Saturday, November 15, 2014

Good Product Management is Based on Good Data

I harp on this all the time and I'm sure my colleagues are tired of me saying it - but I'm of the firm opinion that we should DO NOTHING in regards to product management or product development, without the data to support our decisions. The foundation of any change is underpinned by data to support that change - whimsical changes or even changes supported by some perceived need, mean very little without supported analytics. Foregoing the due diligence, even for small changes can not only be detrimental to the application but can also ultimately impact your company's bottom-line. You do not want to be in the position of defending your actions, even if they seem innocuous, against a product backlog that has real calculated ROI.

Even Dev Prospecting (the idea that sometimes we need to push a change that could garner new business or customers with some nebulous "maybe" result through innovation) should have a minimal data construct projecting what may come from doing so. For this I look at data models in parallel or similar affinity vertical markets.

If you aren't paying attention to the data, making some effort to understand the trends, and have the ability to filter the noise and make some decent projections based on what you see, you're doing more harm than good. So as a Product Manager, what can you do to get this data?

Research: Public Searches. The first avenue for any good product manager is to start doing some searches online using probably keywords. I think most product managers already have some ability at finding public information - it's a skill one develops over the years and is a good starting point for just about any knowledge gathering. Google is your friend, but this is only a starting point. I'd suggest you start brainstorming and as you broaden your searches, additional keywords will suggest themselves in the results you find. Make sure you note what you find but stay relatively focused using the additional words as possibilities as they can get you really distracted (ask me how I know?).

Research: Examine Your Internal Data. The second tool in every product managers tool-kit is internal data. Most of us have applications that have been running for several years and the data is sitting there for the grabbing. Look at the data points you have and see if there's information there that can be used for modeling or to suggest avenues that support your case. Just be careful, especially if you're forecasting to take this data with a grain-of-salt. Using existing data works great to support cost savings; it's much more dangerous to use it to support revenue opportunities.

Research: Use Your Existing Customer Pool. It's always blown-my-mind when I've come to a company and realized that there is almost no interaction with the existing customers, beyond simple support and account management. If you have enough data to identify problem areas in your application via support calls and emails, what's more useful than to reach out to your customers and start a conversation. Begin with what they like, move to what they don't like, then start suggesting things you'd like to do. The information you receive can be quite compelling and leading you to paths making good application decisions.

Research: Use Your Existing Sales and Support Pool. As above, we often receive feedback for what's wrong or bad with what we are doing from an application level. What's harder is to get a sense of what really needs to be changed or fixed. Use data as the foundation, then interviews with your coworkers to gauge what will have the most impact - you'll often be surprised at what you find out and once again, these are leads that can direct you towards real innovation. I love getting sales figures and using the information to defend a case for doing something, or even better, against doing something being driven by someone with influence but no clear understanding of what's needed.

Big Data and DataScience. The last and this is something that I'm a big fan of - hopefully your company has embraced DataScience and hired a good python developer to parse through your tables. It's amazing what can be discovered by trending data and looking for graph-outliers or anti-trends. As product managers, we need to better understand how this last tool can be used effectively, to support our case when making product decisions.
I think most product managers understand all of this, if at a very subconscious level. At minimum, keep your mind open and don't simply disqualify ideas being promoted by your coworkers - I know that we're all busy and that this is easy to do, but your do yourself an injustice and really exhibit a lack of respect for those you depend upon the most.

"I get it...I get it"...

-- John

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