Welcome

Passionately curious about Data, Databases and Systems Complexity. Data is ubiquitous, the database universe is dichotomous (structured and unstructured), expanding and complex. Find my Database Research at SQLToolkit.co.uk . Microsoft Data Platform MVP

"The important thing is not to stop questioning. Curiosity has its own reason for existing" Einstein



Friday, 14 September 2018

Azure Cosmos DB multi-model database

Azure Cosmos DB has to be one of my favorite databases due to the breadth of available database types, its choice of consistency models and elastic scale out.

An introduction can be read here.

A definition for each of these types of databases is given.








Key-value
A key-value pair (KVP) is a set of two linked data items: a key, which is a unique identifier for some item of data, and the value, which is either the data that is identified or a pointer to the location of that data. Key-value pairs are frequently used in lookup tables, hash tables and configuration files.
https://searchenterprisedesktop.techtarget.com/definition/key-value-pair

Column
A column-oriented DBMS (or columnar database management system) is a database management system (DBMS) that stores data tables by column rather than by row.
https://en.wikipedia.org/wiki/Column-oriented_DBMS

Document
Document stores, also called document-oriented database systems, are characterized by their schema-free organization of data.That means records do not need to have a uniform structure, i.e. different records may have different columns. The types of the values ​​of individual columns can be different for each record. Columns can have more than one value (arrays). Records can have a nested structure. E.g. MongoDB
https://db-engines.com/en/article/Document+Stores

Graph
A graph database, also called a graph-oriented database, is a type of NoSQL database that uses graph theory to store, map and query relationships. Every node in a graph database is defined by a unique identifier, a set of outgoing edges and/or incoming edges and a set of properties expressed as key/value pairs.
https://whatis.techtarget.com/definition/graph-database


The five consistency levels offer predictable low latency guarantees and multiple well-defined relaxed consistency models.


Consistency Levels and guarantees

Consistency Level
Guarantees
Strong
Linearizability. Reads are guaranteed to return the most recent version of an item.
Bounded Staleness
Consistent Prefix. Reads lag behind writes by at most k prefixes or t interval
Session
Consistent Prefix. Monotonic reads, monotonic writes, read-your-writes, write-follows-reads
Consistent Prefix
Updates returned are some prefix of all the updates, with no gaps
Eventual
Out of order reads



There is a useful capacity planer that looks at request units throughput per second, request unit consumption and the amount of data storage needed by your application.


Thursday, 13 September 2018

Hortonworks Data Analytics Studio and Open Hybrid Architecture

Hortonworks has announced the general availability of Hortonworks Data Analytics Studio (DAS). A new service to enable enhanced productivity of business analysts by delivering faster insights from data at scale. DAS is part of the Hortonworks DataPlane Service (DPS). DPS enables businesses to discover, manage, govern and now optimize their data spread across hybrid environments. DAS leverages open-source technologies such as Apache Hive to share and extend the value of a modern data architecture in heterogeneous environments. It includes a useful database heat map.




Hortonworks have also shared the Open Hybrid Architecture Initiative, designed to enable big data workloads to run in a hybrid manner across on-premises, multi-cloud and edge architectures.


The Open Hybrid Architecture initiative will

  • De-coupling storage, with both file system interfaces and an object-store interface to data.
  • Containerizing compute resources for elasticity and software isolation.
  • Sharing services for metadata, governance and security across all tiers.
  • Providing DevOps/orchestration tools for managing services/workloads via the “infrastructure is code” paradigm to allow spin-up/down in a programmatic manner.
  • Designating workloads specific to use cases such as EDW, data science, rather than sharing everything in a multi-tenant Hadoop cluster.

AI the art of the possible

During SQL Saturday Cambridge I attended a session by Terry McCann on using AI to write a session submission to SQL Saturday. It was a great session and I would recommend you attending it if you get a chance.

In the new data world it is important to understand the difference between AI, Machine Learning and Deep Learning.


























Then breaking this down further the differences between how machine learning works and deep learning is shown here. Deep learning is really a black box.




















Image : https://www.upwork.com/hiring/for-clients/log-analytics-deep-learning-machine-learning/

Terry mentioned a free book to read to learn more Neural Networks and Deep Learning. 

He mentioned also the book Harry Potter and the Portrait of what Looked Like a Large Pile of Ash which was written by an AI bot.  I hadn't come across this before but it lets you see the art of the possible in the future.

Image: https://imgur.com/gallery/gkLFz


Tuesday, 11 September 2018

Azure DevOps Services



Microsoft have announced new Azure DevOps services than span the breadth of the development lifecycle to help developers ship software faster and with higher quality. Azure DevOps includes:

CI/CD that works with any language, platform, and cloud. Connect to GitHub or any Git repository and deploy continuously. 
Powerful work tracking with Kanban boards, backlogs, team dashboards, and custom reporting. 
Maven, npm, and NuGet package feeds from public and private sources. 
Unlimited cloud-hosted private Git repos for your project. Collaborative pull requests, advanced file management, and more. 
All in one planned and exploratory testing solution. 

PowerBI Report Server

Power BI Report Server is similar to SQL Server Reporting Services (SSRS). Power BI Report Server is on premises, and hosts paginated reports in addition to the Power BI features. There are several types of reports
  • Paginated (standard SSRS type reports)
  • Interactive (PowerBI Desktop)
  • Mobile
  • Analytical (Excel)

PowerBI Pro enables sharing and collaboration with the following features 
  • Build dashboards that deliver a 360-degree, real-time view of the business
  • Keep data up-to-date automatically, including on-premises sources
  • Collaborate on shared data
  • Audit and govern how data is accessed and used
  • Package content and distribute to users with app

There is increased functionality within PowerBI Pro. A comparison between the free PowerBI desktop edition and Pro version is in the table below. Only Pro users can publish content to app workspaces, consume apps without Premium capacity, share dashboards and subscribe to dashboards and reports. Free users can connect to all data sources using connectivity options such as DirectQuery, live connection and use the data gateway. 






























Licensing Power BI Report Server

Power BI Report Server is available in two different license modes: 
A Power BI Premium license enables creation of a hybrid deployment (cloud and on-premises).

Capacity


The session delivered by Isabelle Van Campenhoudt at SQL Saturday Cambridge shared this useful capacity planning  paper. It is based on the sample Power BI Report Server topology using virtual machines. 






















Monday, 10 September 2018

Changing our concept of the world

In Ackoff’s Best His Classic Writings on Management (1999), he shares a way of thinking that as fascinated me for years. It can be used for problem solving and has helped me manage database systems for years. It is about looking at things in a holistic way.  This way of thinking, synthesis, often yields contradictory or conflicting results to traditional analytical thinking. Systems thinking, as it is known, helps expand your viewpoint considering the holistic nature of the environment. Ackoff shared the difference between the two modes of thinking: 

Machine Age Thinking (Analysis)
Synthesis (Systems Thinking)
Analysis precedes synthesis
Synthesis precedes analysis
Focuses on structure
Focuses on function
Reveals how things work
Reveals why things operate as they do
Yields knowledge
Yields understanding
Enables us to describe
Enables us to explain
Looks into things
Looks out of things
Iteration of the parts of the thing to be explained
Iteration of the parts of the thing to be explained, interactions between things in its environment and with its environment itself concerned with functional interaction of the parts of system
Break down large complex problems into solvable or manageable parts outputs assembled into a solution of the whole
Some of the best solution obtained from the parts taken separately is not the best solution of the whole.
The best performance of the whole can be reduced to the sum of the best performance of its parts taken separately
Asserts this is not possible
Reduces the focus of the investigator
Expands it focus of the investigator

As a result, I never look only at the immediate problem but consider all of the interconnecting parts.

Sunday, 9 September 2018

SQLSaturday Cambridge 2018 - Britannia Edition!


I attended PASS SQL Saturday Cambridge 8 September 2018. This was held at The Møller Centre, Churchill College, Storey's Way, Cambridge. Another great training day packed full of lots of new and exciting data technology.

I attended some amazing sessions. These were

  • Data Classification in SQL Server and Azure SQL Database - Mark Pryce-Maher
  • Azure Cosmos DB - What you need to know to build globally distrib - Satya SK Jayanty
  • Using AI to write session submission to SQLSaturday - Terry McCann
  • Power BI server and Office Online server, modernize your on-premises BI approach. - Isabelle Van Campenhoudt
  • Bricking it: An Introduction to Azure Databricks - Simon Whiteley 
  • SQLOpsStudio Vs SSMS - There can be only one - Warwick Rudd
  • Branding Yourself for a Dream Job - Steve Jones

There was some very interesting learning, that I took away, which I will write about in a follow up blog.

