6 Applications that Drive Business Digitisation

Thanks to comprehensive data platforms, companies can quickly adapt to these regulations and compete in a market where making quick and informed decisions is crucial.

To implement an effective data strategy, it is essential to keep up to date in areas such as governance, literacy and cloud costs. In this regard, Qlik highlights 6 benefits to be gained by putting data at the heart of digital transformation strategies:

6 applications for business digitisation

1 – Using real-time data enables more effective and accurate decision making

In recent years, organisations have increasingly understood the importance of using real-time data. However, studies show that more than 88% of companies still need to improve their ability to process it. Data analytics gives organisations the ability to be proactive in their decision making. By having access to real-time data, organisations can act proactively, using up-to-date information to make informed decisions and respond quickly to change.

2 – Decision speed at scale

Once you have access to real-time data, it is crucial to adapt operational decisions at the same speed. Analytics, artificial intelligence and automation can make decisions faster than humans, but the role of people remains critical in areas such as digital hygiene and information filtering. Rapid decision making on a large scale reduces the time between the availability of data and the necessary action.

3 – Process optimisation

Data analytics enables companies to eliminate many manual and repetitive processes that consume valuable worker time. By providing valuable information, companies can identify bottlenecks, inefficiencies and areas for improvement in their processes. Once the data is collected, the workflow can be visualised and patterns and trends can be detected that can affect performance. In addition, by using advanced analytics techniques, such as predictive analytics and machine learning, organisations can foresee future problems and take preventative measures to avoid disruptions or delays in processes.

4 – Fraud prevention

Fraud detection is a constant challenge for businesses, but data analytics has made it more efficient and effective. By using advanced analytics techniques, patterns and anomalies in data can be identified, making it easier to detect illicit activities. Data analytics examines large amounts of information in real-time, which helps to quickly identify suspicious behaviour and potential scams.

This includes the detection of fraudulent transactions, unauthorised activity and unusual behaviour. This solution can also help establish more accurate and up-to-date detection models. By training algorithms with historical data from known crimes, detection systems can be improved. In addition, analytics facilitates collaboration between different areas of the business, such as security, finance and IT, by providing a holistic view of data and enabling a multi-disciplinary approach to fraud detection and prevention.

5 – Improving business performance measurement

Data analytics is an essential tool for driving business growth and operational efficiency. By collecting and analysing relevant data, companies can identify areas for improvement and optimise their operations. This solution allows the impact of implemented improvements to be evaluated by providing metrics and KPIs that measure performance and compare results against established objectives.

The data also enables real-time monitoring of key performance indicators, allowing companies to take proactive measures and adjust their strategies as needed. In addition, it provides valuable information on customer behaviour and market preferences, helping companies to personalise their products, services and improve customer satisfaction.

6 – Boosting Business Intelligence with AI and ML

The combination of generative AI with data analytics will improve the adoption of this tool and increase the operational intelligence (BI) of companies. On the one hand, analytics will be accessible to a greater number of people within the organisation, thanks to the natural language interface used by this AI, which will boost data literacy.

On the other hand, through machine learning, analytics can provide the information and context needed to train and evaluate predictive models from historical data. These models can predict future outcomes or make decisions based on patterns found in the data. Thus, by analysing data in real-time and using machine learning algorithms, it is possible to automate certain decisions and optimise operations without constant human intervention.