Ambidexterity in Management and Productivity Measurement

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Leading and Managing Organizational Resources
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Introduction
Ambidexterity in management is when a person uses both the right and the left hands in
operations. In a company, ambidexterity is when the company cannot forfeit innovation in order
to have something else. Innovation leads to productivity and the reason why we have good
leadership in a company. When hyper convergent solutions only began to be actively
introduced, the main motive of companies choosing this model of building infrastructure was the
desire to consolidate resources while saving and simplicity of solutions. By virtualizing the
infrastructure, they first of all hoped to increase operational efficiency, as well as to make the
infrastructure more homogeneous, while not going beyond the budget (Arena, Cross, Sims, and
Uhl-Bien, 2017).
How to measure and assess productivity in a creative environment (hours/cost/profit)
In the case when a large range of products is produced, the labor method of measuring labor
productivity is applied. The scope of work and production is determined in the labor dimension -
in normal hours and man hours.
For example, As is often the case with young technologies, most companies for a long time did
not venture to bring Tier I-level business-critical applications to hyper-convergent computing
environments, leaving them to work on the basis of traditional servers. Most often, the hyper-
convergent infrastructure became a field for experimentation, as well as a place to deploy low-
priority applications, such as virtual desktops (Ghazzawi, El Shoughari, and El Osta, 2017).But
gradually the situation changes. The experience that companies have already gained allows us to
change the formulation of the question regarding the hyper-convergent infrastructure from “Can

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it support the company's requirements?” To “How well can it support them?”. Most of these
requirements are somehow related to performance.
Traditionally, the criteria for evaluating the performance of a computing infrastructure are the
speed of data processing and the capacity of the channels. The millions of I / O operations per
second (IOPS - input / output per second) and high throughput almost guarantee the choice of
companies in favor of a solution that can demonstrate such results (Keleş, and Keleş, 2018).
Ambidexterity in today's IT market, when the number of hyper-converged infrastructure use
scenarios is growing, various performance testing methodologies are needed that will allow us to
evaluate not only the storage system used. To adequately evaluate the hyper-converged
infrastructure, it is necessary to emulate workflows to test the operation of the computational part
and memory together. In fact, it is necessary to find the answer to three key questions:
1. How quickly can the company's applications run on this infrastructure?
2. What happens if the number of applications and the infrastructure itself grows?
3. How will applications behave in the event of a failure?
Speed is an aspect of performance that shows how quickly the system copes with
workloads. The speed estimate can be divided into theoretical and real (Lane, 2016).Theoretical
performance testing is conducted to determine how the system handles potential workloads and
where bottlenecks may occur. Usually, one resource is taken for this and the maximum of its
capabilities is evaluated, and for this reason, testing can be carried out using public and well-
recommended tools, whose methodology is documented (Martinsen, and Amundsen, 2016).

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How to improve productivity in addition to developing people and becoming a leaning
organization
A semi-natural method of measuring labor productivity is also used. This method expands the
scope of using the natural method of measuring labor productivity. In this case, various types of
products within a certain set of them are equated to a conditionally natural meter by a certain
parameter, in particular, by normalized labor input (McCleskey, 2014). Different varieties of
products are converted to a certain conditional grade. For example, gasoline, kerosene, jet fuel,
fuel oil - for conditional fuel. Or various types of agricultural products are converted to protein
content.
Practical performance can be tested on the basis of an emulator of a real working environment
using real-world applications. For hyper convergent productivity, such testing is very important,
because it uses various resources together, and their performance separately does not reflect the
possibility of sharing them. At the same time, the results of evaluating theoretical performance
can be used to predict the outcome of practical tests.
The problem with practical tests is only that they require much more time and effort to prepare
all the necessary applications, data and environment for testing. In such tests, IOPS, throughput
and response time also remain basic indicators, but specific metrics provide much more insight
into the potential of the hyper-convergent infrastructure (Nauman, and Qamar, 2018). In general,
for a hyper-converged infrastructure, an assessment of the performance of individual resources is
only a necessary first step. Practical testing makes it possible to cluster various computational
and storage resources into clusters in order to understand how they will interact. It can be
described as an understanding of business strategy and its execution. Therefore, this includes