Evolution of Information Systems in Business Decision Making

  “Evolving role of Information Systems in decision making capabilities within organisations”

-A Literature Review

Index

Abstract

Introduction

Information Systems with Organisational Hierarchy

Relation of DSS to Big Data Analytics

Analytics

Evolution of Analytics in Decision making within organisations

Conclusion

References

 “How you gather, manage, and use information will determine whether you win or lose”(Gates, 1999)

Abstract

This literature review gives an explicit elaboration of information systems, their evolutionary history, and the roles played by them in decision making over the years for organisations. It also explores how information systems have evolved decision making capabilities within organisations over time. Moreover, the discussion and sentiments of various authors have been considered and examined for the purpose of this literature review. Information systems have grown over the years which many organizations have embraced them and have adopted them into their daily operations. All levels of management have been using information systems to perform their regular duties and tasks. The paper will expound on the various infrastructure that is used together to form an information system that is effective in an organization. It will emphasize on the current trends of information systems. Future trends also are included in the paper.²

Introduction

Information systems have advanced with time and have helped organisations get the right value of their data. Starting from 1950’s performing simple data transactions to optimizing data and giving the right solutions to the client in 2010’s, information systems have evolved and analytics has played a major role in this evolvement.  Some of the major advancements have happened in the area of customer relationship management, enterprise relationship management, business intelligence, E-commerce, Geo-spatial applications, Sensor -content applications, supply chain management and so on. Customers and organisations can now communicate with each other directly and this can help grow business, improve opportunities and be beneficial for both. With the advent of the internet, evolvement of analytics and information systems, decision making for the organisations has become a vital process. Not only the organisations but also many institutions around the world have the information systems to perform the necessary activities. The inevitable growth and development have not only led to improved performance of business activities but also has helped in the gaining on competitive advantage. Information systems also have a feature of open programming in order to ensure new ideas are added into consideration. Moreover, future trends in information systems are expected to arise. Trends in the mobile applications and m-commerce is also expected to arise. Thus, information system and analytics is the need of the hour  for all the organisations.

Information Systems

Information systems are a set of processes, tools and standard that allow us to manage information strategically, work efficiently, support business processes, communicate effectively and make better business decisions. In other words, Information systems are organised set of components for collecting, transmitting, storing and processing process data into meaningful information and knowledge (Halim, n.d.).

Until 1960’s era, information systems were used for simple operations such as accounting, record keeping. The part of informative report generation was done by the new concept born which is commonly known as Management Information Systems. This part of Information systems was mainly focused on programming business applications that provided the end users on the managerial side with reports needed for decision making. By 1970’s, decision making needs of the management were not adequately meeting the necessary expected levels and so another type of Information system was born named Decision support systems (DSS). The role of DSS was to provide the ad-hoc support to managerial users for constructive decision making. This support was according to the managerial demand as they had to address specific issues and needed solutions to the real-world problems.

By 1980’s the revolution had already began, with the rapid development of microcomputers, application software programs and packages, introduction to telecommunication. The management users now had their own computing resources for working on their job requirements. Later, it become evident that the decision-making reports generated by the management information systems were not directly used by the management of the organisation. It had become more like a hierarchy process were the reports generated by the management information systems were accessed by the end users or the analysts working for the organisations which were then reported to the higher management with proper analysis and formatting. Hence, due to this the concept of executive information systems (ESS) was born. These information systems were extremely informative in terms of reports generation which would give the higher management the edge of report that they wanted. Thus, managers would access information in the format that they wanted which gave them an easy access to constructive information and data with easy walk through.

The following diagram explains how the roles and responsibilities of information systems have evolved over time with each of them deriving as a type of information systems with more added benefits.

Figure 1: The expanding roles of Information systems in Business and Management

In the mid to late 1990’s the emergence of Enterprise Resource Planning came into existence. This organisation specific part of Strategic information systems included all departments of firm such as manufacturing, sales, human resources, customer relationship management, inventory, financial assets control and management. The main advantage of Enterprise Resource Planning (ERP) is that it offers common interface platform for information technology organisations with strict integration and data sharing for the management users in order to carry out constructive decision making.

