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Data Life Cycles in Management Accounting

Instructor: Yusuf Abdullah

Yusuf has taught Science and Mathematics at school level and Finance and Economics at University level. He has recently earned his Ph.D in Financial Econometrics.

This lesson deals with the data life cycle in management accounting. Learn about the different stages of the data life cycle and the characteristics and major requirements of each stage.

Data Life Cycle

Ron is the chief financial officer at Happy Inc. He is meeting with Alice, who is a senior data analyst in the company. The conversation is regarding the data management process for the company. Ron inquires about the process in place for proper data management. Alice informs him that the data from the company goes through the complete life cycle and has proper safeguards to protect privacy. Alice defines the data life cycle as the complete process of sequence in which data is generated, captured, saved and is finally deleted at the end of the cycle. Ron is excited about how the management accounting data goes through this process. He requests Alice to exhibit how sales data for each year goes through the data life cycle.

Life Cycle Stages

Alice informs Ron that the data life cycle has seven distinctive stages but these stages are open to addition or deleting depending on the industry and type of data. The stages are as follows:

Data Capture

Alice shows Ron how the sales data from different stores would be combined to get the total data for the company. This data can also be differentiated region wise or products wise. The collection or capture of data is done through enterprise systems or from the sales force.

Data Maintenance

The next step is data maintenance where the old data is updated with new data by addition or substitution. For our example, sales data for the next quarter would be added to the older data to assess demand. If the company needs data for only 10 years, the oldest quarter may be deleted and a new quarter added.

Data Synthesis

Ron confuses data synthesis with data creation or capture. Alice informs him that data synthesis refers to the creation of data when there is missing data or the creation of a new data set from the original data set. In sales data, a store might be closed for some days. Using original data might not reflect the true demand in the area, and hence data can be synthesized using older data and techniques such as linear interpolation or regression. A complete new data set can also be synthesized such as sales data for total edible items that can be created from the data of different edible items.

Data Usage

The next step is simple. The sales data can be used to forecast demand for the next quarter or year. The data for forecasted demand can be used further to allocate a budget so that the revenue is maximized. Data usage is an important step and depends on the type of data captured or synthesized. The forecasting is as good as the data entered and therefore, the data used should be as accurate and as relevant as possible.

Data Publication

In the next step, the data and the forecasted demand or budget can be published as a report to higher management and even the sales force. The publication can also include the impact of sales on the pro forma figures in the annual report. Again, the publication should be an accurate representation of the actual and relevant data.

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