Unstructured data is usually known to be a form of data that is not organized in any pre-defined format. Typically, it is considered to be reach in text rather than numerical data but it may contain some few elements of data such as dates, mobile numbers and zip-codes (Teinemaa et al. 2016, p. 406). Example of unstructured data include; word processing documents used in organizations for various purposes, multimedia data which involves images, audios and movies. Additionally, mobile text messages and emails form very important part of unstructured data that supports smooth organizational operations. It defines the nature of data that cannot be neatly organized into traditional dataset. In order to be able to work with unstructured data, several techniques such as Natural Language Processing (NLP), data analytics and data mining are specific areas of interest. It is possible to deduce unstructured data concepts and categorize it as the type of data that does not sit on relational databases. In this regard, organizations should come up suitable methods to handle such data. Unstructured data has been observed to form roughly 90% of many organizational data (Gupta & Rathore 2013, p. 973-974). It forms part of organizational data because organizations cannot operate without such data such email messages and other collaboration support tools. Important to note is that unstructured data and its storage serves as complimentary to structured data storage. To store unstructured data, organization should decide on their data sources, implement clear roadmap for managing such data, filter to eliminate any useless data, take required steps towards data storage, choose the right technology to store data and make sure all data has been stored.
The analysis of the unstructured data helps organization decide on several aspects such as its use, the originality of the data as well as uses of unstructured data by organizations. Through such analysis, it would be possible to evaluate the importance of data and possible ways through which it can be used by firms to complement structured data (Shariff, Hussain & Kumar 2011, p. 154). This study would mainly focus on several companies in order to be able to unearth sources of unstructured data, its impotence in the organization, whether it is stored or not, the nature of storage and whether after storage organizational executive make follow ups to use it for decision making.
Unstructured data existed from early as human being because it is obvious that data could not have been structured in any way. Through evolution of human being and technological innovation, data started gaining value due to its use in business operations. As innovation advanced over years, human being started looking for any opportunity to make data more organized. This was meant to make sure data access, retrieval and maintenance was simple (Losee 2006, p. 446). Unstructured data could not offer required efficiency since there was no predefined query to store and retrieve required data. The attributes given to unstructured data has been quite confusing due to variety of names it has been accorded. From as early as 1958 computer experts and specialists were only concerned about unstructured data as it was only data available by then. Due to challenges associated with classification, sorting and querying of unstructured data, an evaluation continued in a bid refine data. It is from need to have simple and ease of retrieving information that triggered innovation to make data more organized. In the year 2004, SAS miner was developed by SAS institute of Technology (Raghavan et al. 2014, p. 218). The SAS miner made use of Singular Value Technology (SVD) in order to reduce the space which was found in hyper-dimensional texts to smaller analytical dimensions. Reducing of textual hyper- dimensional was meant to increase, provide required value as well increase efficiency of unstructured machine analysis. Through technological and mathematical innovation advances, a number of business processes were initiated which later resulted to birth of major fields; mining of customer voice, optimization of call centers and analysis of sentiments.
Studies have unearthed that unstructured data has been growth faster than structured data. In this regard, organizational executives are taking necessary measures to make sure unstructured data does not grow beyond unmanageable state. An evaluation shows that unstructured data is still valuable to organization and cannot be ignored an organization (Basharat et al. 2016, p. 2). A good example can be deduced from video footages that are collected by CCTV cameras. Though it may be disregarded by many people due its nature of storage and retrieval, in cases of security concerns management focuses on these video clips to review past security breaches. On the same note, if organizations need to enhance security measures, it is the same unstructured data that helps executive to review and recommend suitable security improvement strategies. This shows the importance of unstructured data despite it being un-defined. In regard to existence of unstructured data, it is not possible for organizations to present all their data into relational databases. This means that, unstructured data has been there as early as human age and it would continue to exist. Through use of technology, computer experts and data analysts have been coming up with several tools to organize the data. According to Pei, Yang &Yang (2018, p. 20), organizing unstructured data has been noted to be very challenging due to its nature of existence in real life. In many cases, organizing data has been limited by lack of criterions to uniquely identify a certain piece of data. Therefore, it would be important to note that unstructured data has been and its evolution will continue to take place. It evolution has been taking new shape because of tools that have been developed to handle such data.
It is definite that unstructured data is of great importance to an organization. There is no single organization that can claim to operate purely on structured data. It is from the basis of unstructured data that organization deduce structured data. Organizations and data warehousing experts have been dedicating additional support to unstructured data with aim of converting it to structured one for ease of management and exploitation (McAfee et al. 2012, p. 64). To achieve the goal of converting unstructured data to structured one, technology service providers have tried to come up with their tools and platforms such as knowledge management systems. Besides data conversion, organizations need to be able to evaluate and analyze any form of data for use in decision making. This has been necessitated by the fact that, it has been impossible for organizations to load large volumes of data to their knowledge base management systems. Again, it has been a great challenge to hand-code all required metadata in order to facilitate both search and processing of unstructured data because most of the data should be accessed in real-time.
