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Overview: Data Governance Quality Index



Administrative data forms the backbone of decentralized evidence-based decision making in the Government of India. With emerging international evidence of the vital role played by data as an enabler in driving public policy across its lifecycle, the Central and State Governments have paid significant attention to their data systems over the past two decades. Management Information Systems (MIS) and dashboards have been developed for most government schemes and programs. To disseminate this information more widely, Open Data initiatives have also been undertaken. Recently, attempts have also been made to foster data exchange across Ministries/Departments via the Prayas Dashboard at Prime Minister’s Office and the Output-Outcome Monitoring Dashboard at Development Monitoring & Evaluation Office (DMEO), NITI Aayog.

In this context, a comprehensive review of present data preparedness levels of all Ministries/Departments was required to chart way forward and suggest measures for improvement. Against this background, the Data Governance Quality Index (DGQI) exercise was initiated with the objective of assessing data preparedness of M/Ds on a standardized framework to drive healthy competition among them and promote cooperative peer learning from best practices.

Several existing data maturity models were studied to develop DGQI’s methodology. Three key steps of data preparedness were identified: (a)Data Strategy to lay down systemic guidelines, (b) Data Systems to ensure smooth processes of data generation, management and its use and (c) Data driven Outcomes where data is utilized and widely shared by institutions to drive decision making.

While DGQI 1.0 focused on only data systems pillar, DGQI 2.0 aims to assess data preparedness levels of Ministries/Departments across the three pillars.


Data Governance in India: Historical Perspective

Data collection and warehousing started as early as 1881 when the first Census was conducted in India. After Independence, National Sample Survey Organization was established in 1950 and Central Statistical Organization in 1951. Data collected through large scale surveys by these organisations, and the administrative data collected by Ministries and the state Governments led to data-driven decision-making in the Central and the State Governments. Scheme-level information generated and collated at various levels i.e., village, block, district and state levels, assisted programme implementation. However, the whole exercise was done manually on formats individually developed under each scheme and overall scheme progress was mostly tracked inputs (fund releases and budget utilization).

MIS systems and digital data storage facilities became all pervasive in the last two decades. Gradually, activities and outputs started to get monitored. With digitization of data, advent of new techniques and ever-increasing importance of data in public policy, the need for even better management of data was recognized. In order to further India’s vision towards Open Government and Open Data initiative, National Data Sharing & Accessibility Policy was adopted and was launched to provide all relevant data from Government at single place for wider public use. Many schemes also migrated to dashboard based and basic analytics-driven systems which make complex information available to decision makers in simple charts and figures. Intra-government exchange and integration of data is now being facilitated using ICT platforms such as DISHA, Prayas and Output- Outcome Monitoring Framework (OOMF).

Data Governance in India – Current Scenario

As of now, an internal Management Information Systems (MIS) is developed for most government programmes, which provides required information regarding coverage and outputs of the programme, e.g., HMIS for National Health Mission which tracks information uploaded by the States/UTs which enables planning, management, and decision-making based on grading of facilities and various health indicators at block, district, state as well as national level. Such programme MIS typically have capabilities to generate standardized analytical reports on the basis of data collected. Further, Ministry of Statistics and Programme Implementation (MoSPI), through Twenty Point Programme (TPP-2006) and Infrastructure and Project Monitoring Division (IPMD) monitors key infrastructure projects within the Government. The Government also launched Digital India programme in 2015 to ensure digital availability of government services to citizens. This Programme is being managed by National e-Governance Division (NeGD). NeGD provides project development and programme management support to e-governance related measures taken by Ministries. Some of the State Governments also present the work done by their various departments through dashboard based analytical systems (e.g. Pratibimba by Govt. of Karnataka). These measures have ushered in a new era of accountability.

