Emerging Technologies: Strategic Management and Innovation Issues

This essay examines the impact of innovation and emerging technologies on business performance, emphasizing the competitive forces and strategic changes they bring. It discusses how businesses can strategically manage issues arising from these technologies, referencing change management models like Kotter's eight-stage model and Lewin's three-step model. The essay also highlights the role of big data analytics and its influence on business culture, discussing the big data analytics cycle including data acquisition, information extraction, data integration, and modeling. It concludes that sustainable growth requires adapting to change, fostering experimentation, and committing to continuous performance improvement.

RUNNING HEAD: INNOVATION AND EMERGING TECHNOLOGIES 1
INNOVATION AND EMERGING TECHNOLOGIES
Student Name
Institute Name

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Contents
Introduction................................................................................................................................2
Discussion..................................................................................................................................2
Conclusion..................................................................................................................................7
Reference....................................................................................................................................8

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Introduction
The essay will throw light on the impact of innovation and emerging technologies on the
overall performance and operations of the business. It has come up with a very strong
competitive force with important changes in strategy and many other implications. All the
industries can grow around the emerging technology since the current or present industries
are changing in term of progress or destroying completely (Boons and Lüdeke-Freund, 2013).
There is a number of authors who have stated that change in technology and innovation can
work like a force that is associated with creative destruction which later leaves the industries
divided into loser or winners. Factors that divide these companies and their associated
strategies for dealing with transformations in technology need to be addressed. The essay is
all about the study of this implication on the firms that face radical changes in technology and
the options in strategy made by the company consist of so many emerging and advance
technological options.
Discussion
In the present time business environment is rapidly moving changing on a constant basis.
Change has become very crucial for the company since it impacts the ability to transform
because of which companies lose their edge in competition and further fail to address the
needs of the consumers. In order to bring change and further thrive it in long run, the
company requires knowing the process of how it can address and manage any kind of change.
A company has to face so many changes as per the demand of the consumers and it is not the
company that transforms, however the people in the firm (Jin et al., 2015). Transformation
initiate from people and in any successful implementation of change is too important to gain
clarity on how to facilitate any kind of change in a person. The objective of initiatives leading
change in a firm is to improve the company in some of the other ways, whether it reduces the

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overall cost or solve issues or grab available options and further align the strategy which is
able to meet the overall demand of the culture of the company.
In the present time, there is a number of innovative technologies, the new place for the
market as well as high demands from a customer. In addition, regulations also revise from
time to time and becoming more and more tighten which foster company to stay on top in
adapting change and make all possible efforts to survive change constantly along with
competition, the demand and needs for regulations (Jagadish, 2015).
The business culture is majorly influenced by constant changing circumstances. One such
condition is associated with big data analytics. It is becoming the main element that enforces
the company to adopt change. Big data also provides the start where business can take steps
in the forward direction and help the company to grow, evaluate and discover strategies. As a
start, highly advanced analytics is going on the world of this analysis of big data in
companies and the transformations are more associated with changing the thought process
and organizational culture, than it is to get tools as well as skills for analyses and also
evaluating big data (Kotter, 2006). In addition, big data has so many more layers than just
data or technology existing behind the data. The important thing here is to find certain aspects
from the pool of data that can help in driving many decisions that can work as a solution for
so many real business challenges in a company.
The first model which is relevant to the discussion is the change management model which
can be recognized as a part of John Kotter's eight-stage model of change. The eight-step
change defined by Dr John Paul Kotter has worked as a foundation for many theories of
change (Kotter and Rathgeber, 2006). It is used broadly for bringing change which is
transformational by nature in big companies (Appelbaum et al., 2012). As per the book of
Kotter, "Leading change" shows that to make the change which is prosperous by nature in the

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company, there are eight important success elements that lead the company via successful
transformation (Kotter, 2012). The following figure shows those eights steps:
.
Another theory which is very important when it comes to gaining clarity on the change in a
company as well as management change is called as Lewin's 3-step model for change. As per
his research, the analysis and issues related to change need to answer like how to make all the
transformation permanent and influence people to stay motivated to pursue new habits which
means that the new habits or change should be strongly implemented in people (Sarayreh et
al., 2013). The author defined the change elements which can be derived from two distinct
forces and it can be driven internally as well and those impacted by the culture. The
framework is based on looking and finding more answers on how to get implant changes in a
permanent manner with the help of a three-step model. All these steps in the model mention
particular functions that can deal with motivation, execution and promote the change in the
organization (Manchester et al., 2014). The following figure is the detailed discussion of all
the stages of the model: -

