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From Big to Smart Data. How can Data Analytics support Strategic Decisions to gain Competitive Advantage?

Forschungsarbeit 2015 23 Seiten

BWL - Unternehmensforschung, Operations Research

Leseprobe

Table of Contents

Executive Summary

Table of Contents

List of Abbreviations and Symbols

List of Figures

1 Introduction
1.1 Problem Definition and Objective
1.2 Scope of Work

2 Background on Big Data, Smart Data and Strategic Management
2.1 Definition and Objectives of Data Analytics
2.2 Definition and Objectives of Strategic Management and Decision-Making

3 Data Analytics as driver for Competitive Advantage
3.1 Importance and Impact of Data Analytics for Strategic Management
3.2 Data Analytics supporting strategic decisions
3.3 Critical Analysis of the Data Analytics environment

4 Conclusion and Outlook

Bibliography

Appendix A: ITM ChecklistIX

List of Abbreviations and Symbols

illustration not visible in this excerpt

List of Figures

Figure 1: Support of Data Analytics for strategic decision-makingII

Figure 2: Forecast of the data volume generated worldwide in 2005-

Figure 3: From Big Data to Smart Data.13F

Figure 4: Levels of Strategic Management.20F

Figure 5: How Data Analytics support Strategic Management. 53F

Executive Summary

One of the biggest challenges currently and in the upcoming years is the amount of data generated worldwide, which will increase exponentially by factor 10. The challenge for business leaders in the era of Big Data will be to identify and to use the most relevant data for decision-making in the context of Strategic Management.

This assignment analyses which relevance data analytics of Big respectively Smart Data nowadays has and how it can be utilized in enterprises to gain a higher degree of competitive advantage. Therefore a few selected examples and use cases are provided on the Corporate, Business and Functional level of Strategic Management.

Business leaders are using data analytics to understand cost and revenue drivers, to evaluate risks and to predict trends to improve business performance and to foster innovation. Studies show, that Big Data will revolutionize business operations and change the way of doing business. Companies not dealing with Big Data will lose their competitive advantage. With a deeper understanding of customers’ behavior and demands through analysis of Big Data, companies can find new ways to approach existing and potential customers by improved or new products. Criticism related to this is the debate about data security and data privacy and the misuse of personal data. From the explanations provided in this assignment following conclusion about the support of Data Analytics for strategic decision-making can be drawn:

illustration not visible in this excerpt

Figure1: Support of Data Analytics for strategic decision-making. Author's own figure.

1 Introduction

1.1 Problem Definition and Objective

"You can't manage what you don't measure". An old management adage from W. Edwards Deming which outlines the recent explosion of digital data and its importance. By measuring data managers know more about their business and therefore can translate this knowledge into improved decision making resulting in greater opportunities for competitive advantage. Areas which have been dominated by intuition rather than facts will be reduced and managed more precisely than before by better predictions and smarter decisions for strategies.0F[1] One of the biggest challenges currently and in the upcoming years is the amount of data generated worldwide. According to current estimations the global data volume will increase exponentially by factor 10 from current 4.4 trillion to 44 trillion gigabytes until 2020. As shown inFigure 1this means about 40,000 Exabyte of data. Due to a higher degree of digitalization (Internet of Things, sensors and data interfaces) the amount of data increases rapidly. About 35% of this data will be useable for analysis.1F[2]

illustration not visible in this excerpt

Figure2: Forecast of the data volume generated worldwide in 2005-2020.2F[3]

However, Big Data does not create value by itself. The challenge for business leaders nowadays is to identify and to use the relevant and most important data.3F[4] The reduction of information overload is a major problem and is anticipated in the context of smart data or small data how it is sometimes called.

This assignment will identify and analyze which relevance and impact data analytics nowadays has for strategic decisions. Based on the different levels of strategy management few selected examples and use cases will be given showing how data analytics can contribute to decision-making in an enterprise for a higher degree of competitive advantage.

Tools and technical solutions will only be mentioned and not described in detail. Also the theoretical background for the drivers of competitive advantage and a detailed look on the decision-making process itself will be skipped due to the limiting factors of the assignment.

1.2 Scope of Work

A brief introduction into the topic has been given through this chapter. Within chapter 2 the data analytics environment will be introduced by defining Smart and Big Data. The levels of strategic management and objectives of decision-making will be explained. By showing the importance and impact of data analytics and by providing few selected examples on different levels of strategic management, chapter 3 will asses data analytics as a driver for competitive advantage for enterprises. After a critical review a conclusion and outlook will be given in the last chapter.