Overall, it is clear from the background above that governments in India have been quite proactive in ensuring adoption of newer technologies in data management and thereby improving programme outputs and outcomes. However, there still remains lot more to be done with reference to data governance, especially with respect to programme monitoring and management. Given the above, it is imperative that a comprehensive review of data preparedness is conducted for government data systems for scheme management and decision support information systems. Development Monitoring and Evaluation Office (DMEO), an attached office of NITI Aayog, has developed DGQI toolkit to enable a comprehensive self-assessment of data preparedness levels to come up with Data Governance Quality Index (DGQI) for the government agencies at the central and state level.

Objectives of DGQI

The intent of the DGQI is to enable Ministries/ Departments and state departments to assess themselves at various levels of data maturity on the basis of a standardized framework, which in turn would facilitate deepening of digitization in the Government of India.

It is hoped that in the long run, DGQI will help in laying the foundation of more integrated monitoring systems, for e.g., a single, online, API-integrable ‘Overarching Dashboard’ kind of monitoring system of all the CS/ CSS schemes of all M/Ds, ultimately leading to a state-of-the-art data-driven decision making.

The objectives are as follows:

  • To enable review and assessment of data preparedness of the data/ MIS systems of the Ministries/Departments on objective parameters of a standardized framework.
  • To prepare a self-assessment diagnostic tool that will enable the M/Ds to internally contemplate the need for improving data systems.
  • To enable the commissioning agencies to conduct a comparative assessment of data preparedness and source best practices in IT systems which can enable improved cross-learning between the participating agencies.

Under the realm of the overall approach, six key themes have been identified under data systems pillar covered by the Data Governance Quality Index:

  • Data Generation: Data generation measures the ability of the respective ministries/departments to efficiently generate useful data in the course of their programme implementation. It covers areas related to the level of digitization, frequency and granularity of data generation. It also assesses if mobile phones, location tracking and GIS mapping is used to authenticate the generated data.
  • Data Quality: Data Quality covers processes of scientifically and statistically evaluating data in order to determine whether they meet quality benchmarks. The key areas covered under this theme relate to profiling of data, data quality assessment processes (for e.g. data pipeline design, well defined data schema etc.), data cleaning, use of latest technologies and mobile phones in the process.
  • Use of Technology: This theme assesses if emerging technologies are being utilized to improve data robustness. It assesses if MIS of ministries/departments have linkages with PFMS for ensuring transparency and Jan-Dhan, Aadhar and Mobile [JAM-trinity (if applicable)] for delivering last mile services. It also explored if other data sources such as remote sensing or social media data is utilized in addition to data collected by ministries/departments to get a nuanced understanding. Finally, it also measures if emerging technologies like block chain, big data analytics, machine learning, artificial intelligence, IoT are being used to collect data or to draw analytical insights from it.
  • Data Analysis, Use and Dissemination: One of the core themes, it covers if the collected data is being analyzed and used for evidence creation and decision making. Given the present context, it gauges whether ministries/departments are undertaking basic cross-sectional analyses only or regression and predictive analysis as well. The use of dashboards for visualization of data is also checked to ensure that information is disseminated in a user-friendly manner. It also assesses if other social media platforms are also being increasingly used for information dissemination and whether websites have features to support multi-lingual interfaces and are GIGW compliant.
  • Data Security and HR Capacity: While data security requires an in-depth analysis in itself, the same is briefly captured in the index also to reflect its importance. It assesses if antivirus updates and internal audit systems are in place to ensure data is not corrupted or prone to threats. These were identified to be the minimum requirements expected to be met and are not meant to be exhaustive in nature. To look at HR capacity, the existence of dedicated data quality teams has been considered. Again, this is by no means an exhaustive measure of capacity development but was adopted as the starting point.
  • Case Studies: The present questionnaire for this theme focuses on scheme-level MIS. Any intervention done at the Ministry/ Department level or any innovative approach that may not be captured in the structured questions of the tool can be highlighted through best practices. These best practices can be provided as case studies. This theme is expected to help unlock the hidden potential not only in terms of enhanced decision making through inter-ministerial collaboration but also by opening doors for learning from challenges faced and the solutions devised by peer ministries.