Advantages And Disadvantages Of Data Management

According to Berson and Dubov (2011), there are four typical categories of drivers that explain the need for data management: Business Development, Sales and Marketing; Customer Service; Risk, Privacy, Compliance and Control; and Operational

The enterprise data repository (EDR) project at InsuraCorp was developed to be the data warehouse for customer and product data for all InsuraCorp business units. There is a school of thought that data management responsibilities should fall to IT and to the business units themselves. The collaboration between the IT and business users together could produce higher quality data and administer data management more effectively. Everyone who receives or accesses information within an organization is responsible for data integrity so it only stands to reason all parties have a responsibility. Both the information system managers and the business managers, as data stewards, are duty-bound to monitor and control data accuracy. With data, it is as important to have accurate input so that the information that is shared will be useful to other users. Storing data in a holding tank will not solve a bad data problem.

Data management is vital to any business as this is a key tool to an organisations business improvement, as you can refer back to data, and compare them against benchmarks. Analysing data can provide evidence for possible future structure such as identify trends, as well as indicate where improvements can be made. However there are strict procedures to be followed when collecting and storing data.

Data are “raw facts that describe the characteristics of an event or object” (Baltzan 6). Managers used to have to collect data manually along with analyzing it. This was a very time consuming process which could also be complicated in different aspects. If businesses lack data this can cause them to make unethical business decisions. For example, if they don’t have the correct data needed they may order in too many products or not enough causing a surplus or shortage in products/supplies. Being able to collect and analyze data quickly everyday helps make a business make better decision. Data in this day and age is important for a business to obtain so they can make the best possible decisions for their company to be successful. This can help a business know how much to order, how much was sold, and their customer numbers. A company can also see where they are getting most of their customers from when collecting data from surveys. Data allows us to see if a company is doing good or bad and

Data is very important thing in every business, especially in today’s dynamic world where optimal use of data leads to success in shorter span of time as lots of companies are struggling for truthful and accurate data. These data must be analyzed in exact time and in a proper way so that the decision is more effective, but the data we receive are very redundant and carry lot of space in our system. This creates a challenge for the Analytics people to remove the redundancy and bring out only those relevant data that aids in decision making process. Master Data Management is a solution for such Analyst who wants to eliminate the redundant and inconsistent data of the organization (Vinculum, 2016).

When it comes to reviewing healthcare data there are certain data categories an organization needs to keep in mind, those categories include aggregate data, comparative data, and patient-centric data. All of the categories have advantages and disadvantages. In the following paragraphs I will list out the advantage and disadvantages of the categories mentioned above.

Master Data management this type of technique is used when the complexity of business is large

Data Manager- The data manager works in correlation with the Data Analyst to monitor the data and create business databases that can be easily accessed. Constant feedback of communication when there is a potential for business process failure, compromise or monetary loss.

Data collected by a business includes internal data, such as financial or operational information, as well as external data, such as customer or website usage information. Properly analyzing and acting on this vast amount of data can transform the way companies do business and can become their biggest competitive advantage. Leaders of the organization no longer have to rely on their “gut instincts” to make key decisions, instead they will make decisions off historic data and will be able to more easily measure and track the effectiveness of those decisions.

Data in the healthcare industry can include patient demographics, financial information or clinical documentation. Management of this data can include storing in a file, file server, an email, an electronic healthcare system or other locations. Therefore, Data Management is the process of managing how the data collected and how it is stored so that it can be used efficiently and effectively in the future.

Data Analysis is defined as "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data. (Statistician John Tukey, 1961)” In general, Data Analysis is used to check the validation of the vaguely collected data in terms of variations and profitability. However, if Data Analysis is not used we cannot obtain the accurate, factual and optimum solution of the given challenge. Data Analysis tools helps us to understand the question and produces answer in a detailed space. It also helps to find the gaps between data such

when data will be used, Data users are working with the data in their daily jobs.

Determining Databases and Data Communication is important because it allows me to manage data and query, store information, and has proven to be a essential component in day-today business.

Enterprise view of data is required to maximize the efficiency of the organization as a whole (csu, n.d.). The sharing of data and maintaining the transparency is achieved by integrating the data within the system. Integrating the data is a complex task as there is always certain information that is sensitive and it is not meant to be shared with each and every department or individual working within or outside the organization. Making enterprise data strategy demands administrators to understand that the data is critical and is an asset to the organization. Wayne Eckerson describes enterprise view of data as the enterprise data strategy built by the organization to have a successful growth rate plan. He also points out that only one in 10 organizations have such sort of strategy as most of them don’t put together the required soft skills that are needed to manage change incurred by the strategy and the investment for data management techniques and tools to certify the delivery of high quality and consistent data supported with business initiatives and strategies (Eckerson, 2011). Most of the organizations face a lot of difficulties in maintaining the data quality. The cost of data quality incurred is much higher and it sometimes contributes to forty percent of the problems related to IT in a corporation this problem occurs because of the inaccurate data (steria, 2012). One major issue when creating an enterprise view

Managing information for decision making is a key point of any business. If a company has enough effective data collection, the staff can interpret information and then generate knowledge (Marshall, 2012), this will help the managers and staff to make any decisions to satisfy the customers and obtain more profits. And any risks could be predicted, some measures even will be taken to prevent them from occurring.