𖣯Strategic Management: Strategy Design (Part II: A System's View)

Each time one discusses in IT about software and hardware components interacting with each other, one talks about a composite referred to as a system. Even if the term Information System (IS) is related to it, a system is defined as a set of interrelated and interconnected components that can be considered together for specific purposes or simple convenience.

A component can be a piece of software or hardware, as well persons or groups if we extend the definition. The consideration of people becomes relevant especially in the context of ecologies, in which systems are placed in a broader context that considers people’s interaction with them, as this raises to important behavior that impacts system’s functioning.

Within a system each part has a role or function determined in respect to the whole as well as to the other parts. The role or function of the component is typically fixed, predefined, though there are also exceptions especially when the scope of a component is enlarged, respectively reduced to the degree that the component can be removed or ignored. What one considers or not considers as part of system defines a system’s boundaries; it’s what distinguishes it from other systems within the environment(s) considered.

The interaction between the components resumes in the exchange, transmission and processing of data found in different aggregations ranging from signals to complex data structures. If in non-IT-based systems the changes are determined by inflow, respectively outflow of energy, in IT the flow is considered in terms of data in its various aggregations (information, knowledge).  The data flow (also information flow) represents the ‘fluid’ that nourishes a system’s ‘organism’.

One can grasp the complexity in the moment one attempts to describe a system in terms of components, respectively the dependencies existing between them in term of data and processes. If in nature the processes are extrapolated, in IT they are predefined (even if the knowledge about them is not available). In addition, the less knowledge one has about the infrastructure, the higher the apparent complexity. Even if the system is not necessarily complex, the lack of knowledge and certainty about it makes it complex. The more one needs to dig for information and knowledge to get an acceptable level of knowledge and logical depth, the more time is needed for designing a solution.

Saint Exupéry’s definition of simplicity applies from a system’s functional point of view, though it doesn’t address the relative knowledge about the system, which often is implicit (in people’s heads). People have only fragmented knowledge about the system which makes it difficult to create the whole picture. It’s typically the role of system or process operational manuals, respectively of data descriptions, to make that knowledge explicit, also establishing a fundament for common knowledge and further communication and understanding.

Between the apparent (perceived) and real complexity of a system there’s an important gap that needs to be addressed if one wants to manage the systems adequately, respectively to simplify the systems. Often simplification happens when components or whole systems are replaced, consolidated, or migrated, a mix between these approaches existing as well. Simplifications at data level (aka data harmonization) or process level (aka process optimization and redesign) can have an important impact, being inherent to the good (optimal) functioning of systems.

Whether these changes occur in big-bang or gradual iterations it’s a question of available resources, organizational capabilities, including the ability to handle such projects, respectively the impact, opportunities and risks associated with such endeavors. Beyond this, it’s important to regard the problems from a systemic and systematic point of view, in which ecology’s role is important.

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Written: Jun-2020, Last Reviewed: Mar-2024

🛡️Information Security: Threat (Definitions)

"An imminent security violation that could occur at any time due to unchecked security vulnerabilities." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Anything or anyone that represents a danger to an organization’s IT resources. Threats can exploit vulnerabilities, resulting in losses to an organization." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"The capabilities, intentions, and attack methods of adversaries to exploit or cause harm to assets." (Manish Agrawal, "Information Security and IT Risk Management", 2014)

"The potential cause of an unwanted incident, which may result in harm to a system or organisation." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"Any activity that represents a possible danger." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"The danger of a threat agent exploiting a vulnerability." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"A potential for violation of security that exists when there is a circumstance, a capability, an action, or an event that could breach security and cause harm. That is, a threat is a possible danger that might exploit vulnerability." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"A possible danger to a computer system, which may result in the interception, alteration, obstruction, or destruction of computational resources, or other disruption to the system." (NIST SP 800-28 Version 2)

"A potential cause of an unwanted incident." (ISO/IEC 13335)

"A potential cause of an unwanted incident, which may result in harm to a system or organisation."(ISO/IEC 27000:2014)

"An activity, deliberate or unintentional, with the potential for causing harm to an automated information system or activity." (NIST SP 800-16)

