Data Democratization – Data and Analytics Takes a Step Closer to the Masses

by September 10, 2018

Data has charted its course to being a kingmaker. Data captures its power from its ability to collect, store and analyse for business and organisational gains. Of late, access to data has been very unequal concentrated in the hands of a select few companies, selected club to begin with. Giants, harness the data capabilities as they possess enormous resources required to collect quality data and turn it into value. Thus, data utilisation potential for serving the collective good of society comprehensively is limited.

The democratization of data has made it all possible by providing an easy access to data, data correlation tools, contextualizing data and real-time data analysis have brought together an age of data democratization.


The Roadblocks to Data Democratization

Open data or Data from the government sources is accessible to the masses for analysis and processing, data processing tools like Hadoop and Spark, are open source, and there is no technical or legal barrier stopping someone from downloading and running open data of open data processing tools.

Taking data to the masses through the large-scale process of data collection, transformation, storage and/or data analysis is unfeasible for most individuals and organizations in the modern times. There are massive roadblocks to data democratization.

1. Data harvesting takes place at macro levels. Most open-access data is set to broad parameters. If business enterprises need data at micro levels to analyse local parameters, data collection and analysis becomes a concerning issue. Organisations have to deploy and manage their own sensors or other data collectors to collect data, which are often not realistic because due to the lack of affordable, open data sensors.

2. Open Data may be biased, and may not contain all the parameters needed for an organisation’s analytical analysis.

3. Storing data is an expensive task. Data storage can be done on cloud, but when data amasses mammoth proportions to reach terabytes in size storing them becomes a roadblock.  This includes storage costs which add up to organisations budgets.

4. As times change, data becomes out-dated, pre-collected data that was collected by external agencies have a huge probability to become stale by the time it is accessed. Additionally, data cleaning and transformation to fit organisational requirements is time-consuming.

5. Poor incentives for data and AI sharing are another challenge towards data democracy. Few organizations offer incentives towards data sharing and proprietary AI tools. Currently, organisations practice data monetization it through advertising or internal research, there is little initiative to share data to third party vendors.

These reasons make data accessibility and analysis very undemocratic. This makes giants more powerful to leverage data accessibility to undertake large-scale, proprietary data collection and analysis programs, whereas small or mid-size enterprises struggle to make sense from the biased macro data available through open data sets. Even if businesses find access to meaningful, relevant open source data, the availability of advanced AI tools those are necessary to turn the data into value may be a tough call.


All That It Takes Towards Data Democratization

The challenges and roadblocks towards data democratization lay aside to make data democratization practical for individuals and businesses drive value. The essential requirements of the hour are

•  Storing the data in an open format, accessible to the masses. The shared data would be easily accessible to the people who need it. An intelligent data storage aspect is to allow data placement from different sources on the same plane and in the same context, for maximized visibility and insights.

•  An incentive system that rewards people for data sharing making data sharing feasible for revenue monetization in ways that are not purely self-serving.

•  Free and easy access to AI-powered data analysis tools to drive maximum value.

•  Open, affordable data harvesters which can be deployed and used by the masses.

Data is driving mankind to leverage the maximum value from AI and Machine learning technology.  Data availability and accessibility will be the need of the hour coupled with data storage and open data tools to be available for valuable data analytics change the world has seen and will witness in the times to come.