Looking for a new analytics solution? Make sure to consider the openness and flexibility of the platform, or you may be too dependent on it long-term.
Many organizations have considered or experienced a desire to take advantage of a cheaper platform, more feature rich-software, or to divorce from a vendor who is not delivering. But the pain of moving is simply too great to consider doing it. This is the state of being locked-in.
In this blog, we explain what lock-in is, what it means for businesses searching for new embedded analytics solutions, and why it should be a consideration when choosing a platform.
What is vendor lock-in?
Lock-in is a state that occurs when the cost or effort of moving away from a technology platform outweighs the benefit of doing so, even if that choice is good for the business.
While it’s not a state exclusively limited to business intelligence (BI) solutions, vendor lock-in is a common pain-point and is a key consideration to be aware of when adopting any new analytics platform. But why?
The answer is simple: No-one wants to be locked-into a solution, unless that solution delivers compelling value on an on-going basis, with Apple being a popular example. Imagine a day in the future where Apple slips behind the pack. How hard is it going to be to unwind one’s life from the Apple ecosystem? The stakes get significantly higher when it comes to organizational technology decisions. In particular, lock-in can be problematic when choosing new software platforms for business operations, analytics included.
Some might argue it is in the interest of the vendor to lock you in, particularly in the age of Software-as-a-Service (SaaS) and subscription software models, where use of software requires ongoing payments. The harder it is to leave, the higher the likelihood those payments will continue into the future. But this truth is still less than ideal when seeking business flexibility.
3 typical signs of vendor lock-in with BI platforms?
Vendor lock-in varies depending on the type of technology solution being adopted, but for BI and analytics platforms in particular, there are three key signs that determine whether the platform will allow for flexibility in choice, both short-term and long-term.
1. Proprietary data storage
For many years, data cubes or in-memory database storage was a feature of BI platforms. Ingesting data into the proprietary data storage layers of these tools often provided a performance boost to data discovery processes, especially where personal copies of data could be carved out from corporate data stores.
This convenience, however, came at a cost: Data proliferated in an ungoverned way across organizations, but more importantly the greater the investment in these data layers, the tougher it became to switch. Data stored in these types of proprietary layers is generally only accessible from the tools provided by the BI platform provider. While it’s not as common a problem in modern platforms, it’s still there - hence the need for due diligence before adopting the platform.
The reality is organizations today are spoilt for choice with fast, analytic databases, and the need to store data in proprietary formats within a BI platform is no longer necessary. Avoiding this feature is recommended to eliminate future headaches when it comes to storing key data.
2. Cloud-aligned platform providers
The mega-cloud providers, Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure have been in a war to win as many computing minutes from as many organizations as they can. This not only means being able to run any type of computing workload, but also by offering compelling regularly discounted software offerings that are exclusive to their platform, or work most optimally on their platform. These vendors have been developing their own offerings or gathering capability through acquisitions. The challenge comes when organizations want to move some computing workload to another provider to achieve improved price or performance; software choices they have made often lock them into a particular cloud provider, leaving them with an expensive, complex and difficult task to leave.
3. Proprietary skill sets
Some business intelligence platforms offer the opportunity to extend the platform’s capabilities. For example, by creating custom connectors to data sources, implementing custom charts, creating custom calculations or implementing custom user interface (UI) elements. Many vendors also require developers to learn specific scripting or programming languages and frameworks. This means specific extensions will likely need to be redone if a new platform needs to be chosen, and significant time is required to learn these proprietary languages, which is completely throw-away as your team will not be able to use them once moving to new tools.
How to avoid vendor lock-in when choosing a BI platform?
Making the wrong decision on which technology to adopt can impact costs, customer experience, competitiveness, and ultimately the success of the organization’s mission.
While there is no guarantee that the right decision will always be made, or that a right decision today (based on all the available evidence) will not be the wrong decision tomorrow, choosing a platform with inherent flexibility that allows your business to rapidly respond to changes when necessary by making new choices, should always be a key part of any architectural decision.
This is where open architecture comes in.
Technologies that allow for flexibility and provide architectural choices are referred to as ‘open’ and firmly defined as the opposite of being locked-in. Architectural choices, which make it simpler in the future for new choices to be made, can provide some protection against making the wrong decision today, as the consequences of a change of mind down the track are not as significant. A solution that accommodates an open architecture is ideal, which makes identifying and understanding what an analytics solution that offers open architecture looks like critical.
Leveraging Yellowfin’s open architecture
Yellowfin has always been built on the principle of maximizing choice for our customers. We’ve built out a number of capabilities to provide ample options suitable for a variety of use cases, including:
Flexibility - Because it is built on the Java platform, Yellowfin is a fully integrated and portable solution that can run on any modern operating system, run optimally on premises, or on any cloud provider.
Web-based - There are no desktop components of Yellowfin; all user access is done via browser, simplifying deployment.
Direct Query - Yellowfin does not require the loading of an organization’s data; instead, it will connect to any desired data source and run queries live against that data source.
Extensibility - Extending Yellowfin does not require knowledge of specific languages or proprietary scripts, as extensions can be coded using familiar developer languages like Java, JavaScript, CSS and HTML.
Platform-agnostic - Yellowfin’s non-proprietary data preparation outputs can be used with other analytic tools, and its in-built data storytelling capability Stories can integrate dashboards and reports from competing platforms like Microsoft Power BI, Qlik and Tableau into long-form data story content.
We believe Yellowfin is one of the best examples of a BI solution built around the benefits of open architecture, so much so we’ve been recognized for it. We recommend reading Gartner’s Magic Quadrant 2021 in Business Intelligence and Analytics Platforms report to see for yourself.
Ultimately, no decision can be completely future-proofed, but it’s important to choose a solution that ensures future changes to your business can be made with minimal disruption and regret.
Whitepaper: Why Open Architectures Matter in BI
Learn more about the importance of open architectures for long-term flexibility and future-proofing when deciding to adopt a business intelligence (BI) and analytics platform.