Saturday, 8 September 2018

SQL Relay: the travelling conference



Registration is open for SQL Relay. This is a conference with a difference. It tours round the UK for 5 consecutive days bringing international and MVP speakers to your local area. It is an amazing conference to attend with lots of sessions to choose from. This years events are at

Newcastle - Monday 8 October
https://sqlrelay2018-newcastle.eventbrite.co.uk/

Leeds - Tuesday 9 October
https://sqlrelay2018-leeds.eventbrite.co.uk/

Birmingham - Wednesday 10 October
https://sqlrelay2018-birmingham.eventbrite.co.uk/

Reading - Thursday 11 October
https://sqlrelay2018-reading.eventbrite.co.uk/

Bristol - Friday 12 October
https://sqlrelay2018-bristol.eventbrite.co.uk/

Sunday, 19 August 2018

Storyboard Books and 3D Printing

My printed PhD storyboard books have arrived. If you're curious what's inside, you can see the storyboard here . 

Also you can what the video


Together with my 3D printed Data models my brother made, they tell the story of 7 years of work. An amazing reminder of the time, the PhD graduation and the receipt of a research award the, AOUG Will Swann Award for Innovation and Knowledge Development. I am proud to have studied with The Open University. 



Monday, 13 August 2018

Microsoft Faculty Summit Research Commitment

The Microsoft 2018 Faculty Summit entitled: Systems | Fueling Future Disruptions left you thinking about the many possible research avenues that are underway and what research problems there are that need to be addressed. I found the conference inspiring and full of new ideas that fueled my research curiosity.

It is great to see the investment in research to tackle industry challenges. 






















The summit discussed many technologies and the promise of AI.






















AI has many use cases that will enhance society but it is important to think about the need for an ethical framework.

It will be possible to watch the sessions on-demand at MicrosoftFacultySummit.com in a couple of weeks.

Saturday, 4 August 2018

MSBizAppsSummit Viewing




I am pleased to see the Microsoft Business Applications Summit has posted the sessions of the conference to view on-demand. These cover Dynamics 365, Power BI, PowerApps, Microsoft Flow and Excel.  The sessions cover the opening keynote, product visions and roadmaps and the intelligent edge, intelligent cloud and mixed reality, so plenty of viewing to catch up on if you missed out on the conference.

Thursday, 2 August 2018

Understanding Complexity with Systems

I have today been watching the many butterflies flapping their wings around a Buddleia bush. The butterfly bush with its abundant deep violet-purple flowers attracts the butterflies, which surround the bush. I always wonder if this activity will cause a transformation round the globe with the much discussed 'butterfly effect'. The classic quote 'the notion that a butterfly stirring the air today in Peking can transform storm systems next month in New York [...] tiny differences in input could quickly become overwhelming differing in output' Glick (1987) . This is known as Chaos,  the term coined by Lorenz for a system with unpredictable outcomes.  The problem is that it is difficult to know the exact starting point of a situation accurately enough to put it into a mathematical formula. Thus each step in the process in the system moves further away from where you thought it should go. The errors or uncertainty multiply and can cause turbulent features. 

The natural world is unpredictable and has many patterns of behaviour. To understand any system well enough it is necessary to understand the inputs and outputs. To understand database systems there is much complexity that needs to be considered. This graph shows some of the theoretical components.
























To apply this understanding and advance a system using artificial  intelligence (AI) you need to fully understand the system under investigation. For data and database systems people gain that experience and learning over many years. The art is to document the inputs and outputs and identify the best practices which have worked and those that have not. Also to create a method to connect the continuously evolving and changing best practice. Only then can you identify the opportunities within the system to improve management and create a state where AI is embedded within a helpful tool that could improve efficiency and performance.

My research examined the complexity of managing database systems and as such has the building blocks to begin to build an autonomous AI system to help manage database systems. Below shows the components that are interconnected in the the management of database systems.




Complexity is always changing and migrating with the passage of time and the trend will be from order to disorder, thus creating an autonomous system to help prevent that could be beneficial.The aim would be to create a self organizing system based on feedback from a persons behaviour, decisions, documentation, operational configuration, meetings and actions. 

Wednesday, 1 August 2018

Power BI World Tour





















The Power BI World Tour is a two-day event that will cover technical content designed for the Power BI Analyst, Developer/IT Admin, and new professional, by local industry experts. The dates are:

Melbourne, AUS August 21-22
Charlotte, NC August 28-29
Seattle, WA October 29-30
Montreal, CAN November 14-15
Sydney, AUS August 28-29
Copenhagen, DEN September 11-12
Dubai, UAE November 13-14
Dallas, TX November 28-29

Monday, 30 July 2018

Systems | Fueling future disruptions


It is an exciting time of year with so many informative and knowledge sharing conferences from Google to Microsoft. It is the 2 day Microsoft Faculty Summit 2018, 1 - 2 August 2018. This brings together leaders and researchers from the broad systems research area in computer science. Systems research is the foundation innovation grows from. It has the potential to disrupt the future.


