Midsize company leaders are right to be excited about the opportunities for harnessing the value in their large datasets. But the data in midsize companies tends to be messy — spreadsheets and plain-text files, many in different formats, are difficult (if not impossible) to integrate. It takes a lot of time and money to clean it up to make it useful. Poor-quality, disintegrated data can sabotage even the best initiatives, including AI designed to increase value and efficiency. HdL Companies, a Brea, California–headquartered government services firm, used their data strategically and has seen significant efficiency gains. The author offers three lessons for leaders to consider when getting started with automating data analysis.
As midsize companies grow, they develop data flows and data lakes (repositories for both structured and unstructured data) that are too big for one person, or even a team, to manipulate and use effectively. And even if a