Ever-Bigger Big Data

With increasing migration to the cloud, and a growing need to bridge diverse architectures and reduce data silos, the pharma industry is giving software architecture a hard look.

The sheer quantity of data companies collect is – of course – still growing. It’s an insatiable beast.

According to Forbes:
“The amount of data we produce every day is truly mind-boggling. There are 2.5 quintillion bytes of data created each day at our current pace, but that pace is only accelerating with the growth of the Internet of Things (IoT). Over the last two years alone 90 percent of the data in the world was generated. “

In the pharma industry, much of the attention is on Big Data – data that, according to Gartner, contains greater variety arriving in increasing volumes and with ever-higher velocity. Big Data is typically measured in terms of those “three Vs”: volume, velocity, and variety.

ZDNet captured it well: “Once you start talking about data in terms that go beyond basic buckets, once you start talking about epic quantities, insane flow, and wide assortment, you’re talking about Big Data.”

The arrival of Big Data has proven a major disruptor for the life sciences industry – impacting everything from business intelligence and healthcare outcomes to drug discovery and marketing. But it has also shone a spotlight on the need for better harmonized software & infrastructure architecture to ensure maximum value can be extracted from that data.

Data in Isolated Stacks

For most life science companies, leveraging all of that data to make informed business decisions has proven a challenge. One of the barriers? Data caught in silos.

Data silos

Isolated and disconnected data sets are a growing complication for any data-driven company or organization. Silos emerge when data resides in very different systems, with isolated architectures or stacks, and incompatible modes of communication and sharing.

Software connectors to overcome these barriers have become essential to the pharma software development space. Avoiding the need to retool platforms outright, custom coding can create bridges between diverse platforms for data-sharing.

For example, we recently completed a project bridging an off-the-shelf Member Management Portal developed on top of SalesForce with an off-the-shelf SaaS reporting tool.

The Best Software Development Architecture for Pharma

Organizations confronting Big Data have raised questions (and we’ve fielded many ourselves) around how to select the best software infrastructure in order to keep up with the need to capture, manage and harness data.

The answer: It depends.

Different organizations can have very different software solutions. By way of example:

  • Company A may be confronting issues of scalability with their current systems, platforms & processes, but needs a bespoke solution to maintain highly-customized workflows.
  • Company B suffers from fragmented, siloed data that needs to be structured or integrated for further analysis. They have systems which do not talk, and need to create bridges between the platforms.
  • Company C wants to simplify existing workflows, or automate tasks. They are open to adapting their current operations to an enterprise solution.
  • Company D simply needs to combine data from their public website and their CRM to improve sales targeting.
  • Company E needs to migrate fully away from an existing platform that isn’t flexible enough to adapt to changing industry standards and compliance practices.
  • Company F uses a platform that is no longer being appropriately updated and patched.

Each of these represent a different audience, with potentially very different needs.

There are, however, commonalities they all must consider.

What Really Matters When Selecting Software Architecture

Here’s what really matters when it comes to software development: the selected underlying technology is not nearly as important as how it is applied.

Good software architecture, built to interface with your other existing systems (both custom and off-the-shelf), makes it easier for your team to do their jobs. It creates efficiencies, it improves & simplifies compliance, and it empowers your organization’s decision-making.

Proper software and infrastructure stack selection is essential. There are a number of variables involved in the final selection (e.g., existing systems & platforms, overall software development objectives, long-term scalability), and the choice can impact everything from operating efficiencies and R&D collaboration to key business decision-making and even patient health.