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 In Data Analysis Tools, Data Journalism, How To

What do we know about this?

Is this picture good or bad?How will we support this?

Should we take action, or just observe?Is this a good thing?

We can only guess.Are we having a positive impact?

Have we helped, hurt, or had no effect at all?
Working with only small pieces of data is the same as trying to understand this child with only small pieces of the photo.

With all the data, we gain a better understanding.

With data sharing, we can see the entire picture and make better decisions and plans. Without the full picture, we will never be able to support your organization, your project, your mission.

What is Data Sharing?

To understand the importance of data sharing, we must first understand the different kinds of data we deal with:

Open Data

At the most basic level, open data is data you can re-use, analyze, compare across dimensions, benchmark, and find patterns you may not have realized or understood previously. It is generally available in a machine-readable format without restrictions on the ability to use, consume, or share the information.

Public Data

This is data that is available to the public to collect or look at. We have access to a huge expanse of public data, but it can be virtually useless — because it’s not easy to read, analyze, share, or even obtain, in some cases. The World Bank Institute has explored ways to use public data as open data to make development aid more effective.

Private Data

Private data is just what it says on the tin — data that is stored privately and inaccessible to the public. Last year, The New Yorker highlighted some of the potential human consequences associated with private data last year in profiling a family whose son was born with a mysterious disease. The boy’s condition eluded rapid identification because so many medical datasets were maintained in silos. Only when the story broke on social media were others with the same disease located — the open networks of Reddit were fundamentally richer than the private medical data.

The Problem with Data Silos

As I explained in Don’t Get Stuck in a Silo, it’s very easy to misunderstand what’s actually happening with data if:

  1. You’re working with data in silos
  2. You’re only looking at tables of counts

If you want to discover what’s actually happening in the world using data you must:

1. Collect data collaboratively
2. Use proper statistical methods

Let’s look at an example:

We have a community concerned about youth suicide rates. Local social service agencies start to look at who’s at risk, so they can do some preventative work.

Let’s look at the tables these well-meaning organizations produce to study youth suicide:Different agencies provide conflicting data on suicide risk.

Data sharing reveals results that are not what you'd expect.

With data sharing, we could all be working together and using collective impact. Using our combined data, we could make a three-dimensional table. If the data above was in a shared database, instead of in silos, we could know that:

Data sharing helps us combine data to better understand what's happening.

Data Sharing is Real Evidence

  • Data helps identify and verify issues and perceptions
  • Data helps proactively address issues, measure progress and capitalize on opportunities
  • Data helps build trust, respect, and security

Data sharing allows you to:

• Get a more accurate picture
• Spend money more effectively
• Have more effective systems, practices, and policy

As I wrote about in Knowledge is Power, the trend is for organizations to move away from data silos. Not moving in this direction can not only impact outcomes but also restrict the flow of funding for projects.

Isolated service models are:

  • Expensive
  • Unresponsive
  • Insufficient

Collaborative approaches bring:

  • Efficiency
  • Effectiveness
  • Timeliness
  • Greater accuracy
  • Better outcomes for our communities

Collaboration, by definition, demands sharing data and information to the extent required for timely, appropriate and supportive programming, while still respecting the privacy protection responsibilities that apply to the respective data owners across the system.

If we hope to work together as a system, we must measure a system. This can not be accomplished by different agencies measuring the same thing and sharing their reports. The first steps to data sharing, collaboration and stepping out of our silos are:

  1. Collecting data together
  2. Combining aggregate data
  3. Combining individual data

Use online open data sources, including data catalogues, data dashboards. Along with our downloadable guide, Datassist told subscribers where to find the data they need in our Datassist Monthly Resource Newsletter (December 2014).

Let Us Help Empower You

Datassist wants to empower you with the possibilities of data and data visualization. We not only provide impeccable research and insightful, creative graphics — we also understand the analytics science and the context interests of organizations in a way that facilitates agile, focused discovery, and execution.

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