Data Visualization: Buy, Build or Both

When fulfilling your need for data visualization, there are numerous options to consider, but those ultimately boil down to three choices. Do you want to purchase your solution, build it in-house or hybrid the two? We share a few factors to consider before deciding.

Have you ever needed a data visualization solution in your organization and automatically jumped to thinking your main decision is which visualization suite you should purchase — Tableau, Power BI, Looker, Alteryx or something else? Unfortunately, this tends to be the first line of thinking when considering a technology update.

I also used to think this way, but I grew my understanding of data and tech, my thoughts changed. Now, I want to share what I learned with you, and hopefully, it will help you approach your decision about your data visualization needs more strategically.

As humans, we tend to like visual presentations of information. They can be aesthetically appealing and succinctly convey a lot of information. So, it is understandable we try to use visualizations as frequently and quickly as possible. I have seen many businesses becoming quite frustrated with IT not rolling out the solutions quickly enough.

They saw how the demo team did it so effortlessly. But the sales teams for these demos use well-curated (pre-processed) data typically sourced from a modern analytics platform. In reality, creating a data visualization solution is a much more involved process.

An Important Detour: Data Governance and Stewardship

Before we get into this journey, we must take a detour. Think of it as an intervention to mitigate frustration with data visualization delivery and ROI realization expectations.

Remember, your initial investment should not be in a visualization tool or suite but rather a modern analytics platform. If you take this approach, you will significantly improve your opportunity to maximize ROI. Like any other asset of your enterprise, those well equipped to do so need to manage your data.

In addition to the infrastructure investment, you will need to create a Data Governance and Stewardship (DG) organization. The scope and structure will vary by company, but the goal is the same: manage data assets to maximize value for the firm.

DG warrants its own discussion, but I want to keep our focus on data visualization. However, it is essential to highlight DG’s significance since companies often view it as optional. I submit DG is a must.

Data Visualization: Should You Buy, Build or Both?

When it comes to deploying your solution, you can purchase a tool or suite, build it or both.

When deciding which option or combination is ideal, your company will have to assess and score several factors to determine fit. Before getting into these factors, let’s consider what buying a solution versus building one entails.

As mentioned before, your most common buy options are typically Power BI, Tableau, Looker and Alteryx, to name a few. In contrast, your build option could entail the use of Opensource libraries and tools like d3.js, Plotly, Matplotlib, Leaflet, TimelineJS, HTML5 and JavaScript, among others.

These platforms are not a comprehensive list but rather a few solutions you can consider. I also want to make a distinction between engineering and configuration. Engineering refers to developing new capabilities through the enhancement of core or base code. Configuration refers to adjusting built-in parameters to enhance the user experience without any changes to core capabilities.

Building a solution offers flexibility and control. Think of flexibility and control broadly: design, architecture and feature roadmap. The flip side is your investment cost tends to be higher. Besides direct cost, you also must consider recruiting scarce talent, ongoing maintenance, and potentially unrealized costs due to a longer go-live runway.

The buy option transfers the engineering, core capabilities roadmap and advance technical talent recruitment to the vendor. However, you transfer some creative control to the vendor. The tradeoff is you get to leverage the coding and analytics expertise embedded in the platform. Standing on that knowledge and capability can be invaluable on your data transformation journey.

Breaking Down Your Main “Buy vs. Build” Decision Factors

In the context of the pros and cons of the buy-build decision, you need to assess and score the following factors:

•          Investment cost

•          Time to market

•          Resource capabilities: competence, abilities, skillsets

•          Need for customization and application integration

•          Audience: internal, external or both

•          Security concerns: internal and external (GDPR)

•          Third-party vendor relationships

Let’s explore these factors in more detail, looking at a few questions you need to ask yourself regarding each:

Investment Cost

You should view investment costs holistically. This includes the cost of initially standing up the solution as well as the ongoing maintenance and upgrade. There is both an absolute and a relative view of this cost. The absolute view speaks to your total cost. The relative view focuses on answering these questions:

1. What can you afford?

2. Even if you can afford it, is it worth the value created?

Time to Market

View time to market in the context of when you need your data visualization solution up and running. Which option will best deliver? What are the opportunity costs of the longer runway? What compromises do you need to make to fit into a particular critical timeline?

Resource Capabilities

This factor considers whether the talent you need is already on board, and if so, what do they need to forgo to deliver this solution. If you do not have the skill or the bandwidth, what is the cost of hiring and onboarding new resources? How does this affect the timeline? Once the solution is live, how would you keep these resources fully engaged? Do you use contractors who could leave with critical knowledge of the solution infrastructure?

Customization and Integration

First, customization considers if the solution would be largely homogeneous, or will you tailor it to different audiences? Who are your consumers or customers? What are their behavioral characteristics? How is the user experience, capacity and scalability?

Second, integration considers whether the capability will be part of a web-based application or merely a standalone dashboard, for example.

Audience

Is your data visualization audience internal or external? How large is your audience? What are their needs? Is it a highly varied customer base? Having both internal and external customers may be a critical factor in deciding that the firm needs a combined buy and build solution.

Security Concerns

This factor centers around building solutions compliant with internal and external requirements (e.g., GDPR, CCPA). Is your company comfortable with external vendors having access to your data and how? Is there a vendor that can deliver the functionality as well as the security? Can the platform support’ role and row’ level security? What are the authentication capabilities?

Third-Party Vendor Relationships

The third-party vendor relationship is significant to the successful delivery of any project. Many projects struggle because of client-vendor relationships and not the technology itself. First, do you already have a working relationship with a strong vendor in this space? If not, remember, as a general rule, you get what you pay for. Don’t forget to assess customer support service as well as delivery capabilities. And lastly, consider vendors that are experts in the field, so they can consult you rather than just implement the ask.

Conclusion

I couldn’t highlight everything you should consider when deciding between buying or building a new data visualization solution. However, I hope it gets you thinking about making an informed decision around your data visualization needs.