"Any circumstance or event with the potential to adversely impact organizational operations (including mission, functions, image, or reputation), organizational assets, or individuals through an information system via unauthorized access, destruction, disclosure, modification of information, and/or denial of service. Also, the potential for a threat-source to successfully exploit a particular information system vulnerability." (FIPS 200)

"Any circumstance or event with the potential to cause harm to an information system in the form of destruction, disclosure, adverse modification of data, and/or denial of service." (NIST SP 800-32)

"An event or condition that has the potential for causing asset loss and the undesirable consequences or impact from such loss." (NIST SP 1800-17b)

"Anything that might exploit a Vulnerability. Any potential cause of an Incident can be considered to be a Threat." (ITIL)

"The potential for a threat-source to exercise (accidentally trigger or intentionally exploit) a specific vulnerability. "(NIST SP 800-47)

🗄️Data Management: Business Rules – An Introduction

Data Management Series

    "Business rules" seems to be a recurring theme these days – developers, DBAs, architects, business analysts, IT and non-IT professionals talk about the necessity to enforce them in data and semantic models, information systems, processes, departments or whole organizations. They seem to affect the important layers of an organization. In fact the same business rule can affect multiple levels either directly, or indirectly through the hierarchical or networked structure of causality it belongs to. When considered all the business rules, the overall picture can become very complex. The fact that there are multiple levels of interconnected layers, with applications and implications at macro or micro level, makes the complexity to fight back because in order to solve business-specific problems often you have to go at least one level above the level where the problems were defined, or to simplify the problems to a level of detail that allows to tackled.

    The Business Rules Group defines a business rule as "a statement that defines or constrains some aspect of the business" [1], definition which seems to be closer to the vocabulary of IT people. Ronald G. Ross, in his book Principles of the Business Rule Approach, defines it as "a directive intended to influence or guide business behavior" [2], definition closer to the vocabulary of HR people. In fact the two definitions are kind of similar, highlighting the constrictor or guiding role of business rules. They raise also an important question – can everything that is catalogued as constraint or guidelines considered as a business rule? In theory yes, practically there are constraints and guidelines that have different impact on the business, so depending on context they need to be considered or not. What to consider is itself an art, which adds up to the art of problem solving.

    Besides identification, neither the definition nor management of business rules seems easy tasks. R.G. Ross considers that business rules need to be written and made explicit, expressed in plain language, independent of procedures and workflows, built on facts, motivated by identifiable and important business factors, accessible to authorized parties, specific, single sourced, managed, specified by those people who have relevant knowledge, and they should guide or influence behavior in desired ways [2]. This summarizes the various aspects that need to be considered when defining and managing business rules. Many organization seems to be challenged by this, and it can be challenging when lacks business management maturity.

    Many business rules exist already in functional and technical specifications written for the various software products built on request, in documentation of purchases software, in processes, procedures, standards, internal defined and external enforced policies, in the daily activities and knowledge exchanged or hold by workers. Sure, the formulations existing in such resources need to be enhanced and aggregated in order to be brought at the status of business rule. And here comes the difficulty, as iterative work needs to be performed in order to bring them to the level indicated by R.G Ross. For sure Ross’ specifications are idealistic, though they offer a “framework” for defining business rules. In what concerns their management, there is a lot to be done within an organization, as this aspect needs to be integrated with other activities and strategies existing in an organization.

    Often, when an important initiative, better said project, starts within an organization, then is felt in particular the lack of up-front defined and understood business rules. Such events trigger the identification and elicitation of business rules; they are addressed in documentation and remain buried in there. It is also true that it’s difficult to build a business case for further processing of business rules. An argument could be the costs associated from decisional mistakes taken by not knowing the existing rules, though that’s something difficult to quantify and make visible in an organization. In the end, most probably an organization will recognize the value of business rules when it reached a certain level of maturity.

References:
[1] Business Rules Group (2000) Defining Business Rules - What Are They Really? [Online] Available from: http://businessrulesgroup.org/first_paper/BRG-whatisBR_3ed.pdf
[2] Ronald G. Ross (2003) Principles of the Business Rule Approach. Addison Wesley. ISBN: 0-201-78893-4.

