Business intelligence and data insights solutions depend on the right technologies and making your data readily accessible to your entire team — on demand.

Today’s businesses generate an unprecedented volume of data. This continuous stream comes from multiple sources, from internal systems and online ads to click data and order fulfillment. What this means is that all companies are now, in effect, data companies.

Properly utilized, all that data has the power to reveal key insights and drive smarter business decisions. As a result, savvy businesses treat their data as one of their greatest assets. 

But if yours is like many organizations, it’s probably more accurate to say that your data has the potential to be one of your greatest assets. That’s because many companies — including Fortune 500s — still don’t leverage their data as fully and effectively as they could.  

Why is that? And what can you do to ensure that your organization treats its data as the priceless resource it is?

Smart data stumbling blocks: legacy models, data silos, and fragmentation

We’ve observed four primary reasons why so many companies struggle to realize the full analytic potential of their data. These problems don’t reflect a lack of motivation to use data to drive insights. Instead, they revolve around the ways organizations manage their data supply chains and thus make data available to their internal teams.

Legacy data management models

First, many companies still store and manage their data according to an outdated legacy model, one that predates modern cloud storage solutions. Before the advent of cloud-based storage, everything related to capturing, storing, and transforming data was much more expensive. The more data you stored on physical hard drives at a data center, the more money you spent. So it made sense to keep only the data you knew for sure you needed.

Companies that still operate on this legacy model are conditioned to capture and store less data. And less data eventually means fewer insights. But that’s not all. Legacy models are also slower and less flexible when it comes to actually plumbing the data. 

Modern, cloud-based data management solutions are completely different. We’re no longer limited by hard drives or storage space in a data center. Plus the price of data storage has plummeted. This totally changes the way companies can (and should) approach data management. After all, if data is one of your most valuable assets, and data storage is cheap, then it follows that you shouldn’t throw any of your data away — even data you don’t yet know you need. Doing so is like throwing away your competitive edge.

The business vs. IT data divide

There’s another common problem with legacy data management models. Within this arrangement, the IT team is traditionally tasked with building systems to collect, store, and transform data. The business teams, meanwhile, are the ones who actually analyze the data. This arrangement is less than ideal for a number of reasons. 

To begin with, there’s an inherent disconnect between the business and IT teams. Because the IT team doesn’t actually use the data to perform analytics, they don’t always build systems that optimally address the business team’s requirements. The business teams, meanwhile, often don’t have a solid enough understanding of how the data is structured. And this means they may not fully comprehend the very metrics they are using to identify meaningful trends and takeaways.  

Another problem with this setup is that reporting isn’t self-serve. Business leaders are forced to request data from the IT team, which can cause delays and bottlenecks. Often, by the time a report is generated, it’s too old to proactively inform current decisions. In addition, changes to reports and key metrics can be slow and costly, which leads to outdated reporting models.  

We believe that this gap can and should be bridged. As data analytics consultants, we help our clients put their data at the fingertips of their business leaders. And we help them derive key insights from that data, too.

Data fragmentation

Cloud-based data technologies are fast becoming the norm. But with so many new tools out there, it can be hard for companies to figure out how to differentiate between competing technologies and compose them into a complete solution.

To create an end-to-end data management system, you must layer several technologies into a holistic stack. You’ll need one solution for collecting raw data, another solution for storing it, and yet another to transform the data into a business-friendly format that can be visualized and analyzed. (You’ll likely need other tools, as well, depending on your firm’s specific data needs.) Beyond that, you’ll need those individual solutions to play well together. And you’ll need them to include the right functionalities to meet your business’s needs.

In converting to a cloud-based data management system, business leaders suddenly find themselves having to choose among hundreds of solutions for each layer of the tech stack. Without a deep understanding of what differentiates one vendor from the next, it’s all too easy to put together a mismatched assortment of tools that doesn’t harmonize into a holistic solution.

Many data analytics consultants are tool-agnostic. That means they leave these challenging decisions up to their clients. At DAS42, we have strong opinions about which tools make the most sense. We’ve tried dozens of these technologies. Along the way, we’ve identified a small set of best-in-class solutions that we believe in.

Narrowing down the technological options allows your team to focus on solutions rather than columns of seemingly identical feature sets. In addition, it enables us to support your team with our deep expertise of each technology in the stack.

Data silos and inconsistencies

As your company produces more and more data across different applications and streams, your data is also more likely to be siloed. 

For example, your Facebook Ads data may be siloed within Facebook, while your Google Ad Words data is housed within Google. If you want to get a holistic picture of your marketing spend, you have to combine both of those data sets. You’ll probably also want to merge them with your sales data, which may live with your sales team. And don’t forget your customer data, which is owned by your CX team. 

When data lives in various silos, it can’t be easily related or merged. As a result, your staff wastes time reconciling different numbers for the same metric. Manual data reconciliation leads to more errors. And it also acts as a disincentive to more frequent reporting. Even worse, it makes it hard for your organization’s cross-functional teams to speak the same language and measure success in the same ways. 

Making data operational with the fullstack philosophy 

One thing is clear: You can’t make your company’s data fully operational without the right set of technologies. And it’s equally critical that you make your data readily accessible to your entire team on demand. 

At DAS42, we have a distinct philosophy about how companies should approach their data to fully realize its value. We call it the FullStack Philosophy for two reasons.

First, we believe each company should own its data across the full stack of technologies, from collection and storage to transformation. Our recommended architecture facilitates that goal by creating a flexible, scalable, and trusted platform for enterprise analytics. 

The second reason is that our consultants are uniquely capable of working across the data supply chain from both the technical and business perspectives. In other words, we bridge the gap between your IT and business teams. We do this by building technical solutions that fluently speak your business team’s language.  

The FullStack Philosophy allows your company to: 

• Liberate your data from third-party vendors and data silos
• Own and centralize your data to answer deep questions
• Stop having discussions about your data’s reliability by creating a single source of truth with standardized metrics, reports, and KPIs across systems
• Enable product, sales, and marketing teams to answer their own data-driven questions on demand, without having to go through the IT team first 
• Make data operational as it is generated to better inform today’s and tomorrow’s business decisions

Want to learn more about the FullStack Philosophy and how it can help you unlock your data and drive better business outcomes? We’d love to talk.