-data-Architecture-tips-for-building-security-in

[23] http://www.nbcnews.com/business/40-million-credit-debit-card-accounts-may-be-hit-data-2D11775203

[24] https://en.wikipedia.org/wiki/Enterprise_information_security_architecture

[25] https://en.wikipedia.org/wiki/Enterprise_information_security_architecture

[26] http://www.ey.com/Publication/vwLUAssets/EY_-_Big_data:_changing_the_way_businesses_operate/$FILE/EY-Insights-on-GRC-Big-data.pdf

[27] http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics

[28] http://www.revolutionanalytics.com/sites/default/files/modern-data-architecture-predictive-analytics-hortonworks-revolution-analytics.pdf

[29] http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm

[30] http://www.revolutionanalytics.com/sites/default/files/modern-data-architecture-predictive-analytics-hortonworks-revolution-analytics.pdf

[31] https://blog.kissmetrics.com/real-time-analytics/

[32] http://www.opengroup.org/architecture/0404brus/presents/rajesh/aandc1.pdf

[33] http://www.dbta.com/Editorial/Think-About-It/8-Steps-to-Building-a-Modern-Data-Architecture-101417.aspx

[34] https://tdwi.org/articles/2008/05/27/data-integration-architecture-what-it-does-where-its-going-and-why-you-should-care.aspx

[35] https://tdwi.org/articles/2008/05/27/data-integration-architecture-what-it-does-where-its-going-and-why-you-should-care.aspx

[36] http://www.eiminstitute.org/library/eimi-archives/volume-2-issue-4-july-2008-edition/201cwhat2019s-in-your-data-architecture-201d-part-three

[37] http://searchbusinessanalytics.techtarget.com/news/4500243099/Self-service-analytics-needs-strong-data-architecture-foundation

[38] http://www.compositesw.com/data-virtualization/data-abstraction/

[39] http://www.informationbuilders.com/applications/foodlion

[40] http://www.gartner.com/it-glossary/self-service-business-intelligence

[41] http://searchbusinessanalytics.techtarget.com/feature/Recipe-for-self-service-BI-calls-for-flexibility-governance-user-aid

[42] http://agiledata.org/essays/legacyDatabases.html

[43] http://www.businessdictionary.com/definition/data-architecture.html

[44] https://en.wikipedia.org/wiki/Zachman_Framework

[45] http://www.ibm.com/developerworks/library/bd-archpatterns1/

[46] http://international.informatica.com/Images/02354_next-generation-data-integration-transform-data-chaos_wp_en-US.pdf