Real-Time Data Trends Pushing Democratization

Real-Time Data Trends Pushing Democratization

Enabling the democratization of real-time data is not an easy task philosophically, organizationally or technologically.

In the early days of data democratization, the biggest barrier was philosophical and bureaucratic. Organizations of all sizes operated on the now-obsolete concept that certain people in specific departments had the access required to integrate data sources, manage data infrastructure, and run analytics software.

Once organizations broke free from this perception and started letting other business users run analytics on their data, regardless of their technical savvy, they began to unlock performance and data benefits. efficiencies they never thought possible. And many tools, like low-code/no-code analytics platforms, have emerged to meet a growing demand for visualizing data in a way that more people can understand.

But this change, however positive, was also short-sighted. For the most part, data democratization has focused on analyzing historical batches on stable, stored data. Think of people in marketing trying to figure out which version of their promotional material turned into the highest average lifetime value for the company. Or for customer service teams to understand, broadly speaking, how a new effort to document their APIs reduced the volume of help desk calls, made customers more proactive and profitable, and ultimately improved the company.

Download the infographic now: Manufacturing leaders' views on edge computing and 5G

The next frontier is the democratization of real-time data – the idea that everyone within an organization should have the access and tools to analyze and make sense of what is happening at present to make faster and more proactive decisions about their KPIs and overall goals.

See also: Can democratization save companies from drowning in IIoT data?

Here are some technology trends supporting this trend of real-time data democratization.

Automated integration tools: These tools relieve technical staff of being gatekeepers – or facilitators – to the masses of professional users who wish to connect platforms X, Y and Z together. Instead of manually connecting APIs or mapping fields through new code, automated onboarding tools use tools like AI to develop models that teams can then leverage to quickly de-silo their data.

Active metadata: Metadata is the context of information, such as creation date, source, tags/organizational tags, etc. In the past, metadata was a static resource and generally not considered as valuable as the data itself.

But with active metadata, there is a huge opportunity to apply machine learning (ML) or other automated processing techniques to large datasets in real time to ensure the data is interpreted correctly. New metadata techniques also help collect and cleanse data, helping business users focus on what’s really important.

Synthetic data: For organizations that want to refine their ML training or analysis algorithms, but don’t have enough data (or the right data), synthetic data could be a huge opportunity. Synthetic data involves generating new artificial datasets, based on a real-world “seed,” to diversify and expand the possibilities for testing theories and removing harmful biases.

Data Service and Delivery Layers: We have all heard of data warehouses and data lakes, but the data lakehouse, which is a new data management layer built from open source technology. The Lakehouse enables organizations to store all structured and unstructured data, but with the powerful combination of low-cost storage from data lakes and ACID-compliant transactions from warehouses. Early adopters like Disney, Twitter, and Walmart are finding huge benefits in reliable data storage and quick query.

Data structures: When building a modern layer of data storage and analytics, many organizations find themselves in the opposite situation: their data is not siled, but they have too many technology tools to make sense of it. Data catalogs, knowledge graphs, preparation layers, recommendation engines, etc.

The Data Fabric is a unified data delivery platform that hides all the complexity and exposes data in formats suitable for businesses, regardless of where it comes from. Add a few semantics and governance rules, and you have a powerful way to expose data with all the right cues.

Extensive data, not big: Every organization is relentlessly focused on sucking in more and more real-time data, but that can come at the expense of variety. Big data is the idea that organizations should take advantage of integration, source and analysis tools that do not distinguish between internal, external, structured and unstructured data.

Why variety? Most AI-based apps simply can’t function without it, which is exactly why synthetic data is just above at #3. The more variety an organization has in its real-time data set, the more likely it is to find interesting new correlations or be able to validate the quality of what it is already collecting.

Download the infographic now: Manufacturing leaders' views on edge computing and 5G

Knowledge graphs: Most real-time structured data comes in the form of tables, columns, and rows, like you’ll find in any relational database. It’s useful if you have tons of SQL experts on hand to write new queries, but it’s also counter to the democratization of real-time data.

Knowledge graphs rely on a graph database, which stores nodes and edges of data (i.e. relationships between nodes), to create a knowledge “map” of a organization using a flexible schema and support for structured and unstructured data. Anyone can now follow this map, with easy-to-understand contextual details and easier queries, to create new visualizations or more efficiently create insights from incoming data.

Enabling the democratization of real-time data is not an easy task, from a philosophical, organizational or technological point of view. At a time when 75% of business leaders don’t trust their data but want more value from the current cost of storing that data, we’ll likely continue to see technology changes at the controls. Once they have the right tools, whether it’s a lake house, fabric, or agnostic integration, they’ll find a way to get their employees to follow suit.

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