Why data is the lifeblood of digital transformation

Michael Hiskey, CMO of Semarchy, explains how to gain actionable insights from your big-data stockpile.

Why data is the lifeblood of digital transformation Michael Hiskey, CMO of Semarchy, explains how to gain actionable insights from your big data stockpile.

Michael Hiskey, CMO of Semarchy, explains to TechRepublic's Dan Patterson how to gain actionable insights from your big data stockpile. The following is an edited transcript of the interview.

Dan Patterson: In this age, especially post Facebook, post Cambridge Analytica, the word data kinda has a bad connotation to it, but every company, whether they're an S&B startup company or enterprise company, uses data for non-nefarious purposes. How is this data used to not just market to you, but to help improve life?

Michael Hiskey: Data's the life blood of your organization. Whether you're a municipality like the city of New York or you're a large organization in the consumer-products area, your requirement is to sort of move the data through the various cycles of what you have in your environment, and make it meaningful for the business users that need to make the right decisions on it, and then make it consumable for downstream audiences, with the ultimate goal of making the customer interaction better. It seems to be what most organizations— be they municipalities or consumer products companies or whatever— need to accomplish. They need to create a better experience for their users or for their citizens or patients, et cetera.

Dan Patterson: You said some magic words there: make the right decisions. I know that when I look at data, even personal data, I can feel overwhelmed. What does "make the right decision" mean, and how do I do that?

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Michael Hiskey: Well, the more important aspect of making the right decision is being able to trust the underlying data. Now, I know a lot of organizations that have really beautiful business intelligence type visualizations and can draw incredible charts and show some really interesting things with data science, but hand to heart, they don't feel overly confident about the quality of the underlying data they're using to make those decisions.

So the key to making the right decisions, is being very confident that you can trust the data that you have, that it's correctly attributed and clean and enriched, and all that fashion.

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