Finally, the introduction of Internet proved to be a game changer to the ever emerging and evolving Information technology. The rapid growth of internet and other network related technologies changed the capabilities of information systems and gave them the edge that they needed to evolve in the field of decision making.

 Information Systems with Organisational Hierarchy

Figure 2-Types of Information Systems with Organisational Hierarchy (Gabriel, 2012,89)

1] Transaction Processing Systems (TPS): TPS is also called as data processing system. It performs the daily routine task of collecting and processing transactions for the organisations. TPS are part of day to day operations and is operated by the lower hierarchy of the organisations.

2] Office Automation Systems (OAS): OAS supports wide range of business activities and are used by organisations to improve the workflow and bridge communication among employees irrespective of their geographical locations.  This communication can be possible in the forms of document sharing, scheduling meeting. OAS is operated by the Support staff of the organisations.

3] Management Information Systems (MIS): MIS is defined as a system which provides information support for decision making in the organization (Barton & Parolin, 2005). MIS is capable of producing reports which are tailored according to the needs of the managers who belong to specific departments or functions in the organisations. MIS is normally handled by the Tactical management team of the organisation.

4] Decision Support Systems (DSS):  Decision support system (DSS) is an integrated set of computer tools allowing a decision maker to interact directly with computer to retrieve information useful in making semi structured and unstructured decisions (Power, 2002, Ezine, 2010. James, 1998). In other words, DSS is a term which describes information systems providing analytical modelling and information to support semi-structured and unstructured organisational decision making. DSS is normally used by the middle management team.

                         Figure 3- A Simple view of DSS (Kumar, 2006, 75)

5] Executive Information Systems (ESS): ESS is basically a combination of the features of MIS and DSS. ESS is normally used by the executives and the senior management of the organisation. In ESS, the information such as graphical displays and charts are tailored according to the needs of the top management of the organisation using the system.

Relation of DSS to Big Data Analytics

The change in skills and decision-making activities is an ever-evolving process. One of the types of Information system named Decision Support Systems (DSS) is used by managers and members belonging to the middle management tier for decisions based on graphical figures and graphs. DSS has a more vital role in organisations for supporting decisions and analysing data. There are two types of DSS namely:

1)      Model driven DSS.

2)      Data driven DSS.

Model driven DSS: Model driven DSS has a special functionality or modules which are responsible to analyse quantitative data in order to get solutions to respective problems.

Data driven DSS: Data driven DSS normally uses large repositories or organisational and environmental data in order to establish relations and patterns between them. Data driven DSS has been involved in analysing and processing large data sets. These large data sets are also complex in nature are using traditional data storage and manipulation techniques commonly referred to as Big Data. Big Data includes large amount of unstructured data that has to be analysed in real time. Big data analytics is a field which has taken the attention of the academic and commercial sectors, who all strive to obtain value from Big data³. The use of Big Data with DSS is facing some important issues such as limited availability of expert personnel, high cost of underlying technologies as they are in the initial emerging stages, difficulty in tailoring the new systems according to the requirements without the support of major software development projects⁴    

Analytics 

Analytics refers to the quantitative and statistical analysis of data. Analytics is an encompassing and multidimensional field that uses mathematics, statistics, predictive modelling and machine learning techniques to find meaningful patterns and knowledge in recorded data. Data is normally analysed in order to uncover correlations and patterns. There are three types of analytics in use today.

• Descriptive analytics:  Descriptive analytics uses data aggregation and data mining to provide insight into the past and answer the question: “What has happened?” These are the models that will help you understand what happened and why. There are number of  descriptive analytics in use today – everything from number of clicks a page receives to number of units are produced and number of them sold.
• Predictive analytics: Predictive analytics uses models related to statistics and forecast techniques to understand the future and answer the question: “What could happen?”. These models use old data and predictive algorithms to help determine the probability of what will happen next.
• Prescriptive analytics: Prescriptive analytics uses optimization and replication algorithms to counsel on possible after-effects and answer: “What should we do?” Prescriptive analytics answers the question of things to be done by providing information on prime decisions based on the predicted future circumstances. The crucial part to prescriptive analytics is that one can be able to use big data, contextual data and lots of calculating power to produce solutions in real time frame.