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The third important model which is important to discuss here is big data analysis. In the
current time of big data, all the discussed approaches are considered incomplete (Jordan et
al.,2015). The big data analytics cycle tries to cover all important element which are
discussed in detail below: -
Data Acquisition

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The step is important in life cycle since it discusses kinds of profiles needed to offer the final
data product. Gathering of data is a non-trivial kind of step which usually consists of
collecting a lot of unstructured data from a number of sources (Jagadish, 2015).
Information extraction and cleaning
When the data is taken out or retrieved for instance from a website, it must be stored in a
consumer-friendly format. To combine data sources as well as decision that must be formed
to make such responses representation equal. This consist of conversion of initial data source
answer representation to the second kind by focusing on all negative and positive aspects.
The process usually needs a large scale of time allotment so that it can be delivered with high
quality.
Data integration aggregation and representation
Data processing must be stored in a database and there are so many technologies in big data
that offer so many options related to this point. Common options are based on utilizing the
Hadoop File structure for storage that gives a user a restricted type of methodologies like
SQL and many more (Jordan and Mitchell, 2015). This permits most task of analytic to be
finished in the similar manner as would be performed in a traditional manner based on BI
data warehouse.
When data is cleaned and saved in a method that insights can be properly taken out from it,
along exploration of data step is crucial. The goal of the step is to gain clarity on the data,
which is usually done with many statistical methods as well as plotting the information. This
step is relevant to evaluate the information in right manner (Anon, 2017).
Modelling and analysis

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The step consists of reshaping the cleaned data which can be taken in past and use statistical
based pre-processing for so many missed imputations of values along with detection of an
outlier, extraction of feature and selection of characteristics (Brunswickera et al, 2015).
Interpretation
This stage cover development of data and its implementation in the pipeline where data is a
store. This consist of setting a validation scheme where the data products are functioning so
that performance can be tracked.
Conclusion
The present dynamic culture asks companies to adapt to the change in incremental as well as
radical manner. sustainable growth cannot happen with any relevant innovation. It is also
important for a company to transform the method of operations and also transform the
products as well as services. It needs a company that motivate experimentation and also
supervise the culture, test the performance and is also committed to constant performance
improvement (Brunswickera et al, 2015). Management of innovation is multifaceted in so
many ways and consist of many aspects.

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Reference
Anon, 2017. Mind Blowing Growth and Power of Big Data. Available at:
http://uk.businessinsider.com/mind-blowing- growth-and- power-of- big-data- 2015-6?
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Appelbaum, S.H., Habashy, S., Malo, J.L. and Shafiq, H., 2012. Back to the future: revisiting
Kotter's 1996 change model. Journal of Management Development, 31(8), pp.764-782.
Boons, F. and Lüdeke-Freund, F., 2013. Business models for sustainable innovation: state-of-
the-art and steps towards a research agenda. Journal of Cleaner Production, 45, pp.9-19.
Brunswicker, S., Bertino, E. and Matei, S., 2015. Big data for open digital Innovation–A
research roadmap. Big Data Research, 2(2), pp.53-58.
Jagadish, H.V., 2015. Big data and science: Myths and reality. Big Data Research, 2(2),
pp.49-52.
Jin, X., Wah, B.W., Cheng, X. and Wang, Y., 2015. Significance and challenges of big data
research. Big Data Research, 2(2), pp.59-64.
Jordan, M.I. and Mitchell, T.M., 2015. Machine learning: Trends, perspectives, and
prospects. Science, 349(6245), pp.255-260.
Kotter, J.P. and Rathgeber, H., 2006. Our iceberg is melting: Changing and succeeding
under any conditions. Macmillan.
Kotter, J.P., 1996. Leading change. Harvard business press.
Manchester, J., Gray-Miceli, D.L., Metcalf, J.A., Paolini, C.A., Napier, A.H., Google, C.L.
and Owens, M.G., 2014. Facilitating Lewin's change model with collaborative evaluation in

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promoting evidence-based practices of health professionals. Evaluation and program
planning, 47, pp.82-90.
Sarayreh, B.H., Khudair, H. and Barakat, E.A., 2013. Comparative study: the Kurt Lewin of
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pp.626-629.