Digital transformation is a buzzword widely used across many sectors – and rightfully so. It is a game-changer for businesses, brought by modern technology and artificial intelligence. The definition is so widely spread it’s no longer seen as a concept but a movement.
Companies spend billions of dollars innovating and reviewing their processes with the use of technology. Adopting digital transformation as a mindset and practice enables them to achieve business goals such as increasing performance, reach or other metrics they aim to improve. However, technology is just a set of tools used to execute a company’s business strategy.
Digital transformation isn’t about who has the best technology – it’s about who uses the data they’ve got the wisest.
Everyday businesses are getting loaded with data of different formats, coming from different sources. This data can be used for multiple purposes, from getting a better understanding of your clients’ needs and improving customer experience, to discovering gaps in the competitor’s strategy, thus enhancing growth and scalability of the business.
However, a lot of the data goes amiss without processes set to connect, structure and analyse it. The only way of enabling efficient and ROI-driven digital transformation is making sure the data you collect isn’t going to waste through using relevant digital tools that deliver data visualization.
In this article we are going to talk about how data science and digital technology enables successful digital transformation. If you would like to discuss it with us in greater detail, please book an appointment.
Often organisations don’t understand the importance of data health. Poor data quality leads to poor decision making which in turn brings financial losses. Ingesting huge volumes of data without cleaning it is a scarily common practice within many business models, and the longer it stays unaddressed, the deeper the issue roots.
To avoid it from happening altogether a firm data quality plan and methodology are needed. Both transactional and unstructured data need to be addressed, cleaned and error-corrected as soon as it’s gathered. Consistent high quality of the data leads to good business decisions, growth and effective digital transformation initiatives.
Data science is enabling digital transformation by being the moving force behind creating solutions that offer high probability future forecasts based on patterns of the past. Accuracy of these will allow you to see a clearer picture of opportunities as well as potential roadblocks.
Digital transformation backed by data science enables decision makers to take the guesswork out of future planning. Data-driven forecasts mitigate risks in advance and can help decrease costs. Additionally, they put emphasis on best practices that can either be reused in the future or guide towards new opportunities.
Constant reviewing and optimisation of how business processes perform is vital for growth. Data is crucial when it comes to evaluation of effectiveness as it indicates areas that need to be addressed. Digital transformation is all about optimising processes to be time and quality efficient to maximise growth and scalability of the business – but without data it’s impossible to see which processes need optimisation in the first place.
Simply put, data-driven evaluation is the only way to identify digital transformation opportunities. It provides a clear overview on what specific areas are underperforming, where your resources go and whether the spend is justified. All three may be pointing towards the same conclusion: the need for a digital change. Data can connect processes to people, identify key influencers of change and convert insights into actions.
Machine learning can accelerate digital transformation. For the artificial intelligence to learn, however, it needs copious amounts of data to process. Machine learning is about identifying patterns and anomalies, therefore the algorithm is capable of finding insights without being programmed where to look for them.
Usage of artificial intelligence minimises the chance of human error. Secondly, having learned patterns within the processes it may highlight areas that could benefit from automation as opposed to manual labour. This in turn means your team’s productivity is growing as their focus will be turned to tasks that require human judgement.
Feeding more data analysis into it improves the overall performance of machine learning, therefore adding value to the processes and insights it’s responsible for. With it being such a strong force behind digital transformation, improving processes through machine learning can deliver unexpected results.
We have mentioned abruptly that poor quality data leads to poor business decisions. However, decisions not based on data at all are even more dangerous. The process of digital transformation is complex and requires informed decisions unique to your business.
Companies engaged in data-driven decision-making experience a 5 to 6% increase in output and productivity. This enhances the impact of digital transformation strategy and operations, whether it is in the form of increased revenue, reduced costs, or improved efficiencies.
Collecting and analysing data will reveal whether your business is ready for driving digital transformation and where it can be implemented. To assist that, you can also evaluate your readiness with our automation readiness questionnaire.
Data is everywhere in your business and if it’s managed correctly it will connect you to new opportunities. Being data-driven means making efficient and educated decisions that bring growth to your business through optimisation of both external and internal processes.
Digital transformation is the way forward. While it strongly depends on modern technology, the end result will depend on how well the process was carried out. And if you want to execute it correctly, you will need to collect, understand and use the data. Digital transformation requires more than just cloud computing – data scientists are crucial to your success.
Not sure where to start? Book a call with us.