Digital Divide and the Unsustainable Open Data Deluge

Doug Hadden, VP Products

I was reminded of the open data “caution narrative” during the International Open Government Data Conference earlier this. This narrative is “curb your open data enthusiasm” because:

  1. Digital divide = people who really need the data from using the data
  2. Information complexity = data to too complicated for most people
  3. Information overload = just to much information to use
  4. Unsustainable costs = little value to some/most/all open data initiatives

Gartner Vice President and Distinguished Analyst, Andrea di Maio sums it up:

The downside is a deluge of data. People can easily drawn in raw open data that is either too much or simply meaningless unless some processing takes place.” He sees open data opening a wider digital divide “between those who have skills and resources to interpret open data and those who don’t” and that open data is “already showing sustainability problems.”

Di Maio has been critical of enthusiasm in light of poor open data business cases of high costs with unproven returns.

I have to report that Di Maio was proven right at the Open Government Data Conference. Enthusiasm was high and open data business cases were primarily anecdotal. (I’ve curated tweets below on these subjects.) My sense based on the conference is that most of the four issues, with the exception of proven return, were addressed effectively:

  1. Digital divide continues to narrow, there are emerging techniques from reaching the poor (i.e. visualization on walls) through intermediaries and costs for civil society to reach the poor is going down
  2. Information complexity is being sorted out thanks to semantic technology and better user interface design
  3. Information overload, as Marshall McLuhan said, leads to pattern recognition – in the case of open data:  visualization
  4. Costs can be sustainable through automation, cloud computing and lessons learned in scaling up open data

Is there an evidence-based business case for open data?

I moderated an “unconference” discussion about the open data business case for financial data. My thinking was that it’s best to narrow down the domain in order to get more concrete ideas. Some progress was made. We identified areas for financial open data (budget, procurement, revenue, human resources, audit, aid, grants and performance).

We developed a framework to describe direct effects such as open procurement leading to increased competition and reduced costs and indirect effects such as increased trust through open budgets that leads to more investment to build more economic activity that generates tax revenue. There really wasn’t enough time to build out a full framework – but that’s what we need to do. Then we can validate this framework through real cases and provide effective business cases.

Why do we need a business case framework?

Technology disrupts how organizations operate. The business case for previous technology is often inadequate to effectively measure long term costs or returns that were not available in the previous technology. An open data business case will address potential new effects such as:

  • Return on the network effect where each new data set can add value to a previous data set
  • Return in taxes and economic development through the notion of government as platform – in a measurable way
  • Reduction in costs by eliminating IT functions that are no longer necessary in the age of open data
  • Return from better government decision-making through crowdsourcing
  • Return from better personal decision-making through open health, weather and financial data

In particular: we need to be able to prove these suppositions.

#IOGDC Tweets

1. Digital divide

1.1 Digital divide is being overcome

1.2 Intermediaries like the press help bridge digital divide

2. Information complexity

2.1 Linked Web, Semantic Web brings meaning to complex data

2.2 User Interface Design

2.3 Provide the right information to target

3. Information overload

3.1 Visualization helps overcome information overload

4. Unsustainable costs

4.1 Ways to calculate open data value

4.2 Examples of open data return: direct returns

4.3 Examples of open data return on investment: indirect returns

4.4 Open Data used internally by governments

4.5 Recognition that there are not good practices in determining open data return:

4.6 Techniques for making open data sustainable

  1. @alkags ‪#opendata step 1: build community catalysts ‪#IOGDC
  2. @alkags ‪#opendata step 2: build skills, masterclass ‪#IOGDC
  3. @alkags ‪#opendata step 3: embedd change agents, help develop‪#datajournalism ‪#IOGDC
  4. @alkags ‪#opendata step 4: rapid ‪#prototyping, incubators ‪#IOGDC
  5. @alkags ‪#opendata step 5: proof of concept, provide seed funding‪#IOGDC
  6. @alkags ‪#opendata step 6: scale success ‪#IOGDC

4.7 Recognizing the key internal government issues to make open data sustainable

4.8 Difficulty Calculating full economic impact of open data

4.9 Network effect

 

 

 

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