All technology investments should include some conversation about ROI, or what a company or organization hopes to get in return for the up-front cost of investing in new technologies, systems or infrastructure. Some investments make more sense than others and it can often be hard to make the business case to move in a new direction. But when it comes to investing in data-related modernization, I have some great news – ROI should be very easy to calculate and quantify, and it’s all about time!
As companies and organizations ponder the potential value of far-reaching, future-oriented data strategy ideas like big data, machine learning and IOT (the internet of things), the fact remains that there’s a much more practical and immediate application for data modernization. At most organizations, large and small, there are people and teams who every day spend a lot of time analyzing, cleaning and preparing data. More often than not, these manual workarounds exist as a result of outdated, legacy data infrastructure which lacks modern data quality capabilities and tends to be very siloed and disconnected.
It’s not uncommon for individuals or teams to spend hours every day or week, manually analyzing, cleaning and preparing data for critical reporting to be used by customers, members, employees or leadership. As a result, these manual processes simply cannot be eliminated and in fact, as the data set continues to grow, so too will the amount of time it takes to manage that data. These processes not only take up a lot of time, they also increase the likelihood of mistakes and employee frustration.
With this in mind, what steps can your company or organization take towards making the business case for an investment into a phased data modernization initiative?
- Assess your current situation. Where are the most obvious examples of time being wasted as a result of current manual data workarounds? Who is the most frustrated? Where is the most time being wasted?
- Calculate the rough cost of that manual activity. How many people are involved in those activities? How many hours a week on average are each of those people spending on those activities? As an example, if you have one person making $75,000 a year, or $37.00 an hour, who spends ten hours a week on manual data workarounds, that translates into $19,240 a year. If you have five people doing the same kind of work, that comes out to $96,200 per year.
- Consider the opportunity costs of other activities. Once this time is freed up for each of these people, how could that time be reallocated to something more productive?
- Determine feasibility. Based on what this might look like at your company or organization, what type of investment would make the most sense?
Data modernization is an iterative process that happens over time. UDig can help you to identify the areas of your current data infrastructure and process that are causing the most problems and would offer the most immediate ROI if fixed. Depending on what your current infrastructure and data processes look like, you may be spending more in a year on manual workarounds than you would spend to simply fix the problem.