🗄️Data Management: The Data-Driven Enterprise (Part I: Thoughts on a White Paper)

Data Management Series

I read today ‘The Data-Driven Enterprise’ White Paper from Informatica, quite useful paper, especially when it comes from one of the leaders in integration software and services. In this paper the term data-driven enterprise refers to the organizations that are “able to take advantage of their data assets to work faster, better and smarter” [1], in order to achieve this state of art being necessary to” invest in the people, processes and technology needed to know where the data resides, to understand it, to clean it and keep it clean, and to get it to where it is needed, when and how it is needed” [1]. 

It seems that the data-driven enterprise, same as data-driven corporation [2], is just an alternative term for the data-driven organization concept already in use since several good years. Following the DIKW pyramid a data-driven organization follow a four stage evolution from data, to information and further to knowledge and wisdom, of importance being especially how knowledge is derived from data and information, the organizations capable of creating, managing and putting knowledge into use being known as knowledge-based organizations. It’s interesting that the paper makes no direct reference to knowledge and information, focusing on data as asset and possible ignoring information respectively knowledge as asset. I think it would help if the concepts from this paper would have been anchored also within these two contexts.

The paper touches several important aspects related to Data Management, approaching concepts like “value of data”, “data quality”, “data integration”, “business involvement”, “data trust”, “relevant data”, “timely data” “virtualized access”, “compliant reporting”, “Business-IT collaboration”, highlighting the importance of having adequate processes, infrastructure and culture in order to bring more value for the business. I totally agree with the importance of these concepts though I think that there are many other aspects that need to be considered. With such concepts almost all vendors juggle, though what’s often missing is the knowledge/wisdom and method to put philosophies and technologies into use, to redesign an organization’s infrastructure and culture so it could bring the optimum benefit.

Since the appearance of data warehouses concepts, the efficient integration of the various data islands existing within and outside of an organization become a Holy Grail for IT vendors and organizations, though given the fast pace with which new technologies appear this hunt looks more like a Morgan le Fey in the desert. Informatica builds a strong case for data integration in general and for Informatica 9 in particular, their new infrastructure platform targeting to enable organizations to become data-driven by providing a centralized architecture for enforcing data policy and addressing issues like data timeliness, format, semantics, privacy and quality[3]. On the other side the grounds on which Informatica builds its launching strategy could be contra-argumented considering the grey zone they were placed in.

Quantifying Value of Data

How many of the organizations could say that they could quantify (easily) the real value of their data when there is no market value they could be benchmarked against? I would say that data have only a potential value that could increase only with its use, once you learned to explore the data, find patterns and new uses for the data, derive knowledge out of it and use it wisely in order to derive profit and a competitive advantage, and it might take years to arrive there. 

People who witnessed big IT projects like ERP/CRM implementations or data warehousing have seen how their initial expectations were hardly met, how much are they willing to invest in an initiative that could prove its value maybe only years later, especially when there are still many organizations fighting the crisis? How could they create a business case for such a project? How much could they rely on the numbers advanced by vendors and by the nice slogans behind their tools just good for selling a product? 

Taking a quote from the video presentation of Sohaib Abbasi, Chairman and CEO at Informatica, “70% of all current SOA initiatives will be restarted or simply abandoned (Gartner)” [3], and I would bet that many such projects are targeting to integrate the various systems existing in an organization. Once you had several bad such experiences, how much are you willing to invest in a new one?

There are costs that can be quantified, like the number of hours employees spent on maintaining the duplicate data, correcting the issues driven by bad data quality, or more general the costs related to waste, and there are costs that can’t be quantified so easily, like the costs associated with bad decisions or lost opportunities driven by missing data or inadequate reflection of reality. There is another aspect, even if organizations reach to quantify such costs, without having some transparency on how they arrived to the respective numbers it felts like somebody just took out some numbers from a magician’s hat. It would be great if the quantification of such costs is somehow standardized, though that’s difficult to do given the fact that each organization approaches Data Management from its own perspective and requirements.

