The Hidden Costs of Data Analytics Software
Content Marketing Manager, Sigma
Anyone that has purchased a flight on a discount airline recently is familiar with the concept of hidden costs. Discount airlines can seem like a real bargain at face value. But when it comes time to take your trip, hidden costs suddenly begin to emerge left and right.
That bag you want to carry on; it’s going to cost you extra. That mid-flight soda will cost you too. Oh, you don’t want to sit in the last row middle seat? It looks like you have to cough up some extra cash to pick your seat, as well. It turns out that the “discounted” ticket might not have been such a good deal after all.
These small charges or fees are hidden costs— costs that aren’t usually associated with the initial purchase price of a product or service. But hidden costs don’t just apply to your next flight home for the holidays. Like so many other things in life, there are hidden costs associated with data analytics software.
Most of us have a solid understanding of the known costs of data analytics software: licensing fees, hardware costs (if you’re on prem), integration costs, and the human resources to get things off the ground. But if you’re considering a new data analytics tool for your company, you need to think about what might be lurking below the surface. Otherwise, you won’t get a real apples to apples comparison of any two data analytics tools. And even worse, you may end up spending way more than you planned.
Here are a few of the most common hidden costs companies miss during the buying cycle that can come back to haunt you later on.
The cost of implementation
Beyond the initial licensing costs that usually come with data analytics software, there are often costs associated with implementing the tool within your organization. Depending on the tool you choose, you may need to invest extra time to build your server, for example. Some tools require you to learn proprietary coding languages to model your data. And if the software is complex, you might have to set up a training program before employees can get the full value.
All these costs can add up and significantly lengthen the time it takes to implement a data analytics solution. In some cases, this can delay deployment six months or more.
Knowing the time and resources it takes to get up and running is critical if you’re going to understand the total cost or accurately compare software.
Learn how to calculate the total cost of ownership (TCO) of your analytics investment in our free eBook.
The cost of growth
When it comes to data, things are always changing. Your analytics needs today likely won’t be the same as they are in the future. And as those needs change over time, your costs can increase. So you’ll want to consider how an analytics solution solves the problems you have today and how your needs may shift downstream—and whether your costs will change.
Some of the biggest drivers of growth costs stem from generating and storing more data over time, and the need to add more software licenses. Scale can also add complexity to your data stack, which can require additional maintenance and human resources to manage.
Will your data analytics software stand the test of time and scale with you? If not, you may be faced with additional maintenance costs, switching costs, and the downtime that comes with it.
Uncover the Hidden Costs of Business Intelligence Software
In this eBook, we explore:
- How to develop a framework for calculating the total cost of ownership (TCO) of your analytics and BI investment
- The most common hidden costs of business intelligence software
- How to accurately compare BI vendor pricing side-by-side
The cost of low adoption
Investing in data analytics software is no small decision. And there is a significant amount of work required before you will see ROI. So the last thing you want is all that work to be undercut by low employee adoption.
Despite the desire to be data-driven, only 30% of employees use analytics software. This adoption gap largely stems from the complexity of many solutions, and the technical skillset people need to ask questions of their data. When choosing a new data analytics tool, make sure it’s accessible to people outside of the data team. Otherwise, the money you spend can only go so far, and employees will seek out alternatives like spreadsheets—which come with additional risks (more on this later).
To get the full value out of your data, consider a low-code or no-code analytics tool like Sigma. These types of software help foster a data-driven culture where anyone can join the data conversation and use insights to steer decisions.
The rate of BI adoption
of all employees.
The cost of a security breach
So far this year, more than 4 billion records have been breached. Let that sink in.
At the same time, organizations spend millions on their data warehouses, security solutions, and compliance initiatives. But all of that spend can instantly be rendered useless by the everyday business workflows like downloading data to a Microsoft Excel spreadsheet.
Business experts aren’t looking to circumvent enterprise governance practices. Instead, they are trying to get answers that can inform the next decision. And because they lack the programming expertise or extensive training required to work with data directly in most analytics tools, they are often powerless to answer the questions raised in the last meeting. So they turn to what they know best: the spreadsheet.
By investing in accessible cloud analytics software (like Sigma), you can avoid shadow IT scenarios and eliminate spreadsheet sprawl that leads to data breaches. It might seem like downloading data to a personal computer is a trivial issue. But real-world events suggest otherwise. The average data breach costs companies $3.86 million, according to the “2018 Cost of a Data Breach Study”—up 6.4% from 2017.
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Making the switch to cloud analytics? Read our buyer’s guide first.