Although analytics has been around for a long time, business leaders and enterprises started becoming aware of the massive potential big data analytics has in the past decade or so. We can attribute this to the massive amount of data that businesses started collecting at the time the utility of the internet was expanding, or the advancements in technology that have made it possible to take advantage of big data analytics. Data analytics has helped businesses and organisations make use of these vast amounts of data and also use them for decision-making by drawing actionable insight from them.
Data Analytics Is The Future
As we move on to a new era, it has become increasingly clear that anything to do with data, be it collecting, analysis, management, or visualisation, will see its importance increase. We live in an era of cloud architectures, design patterns, and information growth that have all raised the stakes for businesses and organisations seeking to take advantage of any or all three.
While no one can predict what the future of data and data analytics holds, some trends point us towards what we can expect. In this article, we will be looking at some broadly identifiable trends that can help us forecast where the future of data and data analytics is headed.
Artificial Intelligence and Machine Learning Will Keep Growing
Some of us might be disappointed that the flavour of artificial intelligence we have today might not be what was predicted in sci-fi movies of the past era, but there is no denying that it has become an important part of our personal and professional lives. We cannot also ignore the pivotal role that it is playing in data analytics.
Machine learning is perhaps the most recognisable strain of artificial intelligence. The algorithms used in machine learning applications help us collect, distribute, analyse, and process amounts of data that we would not have just two decades ago, not promptly anyway.
Machine learning algorithms have become such an important tool because they learn from their past work and the data that passes through them. This means they are always undergoing self-refinement and improvement, meaning they are always getting better.
These refinements and improvements are the reason for the improvement in the quality of results we can get from machine learning tools. The quality of these results also continues to improve as the algorithms behind them do so too.
Additionally, they both unlock new potentials for uses like prescriptive and predictive analytics. Both of these are already helping businesses use machine learning to predict the behavior of individual customers or whole demographics, for market forecasts and predictions, to do market research, and help improve strategies in areas such as marketing, sales optimisation, and advertising.
With all these uses, it is not surprising to learn that the use of artificial intelligence and machine learning for data analytics is a trend that is predicted to continue growing for decades to come.
To take full advantage of these technologies, enterprises, and organisations will have to scale up machine learning and artificial intelligence operations. This is in response to the expanding volumes of data that they will continue to collect.
This will be especially important when you consider that we are always increasing the number of data sources businesses have access to, which directly translates to more data collected.
More Data in Various Cloud Architectures
We are already seeing numerous enterprises moving their data and the applications they use to the cloud.
Less than 25% of all businesses will have physical databases by the end of this year, with this remaining percentage predicted to take up cloud services in the coming year. Gartner, a research and consulting firm, says that over half of all revenue in the database market will come from cloud database management solutions by the end of this year.
There are reasons for keeping on-premises data because there are lots of amazing solutions for that, but it is unlikely that the migration to the cloud will halt or even reverse.
The variety and types of data being moved to and from the cloud point to enterprises using one of three solutions and architectures. The three main types of cloud architectures that pertain to this discussion are intercloud, hybrid, and multi-cloud architectures.
Some are using multiple cloud solutions from the same cloud service provider for their multi-cloud solutions, some are linking multiple public clouds to form intercloud architectures while others are using a hybrid model that uses cloud and on-premises resources at the same time.
All of these architectures have shown to be great strategies for businesses because they can move workloads to the cloud that better serves that workload.
There are also cloud service providers who now cater to specific business needs. When it comes to data analytics, it is important to remember that there are cloud services that offer adequate or inadequate computing power for enterprise needs.
However, this is not something that many businesses need to worry about because most of the available solutions have enough power for analytics. The most important thing to remember is that regardless of the architecture, the one chosen should ensure data analytics tools are cloud-ready.
A Growing Demand for Chief Data Officers and Data Scientists
As more enterprises look to leverage the power of data and data analytics, there will be an increase in the demand for people who work with the data. These are the people who help make sense of this data through analysis so enterprises can use it.
According to the Bureau of Labor Statistics, there are not enough data scientists and chief data officers to go around. The Bureau reports that only about 60,000 to 70,000 individuals in their database reported that they had the title data engineer, data scientist, or similar.
This means that there is a lot of demand for individuals with these skills but not enough of them. Seeing the trends, it is unlikely that the demand for data scientists will diminish any time soon. When you combine both of these, you can see that competition for these individuals will be fierce.
According to MIT, businesses are starting to take note and are crafting ingenious solutions. One of these is encouraging training for their employees who are outside data science but who have the skills and knowledge of the technologies required to complete advanced computer science degrees that will turn them into data scientists.
There is also a trend of those who don’t have a computer science degree being encouraged to pursue a master’s degree. Some universities are offering online masters in computer science without CS undergrad degrees as long as you have a degree related to the field and that includes software engineering, object-oriented programming, data structures, and other computer science topics.
With these approaches, companies can create data scientists from within their workforce instead of relying on the broader market where they may not be able to find the right candidates for their needs.
This trend is likely to continue if the shortage of data experts and analysts persists, which every indication says it will.
The Popularity and Increasing Use of Data Mesh Design Patterns
To ensure they are getting the best results out of their data analytics efforts, enterprises must start thinking about how their data is arranged in addition to where it is stored. This arrangement is about architecture and delineations.
Some popular options that have arisen over the past few years include data warehouses, data meshes, data fabrics, data lakes, and data lake houses. Because all of these terms are confusing, we will be focusing on the most popular option, data meshes.
When using data meshes, the enterprise sets up its architecture such that all its data from different departments is controlled independently by the people who are responsible for it in the different departments.
This is different from siloing because although this data is controlled independently, it is still intertwined with other data and available to others who need it.
Since each data domain is helped in a different scheme, bottlenecking is never an issue as is often the case where data is held centrally. Each data schema and domain can have its own rules for governance, but these rules must be crafted so that they support and encourage interoperability and can be used across the organisation.
The main advantage of a data mesh is that it allows for quick creation and delivery of data products no matter how complex they are while also allowing for these products to be shared easily. These advantages are crucial for businesses that rely on collaborating teams within the business.
Data mashes do have some downsides such as data duplication, quality degradation, and control issues. However, large enterprises that have large amounts of data or processes can leverage all the data they have available to them easily.
Conclusion
These trends are not the only notable ones in data analytics, but they are the ones that businesses should be keeping an eye on for the foreseeable future. This is especially if they want to keep taking advantage of them and find new ways of leveraging the vast amounts of data they collect.