From Data to Meaning

Reports are used only to aggregate, analyze and navigate data, while it’s in Users attribution to give adequate meaning to the data, and together with the data analyst to find the who, how, when, where, what, why, which and by what means, in a word to understand the factors that impact the business positively/negatively, the correlation between them and how they can be strengthened/mitigated in order to achieve better quality/outcomes.

People want nice charts and metrics that can give them a birds-eye view of the current state, though the aggregated data could easily hide the reality because of the quality of the data, quality of the reports itself, the degree to which they cover the reality. Part of the data-driven philosophy resume in understanding the data, and reacting to data. I met people who were ignoring the data, preferring to take wild guesses, sometimes they were right, other times they were wrong.

From Functionality to Usability

There are Users who once they have a tool they want to find all about its capabilities, play with the tool, find other uses and they could even come with nice to have features. There are also Users who don’t want to bother in getting the data by themselves, they just want the data timely and in the format they need them. The fact that Informatica allows Users to analyze the data by themselves it’s quite of a deal, though as I already stressed in a previous post, you can’t expect from a User to become a data expert overnight, there are even developers that have difficulties in handling complex data analysis requirements. 

The guys from Informatica tried to make simple this aspect in their presentation though it’s not as simple as it seems, especially when dealing with ERP systems like Oracle or SAP that have hundreds of tables, each of them with a bunch of attributes and complex relations, one of the important challenges for developers is to reengineer the logic implemented in such systems. It’s a whole mindset that needs to be developed, there are also best practices that needs to be considered, specific techniques that allow getting the data in the most efficient way.

Allowing users to decide which logic to apply in their reports could prove to be a two edged sword, organizations risking ending up with multiple versions of the same story. It’s needed to align the various reports, bring users on the same page from the point of view of expectations and constraints. On the other side some Users prefer to prepare the data by themselves because they know the issues existing in the data or because they have more flexibility in making the data to look positive.

Trust, Relevance and Timeliness

An important part of Informatica’s strategy is based on data trust, relevancy and timeliness, three important but hard to quantify dimensions of Data Quality. Trust is often correlated with Users’ perception over the overall Data Quality, the degree to which the aggregated data presented in reports can be backed up with detailed data to support them, the visibility they have on the business rules and transformations used. If the Users can get a feeling of the data with click-through, drilldown or drill-through reports, if the business rules and transformations are documented, then most probably that data trust won’t be an issue anymore. Data relevancy and data timeliness are heavily requirement-dependent, for some Users being enough to work with one week old data while others need live data. In a greater or less degree, all data used by the business are relevant otherwise I don’t see why maintaining them.

Software Tools as Enablers

Sometimes being aware that there is a problem and doing something to fix it already brings an amount of value to the business, and this without investing in complex technologies but handling things methodologically and enforcing some management practices – identifying, assessing, addressing, monitoring and controlling issues. I bet this alone could bring a benefit for an organization, and everything starts just by recognizing that there is a problem and doing something to fix the root causes. On the other side software technologies could enable performing the various tasks more efficient and effective, with better quality, less resources, in less time and eventually with lowers costs. Now what’s the value of the saving based on addressing the issue and what’s the value of saving by using a software technology in particular?!

 Software tools like Informatica are just enablers, they don’t guarantee results and don’t eliminate barriers unless people know how to use them and make most of it. For this are needed experts that know the business, the various software tools involved, and good experienced managers to bring such projects on the right track. When the objectives are not met or the final solution doesn’t satisfies all requirements, then people reach to develop alternative solutions, which I categorize as personal solutions – spreadsheets, MS Access applications, an organization ending up with such islands of duplicated data/logic. Often Users need to make use of such solutions in order to understand their data, and this is an area in which Informatica could easily gain adepts.

Business-IT collaboration

There is no news that the IT/IM and other functional departments don’t function as partners, IT initiatives not being adequately supported by the business, while in many IT technology-related initiatives driven by the business at corporate level the IT department is involved only as executor and has little to say in the decision of using one technology or another, many of such initiatives ignoring aspects specific to IT – usability of such a solution, integration with other solutions, nuances of internal architecture and infrastructure. Of course that phrases like “business struggling in working with IT” appear when IT and the business function as separate entities with a minimum of communication, when the various strategies are not aligned as they are supposed to. 