The below figure shows the difference between Reporting and Analytics and categorises them into four sections such as Purpose, Tasks, Results and Value. Moreover, it also explains the meaning between what does Reporting and Analytics mean to convey.

     Figure 4: Graph of Analytics v/s Reporting Salesforce Pardot. (2018). 

Evolution of Analytics in Decision making within organisations

Over-dependency on managerial judgement

Before Analytics came into picture, management of the organisations which used to have a firm control over all the decision makings. These decisions were based on the intuitions and instincts of the management. This behaviour was also called as Rational decision making. It was clearly dangerous to excessively rely on the managerial instincts and experience. Survey carried out by Harvard Business Review Analytic Services Management states that management judgment remains the most common factor in decision making even today—84 percent of survey respondents say it was a strong factor, and a large number of them rated it as the top factor⁶

`

 Figure 5: Key factors in decision making (Harvard Business Review Analytic Services, 2012)

1. Analytics 1.0—The age of Business Intelligence

Analytics 1.0 was a time of real headway in gaining a purpose, depth understanding of the business phenomena and giving the managers of organisations the fact-based comprehension to go beyond intuition when making decisions. The Evolution of Analytics began with Analytics 1.0 or Business Intelligence Era in 1990’s with pre-defined queries and descriptive/historic views leveraging structured data like financial data, sales data, customer records etc. In this era, it was the first time when data about customers, sales, production processes and much more was collected, aggregated, analysed using relational database management systems. Thus, Business Intelligence and Analytics has its base in the database management field. The Analytical methods used in the Business Intelligence and Analytics draw their grounds from statistical techniques and data mining techniques developed in 1970’s and 1980’s respectively. Analytics 1.0 has its roots from Database Management and Data warehousing. Following organisational capabilities were considered to be a part of Analytics 1.0 and they are as follows:

• Ad-Hoc query.
• Search-based Business Intelligence.
• Reporting.
• Dashboards.
• Online Analytical Processing.
• Data mining

OLAP and Data Warehousing are both hand in hand equally important for decision making in the database industry.

In this era, job positions such as Business Analyst, Data Analyst came into picture and had a high demand in the employment industry. Analysts belonging to the 1.0 era spend most of their time in preparing the data for analysis and relatively less time on the analysis of the data. Most of the analytics in 1.0 era belonged to the activities having descriptive analytics. However, the disadvantages of Analytics 1.0 were that it was internal painstaking, backward-looking and slow.

2. Analytics 2.0—The age of Big Data

At the start of 2000’s,  the Internet and Web were offering unique data collection, analytical research and development opportunities. The HTTP-based Web 1.0 systems which was characterized by Internet search engines such as Google and Yahoo and e-commerce websites such as Amazon and eBay allowed organizations to present their business online and interact with their customers. Due to the Web, considerable amount of information would be gathered about the customers, company, products, domains, sectors by using web based and text-based mining techniques. Google Analytics, founded in 2005 was one of the major players in the Analytics market. This analytical company used to track the clickstream data logs of users in order to check the user’s browsing and purchasing patters on the Internet. Web Analytics was one of the vital reasons for decision making in the organisation. For decision making strategies, product placement optimization, market structure research, customer transaction analysis, product suggestions were some of the points possible by using Web analytics. Moreover, Social media analytics also played an important role in the decision making with the necessary tools required for decision making.

Starting at the mid- 2000, the world also started witnessing the term “Big Data” which is well known among the technology sector. Big Data is a concept which everyone talk about but not everyone is familiar with. This term has received tremendous amount of hype from the sources inside and outside the technological sector. Big Data is normally used to describe large datasets. If compared to the traditional databases, Big Data includes a large amount of unstructured data which has to be analysed in real time phases. Big data is also called as tool used for decision making and decision support in the organisations. Big Data basically contains structured and unstructured data where structured data comes from relational databases while unstructured data comes from images, video, emails, social media interactions. The main challenge that posit is about the analysis and extraction of information with value to the organisation.

