Ivan Murenzi, the Director General of the National Institute of Statistics of Rwanda (NISR) was appointed in June to lead the institution, replacing Yusuf Murangwa who became the Minister of Finance and Economic Planning.

Murenzi had served as the Deputy Director General at the same institution for more than seven years.

As he settles into the new role, The New Times spoke to him exclusively to understand NISR future priorities and how the organisation is using data to inform decision making.

Below are excerpts:

As the new NISR DG, what are you focusing on as your priorities for the institution?

Our primary objective is to maintain the progress we have made in producing critical data, such as GDP [gross domestic product], inflation, and labor force statistics. We aim to ensure that data production remains consistent while adding value through improved presentation and interpretation. This approach will facilitate greater utilisation and impact of our statistics.

With a forward-looking perspective of 20 years, we recognise the necessity of integrating new data sources and technologies, such as big data and data science. To prepare for the future, we are investing in building capacity within NISR as well as across government sectors. We are also forging global partnerships to remain at the cutting edge of data science in Africa, ensuring that new methodologies complement and enhance traditional statistical practices.

We will also prioritise enhancing awareness and engagement with statistics across various sectors, including government, private industry, and academia. Establishing a dedicated department for communication and engagement will be crucial to ensure effective use of statistics. This engagement is expected to drive innovation and foster overall development.

How has NISR been using data to inform policymaking decisions?

In Rwanda, unlike in some other countries, the statistics office works very closely with policy makers, providing technical guidance on data interpretation, the formulation of key indicators, target setting, and monitoring. We also conduct annual evaluations of ‘Imihigo’, which allows us to give feedback to various government entities, including districts, on areas of improvement.

Over the past 12 years, particularly since the NST1, the Ministry of Finance and Economic Planning recognised the importance of involving the statistics office in sector working groups. This involvement means that our officers or staff are part of the sector steering committees. This helps in three ways:

First, NISR provides input in identification of relevant indicators to measure progress during the initial planning of sector strategies and interventions. For instance, if the agriculture sector plans to increase its yield, we offer guidance on key indicators that should be tracked to measure progress.

Secondly, NISR provides baseline data, which allows institutions to set more accurate targets. Our role extends beyond planning to include monitoring. The statistics office is involved in various sectors across the country, ensuring that data is effectively used in planning and monitoring processes.

Third, NISR is also called upon to contribute to policy development and the formulation of national plans, such as the NST. We provide insights based on the data we collect. For instance, if there are policies aimed at increasing employment, we offer inputs and ideas based on our data analysis.

To further promote the use of statistics, we continuously disseminate the statistics we produce through reports and workshops, inviting stakeholders to understand the implications of the data.

This comprehensive approach helps to increase the use of statistics in planning processes. However, as I mentioned earlier, there is still work to be done in this area. The goal is to move beyond strategic planning and to see statistics applied in operational decision-making.

For example, during the COVID-19 pandemic, when decisions needed to be made about closing sectors of the economy, the implications of these actions were considered using data. The aim is to integrate statistics into regular, operational decision-making, not just at the strategic level. However, more capacity building is needed in this area.

What have been the major challenges encountered in the production and use of statistics in the past 20 years?

The first challenge has been the limited use of statistics in decision making, especially operational decisions. This can be attributed to a general gap in the interpretation and understanding of statistics. We observe cases where an intervention is implemented lacking the required analysis, which leads to poor outcomes or no results. This challenge cuts across various users of statistics including public sector, private sector, academia, and even the media. The understanding and interpretation of statistics, and their integration into interventions, remains an area of improvement.

In response to this challenge, over the past seven to eight years, we have embarked on a programme aimed at building the capacities of statistics users. This involves training various groups on the statistics we produce, what they mean and how to interpret them. We engage with students and universities, providing internships to foster an understanding of how statistics are used.

We also collaborate with academia, helping professors to comprehend the data we have so that they can incorporate it into their analyses, research, and teaching. Moreover, we work with planners and statisticians across government sectors, focusing on capacity building. The primary challenge here is the limited understanding and the cultural approach to using data in decision-making processes.

Second challenge is retention of expert analysts. Our analytical work requires high level skills which can only be developed within a statistics office. As we advance into the era of big data, the need for high-level analysts, data scientists, and programmers becomes increasingly critical. The expertise required is not readily available in the market.

At NISR, we develop our experts internally, we hire statisticians and build their capacity over the years until they become proficient analysts. However, after six to eight years of experience, some of our skilled personnel leave for better opportunities, which presents a retention challenge. We are trying our best to manage this challenge by running an internship programme that ensures continuous training of analysts that are absorbed at NISR or other institutions.

What measures are the institution taking to improve data interpretation and ensure that statistical information is easily understood and effectively utilised?

Improving data interpretation is an ongoing process. While we have made progress, there is still work to be done. We conduct a user satisfaction survey every two years to gather feedback on how well our statistics are understood. From these surveys, we've learned that when we effectively explain our statistics, many of the users' questions are answered.

For instance, we regularly publish the Consumer Price Index (CPI) every month. If we ensure that users clearly understand terms like urban inflation, rural inflation, and overall inflation, it significantly reduces confusion. As we receive feedback, we continuously improve our approach.

In the near future, we plan to establish a fully-fledged department for engagement and communication because we recognize that as statisticians, we are not necessarily experts in communication. However, even before this department is established, we are starting to develop easier-to-understand materials, such as infographics and videos, to help the average person comprehend key statistics.

How does the institution conduct research in the field of big data, ensuring that the data collected is accurate and reliable, given the evolving nature of this technology?

The field of big data is a relatively new area globally, particularly when it comes to its application in official statistics. As such, there is ongoing work at the international level to develop standards and methodologies to ensure the reliability and representativeness of big data. The aim is to produce statistics that accurately reflect the situation in Rwanda with a high level of confidence, ensuring that the data is both consistent and reliable.

Traditional methods of data collection, such as surveys and censuses, have established protocols that provide this level of confidence. However, big data, which includes new sources like mobile data, offers significant potential because of its ability to provide more regular and detailed insights. For example, mobile data can be particularly useful in Rwanda, where over 80 per cent of the population uses mobile phones, allowing for a deeper understanding of financial transactions and other behaviors.

Currently, the institution is in the testing phase for big data applications. This includes analyzing satellite images to monitor weather changes and their impact on agriculture, as well as exploring the use of mobile money data to estimate economic activity. While these approaches show promise, they require the development of new systems and methodologies to ensure their accuracy and reliability.

At present, Rwanda is working alongside the international community to establish the necessary standards and build the technical capabilities needed for big data analysis. This is an ongoing process, and it may take several years before these methods are fully integrated into official statistics.

However, the Government of Rwanda has been proactive in recognizing the importance of big data, approving the Data Revolution Policy in 2017, which led to the creation of a department dedicated to coordinating big data initiatives. This policy underscores the government's commitment to investing in this area and gradually building the necessary infrastructure across various institutions.