Maximise the returns from your data

Table of Contents

Technology is constantly evolving over time. Of particular importance is the developments in computing power, artificial intelligence and data storage. This has propelled options in analytics forward tremendously with data leaders a key hire for large corporations and small businesses alike. 

The aim of these hires is to ensure that businesses can unlock the power of big data and ensure that they can maximise returns. This process needs to be completed efficiently with the focus on gaining key insights that will drive businesses forward. 

It’s important to note that there are challenges that must be overcome. Data is often locked away in applications that are not effectively connected. Strategies to claim this data require massive amounts of time and heavy financial investment. Furthermore, changing policies in data-privacy are also impacting whether companies can access the data they want. Ultimately, this has made data difficult to acquire, utilise and manage. However, there are solutions that allow businesses to discover the benefits of data without apparent issues. 

Here are some of the factors that should be considered within a data strategy. 


Create A Data Strategy

The first step is always going to be ensuring that there is a data strategy in place. While this might seem like an obvious step forward, it’s worth noting that various leaders fail to focus on a data strategy. 

It’s important to focus on architecture as well as governance that is robust while ensuring that there is an understanding of the impact this has on business. The aim should be to demonstrate value early and potentially centralise data to ensure that it is easy to access. 

Many financial leaders are also focusing on ensuring that a clear path for data collection and storage is mapped out. Instead of trying to connect multiple systems, other leaders instead build a master system. This could take several years however it has the potential to be effective. 

One example of this type of system was set up for a bank. Here a layer was placed over an existing system to provide a full, clear view of the lending process. The system was created utilising multiple systems with different signals for individual processes. 

The setup ensured that their processes on different systems were still accessible and understood by the whole team. 

Many businesses are also purchasing external data ecosystems as part of their strategy. These can be accumulated from a range of different sources. It allows businesses to engage with customers and suppliers effectively. The processes at work here have been described as the early days of the internet. However, many feel that new developments in smart cities and resource management could lead to the leaps forward necessary for more effective data collaboration. This would ensure that data could be shared in a way that is trusted and secure. 

That is crucial due to the new importance and focus on data privacy regulations, including the General Data Protection Regulation (GDPR) as well as the California Consumer Privacy Act (CCPA). There is no company that can operate without considering these regulations, regardless of the locality of the business model. 

As such, it is essential to ensure that systems are built which allow for flexible and fast data transfer that operates effectively and works within the boundaries of new regulations. Understandably, ensuring businesses remain compliant does require further use and commitment to the latest technology. 


New Innovations Trigger Data-To-Value Gains 

Due to the advances in new technology, there are now a variety of different plug and play solutions that could benefit businesses by increasing the speed of data processes. Companies are keen to explore these options from different data service providers. Doing so can help a business maintain a competitive edge. 

There are a variety of different examples of this software on the market including cloud-based software like Amazon SageMaker. With this tool, it’s possible to utilise machine learning to automatically label data sections. 

This isn’t the only benefit that machine learning can provide. We are only just beginning to scratch the surface of the advantages that this option could bring to companies. Indeed, there is a range of processes that could be automated with this type of software. This could solve issues with both anonymising data and maintaining the quality of different sources. 

Hadoop could be another useful piece of software for businesses. This can help eliminate the extensive amount of time that is typically needed to assess and understand the relationship between different pieces of data. Instead, it makes the process of data analytics far more flexible. This ensures that value-creating insights are discovered at a more rapid pace. 

Algorithmic models can be quite complex and difficult to comprehend. This once again leads to a reduction in how quickly businesses can obtain data. However, there are now solutions such as local interpretable model-agnostic explanations. This ensures that there is a reduction in the time spent on exploring the legal issues with data collection. 

In terms of analysis, a variety of algorithms that have been pre-built can be utilised for a number of common use cases. The speed can be reduced when creating custom-build models. Furthermore, processing power from the cloud can also be rapidly increased and this in term helps make model training a more efficient process. 


Communication and Cultural Changes 

It’s important to note that it is not your technical issues that are part of the challenge that prevents businesses from gaining a higher return when utilising data. There is also a cultural shift that must be considered, particularly when building a model of operations that is more data-driven. 

Indeed, leaders often use a variety of different strategies to alter the mindset within an organization and ensure that the culture fits the new working model. This includes everything from data-visualisation competitions to hackathons. It also often involves ensuring that businesses have a way to share their successes in data use with the whole businesses through the right platform. 

Although these strategies can prove to be beneficial, there are certainly more requirements. The main focus here should be on building relationships with leaders and ensuring that there is a high level of communication. 

It’s important to be aware that through the use of data, a business will undergo a transformation. To ensure that there is critical business owner support, it is crucial that data processing and analysis is viewed through this lens. 

As such, there needs to be a dual focus on both business accountability as well as communication. This should be constantly measured through key performance indicators that match the intended outcomes of a particular initiative. 

It’s not a one-person job either. Instead, those operating on data initiatives and the businesses themselves must work as one to provide the optimum results. This ensures that gaps between the technical experts and the business can be repaired. For instance, data teams should always be willing to provide evidence on how new data-driven tools could assist in areas such as sales. This guarantees that business teams are more likely to use these tools because they will understand the benefits of doing so through this form of cross-functional collaboration.


Final Thoughts

While there is still a significant distance to go before we can access the full potential of an enterprise that is data-driven, businesses are already taking strides forward here. It’s important for leaders in the industry to highlight the benefits of data to businesses and provide the right, innovative solutions that they need. This will ensure that companies are able to maximise on the returns from the data currently locked in their business model. 



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