Knowledge Management – The way forward.

The source of a sustainable competitive advantage for any organization is derived from the access and subsequent exploitation of resources, and today, knowledge is being heralded as the most important of such resources that is available to organizations (Drucker, 1993). For the larger and mature organizations, often process capability knowledge is the primary source of advantage, whereas for the organizations seeking to grow and out-grow competition, in addition to process capability knowledge, knowledge management focus would also include market knowledge, rapid product development, or the creation of knowledge through research. However, it is important to recognise that even mature organizations need to create knowledge to avoid falling into the stagnancy pit. This is exactly what knowledge management attempts to provide more succinctly to the organizations. Knowledge management aims are to create conditions under which competitive advantage can be maintained, by creating,  acquiring, retaining and exploiting the knowledge for the welfare of the organisation.

Knowledge management is the way organizations today are attempting to make the otherwise intangible knowledge tangible and distributable, throughout the organization, in search of the illusive competitive advantage. Today, in this evolving world where insights on data or business intelligence plays an extremely crucial role for the sustainable development of an organization, knowledge management has become a key area of focus.

Beckett et al. (2000) has provided an interesting framework by which many organizations are actually managing their data within with a growing focus to manage the data outside the organization also. Through research, the authors highlight how effective knowledge management can provide a wider scope of continuous improvement to obtain benefits for the parent organization, by providing higher quality information, better quality information, removing information asymmetry, and subsequently increasing the levels of organisational expertise which can be applied to it to create substantial improvements for the organization.

The sole focus of organizations today is to convert internalized tacit knowledge into explicit knowledge, so that it can be commoditized and less dependency is there on an individual for being the source of knowledge. With a high attrition rate in organizations across industry, it becomes extremely pertinent that knowledge once created within the organization stays inside the organization and does not become unusable once the creator of the same changes base, within or outside the organization. That is the sole objective of the initial knowledge management systems.

For improving the knowledge management practices within the organization, companies today are increasingly adopting rewards systems, collaborative systems, post-project reviews, knowledge mapping, establishing communities of practice with cross-project learning platforms, creating expert directories, competence management systems, best practice transfer, mentor-mentee relationships, knowledge fairs, formal knowledge repositories, measuring and reporting intellectual capital, knowledge brokers, social media and social network mining systems.

While it is important for organizations to understand the importance of knowledge management systems, even one aspect that many organizations often overlook is using the knowledge outside the boundaries of the organization, but within the value chain. Realizing this, the recent focus has been the development of customer knowledge management systems, where customer tacit knowledge is use to co-create value for the customer in the best possible way, and finally value for the company. Another group of stakeholders who are being introduced into the knowledge management realms are the supplier networks, where knowledge management is often used to create otherwise non-contactable value for the stakeholders. The way forward for knowledge management systems into the future is to capture the tacit knowledge outside the organization but within the value chain (and within multiple stakeholders) to create value for both.

Today, it is pertinent that all the senior executives of organizations realize the potential benefits of effective introduction and management of knowledge management systems, that can benefit the organization. Today, while organizations are facing increasing levels of competition due to the effects of greater competition, knowledge management provides an important way for organization to utilize the most valuable resource available to them, to gain competitive advantage.

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A discussion of 3 core issues in Information Systems Research

Information systems research looks at the interface of three aspects of the firm, Technology, Process and People. In this essay, we look at the issues discussed by three of the highly cited papers in Information Systems research.

The article “A Framework for Research in Computer Based MIS” by Blake Ives, Scott Hamilton, Gordon Davis talks about 5 major research models by “Mason & Mitroff”, “Chervany, Dickson & Kozar”, “Lucas”, “Mock” & “Gorry & Scott Morton”. It also forecasts a model to generate future potential hypotheses for research. In the Mason & Mitroff Model, the focus is on defining the IS as A person of a certain Psychological type who faces a problem within some organizational context for which he needs evidence to arrive at a solution, where the evidence is made available through some mode of presentation. In the research by Chervany, Dickson & Kozar, the focus is on the isolation of dependent and independent variables which determine effectiveness of an IS.  Lucas formed a descriptive model of situational, personal & attitudinal variables & their impact on usage of the system & performance of the IS user, where behavioral issues have been studied in depth. Mock studied how behavioral constraints on system developers and users, impact the usage of the system and their individual performances. Gory and Scott Morton focused on how an IS provides information for management decision making. The authors of the paper designs a model with 3 IS environments, 3 IS processes & 1 IS subsystem, existing within an Organizational & External environment. On the basis of interaction between the variables of these types, the author divides IS research into broadly 5 types, 1 to 5, which again are subdivided into multiple subtypes. The framework focuses on providing a basis to formulate a hypothesis which is relevant to MIS research.