2 Background on Big Data, Smart Data and Strategic Management

2.1 Definition and Objectives of Data Analytics

A lot of concepts have been communicated by the term "Big Data", including huge quantities of data, real-time data or social media analytics.4F[5] Gartner provides the original (Doug Laney in 2001)5F[6] and one of the most used definitions: "Big Data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making"6F[7]. According to McKinsey "Big Data refers to datasets whose size are beyond the ability of typical database software tools to capture, store, manage, and analyze"7F[8]. Therefore Big Data is characterized by four main features (4-Vs):

- Volume: The amount of data (records, files) as presented in chapter1.1and not explained further.

- Velocity: Frequency of data generation, its transfer and processing. More data is generated in a shorter time period due to evolving technological developments (e.g. Internet of Things8F[9] ).

- Variety: Big Data includes the analysis of all kind of different data types - structured and unstructured and generated from multiple sources like internet clicks, mobile transactions, user-generated content or through business transactions like purchase and sales transactions.9F[10] Following data types respectively sources are identified as the most relevant ones for the success of a company - clustered by high and low importance to set a priority for further discussion.

High importance:

- transactional data, log data, e-mails, mails, events, social media
- open data, web content, sensor data
Low importance:
- RFID scans, POS data
- free text, pictures, audio, video10F[11]

- Veracity: Describes the uncertainty of data due to inconsistency and incompleteness, leading to distrust into information generated from them. Poor data quality leads to yearly costs of approx. $3.1 trillion for the U.S. economy.11F[12]

Currently there is a switch in the mindset from Big Data to rather Smart Data which is focused more on the content and level of information within data instead of big volume of data.12F[13] As presented inFigure 2the relevant respectively Smart Data is just a subset of available data. The challenge of identifying this data remains as a big task in the Big Data environment.

illustration not visible in this excerpt

Figure3: From Big Data to Smart Data.13F[14]

By providing e.g. descriptive statistical methods and mathematical modeling techniques analytical tools support complex decision making by correlations, cluster analysis, filtering, decision trees, neural network analysis, regression analysis or textual analysis.14F[15] Big Data can offer a large volume of information over multiple periods (from seconds to years) in real-time if needed and by collecting and connecting data from multiple sources, multidimensional datasets can be build up and used for strategic decisions. But beside just analyzing patterns and correlations data analytics nowadays can also provide the predictive likelihood of future events, which can be utilized in a strategic decision making process.15F[16] Prescriptive analytics offers a number of different possible decisions showing the implication of each decision option. It provides information on why something will happen, not only on what and when. Through each prescribing process with updated data, the accuracy increases allowing automatic decision making.16F[17]

2.2 Definition and Objectives of Strategic Management and Decision-Making

“Strategy is a course of action for achieving an organization’s purpose”17F[18] whereas strategic management describes the process of situational analysis, strategy formulation, implementation and evaluation.18F[19] As shown inFigure 3strategy can be applied on different levels of hierarchy within larger and diversified companies.19F[20]

illustration not visible in this excerpt

Figure4: Levels of Strategic Management.20F[21]

Corporate strategy applies for a whole enterprise and determines goals and objectives as well as principles and plans how to achieve these goals. It defines a “range of business the company is to pursue, the kind of economic and human organization it is or intends to be, and the nature of the economic and noneconomic contribution is intends to make to its shareholders, employees, customers and communities”21F[22].

The business strategy (also called competitive strategy) is inherited from the corporate strategy and implies the choice of an offering (product and/or service) and a market or industry for each individual business of an enterprise.22F[23]

Functional or operational strategies are applied for organizational units of a business, such as finance, production, marketing, IT, research & development and human resources. Decision about and strategic management of core competencies, capabilities and processes, technologies or resources in functions are key being able to deliver combinations of services or products demanded by a customer.23F[24]

Managers and employees at all hierarchical levels are part of the strategic management process and have the responsibility to transform defined strategy into action, to evaluate success of strategy and to adjust where needed. At all levels decisions need to be taken to reach strategic goals.24F[25] The main drivers for decision taken in a business organization are the maximization of profit or the reduction of costs. But often the result of the decision-making process is based on intuition and focused towards the past rather than on facts and on future. Therefore many decisions are delayed and often not repeatable.25F[26]

The purpose of data analytics for strategic management decisions is to overcome these obstacles and provide a more evidence-driven approach. How important analytics is and how it can support will be elaborated in the next chapter.

