Big Data and Business Intelligence

Question:

Understand how and why database systems are specified, designed, implemented, tested, maintained and used.

Answer:

Introduction

In order to understand how Management Information Systems can be utilized for knowledge management how they can assist managers in taking decisions in retail industry, the first step would be understand the concept of knowledge management and what role MIS plays in the same.

Knowledge Management

For a retail sector to grow, knowledge management plays an important role as an asset to the retail sector. Knowledge is more than random information as it is an outcome of organized data and is obtained from resources that are intellectual. Knowledge management distributes valuable knowledge of a retail sector across disciplines and departments that can include customer support, IT, help desk and so on.

Knowledge management comprises of systematic processes aligned in a flow that include search, selection, organizing, distillation and presentation of relevant information. Data is used for problem solving, dynamic leaning, decision making and strategic planning. It is like an enabler for an retail sector planning to achieve its strategic objectives and can be considered as an audit for the intellectual assets of a knowledge driven retail sector. It promotes integration of processes and learning from experiences (King, 2008).

Components of Knowledge Management

Key components of knowledge management include people, processes and technology – that together make the knowledge portfolio of a retail sector. Out of these components, people part makes the most challenging thing for a retail sector as it requires people to effectively coordinate, share resources and re-use other knowledge components to achieve strategic business results and thus, are at the core of the knowledge management. Successful knowledge management thus requires visibility and recognition of people such that specific people are identified as experts by the system in their area of specialization and their expertise can be leveraged upon.

Process component of the knowledge management system involves standard processes of an retail sector such as content management, content retrieval, community practices, implementation of projects, and documentation such as best practice documents and case studies.

KM technology component includes technological solutions and tools that provide support to the business environment through knowledge sharing, workflow, collaboration, document management and so on.  Most of the retail sectors using knowledge management systems in an organized manner usually use a knowledge portal through a corporate intranet using prominent technologies such as Lotus Notes and Microsoft Technologies.

KM assets and processes

There are six types of knowledge assets of the retail sector including stakeholder relationships, human resources, physical infrastructure, culture, practices and routines, and intellectual property.

Challenges in Knowledge Management

Some of the key challenges of the knowledge management discipline are:

• Knowledge acquisition: Collection of data is easy but its conversion into knowledgeable information is a challenging task as it involves a lot of identification of gaps in the field of knowledge, integration of data from multiple sources and acquisition of knowledge from unstructured data. There a significant amount of research in the field of KM which is devoted to development of tools and methods for management of knowledge content.
• Knowledge Modeling: The gap between the acquisition of the knowledge data and the use of the same for decision making or other strategic purpose of an retail sector requires modeling which is exercised using knowledge model structures representing the company. Ontology is one area of modeling which is use for problem solving and involves specifications of attributes, concepts, axioms, and relationships such that their ontology acts as a placeholder assisting in organizing structures of the knowledge that is acquired by an retail sector. Ontology also provides a method for understanding how the knowledge obtained would be used by the management of the retail sector. Development of ontology is both complex and critical to the retail sector.
• Knowledge retrieval: With data generated in a retail sector very frequently and the same updating in this knowledge management systems, the retrieval of information becomes challenges with huge volumes of data to be searched from. It requires storage of information and searching of the same and this needs understanding of the structure such that it can be navigated for search.
• Knowledge RE-USE: When knowledge is constructed afresh every time, it can be a major cost to the company and thus, creation of reusable knowledge is critical as it can lead to cost effective solution for such a problem. However, re-use of knowledge can present challenges in presentations due to different representations of the knowledge. Here, understanding of knowledge can get more returns against the investments done on knowledge assets which are the most challenging task.
• Knowledge publishing: The right knowledge in right format has to be obtained from the right place such that it can be presented to the right people at the right time. This can be challenging with different users needing different formats and methods of presentations based on their preferences.
• Maintenance: The knowledge repository needs to remain functional and always updated with the changes required to be done in the content taking care of the longevity needs as well as the quality of data through verification and validation(G., 2005).
• Certain issues with KM were also identified by King et al. (2002) in an empirical study and these included usage, top management support, maintenance of updates, motivating users to contribute, capture of knowledge, assessment of costs, assessment of benefits, verification of legitimacy, efficiency, and relevance of knowledge and designing and development of a knowledge management system suiting needs of the retail sector, sustain its progress as well as ensure its security.

Management Information System

A major use of MIS is provision of assistance in decision making for the management of an retail sector. Management information systems can be conceptualized in three ways that can emerge as solutions that include Transaction Processing systems, management information systems and Expert systems. Decision Support system is a subset of Management information system which is used for analyzing data such as comparison of sales figures, forecasting revenue figures, and understanding of consequences of the past experiences of certain decisions(Ohlhorst, 2013). DSS is used by top management for taking strategic decisions while supervisor handling first lines use the same for taking routine decisions. A good MIS system would assist in making right decisions while bad MIS can also lead to taking of wrong decisions. Thus, it needs to be developed and used very carefully. Decision making capacities provided by the MIS solution can help decision makers acting as guidelines for decision making as facilitate the progress of the retail sector (Nowduri, 2010).

Big Data Analytics

Big Data technologies are focused on intellectual capital of a retail sector that includes culture, systems, and processes and thus, assist a retail sector in building of knowledge assets. Big data tools can be used for overcoming challenges faced in knowledge management such as acquisition of information which is explicit and taking care of concerns of data arising from its complexities and stickiness. IT-based knowledge markets and communities of practices that are established in big data technologies field can lead to creation of full awareness of business circumstances such that better decisions can be furnished by a management. While data that is obtained through the use of traditional tool does not make any sensible information, big data technologies can be used to convert the data intelligently into information that creates a knowledge base for an retail sector through organizing, processing and analyses of the same for development of tactical and strategic insights. KM and Analytics have similarities in their approaches and goals.