The conference guide can be downloaded

Saturday, 28 July 2018

What is Best Practice?


Best practice is a pervasive term that means different things to different people. Best practice has been defined in various ways (Dembowski 2013; Wellstein & Kieser 2011; Sanwal 2008). Dani et al. (2006) stipulate “A best practice is simply a process or a methodology that represents the most effective way of achieving a specific objective”. Jarrar & Zairi (2000) state that the term best practice is often used within organizations to depict leadership and is recognised as the best way to achieve superior results. In the glossary of benchmarking terms (American Productivity and Quality Centre 1999) cited in (Jarrar & Zairi 2000, p.S734) best practices were defined “Those practices that have been shown to produce superior results; selected by a systematic process; and judged as exemplary, good, or successfully demonstrated. Best practices are then adapted to a particular organisation”. Many different situations require different best practices and with new technology evolving ‘best’ is a moving target (Jarrar & Zairi 2000). 

Markus (2011, p.4) argued that the cultures and practices that develop over time in organizations have changed to become “off-the-shelf” services labelled best practice standards, which organizations needed to adopt and understand. Markus argued the change from unique coded management ideas for handling packages to standard software with relentless upgrades requires knowledge development and standard practices.

Sanwell (2008) stated that the use of best practices are affected by certain beliefs:
  • Best practices help make decisions quickly in a complex uncertain world. 
  • Best practices are easier because they have been proven by other organizations who also operate with complex and uncertain elements. 
  • Management understanding of other organizations in the field are organizational specific. Best practices are often developed later and often already behind leading organizations. 
  • Value must be gained from best practices as other experts, consultants and vendors share them for current trends. 
  • Best practices can improve performance. 

Falconer (2010) argued to the contrary that best practice exacerbates failure:
Best practice is flawed because it acts as a placeholder for proper management practice, displacing accountability for effectiveness and fit. Best practice is flawed, further, because it supplants strategy, adopting solutions out of convenience or copying them reactively, and supplants innovation, allowing “the best we know about”, “the best we’ve come across”, or even “the best we’ve done before” to be adequate. Best practice considers the world predictable, and discounts the emergence of better, novel ideas(Falconer 2010, p.754)
Falconer thought that problem situations are being incorrectly handled due to best practices replacing analysis.

Sanwell (2008) pointed out that changing these best practices in the multidimensional world requires consideration of organizational culture and behaviour, organization processes and organizational systems. As Gonnering stated,
“Best Practices” can serve as a beginning but adaptation will most likely be necessary. Outcome is an emergent property, and the organization that has taken the time to learn the methodology of improvement will reap the benefits. The “continuous” in “continuous quality improvement” depends upon rapid-cycle, small-scale serial innovation and not a static and dogmatic adherence to past processes.” (2011, p.100) 

Gonnering argued that complex problems using best practices failed to have positive outcomes and forced the complex systems to become chaotic. Bretschneider et al. (2004) highlighted three important characteristics of best practice: a comparative process, with action, and linked to an outcome or goal. Nattermann (2000) suggested best practice might be the most widely used management tool in business and important for improving operational efficiency, but for strategic decision making, best practices might not be the best way forward to increase profit margins. Best practices management could be used to benchmark performance, with certain benchmarks being required to demonstrate best practices.

The core or classic best practices utilised within the database community have been developed through the sharing of knowledge, experience and actual outcomes across the sector. The improvement of these best practices were raised by Gratton & Ghoshal (2005) with the term “a signature process”, a process that envelops the company’s character and idiosyncratic nature. This signature process could advance the company although it required careful adaptation and alignment to business goals to succeed. However the allure of classic best practices that were clear, logical and easy to understand were the ones shared within the database community, the body of knowledge often yielding optimal results (Tucker et al. 2007). Some best practices were tightly coupled with their organizations and inseparable from the context (Becker 2004).

Jarrar & Zairi (2000) identified three types of best practice: proven best practice across organizations, good practice techniques for an organization, and unproven good ideas based on intuition. There were drawbacks with unproven ideas that could be a matter of luck and the lack of information to reduce the risk, lack of situational context, application criteria or success measure (Falconer 2011). This serendipitous discovery could lead to ease of deployment and innovation.