If you’re not informing the IT department on the expectations, and vice-versa, each department will reach to address issues as they appear and not proactively, so there will be no wonder when it takes weeks or months until a solution is provided. The responsiveness of IT is strongly correlated with the resources, the existing infrastructure and policies in place. In addition for the IT to do its work the business has to share the necessary business knowledge, how can you expect to address issues when even the business is not able to articulate adequately the requirements – in many cases the business figures out what they want only when a first solution/prototype is provided. It’s an iterative process, and many people ignore this aspect.

No matter of the slogans and the concepts the vendors juggle with, I’m sorry, but I can’t believe that there is one tool that matches all requirements, that provides a fully integrated solution, that the tool itself is sufficient for eliminating the language and collaboration barriers between the business and IT!

Human Resources & Co.

Many organizations don’t have in-house the human resources needed for the various projects related to Data Management, therefore bringing consultants or outsourcing parts of the projects. A consultant needs time in order to understand the processes existing in an organization, organization’s particularities. Even if business analysts reach to augment the requirements in solid specifications, it’s difficult to cover all the aspects without having a deep knowledge about the architecture used, same as for consultants it’s difficult to put the pieces of the puzzle together especially when more of the pieces are missing. The consultants expect in general to have all the pieces of the puzzles, while the other sides expect consultants to identify the missing pieces.

When outsourcing tasks (e.g. data analysis) or data-related infrastructure (e.g. data warehouses, data marts) an organization risks to lose control over what’s happening, the communication issues being reflected in longer cycle times for issues’ resolution, making everything to become a challenge. There are many other issues related to outsourcing that maybe deserve to be addressed in detail.

The Lack of Vision, Policy and Strategy

An organization needs to have a vision, policy and strategy toward data quality in particular and Data Management in general, in order to plan, enforce and coordinate the overall effort toward quality. Their lack can have unpredictable impact on information systems and reporting infrastructure in particular and on the business as a whole, without it data quality initiatives can have local and narrow scope, without the expected effectiveness, resulting in rework and failure stories. The syntagma “it’s better to prevent than to cure” reliefs the best the philosophy on which Data Management should be centered.

Lack of Ownership

In the context of the lack of policy and strategy can be put also the lack of ownership, though given its importance it deserves a special attention. The syntagma “each employee is responsible for quality” applies to data quality too, each user and department need to take the ownership over the data they have to maintain, for their own or others’ departments scope, same as they have to take the ownership over the reports that make scope of their work, assure their quality and the afferent documentation, over the explicit and implicit islands of knowledge existing.

References:
[1] Informatica. (2009). The Data-Driven Enterprise. [Online] Available from: http://www.informatica.com/downloads/7060_data_driven_wp_web.pdf (Accessed: 6 February 2010).
[2] Herzler. (2006). Eight Aspects of the Data Driven Corporation – Exploring your Gap to Entitlement. [Online] Available from: http://www.hertzler.com/php/portfolio/white.paper.detail.php?article=31 (Accessed: 6 February 2010).
[3] Informatica. (2009). Informatica 9: Infrastructure Platform for the Data-Driven Enterprise, Speaker: Sohaib Abbasi, Chairman and CEO. [Online] Available from: http://www.informatica.com/9/thelibrary.html#page=page-5 (Accessed: 6 February 2010).

🗄️Data Management: Data Quality (Part I: Information Systems' Perspective)

Data Management Series

One LinkedIn user brought to attention the fact that according to top IT managers the top two reasons why CRM investments fail is: (1) managing resistance within the organization; (2) bad data quality.

