Data Visualization: Buy, Build or Hybrid?7 min read

There are various ways to explore when satisfying the need for data visualization, but these ultimately boil down to three options. If you want to buy the solution, create it yourself, or mix the two? We’ve mentioned a few things to think about before making a decision.

Have you ever wanted a data visualization solution in your company and immediately assumed your key choice would be which visualization suite to buy — Tableau, Power BI, Looker, Alteryx, or something else? Unfortunately, when it comes to infrastructure updates, this is always the first thing that comes to mind.

The 5 Things You Need to Remember When Relying on Data Visualization |  Inc.com

I’d like to share what we’ve learned with you in the hopes that it will help you make a more strategic decision about your data visualisation needs.

As humans, we prefer visual representations of information. They can be visually enticing but still conveying a lot of detail succinctly. As a result, it’s natural that we want to use visualisations as often and as easily as possible. Many companies have expressed dissatisfaction with IT’s inability to provide solutions fast enough. They saw how easily the demo team did it. However, for these demos, sales teams use well-curated (pre-processed) data, which is usually sourced from a modern analytics platform. Creating a data visualisation solution, on the other hand, is a far more involving operation.

An Important Turning Point: Data Governance and Stewardship

We must take a detour before embarking on this journey. Consider it an intervention to alleviate dissatisfaction with data visualisation distribution and ROI realisation goals.

Remember that the initial investment should be in a modern analytics platform rather than a visualisation tool or suite. If you take this approach, you can greatly increase the odds of optimising ROI. Those well-equipped to handle your records, like any other commodity in your business, are required to do so.

You will also need to create a Data Governance and Stewardship (DG) organisation in addition to the technology investment. The complexity and structure can differ depending on the organisation, but the aim is the same: leverage data assets to optimise firm value.

Although DG deserves its own debate, I’d like to keep our attention on data visualisation. However, it is critical to emphasise the importance of DG because businesses often see it as optional. I believe DG is a must.

Data Visualization: Should You Buy, Build or Both?

When it comes to installing your solution, you have the option of purchasing a tool or suite, building it from scratch, or doing both.

Your organisation would have to evaluate and score multiple criteria to decide fit when determining which choice or mixture is best. Before we get into these details, let’s look at the discrepancies between buying a solution and building one.

As previously said, the most popular purchase options are usually Power BI, Tableau, Looker, and Alteryx, to name a few. Your build option, on the other hand, could require the use of Opensource libraries and resources such as d3.js, Plotly, Matplotlib, Leaflet, TimelineJS, HTML5, and JavaScript, among others.

These platforms are not an exhaustive list, but just a few options to explore. I’d really like to differentiate between engineering and configuration. Engineering is the process of creating new capabilities by improving fundamental or base code. The configuration is the method of changing built-in parameters to optimise the user interface without compromising core functionality.

Creating a solution allows consistency and power. Consider the following facets of versatility and control: concept, layout, and function roadmap. The flip side is your investment cost tends to be higher.

Aside from direct costs, you must also consider the expense of hiring limited resources, continuing repairs, and potentially unrealized costs attributable to a longer go-live runway.

The buying choice delegated to the vendor the engineering, key skills roadmap, and advanced strategic talent recruitment. You do, however, relinquish any creative control to the vendor. The trade-off is that you have access to the platform’s coding and analytics skills. Standing on your experience and capability will be incredibly useful when you embark on your data transformation journey.

Dissecting Your Main “Buy vs. Build” Decision

You must evaluate and rate the following considerations in the light of the pros and cons of the buy-build decision:

• The cost of investment
• Time to Market
• Resource capabilities: competence, talents, and ability sets
• Need for customization and application integration
• Audience: internal, external, or both
• Internal and external security concerns (GDPR)
• Relationships with third-party vendors

Let’s look at each of these variables in greater depth, starting with a few questions you can ask yourself:

Investment Cost

You should look at investment costs holistically. This covers the initial cost of setting up the solution as well as continuing repairs and upgrades. This expense can be perceived in both absolute and relative terms. The net cost is expressed in the absolute view. The relative perspective aims to address the following questions:

  1. What are your financial resources?
  2. And if you can afford it, is the value produced worth it?

Time to Market

Consider time to market in terms of whether you expect your data visualisation solution to be operational. Which choice would have the best results? What are the opportunity costs associated with the longer runway? What sacrifices do you make to reach a certain crucial timeline?

Resource Capabilities

This factor considers whether the necessary talent is already on board, and if so, what they must forego to deliver this solution. What are the cost of recruiting and onboarding additional resources if you don’t have the expertise or the bandwidth? What effect does this have on the timeline? How can you keep these tools completely committed until the answer is live? Can you work with consultants who can leave with vital knowledge of the solution infrastructure?

Personalization & Integration

To begin, customization considers whether the approach will be essentially homogeneous or will be tailored to different audiences. Who are your clients or consumers? What are their personality traits? What are the user experiences, capacity, and scalability like?

Second, integration takes into account whether the capability would be part of a web-based platform or a standalone dashboard, for example.

Audience

Is the audience for your data visualization internal or external? What is the size of your audience? What are their requirements? Is that a diverse client base? Getting both internal and external buyers can be a deciding factor in assessing whether the business requires a hybrid buy and build approach.

Security Concerns

This aspect focuses on designing technologies that satisfy both internal and external specifications (e.g., GDPR, CCPA). Is your organization okay with third-party vendors getting access to your records, and if so, how? Is there a provider who can have both functionality and security? Is the network capable of supporting ‘role and row’ type security? What are the authentication options?

Relationship with Third-Party Vendors

The partnership with a third-party provider is crucial to the successful execution of any project. Often programmes fail due to client-vendor relationships rather than the technology itself. First and foremost, do you already have a working partnership with a trustworthy vendor in this field? If not, keep in mind that, in general, you get what you pay for. Don’t neglect to evaluate customer experience and distribution capabilities. Finally, search for vendors who are professionals in their area, so they can guide you rather than just carry out your order.

Final Thoughts

Data Visualization is a topic with a long debate. However, I hope it gets you thinking about making an informed decision around your data visualization needs. Contact us to get more details.

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