How Big is Your Data Outage Tax?

A framework to calculate the true damage caused by breaks in data quality.

Photo by Jp Valery on Unsplash

When you look at data outages in isolation they don’t look so bad.

They always look like small, siloed, one-off issues, and they always seem to only cause limited damage for a single section of your organization.

But when you take a broader view, they seem to cause a large amount of harm. For example, Gartner estimates data quality issues cost businesses $15 million per year. Other trusted sources provide a similar outlook.

Data quality issues are creating alarming levels of topline harm for businesses [image by author].

We wrote this article to close the distance between these two perspectives, and to show you how those seemingly small, isolated data outages can easily add up to millions of dollars of annual losses.

To do so, we’ll explore:

• The three ways data outages cause topline damage.
• Why organizations often suffer more damage than they know.
• How ad hoc remediation creates millions in losses on its own.
• How to calculate your own annual “data outage tax”.

Before We Begin: Common Definitions and Longer Discussions

In this article, we use the term you may not have heard before — “data outage”. We defined data outages in detail in a previous two-part series. (Part one) (Part two )

In addition, this article is a short summary of a longer piece we wrote for The Plumbers of Data Science.

Now, let’s return to our summary of how to better understand and quantify the true cost of data outages.

The Three Ways Data Outages Cause Topline Damage

Data outages appear in many forms and can create topline damage in many ways.

First, they can cause direct damage to topline metrics like revenue. For example, a data outage can cause an airline loses 99% of their revenue on each sale when they start to sell $5,000 tickets for $50.

Second, they can create a cascade of problems that ultimately result in lost revenue, profit, user acquisition, user retention, or the like. For example, a data outage can drop data fidelity of a 3rd party Know-Your-Customer (KYC), causing a drop in user acquisition rates, and ultimately generating a loss in revenue that’s proportional to the Lifetime Value (LTV) of the users they did not acquire.

Third, some data outages don’t produce traceable damage, but it’s clear they are impacting core business performance in some meaningful way. For example, there’s no measurable impact when a new app version releases, begins dropping events and under-represents user engagement metrics, but it’s clear this outage will impair decision-making.

Data quality issues can harm a business through direct impact on topline metrics, indirect impact on stakeholder experience, and unrecognized latent impact [image by author].

Why You are Bleeding More Than You Might Know

Organizations usually don’t know how many outages they have for three big reasons.

1. Data outages don’t trigger existing monitoring tools, so organizations can suffer a lot of hidden problems while thinking everything is ok.
2. Data outages often cause latent, lagged, or slowburn impact, so organizations can bleed for a long time without realizing something’s wrong (if ever).
3. Data outages are often part of a bigger problem, but organizations often treat them in isolation and fail to see the full scope of the issues they’re suffering.

Why data outages are so expensive — they go unnoticed in infrastructure monitoring tools, they often show up with a lag, and they always end up needing a digital war-room to root-cause and resolve [image by author].

How Ad Hoc Remediation Causes Millions in Losses

To resolve data outages, most organizations assign remediation to individual stakeholders, who then have to cross multiple teams to create ad hoc solutions.

The whole process is time-consuming, resource-intensive, and creates significant productivity loss for data engineers and business stakeholders — who put in expensive throw-away work and take shortcuts that accumulate huge technical debt.

How to Calculate Your Own “Data Outage Tax”

Two points are now clear.

First, data outages create much greater impact than they initially present.

Second, you can calculate your annual damages from outages by adding up:

• The direct and indirect damage your outages cause. Every outage we’ve seen that caused direct impact created millions of topline damages on its own.
• The other data outages you have but aren’t seeing. A typical data outage causes problems for 4–5 other downstream data assets and dependencies.
• The additional costs created by remediation. For a pizza-box sized analyst team this can create millions of dollars in annual productivity loss on its own.

Add it up, and you’ll see that Gartner’s estimate of $15 million in annual losses from data quality issues was conservative. Read the full version of this article on The Plumbers of Data Science.

If you want to stop paying this “tax”, then reach out today.

Lightup brings order to data chaos. We give organizations a single, unified platform to accurately detect, investigate, and remediate data outages in real-time.

To see if Lightup can solve your data outages, take the right next step.

• Learn more by visiting lightup.ai.
• Schedule a demo to see our solution in action.
• Or, directly start a free trial now.

References:
[1] S. Moore, How to Create a Business Case for Data Quality Improvement (2018), Gartner
[2] M. Goetz, G. Leganza, E. Miller, J. Vale, Data Performance Management is Essential to Prove Data’s ROI (2018), Forrester
[3] T. Redman, Bad Data Costs the U.S. $3.1 Trillion Per Year (2016), IBM via Harvard Business Review