Big data is the standalone entity in the Analytics 2.0 era which is quite different from the Analytics 1.0 era in multiple manner and ways. Big data has given rise to range of opportunities to improve decision making by utilizing data-driven DSS with Big data analytics to enable better decisions that are supported with data insights from a larger and real-time dataset. The Big data moment is actually denoted by the three V’s namely:

1-      Volume: As of 2012, about 2.5 exabytes of data was created every day which also indicated that more data was created and browsed on the internet every second than that which was stored 20 years ago. Thus, this gives companies an opportunity to work with petabytes of data in a single data set which can be not only from the internet but also from the customer transactions.

2-      Velocity: There are some applications which for which data creation is vital than data volume. Most of the real time applications fall under this category.

3-      Variety: As Big Data supports structured and unstructured data, it can take the form of images, GPS updates on cell phones, text message updates and so on.

3. Analytics 3.0—Fast impact for Data Economy

The era of Analytics 3.0 is called as the data enriched era because majority of the Big Data companies started investing high amount of money in analytics to support customer facing products, services, features. Moreover, they also attracted internet users to their websites by using product recommendations, friend suggestions on social media, optimal search algorithms, target audience advertisements. Analytics 3.0 is majorly swiped by Mobile applications and sensor-based content. Analytics 3.0 has gone ahead with the evolution part and has such open framework such that not only online firms but virtually, any type of firms can participate in the data economy. Moreover, Analytics 3.0 brings challenges and opportunities under its belt not only for the companies that are in the race of becoming the analytical spearheads but also for the suppliers who supply data and tools to implement analytics.

In order to stay afloat, companies will need to identify relative challenges and respond with new capabilities, positions and priorities. Analytics 3.0 is the third level of technology evolution and decision making in organisations. Newer technology including search optimizing algorithms, data mining techniques, faster data processing techniques and so on contribute to the faster paced decision-making organisations where data is of almost importance and keeps changing at the blink of an eye. Hence, to keep up the pace with competitors, it is necessary to keep the following factors in mind for capitalizing on Analytics 3.0 era.

Following are the requirements for maximizing on Analytics 3.0

• Multiple types of data, often combined: Organisations will have to integrate enormous and small volumes of data from internal and external sources in structured and unstructured formats in order to understand and gain value and insights from the data which can be used by the prescriptive and predictive models.

• New set of data management alternatives: In the Analytics 2.0 era, the limelight was on Hadoop clusters and NoSQL databases but now it is all about big data applications, data warehousing, big data environments which combine data query approach with Hadoop, graph application databases and so on. Nowadays, most of the organisations focus on hybrid data environment.
• Faster technology: Big Data is way faster than its previous generation of database management technology. Machine learning is a new technique used to produce data insights at a much faster rate.
• Embedded Analytics: Models belonging to the Analytics 3.0 have characteristics like increased speed for data processing and analysis of data are used for decision making in the organisation. Many multinational firms are using analytics into their systems by using optimizing algorithms and analytic rules and regulations. P&G is one of the organisations which employed analytics in day to day management operations by establishing business decision rooms.
• Data Discovery: Organisations need discovery platforms for exploring data along with the necessary skills and process activities. Enterprise data environments would also be of lesser help to the organisations as transferring data into them is a time-consuming task. Data Discovery environments can be of help as it can determine the essential qualities and features of the data.
• Prescriptive Analytics: Analytics 3.0 includes all the three types of Analytics namely Descriptive, Predictive and Prescriptive. However, it mainly focuses on the Prescriptive part. Prescriptive analytic models need high level optimization and large-scale testing and are means of implanting analytics into vital processes and employee behaviour structure.

Conclusion

Through this literature review, it is evident that Information systems and Information technology are necessary for the growth and advancement of the organisations not only in terms of technical but also management. This paper has explained the importance of analytics and its involvement in the daily life of a normal person. Businesses have adopted the information systems from the early 1960’s till date and this paper shows the advancement of information systems with its functions and characteristics. Since the business in evolving, so is the technology and internet has taken a big role to play in this. In the world of ever evolving technology and cut-throat competition, it is extremely important for the businesses to stay on toes and handle decision making in a very unique fashion. Moreover, this paper also explains about Business Intelligence and its involvement in the decision making of the organisations. The era of Big data i.e. Analytics 2.0 shows the role or Big Data in faster processing of data with the help of optimizing algorithms, data mining. The evolvement into Analytics 3.0 has been dominated by the cell phone revolution. Human computer interaction, location-based awareness, customer understanding, context related analysis and sensor-based analysis.