The article “The Identity Crisis within the IS Discipline: Defining & communicating the disciplines core properties” by Isaac Bensbasat & Robert Zmud, focuses on what is IS research all about and how to make it a distinct area of research. He focuses on how to build IS research by investigating closely related areas than distant issues. He states the 3 requirements for IS research, that of claimed central character, claimed distinctiveness and claimed temporal continuity required for IS research to evolve as a discipline. He stresses on the need for an identity in the MIS field by obtaining cognitive & socio-political legitimacy. The authors are concerned that the Interdisciplinary nature of IS cause application of varied theories from multidisciplinary backgrounds which is important for building theory but may dilute the focus of research. They express their concerns of errors of exclusion of core constructs & errors of inclusion of non-core construct in studies of IS and IT artifacts. The core properties focusing on the managerial, methodological, technological, operational, behavioral issues and the impacts of IT should be studied in IS research, in the context of the nomological net. IS research should also investigate relationships involving 1/more core constructs only, and heavy reliance on theories of reference disciplines to achieve legitimacy should be minimized.

The article “MIS RESEARCH: Reference disciplines & a Cumulative tradition” by Peter Keen also focuses on suggesting a way in which MIS research may evolve into a classical discipline having its own identity. He states that the concept of having microeconomics & computer science as the reference field limits the scope of MIS research. The researchers should focus on defining the dependent variable in their study and build on the work done by predecessors. The research should now focus on building theory from multiple reference disciplines to help IS keep evolving as a discipline and not build frameworks only. The research should not be dependent on technology evolution but focus on management, information and systems issues. Since MIS as a discipline has strong implications for practice, the focus on theoretical research should not be diluted. The focus of the researchers should be to publish in quality IS journals. Research should focus on the management of the use of technology to benefit business processes and resolve technical, managerial & organizational issues.

All 3 papers focus on mentioning the scope of IS research and what researchers should focus on in the future. They stress on the need for building theory to make MIS evolve as a discipline, and not be too dependent on reference disciplines for the purpose. Also they stress ways to evaluate if a research is actually investigating issues closely related to IS or not. Also they stress on how hypotheses formulation should be done for research in IS and what should the constructs aim to achieve.

Expense Management in Telecom

Expense Management systems typically refers to the management information systems adopted by a business enterprises to process, pay, control and audit all expenses that the firm may incur during its day to day functional life. This may include programs like integrated financial management systems which will make it simpler to keep track of costs and help keep your business ahead of problems. Expense management teams govern the policies and procedures for every spending, as well as the technologies and services utilized to process and analyze the data associated with it, and is expected to have a huge impact on the bottom line of the firm.

Most average Fortune 500 company spends more than $100 million on telecommunications services each year. This arises predominantly from communication that may happen internally (Often 40%-60%) and with external stake holders (20% – 40%) Yet, on average, 7 to 12 % of telecom expenses are in surplus of what they could have been brought down to. This means that there is a high possibility of lowering costs by upto even $10 million each year! Think about all the revenue generating opportunities that could be funded with that money and the possible impact such a cost saving may have on the bottom-line of the firm.

It is interesting to understand how the Telecommunications Management Network facilitates the organizations consuming these services to achieve these goals.  This framework enhances the operations management within the service provider, and expense management systems play an integral role in these systems.

Communications tools, such as wireless and wired connections, are crucial in managing the organizational day to day functionalities, communicate with the employees, liaison solid relationships with the customers, suppliers and business partners. Yet, firms are  often forced to delay new communications projects that could give them a competitive advantage using ICTs, such as Voice over IP, because it’s difficult to accurately forecast labor and infrastructure costs.  implementing a proactive Telecom Expense Management program can help defray those costs.  With the telecom managed service from IT Service providers like IBM, Oracle, Infosys, the firms now-days can look beyond just cost savings and implement business process improvements through innovative adoption of communication technologies.
The best part about these services are that they are outsourced completely to the service providers and billing is done entirely based on how much the firms plan to use the benefits of such a service. It means, firms will pay a small percentage based on the dollar benefit they get out of such an engagement. They are completely priced based on usage. (Read   Pricing of Information Technology)

Firms will be able to utilize the business intelligence sometimes bundled with such services to optimize network price/ performance, plan network strategies, forecast spend and justify telecom expense decisions to your management team.

