#eavoices

Smart Apps for the Enterprise – Mendix World 2016 keynote

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Last years Mendix World was centered around Smart Apps. It was the best conference of the year. At least in my opinion, I might be biased… The conference had a great speaker line-up, including keynotes from Geoffrey Moore, Simon Wardley, and Adrian Cockcroft. All slides and videos can be found here. I had the honor to announce Mendix 7, our latest major release. I explored the topic of Smart Apps.

The post Smart Apps for the Enterprise – Mendix World 2016 keynote appeared first on The Enterprise Architect.

The transformation to the virtual cloud enterprise, steps

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To adapt to the increasingly faster market demands, new enterprise business models appeared. Essentially enterprises outsource today resources, value chain activities and IT to a cloud of external providers so enterprises can ramp up and down thei…

Decision-Making Models

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In my previous discussion of the ACPO national decision model (May 2014), I promised to return to the methodological question, namely what theories of decision-making would be relevant to NDM and any other decision models. I have just happened upon a d…

Decision-Making Models

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In my previous discussion of the ACPO national decision model (May 2014), I promised to return to the methodological question, namely what theories of decision-making would be relevant to NDM and any other decision models. I have just happened upon a doctoral thesis by Maxwell Mclean looking at the decision-making by coroners, which analyses local variation in coronial outcomes at three decision-making stages: whether to report the death, whether to advance to inquest, and the choice of inquest conclusion.

Mclean notes that there is no decision-making model for coroners equivalent to the police national decision model and focussed on standards and consistency of outcome. He finds other examples of decision-making models in nursing (Lewinson and Truglio-Londrigan, 2008; Husted and Husted, 1995; Jasper, Rosser and Mooney, 2013); social work (O’Sullivan, 2011; Taylor, 2010); and probation work (Carter, 1967; Rosecrance, 1985). However, several of these are descriptive models rather than normative models.

Within the professions mentioned by Mclean, I found a lot more work on evidence-based nursing as well as some interesting international discussions on decision-making within offender supervision. Looking further afield, I was interested to find an article about a decision-making model in the US Army, but this turned out to be merely a polemical article by a former Navy Seal advocating the use of Design Thinking.

Rosecrance introduces an interesting concept of the Ball Park, where a professional decision is influenced by the anticipated reaction of a more senior professional. For example, the decisions of a probation officer are not solely designed to achieve the desired outcomes for the client, but also designed to meet the approval of (1) judges, (2) prosecuting attorneys, and (3) probation supervisors. When a recommendation seems likely to meet the approval of these three entities, it is said to be “in the ball park”. The “ball park” concept is also used in sales negotiations, and this hints at the idea that the focus here is on “selling” (or at least defending) the decision rather than just making it.

Coming back to the police, this frames the NDM not just as a way of making the best decision but also avoiding censure if anything goes wrong. See my post on the National Decision Model and Lessons Learned (February 2017).

Miranda Boone and Martine Evans, Offender supervision and decision-making in Europe (Offender Supervision in Europe: Decision-Making and Supervision Working Group, 2013)

Jeff Boss, The Army’s New Decision-Making Model (Forbes, 8 August 2014)

Carter, R.M. (1967). The presentence report and the decision making process. Journal of
research in crime and delinquency. 4 203-211.

Jasper, M., Rosser, M., Mooney, G. (Eds.) (2013). Professional Development, Reflection
and Decision-Making in Nursing and Health Care (2nd ed.). Swansea: Wiley Blackwell.

Husted, G.L. and Husted, I.H. (1995). Ethical decision-making in nursing (2nd ed.). St
Louis: Mosby.

Lewenson, S.B. and Truglio-Londrigan, M. (2008). Decision-Making in Nursing, thoughtful approaches for practice. London: Jones and Bartlett Publishers International.

Maxwell Mclean, The Coroner in England and Wales; Coronial Decision-­Making and Local Variation in Case Outcomes (Doctoral Thesis, University of Huddersfield, 2015)

O’Sullivan, T. (2011). Decision making in social work (2nd ed.). Basingstoke: Palgrave
Macmillan

Rosecrance, J. (1985). The Probation Officers’ Search for Credibility: Ball Park
Recommendations. Journal of research in crime and delinquency. 31, (4) 539-554.

Mooi Standing, Perceptions of clinical decision-making: a matrix model (May 2010). This appears to be a chapter from Mooi Standing (ed) Clinical Judgement and Decision-Making in Nursing and Inter-professional Healthcare (McGraw Hill, 2010)

Taylor, B. (2010). Professional Decision-Making in Social Work. Exeter: Learning Matters.

Carl Thompson et al, Nurses, information use, and clinical decision making—the real world potential for evidence-based decisions in nursing (Evidence-Based Nursing Vol 7 No 3, July 2004) http://dx.doi.org/10.1136/ebn.7.3.68

Related posts
National Decision Model (May 2014)
National Decision Model and Lessons Learned (Feb 2017)

Updated 4 March 2017

The Cloud Is Disrupting Hadoop

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Forrester has seen unprecedented adoption of Hadoop in the last three years. We estimate that firms will spend $800 billion in Hadoop software and related services in 2017. Not surprisingly, Hadoop vendors have capitalized on this — Cloudera, Hortonworks, and MapR have gone from a “Who?” to “household” brands in the same period of time.

But like any good run, times change. And the major force exerting pressure on Hadoop is the cloud. In a recent report, The Cloudy Future Of Hadoop, Mike Gualtieri and I examine the impact the cloud is having on Hadoop. Here are a few highlights:

● Firms want to use more public cloud for big data, and Hadoop seems like a natural fit. We cover the reasons in the report, but the match seems made in heaven. Until you look deeper . . .

● Hadoop wasn’t designed for the cloud, so vendors are scurrying to make it relevant. In the words of one insider, “Had we really understood cloud, we would not have designed Hadoop the way we did.” As a result, all the Hadoop vendors have strategies, and very different ones, to make Hadoop relevant in the cloud, where object stores and abstract “services” rule.

● Cloud vendors are hiding or replacing Hadoop all together. AWS Athena lets you do SQL queries against big data without worrying about server instances. It’s a trend in “serverless” offerings. Google Cloud Functions are another example. DataBricks uses Spark directly against S3. IBM’s platform uses Spark against CloverSafe. See the pattern?

As more firms get tired of Hadoop’s on-premises complexity and shift to the public cloud, they will look to shift their Hadoop stacks there. This means that the Hadoop vendors will start to see their revenue shift from on-premises to the cloud.

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Fear of Failure, Fear and Failure

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Some things seem so logically inconsistent that you just have to check them out. Such was the title of a post on LinkedIn that I saw the other day: “Innovation In Fear-Based Cultures? Or, why hire lions to be dogs?”. In it, Michael Graber noted that “…top-down organizations have the most trouble innovating.”: In particular, […]