Maybe it's a subtle strategy by the conference planners, or maybe we get focused on one of the first speaker's points and imagine it being echoed by other presenters. Whatever the source, one key takeaway from this week's SAS Analytics Experience 2016 was that it's time for organizations to make sure that their analytics initiatives aren't just about probing data, but that they have the means to put that data into action.
It was a message hammered home by keynoter Jake Porway, founder and CEO of Datakind, the nonprofit that coordinates volunteer work by analytics professionals to use data for good, helping social organizations address some of the world's challenges: disease, food production, troubled children, racism, and more. It works with organizations that typically don't have the resources to hire their own data scientists.
Porway is what you might call a high-energy guy, as you can see in the video of his speech, which was an update on the presentation he gave at SAS Global Forum in 2015.
He outlined the progress that Datakind has made, having signed up more than 12,000 volunteers and helped more than 135 organizations. In his keynote and in a later interview, Porway discussed successes and failures, and lessons he has learned in the past two years.
The lessons include some relating to how Datakind and other nonprofits put data into action; but I will add, that lesson applies to the business world, too. He listed six key elements of a successful project, with an emphasis on how social organizations will use the resulting data.
"At the end of the day, we don't care if we just build something. We have to make sure it is something they are going to use that they can apply, something that they are going to make a difference with," he said.
The six elements are:
- A "super clear" problem statement
- Datasets (belonging to the organization or in the public domain)
- Data scientists (the Datakind volunteers)
- Funding (most social organizations never have enough)
- Subject matter expertise
- Social actors on the ground
The first four elements on that list are obvious within any data initiative -- for profit or not -- although we all know that the one about the "super clear" problem statement sometimes gets ignored in many realms.
The last two elements -- subject matter expertise and social actors -- seem to be key to putting data to work in any organization. Porway uses the phrase "design with, not for."
The subject matter expertise supplements the analytical and data skills that a data scientist brings to the project. That subject matter expertise could come from someone such as an expert from the social organization, an experienced field worker, or maybe an independent expert. It's someone who helps define the real problem that needs to be solved, perhaps can identify untapped data sources, and can help to analyze the data as it comes together.
"You really have to make sure you have the constituents and social actors there helping you design it in the first place and design the user experience piece. If you don't, you're going to build something that is useless or worse -- dangerous," said Porway.
What he calls the "social actors" are representatives of the people who will use the data in the field. They will provide the input on what actions the users of the application will be able to take, what types of information they need, and what they cannot do for various logistical reasons.
In a way, the world of Datakind and the social organizations it seeks to help isn't that different from the business world.
"It's really parallel to business applications. For-profit companies work to serve their customers, innovate on their products, and ultimately make a certain amount money. Nonprofits aren't that different. Their end goal might be reducing homelessness or pollution. They need to serve their customers and come up with products that do that," he said.
However, there is one key difference. In the business world, while it's possible that a really bad failure could break the company, in most instances the worst-case scenario is that a couple of individuals could lose their jobs. It stinks for them, but the company rolls on. For a nonprofit that fails in its analytics initiative, the wasted funds could mean loss of a sponsorship and the demise of the whole organization. Then those in the most need, those that the organization serves, suffer the most.
Yet, Porway noted, a successful data project could serve as a proof point that encourages sponsors to invest in still more analytics projects. A string of successes could mean that the organization can hire its own data team.
Being the head of a nonprofit that relies heavily on volunteers, Porway used the Analytics Experience platform for recruiting, calling on the SAS professionals in the audience to step up and get involved in Datakind projects. For those who need a job, he added that Datakind is still growing, and he recommended a visit to Datakind.org/careers.
Watch the video of his presentation on demand, and then try to think up excuses for why you wouldn't heed his call for volunteers to spend a weekend working on a Datakind project. It might be hard to say no.