Indian Telecom Sector – Market Analysis

In this report we try to provide some information of the Indian Telecom Sector in June, 2011, based on secondary research. The boom phase for Indian telecom market continues to thrive on the availability of cheaper telecom services alongside an aggressive marketing strategy by the service providers. The growth phase is panning out across India and is not just limited to urban India. While the customer satisfaction has increased substantially in 2010 and 2011, as per a survey done by TRAI, the profitability of the sector has decreased substantially in 2011.

While the market continues to be led by Bharti Airtel, in terms of market share by revenue, it is closely followed by Vodafone and the relative younger player, Idea Cellular. BSNL is continuing to witness a sharp decline in its subscriber base, with probably the highest ever telecom churn in the industry.

One of the major causes while the telecom operators have seen such a mixed bag of fortune is due to the introduction of the Mobile number portability (MNP).  Due to the introduction of this service via regulation, customers will be able carry their mobile number across service providers in India. The biggest affected service providers had been Reliance and BSNL, while Airtel has been the leading gainer from this exercise.

Genetic Algorithm Applications

Today, in this world of information processing, business intelligence and business analytics, are gaining in importance very fast. The three major techniques or groups of algorithms which have gained a lot of visibility in recent times are fuzzy logic, neural networks and genetic algorithms. In this article, we discuss in brief about the possible business applications of genetic algorithms.

In general, genetic algorithms find their application in problem domains that have a complex fitness landscape with many criteria with divergent needs or fitness function curves. Typically, genetic algorithms find application in computational science, engineering, manufacturing, phylogenetics, bioinformatics, economics, chemistry, mathematics, physics and other related disciplines.

The most popular application of Genetic Algorithms is in the field of Search applications.

Mostly, in such search techniques, the end objective may be an optimization function where the genetic algorithm may be applied for Fast Search through a huge pool of possible solutions so as to find the most optimal solution fast, without compromising the fitness of purpose too much.

Similarly, problems which are often solved using genetic algorithms include time-table scheduling and job-scheduling problems. Many scheduling software packages use Genetic Algorithms as a predictor model. GAs have also been applied to classic engineering disciplines besides the information technology, communications, telecommunications, electronics and semiconductor industry. Genetic algorithms are also popular for usage as an approach to solve global optimization problems.

Some of the other popular applications of Genetic Algorithms are in computer-automated design, artificial creativity, automated design of industrial equipment, design of mechatronic systems, in the financial services sector, design of multi-objective decision making problems, bioinformatics, quality control, operations planning, chemical kinetics, clustering,  code decryption, configuration applications,  design of distribution systems, control engineering, computer network design, gene profiling, electronic circuit design, economics modelling, scheduling applications,  marketing mix strategizing, molecular research,  neural network based applications,  software engineering, data compression, plant floor layout planning, and so much more.

By the way, did you read our article on the application of Fuzzy logic and Fuzzy set theory.

Genetic Algorithm

Today, in the world of data mining, business intelligence and analytics, techniques which can learn and provide decision support is gradually gaining in importance in leaps and bound. The three major techniques or groups of algorithms which have gained a lot of visibility in recent times are fuzzy logic, neural networks and genetic algorithms.

A genetic algorithm (GA) is a search based, self learning algorithm (technique) that imitates the theory of natural evolution based on selective screening of results based on fitness of purpose. This self learning algorithm is routinely used to generate solutions to multi-criteria  decision making problems, optimization problems and search problems. Genetic algorithms have evolved from the more popular class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques mimicked from that of natural evolution like those of inheritance, selection, mutation  and crossover.

The structure of a generic technique developed based on the genetic algorithm would follow the mention sequence given in the diagram below:

First while coding using a genetic algorithm, the first activity is to code the data-sets in such a way so as to be equatable  for the fitness of purpose of the problem domain. Then typically, a random sample is chosen from the data set and the fitness of purpose of these data points are evaluated. Now for those points which have a higher fitness score, they are selected for further application of genetic algorithm optimization techniques like those of inheritance, selection, mutation  and crossover. The other data points are rejected and the algorithm continues to recreate data-points from this pool of selected data points using the techniques of inheritance, selection, mutation  and crossover.

Selection: During each successive iteration, a part of the existing population is selected to form a new generation sample pool. These data points are selected through a fitness-based purpose of usage in the problem domain, where fitter solutions (as measured by a fitness function) are  selected preferentially. Some selection algorithms rate the fitness of purpose of each solution and preferentially select the best solutions. Other methods often rate only a random sample of the population, as this process may be very time-consuming.

Reproduction using Cross-over and Mutation
The next step is to generate the next generation population of data points from those selected earlier (using selection), through the operator algorithms called crossover (also called recombination), and/or mutation.