To have better understanding of the unstructured data need by organizations, two scenarios would be considered. The first scenario would involve corporate and their operational strategies from several departments. Accounting department makes use of data stored in spreadsheets, word processing documents, account descriptions, notes and audit trails. In call centers and receptions, all conversation, required response and notes are captured, stored and documented as unstructured data (Lang, Ortiz & St. Abraham 2009, p. 470). Next, in human resource department, all emails, recruitment offers, contract termination documents, job evaluation, employees working manuals and operational policies are part of unstructured data. In the marketing department which is the key to success of the business, advertisements documents, working forecast, targets, seminars, customer contact details and conferences are unstructured. With these departments among others, important to note is that all these types of unstructured data types have different operational characteristics. The second scenario would involve industries which would analyze the type and content of the available unstructured data. Maluf et al. (2005, p. 116) argues that an evaluation of the existing industries, the type of data they have and nature of data involved in business operations shows unstructured data dominates the market. A good example has been observed from banking industry where banks operates on unstructured data than structured one. In healthcare industry, healthcare facilities works mainly on textual data as most of the collected information is in textual format.
Analysis of unstructured data shows that organization has a greater potential of gaining operational advantages by making use of unstructured data. It is obvious that, many organizational opportunities are hidden on unstructured data (LaValle et al. 2011, p. 21). Decision making should not only factor structured data because cross-dimensional analysis should be done to make sure all competitive have been adopted. In this regard, organizations that do not consider incorporating unstructured data in decision making misses’ greater opportunities for market growth. Some of the useful information that an organization can benefit from using unstructured data are; customer feedback which are used to measure customer satisfaction in regard to new service. Available warranties which are used to evaluate the benefits that organization has been getting from available terms and conditions (Doan et al. 2009, p. 16). Similarly, there is an opportunity of analyzing market competition through use of unstructured data because it is possible to measure customer satisfaction. This can be consolidated in terms of efficient and effectiveness of service delivery, support time response. In security measures, unstructured data can help organizational executive to evaluate whether employees are operating in strict conformity with law. The most important aspect that an organization cannot ignore is that, combination of both structured and unstructured data. A good example is on structured data relating to customer response on service delivery customer and unstructured data inform of emails. According to Chakraborty & Krishna (2014, p. 1291), using of structured data to measure customer loyalty as well as satisfaction may not give exact picture of customer satisfaction. However, using both structured and unstructured data from emails presents a comprehensive data that can be analyzed to have better understanding of customer need. This can clearly be explained from the context of having customers’ demographic information and making decision based on such data forgetting the power of communication on customer relationship.
Figure 1: Importance of mining unstructured data
Taparia, R. and Chetty, P. (2018). Text mining as a better solution for analyzing unstructured data. [Online] Knowledge Tank. Available at: https://www.projectguru.in/publications/text-mining-analyzing-unstructured-data/ [Accessed 4 Oct. 2018].
Unstructured textual data challenges
The importance and use of unstructured data has not been into existence without operational problems. Since organization must make use of unstructured data as a complementary to structured data, some operational issues need to be dealt with. First, with unstructured data being stored in many different formats, its accessibility must be guaranteed whenever needed (Kassner et al. 2015, p. 38). Access can be either physical access to storage devices or logical access in a remote manner. In this regard, data shared over the mail can be downloaded and stored in external hard drives in a data center. Contrarily, they can be left on the mail server for online accesses capability. The next problem is the volume of data collected and stored in unstructured form. The primary purpose of data collection is to use it for reference when making decision. In most of the cases, unstructured data volume is very large and its analysis require word by word walk-through. To accomplish an analysis with such sheer volume of data, un-daunting resources should be provided by subject organization. Additionally, the security of unstructured data is of grave concern because they are not securely stored by many organizations. Some of data is very sensitive and cannot be exposed to any employee. To guarantee security of unstructured data, policy framework should be provided by executive to provide guidelines on methodologies of data access, who should be given privileges to access it and the nature of content to access (Mon 2013, p. 1771).
Attributes of unstructured textual data
To be able to use unstructured data in an organization, it is important to understand its several aspects. Direct relevance of data to business operations should be considered because revolves around critical areas such as customer credit reports, business operations legal documentations’, insurance claims by executive airline booking and reservations (Verma et al. 2016, p. 1). Similarly, there are some other aspects not directly related to business and may include; some communications via emails, available man power and evaluation of employees’ performance. The next attribute is on the formality of presenting unstructured data. The organization of the message can be categorized into formal and informal depending on how it has been presented by the relevant stakeholder. Formal unstructured data covers letters to organization and emails. On the other hand, informal one would include; business reports and contracts. Further, unstructured data is usually associated with large sheer of data which require careful analysis and evaluation in order for an organization to benefit from it. Storage and maintenance of data is resource intensive considering infrastructure setups, required expertise and security maintenance (Misra et al. 2014, p. 41-42). In this regard, organization should be ready and willing to provide all required operational resources in order to realize benefits from unstructured data. Finally, repetition of unstructured data is a common phenomenon in many organizations. It is quite possible to find phone call documentation with similar information shared over the mail (Kassner et al. 2015, p. 40). In such scenario, organizations need to look for suitable methodologies to filter data in order to avoid extreme data redundancy.