The two reasons are common not only to CRM or BI solutions but also to other Information Systems, though from the two data quality has usually the biggest impact. Especially in ERP systems the data quality continues to be a problem and here are a few reasons:

• Processes span different functions and/or roles, each of them maintaining the data they are interested in, without any agreement or coordination on the ownership. The lack of ownership is in general management’s fault.
• Within an enterprise many systems arrive to be integrated, the quality of the data depending on the quality and scope of the integrations, whether they were addressed fully or only superficially. Few integrations are stable and properly designed. If stability can be obtained in time, scope is seldom changed as it involves further investments, and thus the remaining data need to be maintained manually, respectively the issues need to be troubleshooted or let accumulate in the backlog.
• There are systems which are not integrated but use the same data, users needing to duplicate their effort, so they often focus on their immediate needs. Moreover, the lack of mappings between systems makes data analysis and review difficult. 
• The lack of knowledge about the systems used in terms of processes, procedures, best practices, policies, etc. Users usually try to do their best based on the knowledge they have, and despite their best intent, the systems arrive to be misused just to get things done. 
• Basic or inexistent validation for data entry in each important entry point (UI, integration interfaces, bulk upload functionality), system permissiveness (allowing workarounds), stability and reliability (bugs/defects).
• Inexistence of data quality control mechanisms or quality methodologies, respectively a Data and/or Quality Management strategy. If the data quality is not kept under review, it can easily decrease over time. 
• The lack of a data culture and processes that support data quality.
• People lack consistency and/or the self-discipline to follow the processes and update the data as the processes requires it and not only the data to move to the next or final step. Therefore, the gap between reality and the one presented by the system is considerable.
• People are not motivated to improve data quality even if they may recognize the importance of doing that.

Data quality is usually ignored in BI projects, and this is because few are the ones that go and search for the causes, making it easier to blame the BI solution or the technical team than to do something. This is one of the reasons for which users are reticent in using a BI solution, to which add up solution’s flexibility and the degree up to which the solution satisfies users’ needs. On the other side BI solutions are often abused, including also reports which have OLTP characteristics or of providing too much unstructured or inadequate content that needs to be further reworked.

Data quality comes on the managers' agenda, especially during ERP implementations. Unfortunately, as soon as that happens, it also disappears, despite being warned of the consequences poor data quality might have on the implementation and further data use. An ERP implementation is supposed to be an opportunity for improving the data quality, though for many organizations it remains in this state. Once this opportunity passes, organizations need more financial and human resources to reach a fraction from the opportunity missed.

The above topics are complex and need further discussion (see [1], [2]).

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Written: Nov-2008, Last Reviewed: Mar-2024

Resources:
[1] SQL-Troubles (2010) Data Management: Data Quality - An Introduction (link)
[2] SQL-Troubles (2012) Data Migration: Data Quality’s Perspective I - A Bird’s-Eye View (link)

IT: Management Information System (Definitions)

"A system created specifically to store and provide information to managers." (Timothy J  Kloppenborg et al, "Project Leadership", 2003)

"A computer-based or manual system that transforms data into information useful in the support of decision making." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"A reporting or Business Intelligence system." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The full range of information technology solutions required by a business to run its daily operations, support strategic planning and process improvement activities, and identify issues requiring management attention for their resolution. See Decision Support System for an example of one of the components of MIS." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"The software applications and computer hardware systems in an enterprise that provide information for management decisions regarding its business operations. Also see Decision Support System." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"Systems designed to provide past, present, and future routine information appropriate for planning, organizing, and controlling the operations of functional areas in an organization." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

IT: Information System (Definitions)

"Computerized or manual structure of procedures and records. (9-11)" (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed., 2011)

"An interconnected environment for managing and processing data using a computer." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"Software that helps the user organize and analyze data" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"1.Generally, an automated or manual organized process for collecting, manipulating, transmitting, and disseminating information. SEE ALSO application. 2.In data management, a system that supports decision-making concerning some piece of reality (the object system) by giving decision-makers access to information concerning relevant aspects of the object system and its environment." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A physical process that supports an organization by collecting, processing, storing, and analyzing data, and disseminating information to achieve organizational goals." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A system that provides for data collection, storage, and retrieval; facilitates the transformation of data into information and the management of both data and information. An information system is composed of hardware, software (DMBS and applications), the database(s), people, and procedures." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management 9th Ed", 2011)

"The varied manual and automated communication mechanisms within an organization that store, process, disseminate, and sometimes even analyze information for those who need it." (Joan C Dessinger, "Fundamentals of Performance Improvement 3rd Ed", 2012)

"System that supports enterprise activities." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)