Cross-over: During cross-over, a pair (or triplet) of data points are chosen from the refined selection and they are combined and data-exchange takes place based on the individual coding. After the data exchange occurs multiple times, the new data points created from this process is returned to the selection pool to be operated by the selection operator.

Mutation: The coded data point obtained after the operation of selection on the sample pool of data points, is modified by tweaking one parameter to test its fitness factor and this continues for the entirely set of selected data points. The newly created generation of data-points are then tested using the selection operator again and the entirely process is restarted.

Although Crossover and Mutation are known as the main operators of genetic algorithm, there are also other operators such as regrouping, colonization-extinction, or migration in genetic algorithms.

The application of these operators and re-selection of these new generation data-points continue till the optimal fitness of purpose is attained. Common terminating conditions are often due to a solution satisfying minimum criteria, fixed number of generations being reached, allocated resources (complexity of computation, computation time, money) reached, the highest ranking solution’s fitness level is reaching or a fitness plateau has been reached such that successive iterations no longer produce better results, manual inspection produces better results or even combinations of these.

Customer sentiment and Opinion Mining

Customer sentiment mining or opinion mining refers to the application of datamining techniques like natural language processing, computational linguistics, and text analytics to identify and extract subjective information from customer data like textual interactions (sms, emails), recorded phone calls (calls to CRM divisions) and more recently usage of products/services (website usage, etc).

There are many popular technology products for customer sentiment mining.

Typically, these products analyze e-mails and telephonic conversations to decide if it is a complaint or a query and forwards it to most suitable agent for immediate resolution thus increasing agent productivity. They can also perform both structured and unstructured data analysis to understand & quantify customer satisfaction, display customer details and past communication, prioritize complaints based on customer satisfaction level the value of the customer, update customer contact details in the data warehouse, and also helps to differentiate services to HNI and LNI clients.

Sometimes, these data-mining systems can even create customized email/sms/ automated phone call campaigns for the marketer to increase revenue from cross-selling and up-selling, analyze customer preferences post campaign-creation, analyze customer actions post email reading, generate reports to facilitate management decision making process (which can be integrated with the existing other report generation tools)and also helps to analyze productivity of agents and channels.

A feature-wise competitive analysis of the 15 most popular instruments available in the market for customer sentiment mining can be obtained on request to arpan.kumar.kar@gmail.com. The comparative report is easy to understand with charts implying a comparison of features an is priced $350 only.

Fuzzy Logic and Fuzzy Systems

A fuzzy system is basically a control system based on fuzzy logic, which is a mathematical system that analyzes input values in terms of logical variables that take on continuous values between 0 and 1 instead of belongingness or non-belongingness to any set, as in crisp set theory or classical or digital logic, which operates either in 1 or in 0  for a logic to be true or false respectively. Fuzzy systems have become extremely popular in recent times for their various business applications.

Fuzzy logic on the other hand is the mathematical theory based on set theory, from which fuzzy systems have been developed. Essentially, fuzzy logic is based on fuzzy set theory which proposes that logical variables can take on continuous values between 0 and 1 instead of crisp belongingness or non-belongingness to any set, as in crisp set theory  which operates either in 1 or in 0  for a logic to be true or false respectively.

Typically, a response in real life situation is not crisp, i.e. its not in black and white. Say one is asked to judge the performance of a technology application, typically the person (judge) will think that for every specification of feature there is a degree of acceptability in performance while in certain dimensions, the performance is not acceptable. This is precisely what fuzzy logic or fuzzy sets attempts to capture.  Here, every item of a set may belong to multiple sets by a degree of belongingness and not by absolution of belongingness or non-belongingness to any set.

Today, Fuzzy sets and systems have taken a major interest from both the academia and the industry for its numerous and unlimited applicability and the scope.  The first wave of application of these systems were primarily in process control and electronics, while slowly, these have made their impact felt in information systems development and deployment in business processes and activities. The largest applications are in the area of decision sciences in business intelligence and decision making, and is being thought of as one of the biggest change creating mathematical tool.

e-Commerce and e-Business Strategies

While there are many e-commerce and e-business models which one needs to be aware of, the dynamics of business strategies change overnight with the adoption of e-commerce and m-commerce business models. The limitations of the more popular strategic frameworks like that of the Porter’s 5 forces model was realized very soon by the researchers in e-business strategy. The generic strategy framework was also somewhat limited in its applicability in pure e-business models.

In view of these changes, there began a serious contemplation of sustainable e-commerce and e-business models for business giants. What would be the mantra for success was long deliberated and various e-business and e-commerce models started getting wider acceptance.