Challenges in “Big Data”

While there are several great advantages of big data, there are a few challenges as well, which companies are struggling with right now. To begin with companies often struggle to identify the right data and determine how to best utilize it. Then companies are also struggling to find the right kind of talent proficient of both working with the technologies and also of interpreting the findings to conclude meaningful business understandings. Also, connectivity and data access can often be a problem. A good no. of data points are not yet linked today and companies often don’t have the right stages to cumulative and manage the data through the retail sector. Also, implementing big data often means functioning across functions like engineering, IT, finance and also procurement, and the ownership of data is divided across the entire retail sector. To look after these retail sector challenges is basically finding new ways of cooperating across businesses and functions. Last but not the least, security worries about data protection are often a major difficulty stopping companies from taking the full benefit of their data(Mayer-Schönberger & Cukier, 2013).

Improved decision making

Big Data helps in decision-making in Retail

• Traditional customer divisions were based on macro variables, which are gender, age, life stages, basket spend, store visits, etc. In modern day, the variables deciding customer segmentation have advanced from the traditional divisions to varied multi dimensions based on customer preferences, basket analysis and social media communications among others. Big Data assists granular and better customer segmentation by gathering customer information from multi feedback and sources from various marketing campaigns. These mechanisms facilitate higher success rates and also create an essential change in how retailers promote and market their products by supporting them to avoid coupon explosion and mass promotions (Mayer-Schönberger & Cukier, 2013).
• Inventory management and Price optimization – Big Data encourages enables predictive alerts, real-time transparency within the supply chain and uncovers global or local supply demand trends. This helps optimizes inventory, helps avoid stock out incidents and minimizes inefficiencies. For example, inventory can be reallocated to stores where it is necessary to completely avoid or reduce stock outs incidents.
• Expanding business verticals – Retailers can also expand into other industries such as financial media services, banking etc. Big Data can enable such enterprises to associate customer data across all industry, ensuring much better up-sell and cross-sell capabilities and also creating a higher value propositions for their customers.
• Crowd-sourcing and new product development – Retailers now a day are adopting innovative techniques to engage customers with their various brands. For example, a leading fashion shop designed an online portal, where customers can create their own fashion style using the retail product collection and also win special offers for the max no. of visits. Such tactics provide insights into what consumers actually prefer, allowing retailers to fashion product lines to ensure stock relevance.

Conclusions

In a growing and challenging economy, insights driven by big data can be a competitive differentiator for a highly profitable and satisfied, existing as well as new consumer base. The main use of big data is not limited as mere analytics-related capabilities or reporting, but what companies actually can do with big data initiatives, which are coming up with solutions for filling business gaps and also addressing customer process difficulties. This includes the ability to access information disturbing the entire business as the data is sourced from multiple locations. This can comprise one or multi sets of data sources, and may have effect on one or many sets of actions, decisions, departments and people. Retail retail sectors which take a strategic tactic to enterprise big data complications and the access to relevant data, how, when and where people need it, will be surely be better positioned to accomplish retail sectoral success.

Recommendations

Big data has lots of potential but its implementation is not very easy, specially when a large number of threats and challenges are already in place. In order to deploy big data, the retail sectors must hire a good profile vendor who can ensure 24×7 availability along with backup and restoring options. In addition to this, the retail sector must ensure that they pay per use model only. Privacy and security concerns should be resolved before the retail sector go for production.

References

G., B. (2005). KNOWLEDGE MANAGEMENT: WHY DO WE NEED IT FOR CORPORATES. Malaysian Journal of Library & Information Science, 37-50.

Frankfurt, R. V. (n.d.). Big Data: Challenges and Opportunities. Retrieved from http://gotocon.com: http://gotocon.com/dl/goto-aar-2012/slides/RobertoV.Zicari_BigDataChallengesAndOpportunities.pdf

King, W. R. (2008). Knowledge Management and Retail sectoral Learning. University of Pittsburgh .

IBM. (2012) IBM big data platform. Retrieved from http://www-01.ibm.com/software/data/bigdata/

James Manyika, M. C. (2011). Big data: The next frontier for innovation, competition, and productivity. Retrieved from http://www.cra.org: http://www.cra.org/ccc/files/docs/init/bigdatawhitepaper.pdf

Joseph McKendrick, R. A. (2012). BIG DATA, BIG CHALLENGES, BIG OPPORTUNITIES:2012 IOUG BIG DATA STRATEGIES SURVEY . Retrieved from http://www.oracle.com: http://www.oracle.com/us/corporate/analystreports/infrastructure/ioug-big-data-survey-1912835.pdf

Mayer-Schönberger, V., & Cukier, K. (2013). Big data. Boston: Houghton Mifflin Harcourt.

Nowduri, S. (2010). Management information systems and business decision making: review, analysis, and recommendations . Journal of Management and Marketing Research , 1-7.

Ohlhorst, F. (2013). Big data analytics. Hoboken, N.J.: John Wiley & Sons.

Schmarzo, B. (2013). Big Data. Hoboken: Wiley.

Zikopoulos, P., DeRoos, D., Parasuraman, K., Deutsch, T., Corrigan, D., & Giles, J. (2013). Harness the power of Big Data. New York: McGraw-Hill.