A data platform is a complete solution for ingesting, processing, analyzing and presenting the data generated by the systems, processes and infrastructures of the modern digital organization. While there are many point solutions and purpose-built applications that manage one or more aspects of the data puzzle effectively, a true data platform provides end-to-end data management.
A data platform is more than a business intelligence platform. While the latter incorporates relevant data to improve a business's decision making, a true data platform manages more types and structures of data across the enterprise, including not only the data used to ensure security, privacy and compliance, but also IT and technical operations data — all the data your business ingests or generates.
Today’s businesses can build an infrastructure drawn from thousands of applications and services to meet specific needs, but individual solutions are often unable to integrate with each other effectively. The result is silos — data sets that can’t be shared with other teams and for other purposes within larger organizations. That frustrates the power of data analytics to identify challenges and opportunities.
One of the many benefits of using a modern data platform is centralization — a single platform that can be used across an entire organization, preventing silos and providing actionable insights based on a holistic view of the organization’s data. But to do so effectively, the one must be able to take in data from nearly any source without introducing additional complexity. Simply put, a data platform should improve your organization’s ability to learn from and act on any and all of your data.
Below, we’ll dig deeper into what defines a data platform and the qualities you should look for when choosing one for your organization.
What is a Data Platform: Contents
A “big data platform” is no different than a “data platform” — both are intended to handle data at scale.
The concept of “big data” was popularized in the 1990s, when the volume of data generated by humanity started on a path of exponential growth. But at this point, all data is big data. Individual consumers have access to hardware and cloud systems with petabytes of storage. Professional organizations — businesses and public sector alike — are generating staggering amounts of data. IDC estimates that by 2025, there will be 163 zettabytes of data in the digital universe.
There are three core characteristics that define “big data” (which we can call “data” going forward):
Because of the extraordinary growth along these three vectors, any data platform that can keep up with current organizational demands can be considered a big data platform.
The advantages of an enterprise data platform revolve around the combination of end-to-end features that replace point solutions previously used to provide data services. Many organizations make do with operational data stores (ODS), data warehouses (DW) or data marts (DMs) that may not work together effectively and limit the ability to scale. An EDP integrates the capabilities of those solutions and brings all the data into one place, where it can be secured, shared and used most effectively.
An EDP offers other significant benefits to large organizations, including:
In essence, an effective enterprise data platform will let you work with any and every data set, regardless of what it is, where it is (structured database or vast data lake), or how much of it there is. And at a speed, and with a degree of trust, that gives you actionable, real-time insights.
A data architecture is essentially a framework for an organization’s data environment. A data architecture is not a data platform. A data architecture is the plan for ingesting, storing and delivering the data, while the data platform is the machine that accesses, moves, analyzes, correlates and validates data for end users.
That’s the importance of a solid data architecture — it’s the backbone of a data-driven organization, the robust infrastructure that supports its existing data requirements and scales to match data and infrastructure growth. With technologies like edge computing and the Internet of Things (IoT), solid architectural principles are increasingly important.
A modern data architecture is built with these three characteristics in mind:
A data strategy is a plan for how an organization will gather, store, secure, manage, analyze and share data and use it to meet organizational goals. It is a central, integrated concept that articulates how data will enable and inspire business strategy. A company’s data strategy sets the foundation for everything it does related to data.
Every organization will have a different data strategy, but a typical data strategy will accomplish the following:
A data strategy combines data science with business objectives, and should be specific and include tactics for implementation, but it should also be flexible enough to adjust to fast-paced changes in the market.
Choosing the right data platform comes down to six core considerations: on-premises vs. cloud, scalability, flexibility, usability/breadth, security/compliance and intelligence/automation. Driving all of them is the essential consideration that you want to be able to work with any data in your organization; regardless of source, format or time scale, you want to be able to ask any question and get actionable insight.
In the future, data platforms will need to handle data sets of greater velocity, variety and volume, while allowing a range of users — from data scientists to business managers — to bring real-time data to every question, decision and action. A data platform must allow users to investigate, monitor and analyze data — and take effective action based on the insights revealed.
Splunk believes that as new technologies bring more data, in more formats, data platforms will have to evolve as well. To meet the challenges of the future, data platforms will need to integrate “smart” technologies — machine learning and artificial intelligence — to proactively assist organizations with their data-related goals. The Splunk approach allows you to complement the expertise of your organization and data with AI and machine learning for enhanced effectiveness and productivity, across industries, use cases and skill sets. Learn more about our approach with the Splunk platform.
With so many options available, choosing a data platform can seem like an overwhelming prospect. Set aside the enormous selection and the various labels for products, services and solutions, and approach the search by starting with your needs: