The new fragmentation ravaging companies

The irony is striking. While organisations have prioritised unifying their data, many have unwittingly opened the door to a new form of fragmentation—exposing them to risk and sabotaging their progress.

Modern organisations have invested heavily in breaking down data silos through cloud-based platform solutions. But not all platforms are created equal. Many popular platforms begin to show cracks when an organisation scales to complex queries or large amounts of data, especially unstructured data—forcing the business to cobble on more solutions as a fix. This only re-creates the fragmentation they were trying to solve.?

Today I’ll tell you how to avoid this trap and create a unified data ecosystem to power your data analytics and AI tools, once and for all.?

AI only adds value when it uses unified, quality data.

Fragmented, inconsistent, and siloed data is the foundation for poor AI—and so it’s no wonder that enterprises have doubled down unifying their data in the AI era.?

It’s been a monumental effort. Many organisations started by moving their data and workloads to the cloud. But they soon realised that simply putting everything in the cloud doesn’t actually solve for fragmentation, and they’re still shackled with the same issues. It’s obviously still worth migrating to the cloud, but true modernisation requires a more intensive effort.

To truly take advantage of the benefits of cloud for data, AI, and networking, technology leaders migrated to data platforms—like BigQuery, Snowflake, Databricks, and Azure. These platforms use data architectures that enable organisations to gain visibility into large amounts of both structured and semistructured data, which is essential for AI models and advanced analytics. And out of the box, these major platforms help enterprises keep their data safe and compliant.

However, enterprises are managing an exponentially increasing amount of data with more complex AI and analytics tasks. Especially with the need to process unstructured data, the data that brings much-needed context to customer engagements, conversations, and product development roadmaps. And not all of the platforms are able to keep up. AI is a huge topic, I’ll dive into this more in my next post.

Fragmentation 2.0?

In my last article, I talked about the importance of empowering everyone in your organisation with the right data. To make it happen, your IT team has to build a platform version that gives a certain team access to the data and insights they need. But inevitably, a different group needs access to different data, and so another platform version is built. And again, and so on…?

Is this looking familiar? Hello, Fragmentation 2.0.

This particular type of platform sprawl happens when a platform doesn’t come equipped with the user access controls that an organisation needs. And it’s not limited to data access.

If a solution doesn’t come with the functionality an organisation needs, they are forced to add on another package for additional features. Before they know it, they’ve recreated the same piecemeal, bolted-on infrastructure and data access they were initially trying to avoid. This, of course, leads to even more challenges, including:

• Performance bottlenecks, especially with complex queries, large data sets, or during peak periods
• Dependency on an ecosystem of infrastructure-specific tools
• Increasing complexity of user access control as the organisation scales
• Increased dependency on expensive and scarce specialist skills

And this leads to:

• Insufficient visibility
• Increased security risk
• Compliance issues
• Inefficiency, in both cost and resources

Clearly, fragmentation is still a problem that needs to be addressed.

Overcoming Fragmentation 2.0

This fragmentation of data and platforms creates a disjointed view of risk, making it challenging for CROs to gain a comprehensive understanding of the organisation's overall risk exposure. And patching these vulnerabilities can require complex security configurations and ongoing monitoring—the antithesis of proactive, built-in risk management.

Clearly, continuing with the status quo isn’t acceptable. So, how can a CRO mitigate this risk? There are two options: keep tinkering with their current platforms or start from scratch.

Many organisations are tempted to keep the platform solution they have, and continue to add on more functionality. This path is well trodden because there is rarely a single trigger point that universally declares the platform has become too fragmented. Sure, tasks may take a bit longer to run. And there is an increased degree of risk. But how slow is too slow? How much risk is too much to bear? Unless a major security breach occurs, the glitches are all too easy to sweep under the rug of ‘how things are’.?

The alternative is daring. It will ruffle feathers. But it will deliver better long-term results. Brave data leaders are scrapping their current systems and starting fresh with a platform built with data unification at its core. They often receive a bit of flack—internal stakeholders are right to ask, ’didn’t we just get a new platform 3 years ago?’. Well, 3 years ago, the world looked rather different, didn’t it??

Let’s get things straight. No one wants to build a new platform. But in this time of tremendous innovation, exponential data growth, and AI, the wrong platform stops your enterprise from moving forward.?

This is the time for CROs to take bold decisions and affect enterprise-wide technological change.?

I wrote this article because over and over again, I’m seeing enterprises who moved to new platforms a few years ago, and now they’re noticing that those solutions just aren’t scaling up.