The abundance of data presents limitless opportunities, and now is an opportune moment to envision the potential advancements in data, analytics, and AI that the upcoming year may bring. While it's difficult to predict the future with certainty, DevPals experts have endeavored to look ahead to what the next year may hold.

The majority of the busnies's objectives revolve around data in some capacity. As a consequence of this, the present moment is the ideal time to make assumptions about the potential for advancements in data and analytics in the next year. In terms of context, the following business parameters appear to be very clear:

• The aftermath of COVID has induced instability due to its potent impact.

• Business innovation is necessary, as the positions of (online) trendsetters and influencers frequently shift.

• Many executives place sustainability at the top of their agenda.
  
  

With that in mind, we anticipate the following may happened in the thriving data-powered business domain:

1. Taking action to reduce data waste

Data waste occurs when opportunities to derive value from data are missed, or when excessive resources are expended on data acquisition, storage, and usage. In large-scale systems, data waste can manifest in various forms, most of which are avoidable but costly. To minimize data waste within your organization, it is crucial to identify and address its underlying causes.In the past, it was common to hoard as much data as possible, in anticipation of future algorithms and at a time when data storage costs were low. However, this approach has resulted in the consumption of significant natural resources and energy, along with a growing pile of unsustainable e-waste.

It is essential to become more mindful of the data that is truly necessary, the number of times it needs to be replicated, and how long it should be retained. Additionally, while AI can play a critical role in addressing climate concerns, it is not without its energy consumption challenges. For instance, the amount of power needed to train a large AI language transformer model is significant. Therefore, combating data waste requires a constant and delicate balancing act.

2. Data mass gravity 

When working with larger and larger datasets, moving the data to different applications becomes time-consuming and costly. This is referred to as data gravity.Although data does not produce a gravitational pull, smaller applications and other bodies of data appear to congregate around large data masses. Moving becomes increasingly difficult as data sets and applications associated with these masses grow in size. This leads to the data gravity issue.When data gravity becomes severe enough to lock you into a single cloud provider or an on-premises data center, it impedes an enterprise's ability to be nimble or innovative. To mitigate the effects of data gravity, organizations are turning to data services that connect to multiple clouds at the same time.

Gravity also weakens with distance. As a result, the stronger the gravitational force of two things, the closer they are to each other. We are seeing an increase in the amount of data held and processed by the industry's main cloud providers, such as Google, AWS, Microsoft and other well-known names. As these data masses grow in size, more market players in the surrounding data platform ecosystem will become more interested in partnering and collaborating as a result of the law of gravity.

3. AI-core products and services

While the energy consumption of sophisticated AI systems is a valid concern, their capabilities and potential benefits are increasingly impressive. In particular, Generative AI systems have demonstrated remarkable abilities in various domains. For instance, they can seamlessly translate texts, generate concise summaries of complex documents, compose well-written articles, craft beautifully finished emails, transform legacy code, create stunning visual arts, and even write poetry. 

Looking ahead, we can expect AI's capabilities to continue to expand and amaze us in the coming year. For instance, AI-powered virtual assistants can become even more intelligent and capable of handling complex tasks, such as booking appointments and managing calendars. The use of AI in healthcare can lead to the development of more accurate and personalized diagnostic tools and treatments. In finance, AI can enhance fraud detection and risk management, while in manufacturing, it can improve quality control and optimize supply chain management. 

However, as AI systems become more powerful, the need to address their energy consumption and environmental impact becomes even more pressing. Researchers are exploring ways to develop energy-efficient AI systems, including the use of specialized hardware and more efficient algorithms. As AI continues to advance and become more integrated into various sectors, it is essential to balance its benefits with environmental sustainability concerns.

4. Data sharing is compassionate

In today's interconnected world, data sharing has become increasingly important for organizations to achieve their goals. The value of data increases significantly when it is traded and shared with others. Collaboration between businesses and other organizations can lead to innovative solutions, increased efficiency, and better decision-making.

Data sharing is not just about growth and cost-effectiveness, but also about sustainability and inclusiveness. By sharing data, organizations can ensure that their products and services are sustainable and meet the needs of a diverse range of customers. For example, sharing data on energy consumption can help businesses identify areas for improvement and reduce their environmental impact.

Moreover, data sharing is a two-way street. In order to tap into the relevant external data sources, organizations must also be willing to share their own data with others. This creates a mutually beneficial relationship where both parties can benefit from the insights and knowledge gained through data sharing.In addition, data sharing can help foster trust and transparency between organizations and their stakeholders. By sharing data openly and honestly, organizations can build trust with customers, investors, and regulators. This can help them to achieve their goals more effectively and sustainably in the long run.

Data sharing is a critical component of modern business and society. It can drive innovation, increase efficiency, and promote sustainability and inclusiveness. Organizations that prioritize data sharing and collaboration are more likely to succeed and thrive in the long run.

5. Data mesh rise

Data Mesh is a strategic approach to modern data management that strengthens an organization's digital transformation journey by serving valuable and secure data products. Data Mesh aims to move beyond data warehouses and data lakes for centralized data management. Data Mesh promotes organizational agility by allowing data producers and consumers to access and manage data without involving the data lake or data warehouse team. Data Mesh's decentralized method assigns data ownership to domain-specific groups that serve, own, and manage data.

The same thing is happening with the concept of "data mesh," which comes from lightweight, ad-hoc networking. Data mesh is currently popular among data platform creators for a good reason: concepts such as data product thinking, domain-central data ownership, federated data governance and self-service platforms enable enterprises to become significantly more data-powered. However, the cultural impact is significant, and getting bogged down in sophisticated technological intricacies becomes alluring - even for the approach's creators.

Conclusion

Regardless of whether the experts' predictions come to fruition, there is no denying that the upcoming year will be a transformative one for businesses. Data is the driving force behind these changes, making mastery of data the most critical skill for success. Companies that can effectively collect, analyze, and utilize data will have a significant advantage over those that cannot. In today's data-driven world, the ability to extract insights and make data-driven decisions is crucial for staying competitive and achieving growth.