The Future of Big Data

The future of big data in a cloud world

Talking about the future of big data is somewhat beside the point, because it's very much a "here and now" phenomenon. Many market leaders are already using big data and analytics in ways that seem futuristic to their lagging competitors but are actually contemporary, albeit future-minded. Such strategies may include everything from using hybrid cloud deployments — to separate sensitive, on-premises data from daily workloads — to establishing complex data fabric architecture.

These forward-looking companies have begun to define their big data futures in meaningful ways. Yet as impressive as these programs sound, they really only scratch the surface. Consider, for example, that there could be up to 74 trillion gigabytes — or 74 zettabytes — of data created worldwide in 2021, according to projections compiled by Statista. That would constitute a sizable increase from the approximately 59 zettabytes in 2020 and 41 zettabytes the year before. Our perspective must broaden to reckon with the scope of big data.

Many of the questions about current big data trends and its burgeoning future are queries focused on leveraging the value of these vast amounts of information as quickly as possible. While this bottom-line concern should not be the only thing you consider as you move toward a more data-forward strategy, it can be a reasonable entry point for discussions about the future of big data at your company:

• How to start monetizing big data? Through actionable insights and opportunistic action. This can include everything from tweaking marketing campaign offers and looking for new strategies for increasing customer engagement to refining operations in production, accounting, R&D, and other departments.
• Who will use big data? Data scientists with years of experience, at the head of a big data analytics center of excellence? Function-specific business analysts? Big data ninjas, black belts, or all of the above? The ideal answer is not just "all of the above" but "everyone in the business," to some extent. Data science and its various applications should not be the sole realm of expert data professionals who focus directly on it at all times. There is plenty of room in the organization for those who Teradata calls "citizen data scientists."
• What new business problems can big data solve? Along similar lines, what new markets might it open? Beyond the surface-level question of "monetizing" data, it will also be critical to look at ways in which the business intelligence (BI) you analyze can lead to substantial and long-term improvements — developments that help bring true gains to your enterprise's bottom line.
• How will big data drive performance management? This is one of the areas in which big data analytics can be truly revolutionary: the development of better, faster, and more agile performance management models. For example, human resources can benefit greatly from the wide variety of key employee performance metrics that can be mined from large data sets and refined into prescriptive analytics. This, in turn, can help drive strategic improvement initiatives for personnel. Financial performance and regulatory compliance can also be quantified in new and useful ways.

At the root of all of these questions — and their possible answers — is the cloud. Without cloud technologies, big data would not be remotely as accessible as it is, and leveraging the cloud to further bolster the usefulness of data is a trend that will only intensify in the near future. Numerous cloud trends will play significant roles in the broadening usefulness of big data, including the increasing use of multi-cloud, hybrid cloud, and intercloud deployments. Enterprises are also becoming more and more comfortable with using the public cloud in conjunction with their on-premises infrastructure.

The cloud is instrumental in maximizing the value of enterprise data from both an internal and customer-facing perspective. This is especially true in a post-COVID world, with so many distributed workforces needing the essential tools of their job to function just as well at home as they did in the office. As for customers, their interactions with businesses are taking place via cloud-based applications more often than ever before, and they similarly expect speed and efficiency. Strategic container deployment, scaling, and management is of the essence to keep all of these cloud services running smoothly, and a cloud-ready analytics platform is equally critical to make sense of all the big data that apps are generating.

5 steps for seizing new big data analytics opportunities

For all of the reasons detailed above, having a sense of the possible — especially in relation to technologies such as the cloud and the internet of things — is of paramount importance. And the possibilities will be nothing short of astounding, blurring the lines of industries and fundamentally altering the way businesses interact with customers and each other.

In preparing for the future of big data, where should executives seeking tangible ROI tomorrow focus their thinking today? Groundbreaking future results start with five disciplined and incremental steps in the near, mid-, and long term.

1. Develop a strategic focus: Today, the most important question to ask is, "How can big data improve business performance?" It may be the most important question tomorrow as well. As such, it will be critical to bring on a chief data officer (CDO) or appoint a data scientist with equivalent experience to that role, if your enterprise hasn't already done so. This individual can spearhead your big data technology, storage, and architecture projects with a strategic approach, leaving the more scattershot efforts that were undertaken in the early years of big data in the dust.
2. Enable operationalization: Moving beyond speculative pilot projects is critical to reaching future scale with big data investments. As pointed out in Gartner's 2021 rundown of the top trends in the big data market, it will become necessary for data and analytics to become core functions of businesses. Instead of being managed by a specific team within IT, data should be a key element of how each of the company's departments carry out their day-to-day responsibilities. Developing and implementing this approach will be much easier if overseen by an experienced CDO.
3. Focus on integration and ecosystems: Holistic, big-picture views are necessary to knit together the right big data repositories. You must consider not only how best to use spaces such as a data lake or data warehouse, but also whether emerging approaches to data architecture, such as a data mesh framework, will be right for your organization. Additionally, you'll need to decide what data should be in the cloud and what should remain on-premises. This will help establish a flexible foundation for the future, with the highest value data readily accessible to the right users and well-defined business rules and governance structures in place.
4. Facilitate cultural shifts: Data-driven business operations and analytics-enabled decision-making processes must become the norm. Based on the direction we seem to be headed, this seems inevitable in the next generation, but the firms that get there first will have a decidedly competitive advantage. Also, this is yet another area of big data strategy to which your CDO or senior data scientists can make invaluable contributions.
5. Cultivate the right people: Having staff with the right skills is of the utmost importance now, working together in teams that have strong and purposeful leadership. Intelligent, resourceful, and creative data professionals will continue to be necessary in the future, especially as cloud storage and computing technologies continue to evolve.

Assessing the right cloud options for big data

Big data and the cloud are effectively joined at the hip. Businesses across virtually all sectors are recognizing this with increasing frequency and adopting cloud-first strategies for their big data and analytics needs, ranging from NBA basketball to discount e-commerce site Groupon.

You have multiple options in your search for the right cloud-first big data solution. If you find that one cloud service provider (CSP) offers everything you need, you can opt for a hybrid cloud platform that splits data between the CSP's public cloud offering and your on-premises infrastructure. By contrast, if you want to avoid vendor lock-in, have a large number of separate data sources, or have different types of data face vastly different regulatory standards, you might opt for a multi-cloud deployment: This could be any combination of Amazon Web Services, Microsoft Azure, Google Cloud, and other vendors, and also often involves at least some on-premises data storage as well. Last but certainly not least is intercloud, which is beneficial if you regularly need to move data from one CSP's cloud to another, though it's worth noting that this can become rather costly.

The right cloud architecture, when planned with your organization's unique ecosystem and goals in mind, can be scaled up or down as needed. Whichever deployment model you pick, keep in mind that you'll get the greatest value from your cloud data strategy by deploying it alongside an analytics tool that can run anywhere: cloud, on-premises, or via virtualized commodity hardware.

Stay ahead of the big data curve with a cloud-first approach

With the future of big data no longer an abstract ideal but a fast-approaching reality, it's time for your organization to welcome it, and make the most of it with the power of the connected cloud.

Your enterprise's big data is too important to manage improperly. As the volume, number of sources, and types of data you handle grows, this will only become more true.

To learn more about how Teradata can quantify and optimize your big data analytics in a connected cloud environment, connect with our team. Or delve into our customers' success stories by checking out customer stories.