3 Data Analytics as driver for Competitive Advantage

3.1 Importance and Impact of Data Analytics for Strategic Management

According to different studies about 63-85% of the respondents see the creation of competitive advantage for their organizations as one of the highest potential of Big Data.26F[27] Another Big Data survey27F[28] shows, that 89% of respondents believe that Big Data will revolutionize business operations like the Internet did in the past and about 85% think that Big Data will change the way of doing business. About 79% agree that companies which do not deal with Big Data will lose their competitive advantage.28F[29]

Due to faster and higher capacity-storage and cheaper technology data analytics has become more powerful. On the other hand globalization and stronger competition have put pressure on improving efficiencies and effectiveness of business to strengthen customer relationships.29F[30] Business leaders are using data analytics to understand cost and revenue drivers, to evaluate risks and predict trends to improve business performance and to foster innovation.30F[31] Each further step in competition requires more analysis to "support strategic, managerial, and operational decision-making".31F[32] This increases the demand for better analytics technology which again increases the level of competition among enterprises. The constant analysis of complex and huge amount of data accelerates the "intelligence" of a company to take smarter decisions on all levels (seeFigure 3) and to gain competitive advantage through the application of business analytics methods.32F[33]

It seems to be crucial to have a central analytical group which coordinates all activities within an enterprise and oversees localized (geographically or business-unit based) teams to avoid silo-thinking and to ensure a one-face-to-the-customer approach.33F[34] When thinking about implementing a data analytics infrastructure (i.a. IT systems, processes, support organization) companies create business cases to value the benefit of an implementation focusing on leveraging new sources of data to improve quality of decisions (smarter decisions), enabling a more real-time based analysis to support faster decisions at the 'point of impact'34F[35] and on focusing towards Big Data efforts in areas which provide differentiation.35F[36]

How Big Data analytics can provide added value to achieve competitive advantage through better decision-making will be elaborated within the next chapter.

3.2 Data Analytics supporting strategic decisions

The challenge of decision-making across all levels (seeFigure 3) is, that on the highest level the nature of problems is unstructured compared to the operational level which allows only non-programmed decisions on the highest level of strategy management but programmed and repeatable decisions on the operational level. The risk impact on the strategic level is higher and decisions are more uncertain than on the levels below.36F[37] Analytics of bigger data for the strategic level provides additional value by supporting the tools within the process and by the content of strategy itself. The SWOT37F[38] analysis evaluating internal capabilities/resources and limitations and positive factors and challenges from the external environment can be conducted with a better degree of information using Big respectively Smart Data reducing uncertainty.38F[39] Open Data39F[40] could be easily gathered to feed the PEST analysis to identify e.g. demographic changes, unemployment rates, GDP development, national debt level, levels of education. Especially when comparing different scenarios of industries or markets to be entered, data analytics unfolds its strengths, e.g. by predicting trends of market development on a broader data basis. To indentify internal change drivers data analytics contributes with data by monitoring and visualizing the network of processes and work activities in the organization, analyzing the inventory or production cycle looking into the whole value chain. Knowing companies strength and weaknesses and opportunities and threats with the help of better data will help managers to formulate and decide on an appropriate strategy and adapt if needed in shorter timeframe.40F[41]

By having a higher degree of analytical capabilities companies can analyze more data in a shorter time when thinking about mergers and acquisitions. Private equity companies e.g. analyze already huge amounts of data when acquiring companies or managing their portfolio with the aim to reduce risks.41F[42]

On the business level data analytics can be utilized to monitor sales and market development to derive innovative and improved products from the information gathered. Typical use cases is this area are monitoring of brand perception, of competitors and market prices, automatic pricing, churn analysis and forecasting or personalized product recommendations for customers.42F[43] The results are not only useful for the optimization of products and processes but also to identify new business areas.43F[44] Gathering data e.g. from social media sources allows a "more complete picture of customers' preferences and demands"44F[45]. With a deeper understanding of customers’ behavior and demands companies can find new ways to approach existing and potential customers.

The examples provided for functional strategies are according to BARC's survey focused on the main areas (ranked by priority for data analytics form high to low): Controlling and Finance, Marketing, Sales, IT, Production, Research and Development, Supply Chain.45F[46]

Financial controlling departments use data analytics to handle the growing quantities of data and increasing complexity. Marketing and Sales functions are focusing on gathering and analyzing customer behavior by integrating social media channels or log-files from e-commerce platforms to generate personalized offerings. They can segment customers in a better way and conduct appropriate campaigns.46F[47] IT departments utilize Big Data frameworks to analyze log-files being able to understand issues across platforms handling support in a faster and more efficient way. Production and Supply Chain are analyzing sensor data to monitor operating conditions or conduct proactive maintenance and ensure quality assurance.47F[48]

[...]


