Data is abundant and has limitless possibilities, and the present moment is the ideal time to imagine what developments in data, analytics, and AI may bring in the coming year. We realize it's challenging to predict what will happen in the world tomorrow, but the IT experts tried to see a year ahead.
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:
- COVID caused instability due to its strong afterburn.
- Businesses must innovate because (online) movers and shakers frequently change positions.
- Sustainability is high on many executives' priority list.
With that in mind, we anticipate the following may happened in the thriving data-powered business domain:
Taking action to reduce data waste
Data waste means passing up an opportunity to gain value from data or paying too much to acquire, store, and use data. Data waste can take many forms in large-scale systems. Some are unexpected, most are costly, and almost all are avoidable. To avoid unnecessary data waste in your organization, you must first recognize it.
We used to be proud of the fact that we held on to as much data as we possibly could because we could, prices were low, and there were future algorithms waiting for it. This, it turns out, consumes a lot natural resources and energy, and generates a growing pile of unsustainable e-waste. We must become more conscious of what data we will truly require, how many times we will replicate it, and how long we will keep it. Furthermore, while AI may be crucial in addressing climate concerns, it consumes energy as well. Consider the power needed to train a large AI language transformer model (hint: really a lot). As a result, the fight against data waste will be a constant and delicate balancing act.
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.
AI-core products and services
Although AI - sophisticated systems do cause energy usage concerns, the results are becoming more amazing. This is especially true for Generative AI systems. For example, AI can generate seamless translations, management summaries, entire articles, beautifully finished emails, legacy code transformations, images or even poetry. Next year we should expect to see even more of AI's amazing abilities.
Data sharing is compassionate
That extra-special time when data becomes that valuable: when information is traded and shared with others, and when businesses collaborate on data to achieve their goals. This is where data sharing is what makes all the difference. Remember that these goals aren't simply about growth and cost-effectiveness. An organization's fundamental raison in terms of sustainability and inclusiveness, may be dependent on its ability to not just share its data with the outside world, but also to tap into the relevant external data sources - because others care about data sharing as well.
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.
Whether or not the predictions of the experts come true, there is no question that the coming year will be yet another transformative one for business.
And the data is the driving force behind everything. As a consequence of this, having a mastery of data is the most essential capability!