Putting down a foundation

12 October 2022


How to Build a More Data-Driven Foundation for Your Enterprise

There’s no denying that enterprise technology is accelerating at a rapid pace. From automated billing systems to segmented email marketing campaigns to 24/7 customer service bots, there’s no shortage of ways that tech has forever altered the business landscape. 

But, today’s enterprises are far from perfect, and there’s still plenty of opportunity for businesses to use the data at their fingertips to accelerate ahead of their competitors. 

In this post, we’ll walk through a few key opportunities for data to transform your enterprise. 

How to Create a More Data-Driven Future for Your Enterprise 

While there are virtually no areas the proper use of data wouldn’t advance, we’ve zeroed in on three opportunities where data-driven decisions will reap the greatest benefits for your enterprise. 

Opportunity #1 – Embed Data Everywhere 

The Way Things Are 

In most organizations, data is a siloed function—data scientists operate in their own team, separated from the rest of the business. They work on their models, they code, and those models are passed along to the appropriate team. 

With this model, there’s a lack of understanding on both sides—data scientists don’t understand how the business operates (thus their models don’t solve their target problems as well as they could), and businesspeople don’t understand how machine learning models generate insights. 

We’re not saying that data isn’t flowing throughout the organization. On the opposite—typically, businesses have mountains of data. The issue is that data isn’t being used to enable the entire organization to work together like a well-oiled machine.   

The Way Things Could Be 

The first issue that needs to be addressed is the silos that isolate data science teams and their technical knowledge. The key to fixing this? Upskilling. Upskilling means educating both teams so that domain experts have a basic understanding of data processes and machine learning, and data scientists understand the full scope of the business case their models are intended to solve. 

The next part is distributing and utilizing those mountains of data you already collect properly. Use data-driven techniques to resolve issues faster. Automate tedious tasks. Liberate your human workers to focus on higher-level work.  

In an ideal world, enterprises would use data in innovative, rather than routine, ways. No more reporting just for reporting’s sake. Instead, use your data to generate insights that can empower employees and improve your end-users’ experience. 

Opportunity #2 – Out with the Old, In with the New (Technology) 

The Way Things Are 

Currently, many enterprises are stuck in the past—and much like a college graduate who won’t stop talking about their “glory days,” it isn’t helping anyone. In fact, it’s more harmful than you might think. 

Outdated, legacy systems prohibit data from flowing seamlessly throughout organizations. Data engineers spend far too much time on data prep and manual processes, rather than innovating and creating algorithms to transform data into usable, actionable information. 

According to a recent survey by IDC, 54% of organizations remain mostly on-premises with some cloud-based systems mixed in. More than half of businesses are missing out on the flexibility of hybrid or cloud-only systems. 

When trying to adopt modern data architectures like HPC (high-performance computing), they run into roadblocks like high cost and issues with data security. 

The Way Things Could Be 

Ideally, businesses would leverage a network of connected IoT devices to transmit data and insights in real-time, enabling business process optimization and data-driven decision making. 

While misconceptions exist around the high cost of cloud-enabled technology (like Cloud HPC), many systems exist that can be scaled up or down depending on users’ changing needs. Cloud infrastructures that run HPC applications are often cost-optimized to be powerful yet flexible. And, as more providers compete to make even more flexible models, costs continue to decline. 

Adaptable data stores like time-series databases and NoSQL databases are great tools to modernize data architecture as well. Data historians, for example, can help manufacturers streamline insights, improve plant productivity, and reduce energy consumption.  

Opportunity #3 – Democratize Access to Data 

The Way Things Are 

Remember those silos we mentioned? Well, they’re not only impacting fundamental business knowledge from flowing freely across your enterprise. They’re so baked into the organization that they can also prohibit data scientists from getting access to the data they need to build specific models, or marketers from getting access to past customer data. 

In many organizations, it’s common that no one is “owning” particular datasets. Rather, one department (or individual, depending on the size of the business), governs access to everything—which is a clunky and inefficient method to approaching data governance. 

The Way Things Could Be 

Imagine if each dataset within an organization was organized according to its use and purpose. Sounds pretty ideal (and logical), right? In this way, entire teams could have self-service access to specific datasets that are relevant to their work. 

For this sort of access to work, a data governance model needs to be in place to ensure that proprietary data and customer data isn’t abused or available to anyone who doesn’t need it. Data lineage processes can help your organization set up checks and balances that allow you to trace any errors in data usage back to their source. 

From a project standpoint, development teams who leverage a CI/CD framework effectively democratizes the data science pipeline, ensuring that multiple people can work on data projects simultaneously. The end result is that only the highest quality models are deployed, and project owners have end-to-end visibility into how projects were developed. 

Wrapping Up 

Now is the time to create and communicate your vision for a data-driven organization. While getting the most out of your data is a top-down initiative, successful execution isn’t possible without buy-in across the organization. 

This is where culture comes into the equation—implementing a company culture that values the use of data to make difficult decisions, modify processes, and optimize communication is the key to unlocking your organization’s full potential. 

Need help getting started? We put together a step-by-step roadmap, A Leader’s Guide to Building a Data-Driven Culture, to making a meaningful cultural change. 

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