With a central analytics platform, companies can make more and better data-driven decisions – creating business-relevant added value in the process. Awareness-raising, communication and training for employees are just as important as introducing new technologies. Only when new processes are accepted can there be trust in data and applications.
By combining and providing high-quality data and the right tools, companies can create the conditions needed to generate added value from data. However, introducing the likes of automation, low-code applications or a central analytics platform in the cloud also means establishing some completely new processes – which can lead to significant changes in how work is done.
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Transparency creates trust
When companies implement new processes, employee acceptance is a must. ‘While the technical implementation may proceed apace, such transformations often fail because too little attention is paid to the needs of users,’ says Ioannis Theocharis, Data ＆ AI Consultant at Swisscom. It is therefore important to define new processes in a clear and comprehensible way and to familiarise units and individual users with them. ‘Right from the outset, this includes communicating transparently and continuously at every stage and training employees accordingly,’ continues Theocharis.
Trust in data and new technologies does not develop on its own. ‘Employees have to be made aware that moving to the cloud or a central data platform will not automatically solve every problem,’ says Dave Schadock, Team Leader Data ＆ AI Consulting at Swisscom. In addition to providing training, it is therefore equally important to communicate the added value of using data more intensively. The benefits of the changes have to be known for the new processes to be accepted.
It must therefore be clear how the processes are created, where the data comes from, how it is analysed and where its added value lies. Dave Schadock gives a simple example: ‘If you show how a report that previously took several hours to prepare in Excel can now be automatically created in a very short space of time, this can cause a rethink among many people.’
The people factor is crucial
New systems built entirely in the cloud or even AI applications are often perceived as abstract and hard to grasp. According to Dave Schadock, this makes it all the more important to be able to establish them in a company. ‘Key figures run through a new analytics platform may deliver different results than with legacy systems – even some that instinctively seem off. This is because more information is now available or the figures were miscalculated in the past.’ In companies where employees have been making business decisions for years or decades on the basis of isolated analysis or gut feeling, the added value of a central, comprehensive data platform may not be immediately recognised and there may be scepticism. Appropriate training is needed to ensure employees understand how such differences arise, what has been changed, which additional data is being used and how the analysis is being integrated into new processes.
‘The technical implementation is often not that complex. The more difficult aspect is establishing the new processes in the company and getting employees to apply them.’
Dave Schadock, Team Leader Data ＆ AI Consulting at Swisscom
Schadock states that the people factor is crucial for many technological transformations: ‘The technical implementation is often not that complex. The more difficult aspect is establishing the new processes in the company and getting employees to apply them.’ This cannot be achieved simply with a slapdash workshop. Instead, what is needed is a specific training concept catered to all employees depending on their level of knowledge, digital maturity, age, etc. Dave Schadock is convinced that many customers massively underestimate the importance of training and communication.
Past projects confirm the success companies can achieve when they get their communication and training right: ‘Sufficient consideration was given to staff training when introducing a full cloud system landscape at Geobrugg AG. This meant that the switch from, for example, analysing countless local Excel files to centralised reporting using a low-code app worked very well – and improved the investment and decision-making basis for the entire company,’ explains Schadock.
Error culture and hierarchies
Ioannis Theocharis says another risk factor that may prevent full exploitation of data analysis potential can be found in a company’s mentality: ‘We had a customer project where employees noticed a better way of using the new tools, but did not feel empowered to bring this to their superiors’ attention.’ For example, errors in key figures had not been addressed and the potential was not being exploited. Open communication and a healthy error culture are therefore extremely important. For all employees to get equal benefit from their use of the data, there has to be awareness of data integrity in all departments – and not just in IT units or among data and AI specialists.
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