Thus, based on the above conclusions and various author’s views its ca be concluded that Information systems have proven to be a vital part in any organisation which can hep them sustain and grow in the ever-changing technology era.

References

1. Renu, R. S., Mocko, G., & Koneru, A. (2013). Use of Big data and knowledge discovery to create data backbones for decision support. [online] https://www.researchgate.net/publication/273846812_Use_of_Big_Data_and_Knowledge_Discovery_to_Create_Data_Backbones_for_Decision_Support_Systems [Accessed 27 Oct 2018]
2. Berman, J.J. Principles of Big Data Preparing, Sharing, and Analyzing Complex Information. Elsevier, Waltham (2013) [online] Available at:

https://books.google.ie/books?hl=en&lr=&id=gEho0DI8a2kC&oi=fnd&pg=PP1&dq=Principles+of+Big+Data+Preparing,+Sharing,+and+Analyzing+Complex+Information&ots=OaSyHPRm73&sig=8BYmx49ue5cAC1UlhM9OqC_6nrE&redir_esc=y#v=onepage&q=Principles%20of%20Big%20Data%20Preparing%2C%20Sharing%2C%20and%20Analyzing%20Complex%20Information&f=false [Accessed 17 Oct 2018].

3. Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.

https://ieeexplore.ieee.org/abstract/document/6567202

4. Sas.com (2018). Analytics: What it is and why it matters. [online] Available at: https://www.sas.com/en_au/insights/analytics/what-is-analytics.html [Accessed 10 Oct. 2018].
5. Kumar, P. K. (2006): Information Systems Decision-Making, Indian MBA. [online] Available at: http://www.indianmba.com/Faculty column/FC307/fc307.html [Accessed 4 Oct. 2018].
6. Tarjomefa.com. (2012). Big Data: The Management Revolution. [online] Available at: http://tarjomefa.com/wp-content/uploads/2017/04/6539-English-TarjomeFa-1.pdf [Accessed 13 Oct. 2018].
7. Halobi. (2018). Descriptive, Predictive, and Prescriptive Analytics Explained. [online] Available at: https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained [Accessed 21 Oct. 2018].
8. Slideshare.net (2018). Analytics3.0 final. [online] Available at: https://www.slideshare.net/albertspijkers/analytics-3-0-final  [Accessed 13 Oct. 2018].
9. Salesforce Pardot. (2018). Reporting Vs. Analytics | Salesforce Pardot. [online] Available at: https://www.pardot.com/blog/reporting-vs-analytics [Accessed 19 Oct. 2018].
10. Emc.com. (2018). [online] Available at: https://www.emc.com/collateral/white-papers/ei-evolving-role-data-decision-making.pdf [Accessed 29 Oct. 2018].
11. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big data to Big Impact [online] Available at: https://ai.arizona.edu/sites/ai/files/MIS611D/chen-bi-december-2012.pdf [Accessed 24 Oct. 2018].
12. Pdfs.semanticscholar.org. (2018). The Roles of Big Data in the Decision-Support Process: An Empirical Investigation. [online] Available at: https://pdfs.semanticscholar.org/c125/ee02945551332deceead0e32d3fdb0efb196.pdf [Accessed 16 Oct. 2018].
13. A-Z Quotes. (2018). Bill Gates Quote. [online] Available at: https://www.azquotes.com/quote/843320 [Accessed 14 Oct. 2018].
14. Halim (2018). Information Systems Evolution (IS). [online] Slideshare.net. Available at: https://www.slideshare.net/SemputLa/information-systems-evolution-is [Accessed 19 Oct. 2018].
15. Use of Big Data and Knowledge Discovery to Create Data Backbones for Decision Support Systems. (2013). Elsevier B.V.  Available at : https://www.researchgate.net/publication/273846812_Use_of_Big_Data_and_Knowledge_Discovery_to_Create_Data_Backbones_for_Decision_Support_Systems [Accessed 19 Oct. 2018].
16.  Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao, Athanasios V. Vasilakos (2015)

Big data analytics: a survey. [online] Available at:    https://journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0030-3

[Accessed 14 Oct. 2018].