Information System of Fibre Fashion Case Study
Introduction
Small business are the thrive and liveliness in the economics of a country, new start-ups with new ideas and that is the reason why SMEs can grab the attention of its audiences efficiently. In a report it was said that SMEs contributed 44 percent of the total economic activities in the US with the provision of two-third of the new net jobs. After all this good news there is an alarming situation as well for these businesses is that GDP share has fallen from 48.0 to 43.5 percent while for new SMEs GDP is increasing 1.4 percent annually and large business 2.5 percent annually (Doré, 2019). In this statement, it can be seen SMEs are not only competing against the mainstream businesses but also alike ones too. Hence for your business to flourish in this competitive market, the first step is to have a manageable system.
Manageable system include primary activities and support activities. According to Thimothy (2018), If a company have a budget to improve its system, the priority should be given to sales and marketing. The sales and marketing is crucial because it helps to connect to customers, build a network in the market which creates a sustainable business and boost up sales, which generates revenue for the company (Sims, 2013). The first step to improve this side of a company is to manage its data or information. Information must be organized to extract the useful data easily with less prone to error and must be protected. On average a data breach can cost a company £3100, which for a SME can become a never-reopen situation (Bedfordshire Chamber of Commerce, 2018). Likewise, huge business have strong protected system this can be a case for SME as well.
Identifying issue with current Information System
The current structure of the Fibre Fashion involves excel sheet and a database. Database stores the information of customers, transactions, orders, and cost of goods, while excel contains the data related to retailers, their sale figures, location, and the current statuses with them.
Overall, looking at the excel sheet the first thing to notice is the inconsistency of data and format. In the sale figure there are empty cell while a few has 0 or even hyphen in it. Then the blue color key represent placed then cancelled and yet there are still sale figure in that collection of the store which is being accumulated at the end in the total. This shows even the quantitative data about the total sales at the end is calculated wrong. Fibre fashion stores sales per brand on their excel sheet but according to the current spreadsheet the sale figures are according to per collection not brand. So, the qualitative data about which brand has the most sales hence more popular cannot be analyze. These are few issues out of many. In general, the spreadsheet is inconsistent and inefficient with respect to Fibre Fashion, the qualitative and quantitative data cannot be analyzed with this data.
Moreover, current database shows that database is not normalized. There are redundant data along with multi-value data, empty cell, functional dependencies, and anomalies. Such as in customer table they may store same customer whenever they purchase from Fibre leading to redundancy and field state also have repeating values. In the transaction table there exist no key such primary, super or candidate key. In the order table the there are many repeated data along a field. The cost of a good or price of a product cannot be identified. This all lead to misinterpretation and loss of useful data for a company.
Both database and excel have few things is common such as being inefficient, have poor quality data and improper data entry which makes this system as a bad marketing information system. Despite all the problems with your current marketing information tools, is it good to use excel for sales and marketing or are there any other alternative for a SME?
Literature Review
Microsoft Excel have been around since 1985 and have no doubt been a revolutionary tool for corporations since then (Rosenberg, 2019). But likewise, in this field of Internet technology nothing is bound to be permanent and are in constant competition with another tool alike. Since then it has been a controversial topic among the marketing data analytics, but according to Rosenburg (2019) excel still exists and is not going anywhere soon. This software is used for data analytics, forecasting, calculation and analyzing and it can be used as an embedded system or independently as well. It takes advantage of Microsoft’s different technologies such as OLE (Object linking and embedding), ODBC (Open Database Connectivity), DAO (Data Access Object) and plug and play (RUSU & RUSU, 1998).
The fame of this tool came from its perspective that it can do anything. It is being used for finance and accounting, marketing and product management, human resourcing and planning etc. Microsoft Excel is a powerful tool, but it cannot stand alone (Rosenberg, 2019). Hence, this is where the controversy arrives because managing data in excel can be tricky, but is it worth it for Fibre Fashion?
Excel and Data quality
Data quality refers to many customized dimensions or it can be said that the data is high quality if it can be used for a desired purpose and is error free and have some desired characteristics. These characteristics includes the ease of interpreting, the usefulness or relevance if they are accurate and consistent. The main components of data quality are accuracy, completeness and consistency. Accuracy refers to as error free such as wrong spelling or an unacceptable value in the data field. Completeness refers to as scheming of data as if there are all entities, within rows data are not missing and along the column field. In this part the problems such as sampling of data and values missing are the most common. Then comes the consistency which deal with validation and checks on the data, such as children cannot be older than their parents ( Babigumira, 2011).
Data consistency control is a central component of the overall data control process, and attempts to increase data security are also closely related to data governance systems that aim to ensure data is correctly structured and used within an enterprise ( Rouse, 2019). Bad data alone can cost companies on average $15 million per annum (Grow, 2019). So, data quality leads to marketing information system to be efficient and this is the first step that Fibre Fashion must do with their data too.
Is excel a correct tool to check for data quality? The answer to this is that it depends, according to Albright (2017), if done properly it can be very handy otherwise it can become nearly impossible to interpret any data. Excel requires some special care for it to hold a good quality data. According to Bhalai & Pawlik (2016), If the sheet has well-structured table so simple techniques such as prior to entering data which assures quality and after entering data which controls quality need to be implemented. Excel does have features such as validation along the column, pivot tables which is used as an organizer of data and many more.
Despite the features provided by excel yet still it does not fall into a great marketing information system tool for enterprises. According to Hermans (2013), the taxonomy of spreadsheet seven audits were studied and out of which 87% of the spreadsheets still had errors in them. The underlying problem of this is that design of the spreadsheet which consist of formulas and worksheets are hidden. The main errors are categorized into qualitative errors are when there is inaccuracy in adding data which leads to quantitative errors as the value calculated is wrong. These errors resulted from mechanical or manual error as spreadsheet involves manual entering of data, logical errors – if a wrong formula of an algorithm is applied and omission error because spreadsheet requires high level data modelling and planning.
For Fibre fashion if excel is to be used for sale and marketing department, it is necessary to hire someone who is an expert of excel as it is a handful tool but with a great learning curve.
