This is no longer the case. The cases have gone digital, which is a good thing as the digital transformation makes it easier to solve daily tasks, also in a time of Corona, when the home office has become an integral part of the daily work. The consequence is that you become even more dependent on valid data and new tools to ensure visibility on tasks and performance in a workday where people are scattered throughout various locations.

The principles for efficient operations management remain the same; it is only the framework that has changed. You still require a focus on core tasks, to know your main processes and management needs and to have access to critical operational data. It also requires a common standard for good operations management and that your employees are involved in problem-solving and continuous improvements.

However, we also need to get better at data discipline. The software and tools are still essential to have in place. Still, it is equally necessary to develop a work culture that can help ensure reliable data supporting decisions on mutual direction.

However, we see several typical challenges and barriers to creating the necessary management transparency. We have grouped these challenges into three categories in the following and offer you our recommendations for overcoming them.

1.      “We have no data” – Sure you do, you just need to find them …

“We do not have the operational data we need.” We hear that complaint from many managers in both private and public organisations. It is difficult to make the right decisions and take action on operational issues if you do not have the relevant data to support your decision-making.

One of the causes of the lack of data may be that managers have been unable to acquire the requisite knowledge and insight into the types of data you can extract from the systems. In our experience, most systems contain enough data to support most of the management team’s operations management needs.

The management team must work together on identifying and specifying their management needs, which means that they should determine which data they need, at which detail levels and with which frequency to manage operations.  The demand is defined based on the core tasks, customer requirements and expectations as well as stated targets and objectives.

Once the management needs are identified, it becomes crucial to involve employees who have the necessary insight into systems and data structure. If you do not have such an employee yourself, an excellent place to start would be with your colleagues in Business Intelligence (BI). In many cases, they can be the key to obtain relevant data and to set up a flexible and robust process for data capture. It sounds easy. It is not. It typically takes some time to generate the wanted data, and you need several dialogues with the management team.

2.      “Our data are not valid” – then create the framework for uniform registration

We often get the complaint, “We have data, but they are not reliable,” from managers. And that is indeed a problem. Valid data are the foundation for efficient operations management.

“Garbage in, garbage out” is a well-known phrase, which is also relevant when it comes to understanding inadequate data validity. The reports used to manage daily operations are not any better than the sum of the hundreds of registrations made by your employees every single day.

The daily work (whether analogue or digital) is important for the validity of your operational data. One example is your customer service filling out data fields with the customer on the phone or the fields filled out by the caseworker when working on a case. If the employees enter the data in different or inadequate ways in these situations, the consequence is more inferior quality in the operational data.

Of course, employees will work in different ways, not least because the systems let them.  There is often a lack of restrictions and transparency on which data to enter in which fields. For example, many systems have free text fields, which makes it more challenging to use these data for management purposes. And many companies and organisations do not have a data registration practice and culture to guide their employees and ensure uniformity in the registration of data. The individual employee consequently has a high degree of freedom in the daily data registration, which results in a more deficient data basis for your decisions.

More system restrictions will enable you to achieve a more robust data basis and will support a higher degree of uniformity in your registration practices. However, this alone will not be enough. It is a good idea to incorporate specific descriptions of the desired registration practice in detailed process descriptions and standards. Employees’ competences and focus on registration practices should be strengthened through training, continuous knowledge-sharing and follow-up from the management team. It will not happen from one day to the next but should be an integrated part of operations management.

3.      “We interpret data differently” – so take the time to build a mutual understanding

Data brings facts to the table. It creates an objective basis for decisions and will lead to better decision-making. However, we see that there are significant differences in how data are interpreted and used. Decisions are made based on data that you do not understand. There is often a lack of data insight and a uniform framework of interpretation that can help managers and employees make the right decisions.

The point is that the people who need the data also need to understand the decisions and calculations constituting the basis of the reporting. One classic example is absence data. Is long-term illness included in the definition or not? In some companies, they work with two absence figures, one with long-term absence and one without, which causes confusion and incorrect interpretations.

It is a good idea to have easily accessible definitions close to the KPIs and operational data used. Review these definitions, together with your employees, to build a shared understanding. Always remember to make the desired target level visible in the data overviews. Examples of targets are the maximum number of phone calls to lose in a day, the number of days it should take to process a case or the maximum number of cases you should be working on simultaneously. The target level is particularly important as it defines what good and bad looks like, and consequently, whether there is a need to launch mitigating actions.

The source of good decisions lies in the underlying data from which the average number is derived. Together with your employees, you should understand the numbers in depth. They provide insight and will lead to better decision-making. For example, it is necessary to understand the underlying data if you want to act with efficiency and focus in the case of rampant error margins. These could relate to the volume data for the most frequent error types, wherein the process they appear and within which time frame, etc. The same goes for numbers for case processing times. If it becomes necessary to stop an unwanted trend, you will need to know whether the long case processing times are a sign of a general development or a few errant cases. It will require extra time to understand the underlying data set, especially in the beginning. But this time is worth investing as you will gain a more in-depth insight that will lead to more effective decisions.

4.      Data-driven operations management is not just about IT

Finally, we would like to emphasise the importance of training managers and employees in understanding their operational data and the link to their success criteria and processes. This understanding and insight will not just happen by itself, and you need to invest time in this.

Most challenges related to data and their use are not founded in technology but rather in data maturity, work culture and daily management. Most organisations already own the relevant operational data but still need to find them. There can also be several benefits to focusing on standardising and training the organisation to register and interpret data.

To sum up, here is our advice for working efficiently with your data:

  • Describe your management need – this will tell you which operational data you need
  • Begin with only a limited amount of data – expand as you develop your shared data maturity
  • Involve individuals with deep system and data knowledge
  • Describe in detail which data your employees should register where in your processes and in which IT systems – this will enable you to have a more valid and uniform data basis
  • Present data in a way that is easy to understand and supports sound decision-making
  • Spend time on training and sharing knowledge about data needs, data validity and data usage. This way, you will slowly but surely develop a higher level of data maturity.