[1] Cp. McAfee, A., Brynjolfsson, E. (2012), p.62.

[2] Cp. IDC / EMC Corporation (2014a).

[3] Figure taken from: Statista (2015), data from: IDC / EMC Corporation (2014a).

[4] Cp. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P. (2012), p.11.

[5] Cp. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P. (2012), p.1.

[6] Cp. Loshin, D. (2013), pp.1-2.

[7] Gartner (n.Y.).

[8] McKinsey Global Institute (2011), p.1.

[9] Referring to sensors and actuators embedded in physical objects and connected by networks.

[10] Cp. George, G., Haas, M., Pentland, A. (2014), p.322.

[11] Cp. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P. (2012), p.11. Cp. Schäfer, A., Knapp, M., May, M., Voß, A. (2012), p.44.

[12] Cp. Liu, Y. (2014), pp.40-41.

[13] Cp. George, G., Haas, M., Pentland, A. (2014), p.321.

[14] Taken from: SAS Institute (n.Y.), p.2.

[15] Cp. Davis, C. K. (2014), p.40.

[16] Cp. George, G., Haas, M., Pentland, A. (2014), pp.321-324.

[17] Cp. Strickland J. (2015), pp.2-3.

[18] de Wit, B., Meyer, R. (2010), p.108.

[19] Cp. Mintzberg, H. (2003), p.6.

[20] Cp. Koontz H., Weihrich, H. (2010), p.119.

[21] Own illustration according to: Hill, C. W. L., Jones, G. R. (2011), p.5.

[22] Cp. Mintzberg, H. (2003), pp.72-73.

[23] Cp. Andrews, K. (1987), p.74ff.

[24] Cp. Lowson, R. H. (2003), p.57.

[25] Cp. Vermeulen, P.A.M., Curseu, P.L. (2008), pp.1-3.

[26] Cp. McAfee, A. (2010).

[27] Cp. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P. (2012), p.1. Cp. Schäfer, A., Knapp, M., May, M., Voß, A. (2012), p.6. Cp. PwC (2015).

[28] Includes 1,000 respondents from companies operating across seven industries and headquartered in 19 countries that had completed at least one big data implementation.

[29] Cp. Accenture (ed.) (2014).

[30] Cp. Davis, C. K. (2014), pp.39-41.

[31] Cp. Hagen, C., Khan, K. (2014).

[32] Davis, C. K. (2014), p.39.

[33] Cp. Davis, C. K. (2014), p.41.

[34] Cp. Deloitte (2013), p.9.

[35] e.g. while customer is having a telephone call with a service representative or navigating on a website.

[36] Cp. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P. (2012), pp.16-17.

[37] Cp. Koontz H., Weihrich, H. (2010), pp.134-136.

[38] SWOT analysis helps to create strategies to exploit strengths, minimize weaknesses, take advantage of opportunities, to avoid threats.

[39] Cp. Kotler P., Armstrong G. (2011), p. 53.

[40] Open data which is available for the public without costs, often provided by the government. Cp. Rodriguez-Bolivar, M. P. (2014), p. 27.

[41] Cp. Hitt, M., Ireland, R. D., Hoskisson, R. (2014), p.27.

[42] Cp. Meyer T. (2014), p.194.

[43] Cp. Schäfer, A., Knapp, M., May, M., Voß, A. (2012), p.8. Cp. George, J. A., Rodger, J. A. (2010), p.3.

[44] Cp. Bitkom (ed.) (2014), p.19.

[45] Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P. (2012), p.7.

[46] Cp. BARC Institute (2013), p.22.

[47] Cp. O'Reilly Media (2012), p.63ff.

[48] Cp. BARC Institute (2013), pp.22-23.

Details

Seiten
23
Jahr
2015
ISBN (eBook)
9783668074705
ISBN (Buch)
9783668074712
Dateigröße
594 KB
Sprache
Englisch
Katalognummer
v309153
Institution / Hochschule
FOM Essen, Hochschule für Oekonomie & Management gemeinnützige GmbH, Hochschulleitung Essen früher Fachhochschule
Note
1
Schlagworte
Big data Data Analytics Strategic Management Smart data data securtiy privacy business leaders

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Titel: From Big to Smart Data. How can Data Analytics support Strategic Decisions to gain Competitive Advantage?