The Cynefin framework (Snowden & Boone 2007) classified and ordered simple systems in the domain of best practices. In an earlier paper in the chaos domain Kurtz & Snowden (2003) argued that applying best practices probably caused the chaos in the first place. They argued that different contexts use different management responses and that there are different tools for the management of complex contexts. The best practices domain is based on cause and effect relationships that have simple contexts, often within areas that do not change frequently.

Wagner and Newell (2011, p.400) stated that “The best way of operationalizing a process in one context and at one point in time may be different in another context and time”. They contended that there is no such thing as best practice, as knowledge is created by engagement in a practice. Practice is always changing and emergent with inconsistencies in the same practice, with best practice being defined locally. 

Wagner and Newell (2011, p.401) suggested a move to negotiated practice with a cooperative approach to best practice adoption. Their aim was to smooth out complex implementation through compromise. They concluded that highlighting problems with identifying best practice (due to it being an interactive process based on learning through implementation with information systems) sometimes required customisation to work well. This approach was also adopted by Avgerou & Land (1992) with their notion of ‘appropriate’ context specific practice, where information systems innovation looked for “best practice, or suitable new organizational form for the information age”  (Avgerou 2011, p.650).

Avgerou drew together organizational and information systems to develop a framework which had one key tenet of a knowledge management system or a best practice solution to help address static and commoditized technology.

Best practices and procedures were continually developed by database software providers (e.g. Microsoft, Oracle and MongoDB) to enable the management of database systems to be carried out to the highest standards. The procedures were based on formal rules the business world defined which were sometimes called standard operating procedures (Becker 2004). Best practices were defined by the software providers as exemplary tested designs for certain configurations or ways of doing things. They were multi-faceted and resided in varying layers from architectural design, through development, to operational management.

The management of database systems utilizes best practices and procedures provided by software providers and often industry best practices shared by the community. McGregor (2007) argued that this rarely leads to great customer service. McGregor’s (2007) idea that “Next Practice” was the future of continually analysing and looking for positive quality products and service in other organizations, would bring ideas and innovation to improve the business. There was an aspiration to improve database management and improve business processes to provide good quality service when managing IT projects and database systems. Best practices might not however be the best solution. Sanwell (2008) raised some key issues with using processes and strategies created by other organizations, and did not believe that following these would create a better organization or bring about improvement.

Within database systems there are various types of practices and procedures that need to be incorporated within change processes. Savage (2014, p.17) stated Stonebraker thought “in memory” database engines will take over online transactional processing systems (OLTP). Savage (2014, p.16) shared Stonebraker’s views on the database world, that it could be divided into three types: OLTP, data warehouses and everything else (Hadoop, graph databases). This was likely to mean three or more database management and best practices models were required.

Best practices operate at different levels within the sphere of database management. There are technology best practices which deal with specific tasks for deployment of databases onto servers or into the cloud; and management best practices which relate to higher level functions and overall processes. In addition there are best practices which are defined by software vendors for their own products.  As technology and management change, in the world market, and more is understood about certain areas, best practices change. Thus best practices are replaced with new best practices. The large collection of best practices created are likely to be defined and owned by a multitude of people. This can cause problems with conflicting best practices. Sometimes there is a mismatch between best practices and a compromise needs to be found where possible.
   
Best practices are intended to be useful for technical solutions to help people provide the required results. They aim to provide a useful guide on what management need to do to perform certain tasks. Best practices are sometimes adapted from vendor or industry defined best practices for nonstandard configurations or different business scenarios. However, sometimes communication is lacking between the management requirements, the vendors’ practices and the technology tasks. Different teams may each create best practice, in places where the technology overlaps, which are not shared. There are therefore limitations to the usage of best practices. The best practices presented are significantly different for ILTM, CMM and ILTIL. There are many different types of tasks from in depth technical ones to higher level models that combined can produce a well-managed database system. Each task, model or part of the database system will have its own best practice, which aims to achieve those reliable results. These best practices at different levels may, in practice, sometimes be in conflict. This discussion on best practice has shown there are many diverse views on the usability and definition of best practice. The working definition in my research (Holt, 2017) for best practice was: a recommended practice for carrying out actions for desirable outcomes, rather than always being the best way of doing something. The research best practice findings are in Holt et al. (2015) and the working cogs of best practice summaries the findings. 

