Let’s imagine for a minute that you’re in the market for a new car and are trying to assess which vehicle offers you the most bang for your buck. It’s easy to be drawn to price and claim the cheapest car as the victor, however, there’s so much more to total cost of ownership than what the sticker price divulges.
Take maintenance fees for example. Some retailers – such as Audi – offer fixed maintenance plans at an additional upfront fee. These services, however, are appreciable savings compared to their separately purchased counterparts. Even if parts and labor rates increase between services, the prepaid cost is still ensured. And what about the fuel economy and tank capacity of your vehicle? This will greatly impact how frequently you visit your local gas station and how much you spend in total fuel costs. Do you plan on primarily driving in the city, or do you see yourself making weekly trips to the cottage? Don’t even get me started on insurance and taxes. There’s a reason the used car market is rapidly outpacing the new car segment in growth.
The moral of this story, is that calculating the cost and value of a vehicle is much more difficult than simply acknowledging its sticker price and basing your decision off of that. The same can be said for business intelligence (BI) and data analytics software. There are innumerable direct and indirect costs associated with these products, and it’s careless for buyers to only take sticker price into consideration when purchasing and implementing a BI solution.
Many analytics vendors tout their software as being priced ‘as low as some dollar amount per user per month’. And while this number might be accurate in the most bare-bones of ways, it does nothing more than help to generate an estimate as to how expensive the software might be as you scale your deployment. This number is rarely concrete, fails to take into consideration the many indirect costs you’re more than likely to encounter, and isn’t indicative of the total cost of ownership.
Data analytics buyers are all-to-often unpleasantly surprised at how much higher the cost of ownership is immediately after purchasing licenses, so, we’d like to remedy that shock by educating you on how you can more accurately calculate the total cost of ownership of business intelligence and data analytics.
There are countless considerations when determining the cost of BI. And while I won’t cover all of them here (many I simply cannot account for), I will discuss three of the most important factors to help you in determining the total cost of ownership and put an end to blind decision-making based on sticker price.
What are the platform costs?
In order to calculate the total cost, you must still include the sticker price; we can’t entirely forgo that step. Platform costs may just be one tiny part of a much larger equation, but a part of that equation they are. The platform costs of business intelligence and data analytics software are typically comprised of two components: software and hardware.
As far as software fees go, licenses make up the majority. In today’s day and age (and to great applaud by customers), many BI vendor’s licenses are available for purchase via subscription (which are usually renewed monthly or yearly), with most of the services bundled together.
Now, the costs of the licenses themselves are typically determined on either a reserved or concurrent seat basis – each person provided with a license granting user-level security is defined as a seat. This means you’ll either pay for a set number of users (such as 100, whereby the 101st user cannot access the software as they do not have a seat), or a floating number of users (such as 50, whereby the 51st user can access the software, but only once one user is no longer using it). While this is a highly simplified description of seat types, it’s important nonetheless to decide on which is more relevant to your business’ specific needs – especially since both will come at different costs.
Seats are typically designed to control the abilities and data access of users in an application. It’s important you purchase the correct types (and quantities) of seats for your users, as it’s highly unlikely that each of them will be consuming data in the same manner. Different seat types are often associated with different costs, so you’ll want to be certain. It’s also important to note that not all data analytics vendors offer flexible seat types. To ensure your software is useful and doesn’t become shelfware, flexible licensing is key. Software is not one-size-fits-all and neither should licensing.
Beyond licenses, it’s important to note that some vendors will also charge additional fees depending on which integrations are enabled, how many data connectors are in use, where you choose to store your data, whether you decide to access APIs, and whether or not license/usage limits are exceeded. While these costs may not make themselves known initially, they still directly tied to the platform and can substantially increase the cost beyond the sticker price.
Most software has minimum requirements that must be met on the client and server sides for it to operate. For example, some applications can only be run on select web browsers or require a minimum amount of RAM for production. Is your organization well equipped to support this type of software or will you have to purchase additional technical infrastructure to get the job done? Try and estimate what you’ll need to (or are comfortable) commit to these resources to ensure the solution works the way you intend it to.
What are the labor costs?
Those interested in business intelligence and data analytics often evaluate licenses and other platform costs but fail to take into consideration any indirect costs beyond that. Labor costs, for example, are often more expensive than the software itself, yet are rarely thought of during budgeting. Common labor costs include the following:
Support & Training
Will the solution you’ve implemented require an IT team large enough to handle a massive volume of support requests, or will your solution be largely self-service, requiring fewer technical folk post-launch? The more people you’re required to employ and train to ensure your BI project runs smoothly will affect the total cost. Speaking of training, odds are you’ll want your power users to be as proficient in the software as possible. Make sure you build training costs (be they onsite or remote) into your calculation.