With increased adoption of E-commerce, firms adopted Pure-Click and Brick and Click Business Models.

  • Pure-Click companies are those that have launched a website without any previous existence as a firm. Ex: AMAZON.com
  • Brick and Click companies are those existing companies that have added an online site for e-commerce but still maintains an offline business model.  Ex: futurebazaar.com

However it was noticed that many (as high as 84%) of the business models met with disastrous results and the companies blew up (bubble burst) within a couple of years of its inception. It was at this juncture that the value proposition of e-commerce models started getting scrutinized and the importance of the complete value chain for e-business models got its due importance.

It was realized that the complete value chain needs to be analyzed before a firm selects a business model for its operations.

The analysis of the core of a firm is crucial for success in this increasingly information age, where business models are attempting a quick fly off the sly to generate revenue and most of which are falling flat.

By the way, have you read our article on the Growth Strategies of Web Based New Generation Firms?

e-Commerce and e-Business Models

Electronic commerce, which is abbreviated popularly as e-commerce or eCommerce, is defined as the buying and selling of products or services over electronic media like the Internet or other Information Technology dependent networks. Sometimes e-commerce is also interchangeably used with the terminology e-business.

Continue reading “e-Commerce and e-Business Models”

Information Technology and Information Systems

People often use the terms Information Technology and Information Systems interchangeably, although both the terminologies have established identities of their own. However it is crucial for every professional and individual to understand the subtle differences that defines the individuality of these disciplines.

Information Systems (IS) is a discipline bridging the business field and the well-defined computer science field (popularly called information technology) that has been evolving since it was coined in the early 1970s.An information systems discipline therefore is supported by the theoretical foundations of management social science, information theories and information technology such that students of the discipline have unique opportunity to explore the academics of various business models as well as related algorithmic processes within a computer science discipline. Typically, information systems include people, business procedures or processes, data, software, and hardware that are used to gather and analyze digital information. Specifically Information Systems are the intersection that people and organizations use to collect, filter, process, create, & distribute data (computing) through its business processes, and implemented by its human capital.

While Information Technology (IT) typically is the acquisition, processing, storage and dissemination of digitized information, often represented technically as “Data” through electronics-based media built upon the disciplines of computing and telecommunications. The terminology was first coined in a 1958 by Leavitt and Whisler who defined it as “the new technology that does not yet have a single established name. We shall call it information technology.” Essentially, in its raw form, it comprises of Hardware, Software, the platforms to support both, communication networks and protocols.

It is crucial to understand that while “Information Technology” is a huge discipline with an identity of its own, it essentially is a subset of the discipline “Information Systems”, although the latter evolved much later. The discipline of Information Systems specifically studies the intersection of Business Processes (which may or may not be technology enabled), People (who will be part of the business processes and will use information technology) and Information Technology.

Hope this clarifies your thoughts. Do let me know what you think or would like to discuss further,

Using Gmail for Business

Many small businesses are now contemplating using G-Mail for business. But is it worth it?

Google’s webmail and complimenting applications (YES, THESE ARE OFTEN FREE ALSO)  require no hardware or software and need minimal administration, creating tremendous time and cost savings for businesses. While these can be entirely browser based, the mails can also be pushed and thus accessed offline too.

Some of the features that Gmail offers ts privileged users are as follows:

  • Each user gets 25 GB of email and IM storage, which is 50 times the industry average. Gmail Business Mail for SMEs/MSEs are designed so employees can spend less time managing their inboxes, and thus increase productivity in the office. Time-saving features like message threading, message labels, fast message search and powerful spam filtering help employees work efficiently with high volumes of email.
  • Gmail is securely powered by the web, so you can be productive from your desk, on the road or at home, even when you’re offline, using your laptop, or even your I-Phone or smartphone.
  • You can Sync your emails with Outlook & BlackBerry. At no extra charge, Google Apps supports over-the-air mobile access on BlackBerry devices, the iPhone, Smart phones, Windows Mobile, Android and many less powerful phones.
  • Gmail provides excellent spam protection. Powerful Gmail spam filtering helps you stay focused on what’s important. Postini filtering lets you customize your spam protection.
  • When you trust your company’s information to Google, you can be confident that your critical information is safe and secure. Google’s information security team, including some of the world’s foremost experts in information, application and network security, are focused on keeping your information safe. Google and many other customers trust this system with highly sensitive corporate data.

So what are you waiting for? Go GET GMAIL for your BUSINESS..

Its much more than what our normal Gmail users get for a minimal price.