How is data benefiting the operations of a company? What is it exactly and what are its types? How is it calculated? How is it changing the way companies compete and operate? These are some of the important questions to consider. So, let's find out.

Types of operational data

There are three types of operational data:

Transactional vs. operational data

An organization has a huge amount of transactional data. Some typical activities that produce transactional data are purchasing products from suppliers, sale of products, location of sale, delivery of items to customer sites, recruitment of employees, etc. hence, transactional data is produced frequently.

The knowledge and understanding of these types of operational data can improve operations and assist in the identification of risks. Thus, in order to make full use of operational data, it is essential to make sure that the data is reliable and of the best quality.

Data-driven business operational processes

When a business implements a data-driven approach, it means that it makes strategic decisions on the basis of data analysis and its interpretation. This approach allows businesses to assess and systematise their data with the aim to better serve their customers. By utilising data to direct its actions, a business can customise its relationships with its customers in order to have an approach that is more customer-centric.

Calculation of operations data

Now let's take a look at some examples of important operations data that businesses should take into consideration.

laboratory productivity

Labour productivity is a measure of the output per employee in a certain time period. It is calculated as:

$LaborProductivity=\frac{Outputperperiod}{Numberofemployeesworkingduringtheperiod}$

The rise in labour productivity can benefit an organisation by allowing it to:

• Boost output without changing costs. With the example mentioned above, if labour productivity per week rises from 40 units/worker to 50 units/worker, then the 10 workers could boost total production from 400 units/week to 500 units (10x50) per week.

• Lower costs without changing output. Based on the above rise in labour productivity, the organization could have less workforce and still produce 400 units per week. With labour productivity of 50 units per week, only 8 workers will be needed to achieve 400 units of output.

The rise in labour productivity can be obtained in different ways. Some of the examples are as follows:

• Introduction of new technology that will accelerate the production process.

• Adjusting the production system so that it works more efficiently.

• Recruitment of new workers with higher skills, experience, and qualifications.

• Offering training and motivation to the workers.

While enhancements to labour productivity are attractive, an organization should be careful due to the following reasons:

• Costs associated with boosting labour productivity: the above-mentioned ways incur additional expenditure, so the organization should only apply them if the enhanced labour productivity produces sufficient revenue to pay for the modifications.

• Conflict with other objectives: if the workers focus only on increasing output other operational issues might be left unaddressed. For example, the quality of production.

Unit costs (average costs)

Unit costs can be calculated with the following formula:

$UnitCost=\frac{Total\cos t}{Unitsofoutput}$

The unit cost is also called the average cost.

Figure 1. Monthly costs and output of a newspaper producer

*Suppose that 50,000 units are the capacity

In this example, the highest efficiency level of output is 50,000 as this is the output at which the unit cost is the lowest (£ 1.40 per unit).

Figure 2. The unit cost of four organizations

In this example, the most efficient company is B. In this company, the unit costs are £7.50/unit, the lowest out of all four companies. Whereas the least efficient company is C, as the unit costs are £9/unit, the highest out of all companies.

Capacity

The ultimate capacity can depend on various factors:

• The level of demand for the product

• Flexibility in production

• Seasonality of the output and demand

• Opportunities for outsourced production

Capacity utilization

Capacity utilization measures the level to which the maximum potential output is being reached.

$CapacityUtilization=\frac{ActualOutput}{MaximumPossibleOutput}$

Capacity utilization can be measured on a daily, weekly, monthly, etc basis.

The connection between capacity utilization and other operational targets

Even though capacity utilization in itself is a target, it is essential as it has an effect on other operational targets.

Capacity utilization and labour productivity: in the case of low-capacity utilization, a lot of machinery will not be employed productively in the business. This will result in less work done by the workers as they rely on machinery. Therefore, output per worker will decrease and labour productivity will also decrease. For more flexibility, the number of workers can be reduced if production levels decrease.

Capacity utilization and unit cost: the greater the level of capacity utilization, the more effectively the company is using its resources. It is important to note, however, that a company will have fixed costs regardless of its level of output.

Big data changing the way businesses compete and operate

Big data is defined as a dynamic, big and distinct volume of data generated by people, tools, and machines. It needs modern and innovative technologies to gather, host and actively process the huge amount of data collected to generate real-time insights for the organisation that may link to customers, performance, risk, productivity, and improved value for shareholders.

Big Data has primarily changed the way companies compete and function. Companies that invest in and effectively generate value from their data will have a definite benefit over their rivals. A performance gap will constantly increase as more significant data is produced. Evolving technologies and digital pathways provide better mechanisms for acquisition and delivery. Below are some of the ways big data has changed the way businesses operate:

Improved business intelligence

Tools like business intelligence were developed to assist in analyzing the company. Business intelligence and big data work together when it comes to managing operations. The bigger the reach of BI, the more improved the insights for the company are.

Improved target marketing

Targeted marketing has supported companies in accomplishing their long-term objectives with great outcomes. Due to increased accuracy, resulting from Big Data, companies are able to fulfil the demands of their customers and create marketing strategies more efficiently.

Satisfied customers

Big Data helps anticipate customer needs in advance and works accordingly to improve customer service and eventually customer satisfaction.