American Productivity and Quality Centre. (1999). What is benchmarking. Retrieved from www.Apqc.org

Avgerou, C. (2011). Discources on innovation and development in information systems in developing countried research. In R. D. Galliers & W. L. Currie (Eds.), The Oxford Handbook of Management Information Systems (p. 650). Oxford: Oxford University Press.
Avgerou, C., & Land, F. (1992). Examining the appropriateness of information technology. In S. Odedra & M. Bhatnagar (Eds.), Social Implications of computers in developing countries (pp. 26–42). New Delhi: Tata McGraw-Hill.
Becker, M. C. (2004). Organizational routines: a review of the literature. Industrial and Corporate Change, 13(4), 643–678. https://doi.org/10.1093/icc/dth026
Bretschneider, S. (2004). “Best Practices” Research: A Methodological Guide for the Perplexed. Journal of Public Administration Research and Theory, 15(2), 307–323. https://doi.org/10.1093/jopart/mui017
Dani, S., Harding, J. a, Case, K., Young, R. I. M., Cochrane, S., Gao, J., & Baxter, D. (2006). A methodology for best practice knowledge management. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(10), 1717–1728. https://doi.org/10.1243/09544054JEM651
Dembowski, F. L. (2013). The Roles of Benchmarking , Best Practices & Innovation in Organizational Effectiveness. International Journal of Organizational Innovation, 5(3), 6–20.
Erica Wagner, & Newell, S. (2011). Changing the story surrounding enterprise systems to improve our understanding of what makes erp work in organizations. In R. D. Galliers & W. L. Currie (Eds.), The Oxford Handbook of Management Information Systems (p. 401). Oxford: Oxford University Press.
Falconer, J. (2010). “Best Practice” as Worst Practice : Broken Metaphor , Nude Emperor. Proceedings of the European Conference on Intellectual Capital, 754–762.
Falconer, J. (2011). Knowledge as Cheating : A Metaphorical Analysis of the Concept of “Best Practice.” Systems Research and Behavioral Science, 180, 170–181. https://doi.org/10.1002/sres
Gonnering, R. S. (2011). The Seductive Allure Of “Best Practices”: Improved Outcome Is A Delicate Dance Between Structure And Process. E-CO, 13(4), 94–101.
Gratton, L., & Ghoshal, S. (2005). Beyond Best Practice. MITSloan Management Review, 46(3).
Holt, V. et al. (2015) ‘The usage of best practices and procedures in the database community’, Information Systems, 49. doi: 10.1016/j.is.2014.12.004.
Holt, V. (2017) A Study into Best Practices and Procedures used in the Management of Database Systems. The Open University. Available at: http://oro.open.ac.uk/id/eprint/50950.
Jarrar, Y. F., & Zairi, M. (2000). Best practice transfer for future competitiveness: A study of best practices. Total Quality Management, 11(4–6), 734–740. https://doi.org/10.1080/09544120050008147
Kurtz, C. F., & Snowden, D. J. (2003). The new dynamics of strategy : Sense-making in a complex and complicated world. IBM Systems Journal, 42(3). https://doi.org/10.1147/sj.423.0462
Markus, M. L. (2011). Historical Reflections on the Practice of Information Management and Implications for the field of MIS. In R. D. Galliers & W. L. Currie (Eds.), The Oxford Handbook of Management Information Systems (pp. 3–15). Oxford: Oxford University Press. https://doi.org/http://dx.doi.org/10.1093/oxfordhb/9780199580583.003.0002
McGregor, M. (2007). When Best Practice is Just Not Good Enough Why and How You Need to be Better than the Best. BPTrends, (July), 1–2.
Nattermann, P. M. (2000). Best practice does not equal best strategy. The McKinsey Quarterly, 2.
Sanwal, A. (2008). The Myth of Best Practices. Journal of Corporate Accounting & Finance, 19(5), 51–60. https://doi.org/10.1002/jcaf
Savage, N. (2014). The Power of Memory. Communications of the ACM, 57(9), 15–17. https://doi.org/10.1145/2641229
Snowden, D. J., & Boone, M. E. (2007). A Leader’s Framework for Decision Making. Harvard Business Review, 85(11), 68–76.
Tucker, A. L., Nembhard, I. M., & Edmondson, A. C. (2007). Implementing New Practices: An Empirical Study of Organizational Learning in Hospital Intensive Care Units. Management Science, 53(6), 894–907. https://doi.org/10.1287/mnsc.1060.0692
Wellstein, B., & Kieser,  a. (2011). Trading “best practices”--a good practice? Industrial and Corporate Change, 20(3), 683–719. https://doi.org/10.1093/icc/dtr011