Five Data Solutions Worth the Investment
In several sectors, executives of large corporations have a sharp eye on which data solutions to invest their resources in. But there are also companies that feel a bit lost in the world of data. Directors of these companies are aware of the importance of data, but do not always know where to start. What investments do you prioritize? Do you invest in Artificial Intelligence or rather in data housekeeping? These companies also question what competitors are focusing on and what the relevant significant topics are. These executives are concerned about missing an opportunity, and that concern is growing.
Much can be gained in different sectors with the right investments in data solutions. Companies in the utilities- or manufacturing sector would do well to put their data management in order to further optimize processes and develop new products or services. Moreover, they could leverage their data to deploy dynamic pricing models. Governments can also work far more efficiently by automating time-consuming tasks. Investing in document mining solutions, for example, allows employees to make faster and qualitatively better decisions.
The top five
There are several reasons for the urgency to invest in data. First, investments provide companies with much more efficient processes. Getting data in order and setting up proper models save manpower. Equally important: deploying technology leads to cost savings and qualitatively better decisions. That ultimately strengthens the organization.
Administrators do not necessarily need to be excessively knowledgeable about data and technologies. It is, however, important to know what the latest developments are and what data solutions competitors are investing in. To assist in this, we compiled the top five investment trends for 2023, as drawn from our practice.
- Data migration to the cloud
Many companies - even the somewhat larger, more cumbersome organizations - still often have their data on-premise, i.e., on local servers. They are increasingly migrating their local data to the cloud. This enables them to work more efficiently, process their data faster, add both internal and external resources, make real-time decisions and leverage scalable infrastructure and services. - Computer vision, sharper than the human eye
Computer vision makes it possible to analyze and interpret visual information. This AI technology was initially embraced mainly by academia, e.g., for medical research, but is now increasingly being used in business. Two practical examples: until recently, the maintenance of train tracks was done by manual inspection. This was enormously time-intensive - inspection teams had to walk along the tracks to assess whether maintenance was necessary on certain sections. However, with AI technology the same work can be done more accurately, with better quality and at a lower cost. This is now happening. Trains are equipped with a camera; the images are used to predict where maintenance will be required in the short term. Another example comes from retail. Web shops are increasingly using computer vision to compare competitors' products with their own. - The importance of data governance is growing
At many companies, data governance - the agreements and standards by which an organization establishes how data will be handled correctly, securely, and reliably - is high on the agenda. Substantial investments are being made in systems to put data governance in order. In part, this investment trend can be explained by increasingly stringent laws and regulations dictating what is and is not allowed with data. As a result, data governance is no longer a free choice, but a necessity. Companies not only risk high fines but can also suffer reputational damage if they fail to comply with laws and regulations. Especially the latter is a doomsday scenario for companies; you can scare off customers if it turns out that you are not handling their data carefully enough.
In addition, more and more companies are realizing that their database needs to be in order to be able to gain valuable insights from it. Companies increasingly value improving the quality of their data. - A modern data platform is a must
Many companies are currently choosing to implement a modern data platform. They are spending a considerable amount of money and time on it. Why? Modern data platforms provide many advantages over traditional data platforms which are relatively static; data is stored and processed into valuable information. This static concept no longer suffices. Data sources are becoming more dynamic, companies want to have real-time information to make decisions. Companies also want to use external data in addition to their own data. Another important argument: unlike traditional data warehouses, modern data platforms are capable of processing unlimited data. In short, for many companies, a modern data platform is an absolute must to have faster and constant access to an inexhaustible mountain of data to enable better decisions. - The use of hybrid data teams is on the rise
Also noteworthy: companies increasingly need rapidly scalable data teams. They might need two consultants today, next week they might need ten data experts. That is not always feasible in the Dutch market. This is why companies are increasingly using hybrid data teams. This means that a number of consultants are with the customer, while the other specialists work remotely. These are often professional data engineers, working from a near shore location such as Croatia, Serbia or Bosnia and Herzegovina. The experiences are positive and companies are entrusting more and more projects to these hybrid teams. While working with hybrid teams is also interesting from a cost perspective, the ability to scale up extremely quickly is the most important plus point.
The impact of AI
There is high demand for support with investments in data solutions and AI advice. Companies want to know what the impact of AI and GenAI - that is, Artificial Intelligence capable of creating new content - is for their organization. And whether they should do anything about it. This is understandable as the media reports on new developments around AI on a daily basis. It is difficult to give a satisfactory and unambiguous answer to this. Every company would benefit by delving into the developments and investigating what AI can do for the specific business. It is clear that AI will lead to efficiency gains in all industries; companies can make faster and qualitatively better decisions and provide smarter solutions to customers.
Yet AI also carries a threat as we cannot yet fully understand the risks. It is better to not rely blindly on generated AI. Algorithms can for example lead to discrimination. In addition, it is not wise at this stage to share sensitive data via AI tools. It is like a black box where it is difficult to predict the outcome. Nevertheless, it might not hurt to experiment with innocent data in ChatGPT, for example. These technologies are, to a large extent, going to define our society of the future. It is better to be prepared for them.
Essay by Sander de Jongh, partner at Valcon. Published in Management Scope 06 2023.