Don’t data despair

In 2019, as data proliferates — from general information to real-time decision support, to robotics that drive cars — we’re struggling to translate that data.

Underpinning this challenge are some fundamental barriers — including accountability, classification, and expertise — that are set to become even more burdensome in the coming years.

“It’s the data, stupid”

One big problem that we’ve identified as being missing from many data-driven technologies is proper ability to filter and understand patterns and variances in data. In particular, there is a gap between current detection mechanisms for unstructured data, such as text, images, video, and other visual and auditory information, and the instruments available to understand such data, such as machine learning algorithms.

Likewise, there is a need for the appropriate classification processes for data in order to successfully process and understand data on premises, in the cloud, and/or in user interfaces.

To overcome these barriers, developers of real-time/spatial data platforms will need to first identify and break apart their data. Perhaps the smartest thing software developers are doing today is attempting to connect these disparate sets.

To achieve this goal, advanced technological techniques that engage two-dimensional, inferencing techniques to connect discrete layers of data in progress are emerging as a solution to address these data-related problems.

How will we fix this? There’s no easy answer. However, there are elements that can help in the construction of new data platforms, more effective data reporting and interpretation, and a better understanding of the corresponding development challenges. Given these realities, the first step is to define data requirements using inclusive criteria that are helpful to both data experts and data developers.

If you look at these terms, you’ll see that they’re not perfect, but they offer a place to start when in discussions about data. Organizations should also pay attention to AI as it begins to play a more prominent role in making data-driven decisions.

Here are some considerations for companies feeling a little overwhelmed by too much data:

1. Data is more than just data 

By reinforcing this assumption, organizations can more easily define their data requirements. Plus, they can establish processes to reach their data goals from a data perspective, with a focus on access, availability, and often, cost. Data is more than just about the network, data platforms and infrastructure. It’s about the insights.

2. Avoid scrambling for data with cross-platform, enterprise-class tools

Bringing disparate projects together will be less complicated if data is understood through reliable, specific data methods. Data developers should use data modelling to support the integration of diverse datasets. Interestingly, even data scientists are learning that it can be possible to approach data using different tools specific to any individual project.

3. Support data distribution and security

Let’s look at this from the other end of the spectrum. No matter how easy this gets, data teams are often required to work with a great deal of data that needs to be handled and/or analyzed. High-end data visualization tools make it easier to assemble data. But are they really necessary? In fact, with the explosion of self-service data discovery platforms, self-service data access is still the most valuable part of the ecosystem.

We’re only just beginning

For people willing to play around with new tools, one could argue that we’re entering a golden age of analysis. Whenever we see intense competition for attention, users always win. And a mass proliferation of tools is a virtual smorgasbord for data scientists wanting to get their hands dirty.

That being said, companies look for predictability, not chaos. It remains to be seen how things will play out, but one could expect that data platforms that can balance the deluge of new ideas with some sort of stability will have a huge market for the foreseeable future.

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