While on this topic, to what extent will your IT staff be involved in creating and curating content for business users to consume? Are you deploying across the entire enterprise and project there to be hundreds of dashboards for tens of thousands of users? Will each of those users merely be viewing dashboards, or will they be combining and manipulating data from disparate sources themselves to perform their analysis? Does your staff primarily consist of technical resources who can manage data analysis on their own? Or do your end-users wish for someone else to create dashboards and reports? IT will need to understand your needs from the start and be involved in some capacity, but to what extent, is dependent on your project.
What about timelines? Depending on when your project needs to be completed, you might consider purchasing consulting services from your data analytics vendor to make the most out of your investment and speed up your time-to-market. Many deployments and environments are unique in type and scope, and to minimize risk and avoid pitfalls, in-house consulting experts from the vendor are often integral in helping buyers establish best practices and achieve their objectives. Many buyers will opt for consulting services as a way to confidently and quickly reap the rewards of business intelligence.
Maintenance comes with the territory when discussing BI and data analytics software. Did you know that many vendors charge for product enhancements and new features? Or that to continue taking advantage of a vendor’s support network you’ll have to pay yearly fees; often a fixed percentage of your license costs? Depending on which license model you’ve opted into, you may be forced to pay to continue benefiting from these services.
It’s also important you consider the opportunity cost of implementation and compare efficiency levels in worker productivity with different BI solutions. Data analysis is intended to help businesses and individuals understand information more quickly and take action. If workers can spend less time on analysis and can achieve what they’ve set out to do, they’ll then have the bandwidth to perform more analysis or can tackle other tasks that drive value for the organization.
A simple way to measure and assess worker productivity across various BI solutions is to track the following:
How difficult is it for developers and data analysts to connect and blend data sources? How much time is required to create different views for analysis: dashboards, reports, scorecards, small multiples, etc.?
How simple is it for business users to consume and understand the data they’re presented with? What’s the experience like? Is it straight-forward? Complex?
Compare the end-user experience and the time-to-output against other platforms relative to your current approach. If the analysis process (from creation through to consumption) takes too long, it’s likely inefficient, too laborious, and is having a negligible or even negative impact on your bottom line. Without clarity and timely answers, the labor cost will far exceed the value.
How many applications are required?
The decision to invest in business intelligence and data analytics is one of the best a business can make. In fact, it’s more important now than ever before. However, you need to be wary of how capable and well-rounded the BI software you’re evaluating is at preparing data for analysis. If you aren’t careful, you may require one or more additional tools to get the job done. The more tools in your stack, the more money out of your pocket.
First and foremost, you’ll want to ensure the platform is more than capable of accommodating and storing your data. Some platforms thrive on analyzing big data, whereas others shine with their data visualizations. There’s no right or wrong answer, you just have to be confident your platform has a means of accessing your information wherever it lives, be it in flat files, or sprawled across a vast network of data stores. You mustn’t be stuck in a situation where you need to spend on enormous hardware requirements such as data warehousing or data marts.
Likewise, if you find yourself spending and relying heavily on extensive data warehousing, you’ll likely be in the neighborhood for an external tool that performs ETL (Extract-Transform-Load) functions for cleansing data and blending data sources. Any combination of business intelligence software, ETL software, and data warehousing software can set you back a pretty penny. If you want to keep your total cost of ownership down, you’ll want a BI platform that does all three and does them well.
At the end of the day, the value of business intelligence and data analytics software cannot simply be assessed by its costs and functionality. Most important are the business benefits that are made possible with accurate, data-driven decision-making.
At Dundas, we’re simply focused on ensuring you walk away with maximum ROI, and that’s only possible when your total cost of ownership is reasonable and expected. Our team is borderline obsessive about ensuring your success, so when you invest in Dundas, your investment extends far beyond our software. We work hand-in-hand with our customers to reduce the barriers to informed decision-making, so they can deploy transformational data analytics solutions quickly, easily, and cost-effectively.
We’ve structured our entire offering in such a way that enables you to get the most out of your investment and extract as much value from your data in as little time as possible, with as little effort as possible, without incurring an obscene amount of hidden, indirect costs somewhere down the line.
If you’re interested in learning more about how Dundas ensures a lower total cost of ownership than other business intelligence and data analytics vendors, I encourage you to connect with our team on any questions you might have. In the meantime, here’s a great video on how to easily calculate the ROI of business intelligence. It’s perfect for those who are struggling to justify the implementation and software costs.
About the Author
Jordan Zenko is the Community & Content Manager at Dundas Data Visualization. As Dundas’ resident (and self-proclaimed) story-teller, he authors in-depth content that educates developers, analysts, and business users on the benefits of business intelligence.