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 \u2014 Tableau, Power BI, Looker, QuickSight or something else? Unfortunately, this tends to be the first line of thinking when considering a technology update.<\/p>\n
I also used to think this way, but as 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.<\/p>\n
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.<\/strong><\/p>\n The sales teams for these demos use well-curated (pre-processed) data, typically sourced from a modern analytics platform and often end up doing an effortless demo. In reality, creating a data visualization solution is a much more involved process.<\/p>\n 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 return on investment (ROI) realization expectations.<\/p>\n Remember, your initial investment should not be in a visualization tool or suite but rather in a modern analytics platform<\/a>. If you take this approach, you will significantly improve your opportunity to maximize ROI.<\/strong> Like any other asset of your enterprise, those well equipped to do so need to manage your data.<\/p>\n 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.<\/p>\n DG warrants its own discussion, but I want to keep our focus on data visualization<\/a>. However, it is essential to highlight DG\u2019s significance since companies often view it as optional. I submit that DG is a must.<\/p>\n When it comes to deploying your solution, you can purchase a tool or suite, build it or both.<\/p>\n 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\u2019s consider what buying a solution versus building one entails.<\/p>\n As mentioned before, your most common buy options are typically Power BI<\/a>, Tableau, Looker, Qlik and Alteryx, to name a few. In contrast, your build option could entail using Opensource libraries and tools like d3.js, Plotly, Matplotlib, Leaflet, TimelineJS, Grafana, HTML5 and JavaScript, among others.<\/p>\n These platforms are not a comprehensive list but a few solutions you can consider.<\/strong> I also want to make a distinction between engineering and configuration. Engineering refers to developing new capabilities by enhancing core or base code. Configuration refers to adjusting built-in parameters to enhance the user experience without changing core capabilities.<\/p>\n 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.<\/strong> Besides direct cost, you also must consider recruiting scarce talent, ongoing maintenance and potentially unrealized costs due to a longer go-live runway.<\/p>\n 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<\/a> journey.<\/p>\n In the context of the pros and cons of your buy-build decision, you need to assess and score the following factors:<\/p>\n Let\u2019s explore these factors in more detail, looking at a few questions you need to ask yourself regarding each:<\/p>\n You should view investment costs holistically. This includes the cost of initially standing up the solution and 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.<\/strong> The relative view focuses on answering these questions:<\/strong><\/p>\n 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?<\/p>\n 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?<\/strong> Do you use contractors who could leave with critical knowledge of the solution infrastructure or reskill them to another tool?<\/p>\n First, customization considers if the solution would be largely homogeneous, or if you will tailor it to different audiences. Who are your consumers or customers? What are their behavioral characteristics? How is the user experience<\/a>, capacity, and scalability? Does the solution need advanced capabilities like AI\/machine learning or sentiment analysis<\/a>?<\/p>\n Second, integration considers whether the capability will be part of a web-based application or merely a standalone dashboard, for example.<\/strong><\/p>\n 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. For example, on Power BI, it becomes a question of using Power BI Embedded, which is a complex implementation with separate costs<\/p>\n This factor centers around building solutions compliant with internal and external requirements (e.g., GDPR, CCPA). Is your company comfortable with external vendors accessing your data, and how? Is there a vendor that can deliver the functionality and security? Can the platform support role and row-level security? What are the authentication capabilities?<\/p>\n 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. 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\u2019t forget to assess customer support service as well as delivery capabilities.<\/strong> And lastly, consider vendors that are technology partners and experts in the field, so they can consult you rather than just implement the ask.<\/p>\n While this may not cover everything you need when deciding between buying or building a new data visualization solution, I hope it gets you thinking about making an informed decision around your data visualization needs.<\/p>\n\n An Important Detour: Data Governance and Stewardship<\/h2>\n
Data Visualization: Should You Buy, Build or Both?<\/h2>\n
Breaking Down Your Main \u201cBuy vs. Build\u201d Decision Factors<\/h2>\n
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Investment Cost<\/h3>\n
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Time to Market<\/h3>\n
Resource Capabilities<\/h3>\n
Customization and Integration<\/h3>\n
Audience<\/h3>\n
Security Concerns<\/h3>\n
Third-Party Vendor Relationships<\/h3>\n
Conclusion<\/h2>\n