Is Your Data Reliable?

You won’t see returns from your investments in data science and RPA unless your data is reliable. 

But here’s the good news. You can recover and accelerate these investments with immediate, low-to-no cost changes to how you capture, pass, and store your data. These changes can also redeploy 15% or more your employee hours otherwise spent on data reliability issues into more productive, value-oriented activities.

When you think about data for data science, and RPA: do you know where the data came from? Is it reliable? How would you convince your board, management, regulators, or customers of this? Is there a measure of reliability that tells you it’s good enough, more than good enough, or excellent? And more importantly, who would you depend on and reward for continued reliability?

Who is responsible for unreliable data that results in:

• Poor investment or management decisions based on good models but flawed data?

• Incorrect operational decisions due to flawed data, leading to fines, loss of customer or public confidence, or even tragedy?

Reliability and how we measure it depends completely on the leadership and accountability we put in place. The value promised by investing in data science and analytics leadership roles will fall well below expectations without equal investment in data governance leadership.

Do You Give Equal Priority to Reliability?

The Chief Data Officer role is best known for MIS, data science, and database architecture. But we need equal attention to core health and controls:

• Do you have a role that is clearly accountable for data health and control?

• Do they have the authority and experience to be effective?

Commitment Matters.

The difference between failure and success will be whether the data governance manager, and their leadership team, are willing to confront occasionally difficult and human challenges.