A data platform is a total stack solution for end-to-end data processing, from ingestion to action. Data platforms are typically used to process very large data sets at scale without compromising on latency.
Companies in all industries are increasingly using modern data platforms to make information actionable and profit from it in real time. Data platforms can be designed for specific sectors or reasons. For example, there are telco data platforms, customer data platforms, as well as data platforms geared for healthcare, finance, public sector organizations, and manufacturing, among other industries, each geared to handle that particular industry’s challenges around processes and analyzing data.
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Difference Between a Data Platform and a Database
A database simply ingests and analyzes data. A data platform manages the full data lifecycle, from ingestion to action, and uses machine learning to apply recent and near real-time historical analysis to learn from every piece of data coming in and use every bit of data to determine if an event is anomalous or not and thus worth taking action on. A database is often a key part of a data platform.
Must Read: Event Driven Architecture Microservices
Main Advantages of a Data Platform
Now that we have our definitions straight, let’s turn our attention to some of the major reasons why organizations are increasingly investing in a new generation of data platforms.
1. Ability to do a lot of things with the same data, and do it fast
Companies need to be able to do lots of things with the same data now. It’s no longer helpful to have something that collects the data, something that stores the data, something that analyzes the data, something that renders dashboards based on that analysis, etc. That’s the fundamental difference between a ‘platform’ and a collection of point solutions.
The 5G era is officially here. Companies can use data platforms to achieve ultra-fast data processing speeds to capitalize on in-event—ie, as the data is being generated and at the place it’s being generated—opportunities around customer management, fraud prevention, mediation, and more. Most data platforms now enable organizations to achieve true real-time round-trip data processing within half a second, although with the combination of 5G and IoT, there are now parts of this process that need to happen within just 10 milliseconds.
2. Stack simplicity
Companies loathe to jettison their legacy technology are finding themselves in a tough spot right now: add more layers to the stack to be able to handle today’s speed and variety of data—at great risk of the entire stack failing and also at the cost of adding latency, since each layer add distance for the data to travel. Or replace their stack entirely.
A data platform, especially one that can easily enhance legacy technology, significantly reduces latency (and ultimately, overhead) through stack simplification. It can do this because it can handle a variety of functions, such as data aggregation, enrichment, and filtering, through a single database cluster, instead of having to perform each one of these tasks in a different layer.
3. Real-time decisions at scale
The proliferation of the internet of things (IoT) and machine-to-machine applications has increased the need for real-time decisioning. With all this data coming in, companies can no longer afford to spend as much time as they do on the data ingestion-to-decision process, because in that time, the website visitor will bounce or the fraudster will enter the network.
But the trick isn’t necessarily to make real-time decisions, it’s doing it consistently and at scale that most enterprises have trouble with.
Leading data platforms can ingest and process thousands of decisions per second while maintaining state, enabling organizations to truly scale data collection, automation, and most importantly, decisioning.
Types of Data Platforms
Data platforms have been around for many years. The underlying technology has evolved considerably over the last two decades, and today there are many options to choose from. A key element of any data platform is, of course, the database, and database technology has evolved rapidly over the last five years.
SQL vs. NoSQL
Up until the late 1990s, most data platforms relied on the Structured Query Language (SQL) for accessing and managing data.
This changed with the advent of “not-only SQL” (NoSQL) and the emergence of open source data platforms designed for handling large volumes of data and storing both structured and unstructured data.
Despite all the hype surrounding NoSQL, the technology is falling out of popularity because NoSQL databases come with some major drawbacks. Chief among them is the lack of ACID properties, resulting in inconsistent transactions at scale, and a lack of consistency.
Looking beyond NoSQL
A growing number of engineers are realizing the pitfalls of NoSQL and seeing firsthand how using it can weaken a tech stack instead of strengthening it. As such, many companies are switching to platforms that support SQL and SQL-like technologies.
In-memory NewSQL data platforms, for example, offer real-time decisioning and actions while also enabling wider querying and more complex event processing.
NewSQL solutions are much more scalable, faster to develop, and easier to deploy. They also maintain ACID compliance, meaning they can analyze events as they happen without compromising on data accuracy.
Simply put, all of this functionality is critical for agility, innovation, and success in the 5G economy.
How to Choose the Best Data Platform
Selecting the right data platform isn’t easy—especially when considering how fast the world of 5G is changing the game for data platforms. It takes a lot of time to research your options and zero in on the solution that meets your needs the best.
At the end of the day, it’s not about finding the best overall data platform on the market. Rather, it’s about implementing the data platform that enables you to achieve your particular business objectives around how you want to use your data.
1. Round up key stakeholders
As you begin searching for a data platform, start by rounding up all key stakeholders — including engineers, product managers, customer service leaders, and anyone who may have a hand in the data collection and distribution process. Get their intel and document it to know where your gaps are and the options available to fill them.
2. Figure out what data platforms are in use
Chances are your organization is already using data platforms for various purposes. As the saying goes, if it ain’t broke, don’t fix it. Figure out what’s working well and identify areas for improvement, and if you can’t realistically use what you already have, or see it becoming too risky or expensive, begin to look for outside help.
3. Outline specific goals
The next thing to do is to outline basic goals for specific use cases. For example, an enterprise may need a way to enable in-event data analysis to supply customer service teams with data to upsell customers during calls. Or, a manufacturing facility may want to expedite data processing to identify production variances early on and shut down assembly lines to prevent waste and reduce accidents.
In both cases, an in-memory data platform could be extremely beneficial.
At the same time, some use cases may be better suited for pure NoSQL deployments (e.g., social media and streaming services). However, this is not always cut and dried.
While NoSQL is typically advertised as a champion for processing unstructured data, some NewSQL systems can handle unstructured data as well.
4. Decide if open source is right for you
Organizations must decide whether to embrace open source software or use closed-source, proprietary solutions.
Open source data platforms typically promise cost savings, access to an engaged community of contributors, and freedom from vendor lock-in. However, open source platforms can also cause a lot of problems—including significant hidden costs and security issues. What’s more, open source licenses are often sold as free services. But vendors often try to convert customers to paid plans, which can be frustrating.
Data teams should talk through the pros and cons of open source to determine what’s best for the company.
5. Test different vendors
Once you nail down the basic preliminary requirements, it’s time to begin researching and testing different vendors. Lay the groundwork for the exact metrics and performance you need—for example, in terms of TPS—before reaching out directly to companies.
After you’ve narrowed your options down, it’s time to begin vetting vendors. IT decision makers should do their due diligence and research as much as possible to get a strong sense of a vendor’s capabilities. This is not an area where it pays to rush or take the least expensive option.
It’s also a good idea to run pilot tests and try various data platforms before deploying them at scale. This way, IT administrators can find out what works while gaining buy-in from engineers and end users.
Volt Active Data—the Data Platform Built for 5G
Volt Active Data enables enterprise-level companies to innovate faster, perform better, and create new revenue streams by unlocking the full value of their 5G data.
The only data platform built for real-time, sub-10 millisecond decisioning, we empower companies to re-engineer their latency-dependent solutions to process more data than ever before at a faster pace than ever before, allowing them to not just survive but thrive in the world of 5G, IoT, and whatever comes next.
By combining in-memory data storage with predictable low-latency and other key capabilities, we can power BSS/OSS, customer management, and revenue assurance applications that need to act in single-digit milliseconds to drive revenue or prevent revenue loss, without compromising on data accuracy.
To learn more about how Volt Active Data can help your organization unlock the full promise of the 5G era, take Volt Active Data for a spin today.