Application of unstructured data
Organizations are trying to make use of unstructured data to gain competitive advantage in the market. The need and use of unstructured data has been necessitated by the fact that use of transactional and sales data do not provide required insight into the market such as satisfying customer and maintaining business loyalty (Kubina,Varmus & Kubinova 2015, p. 564). Holding to the fact that more than half of organizational data is unstructured and organizations want to gain or retain competitive advantage in the market, are currently focusing on unstructured data. To understand how organizations are becoming competitive by using unstructured data, it would be important evaluate how unstructured data has been solving business problems.
Management of customer experience
Marketing department has been collecting a wealth of customer related data which goes far much beyond customer classification and purchase behavior. In cases where a customer makes a call to complain about service delivery, defective products or just give recommendations, all this information is collected as unstructured in a Customer Relationship Management system (CRM). Similarly, unstructured information includes data collected from emails and conducted surveys which are focused on evaluating customer’s view on organizational product or service delivery (Das & Kumar 2013, p. 153). All these sources of information collected are used to study and analyze customer purchase behavior as well as understand specific customer needs. With this pertinent information from unstructured data, it is possible to increase sales volume as well as capture customer need. Generally, these aspects translates to increase in customer consumption, attract more customers’ and increase organizational profit margin.
Figure 2: Measuring customer satisfaction
Ford, M. and Ford, M. (2018). The Customer Experience Maturity Model. [Online] MATTYFORD. Available at: https://mattyford.com/blog/2013/9/18/the-customer-experience-maturity-model [Accessed 4 Oct. 2018].
Preventing fraud in an organization
It has been noted that, fraud originates from within organizational members. Past experience demonstrates that, some financial documentation such as mortgage and insurance are usually recreated from already accepted clam documents in order to create other fake claims. To track such fraud issues in an organization, it would be difficult for organizational executive to rely on structured data only. Combination of both unstructured and structured data would be of great importance because a comparison of available information would be done effectively to unearth any existing disparity (Edge, Larson & White 2018, p. 2). In this regard, executive would be able to use unstructured data to prevent loss of organizational resources. Besides, identification of patterns from unstructured data can be used clarify all issues that are not accurately captured from structured data. Therefore, the importance of data pattern recognition is far much beyond financial implications to other areas such as government entities.
Organizational marketing optimization
Marketing industry has developed several text analytics applications with aim of being able to analyze unstructured data. Organizations are able to search from multiple sources of information such as websites, weekly published journals and press release to gain an understanding of entire market structure (Halaweh & Massry 2015, p. 2). This helps organization to segment available market and prioritize on the most profitable segments while developing potential segments. The more information marketing department collects the higher the chances of have information match in regard to customers’ need. Since marketing is categorized into customer-based and market-based, data collected for use in these two sub-sectors have been over-lapping. Since customer focus is known to be the key business drivers, organizations can take advantage of consolidating its initiative to gain substantial benefits from variety of information. This helps organization to enhance general view of customer by targeting its efforts on customer buying habits and lifetime value.
Healthcare industry and patient care
Management of patient in healthcare industry is an important representation on use of unstructured data. In this case, notes that complements patient records are quite useful to healthcare professionals as they are used to identify medical history of a patient. Through use of unstructured data, it is possible for medical practitioners to narrow down to possible ailment and first-aid prescriptions in regard to historical patterns (Doan et al. 2009, p. 16). Similarly, digital images can be send via mails instead of physical delivery to shorten wait in a bid to speed up treatment. Generally, any data collected and digital images are quite useful in identification of ailment commonalities.
McDonald, C. (2018). How Big Data is Reducing Costs and Improving Outcomes in Health Care | MapR. [Online] Mapr.com. Available at: https://mapr.com/blog/reduce-costs-and-improve-health-care-with-big-data/ [Accessed 4 Oct. 2018].
Conclusion
Unstructured data has been in existence for long time and still continue to be an important aspect in driving business need. Structured data was deduced from unstructured data as computer scientist tried to look for a more organized data. Organizational use of unstructured data cannot be doubted as it offers organization an opportunity to gain competitive advantage in the market. Relying on structured form of data cannot guarantee any organization competitive advantage as well as customer satisfaction. It has been proven beyond reasonable doubts that organizational use of unstructured data offers operational advantage. Despite being unorganized, it is a critical tool for business survival. With growth in technology, organizations have been developing applications that can be used for unstructured data analysis in various industrial application. Unstructured data is quite important and organizations cannot afford to underrate it because there is hidden power that can drive business growth. Therefore, development of more complex tools to help organizations collect and analyze unstructured data successfully ought to continue in order to steer business growth.
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