It is the exercise of authority, control and shared decision making (planning, monitoring and
enforcement) over the management of data assets. 

Data governance refers to the different organizing,decision-making, and accountability processes that organizations, companies, local and national governments, and global entities use to manage, control, share and exercise power over data. Because much data is from or about people, data governance decisions are also decisions about managing, controlling, influencing, and protecting people. 

Components of a data ecosystem

The ecosystem encompasses multiple data communities;distinct types of data;institutions, laws and policy frameworks; technologies,platforms and tools;and the dynamic interactions among
the actors within prevailing technological,infrastructural, legal, policy and other constraints

Why data governance is important

1. Whereas emerging technologies and expanded access and use of digital and mobile communications present new opportunities for Africa to harness new data sources for sustainable development, the increase in digitization brings new challenges. These include trust in data, privacy protection and effective data governance.
2.  Needs of each of the stakeholders (as relevant to the county government) are different. 
3. Information Technology is just an enabler; the interaction between people, processes, technology and culture drive success of data governance
4. Without identifying and addressing all these components, it is not possible to move from an ad-hoc state to a mature, focused process of data governance that is striving towards continuous improvement

Components of a National Statistical system

Many countries in Africa (and beyond) mistakenly refer to the National Statistical System (NSS) as Official Statistics. And have gone further to place the head of the NSO as heads of the NSS. This has starved countries of vital statistical activities without any space for contributions through statistical activities by non-state actors

Official and Non-Official Statistics definitions flow from where the data are produced. This does not minimize the role of NSO’s; they are a key stakeholders within the NSS and other data ecosystems for sustainable development playing the role of facilitator and leader in fostering collaboration, harmonization, and coordination within (inter)national communities

KNBS

1. KNBS is mandated by the Statistics Act of 2006; part of this mandated is the establishment of standards and promote the use of good practices and methods in the production and dissemination of statistical information.
2. KNBS has also recently published a citizen generated data guideline; it is also in the process of developing a Kenya National Quality Assurance Framework for Statistics
3. At the county government (the County Statistics Bill is still under deliberation in Senate)

KNBS Mandates

1. The statistics act identifies KNBS as the principal agency of the government for collecting, analysing and disseminating statistical data in Kenya and permits KNBS to plan, authorize, coordinate and supervise all official statistical programmes undertaken within the National Statistical System (NSS).
2. The NSS includes producers and users of statistics under the supervision and coordination of the KNBS and mainly comprises MCDAs and county governments.
3. KNBS cannot publish, or otherwise make available to any individual or organization, information that would enable the identification of any individual person or entity (Statistics Act - Section 22)
4. KNBS is committed to a quality management principles - aligned to ISO 9001:2015

Kenya’s Digital Economic Blueprint

The Blueprint seeks to:
● Identify the foundations for a Kenya digital economy framework by defining the pillars/enablers of a digital economy.
● Define the imperatives necessary for Kenya to move to a digital economy.
● Identify areas that Kenya can intervene and seize the opportunities therein.

Kenya’s Digital Economic Blueprint

Power relations within the African data ecosystem

● The challenges of data collection, use and sharing in Africa are too often driven by non-African stakeholders.
● Those at the top of the iceberg wield more power and influence than those who remain ‘hidden’ – with individuals/citizens (especially indigenous and marginalized communities) at the very bottom

Minimizing adverse impact of the data revolution

• The best way to avoid data-related risk is to avoid collecting data. 
• However, M&E is a data heavy exercise, so you will likely need to collect data to fulfil your goals.
• Rather than avoiding the collection of data, you should minimize the amount of data that you are collecting.
  ○ Ask yourself: What data do you really need to collect and why? Do you have a specific use and purpose for each data point that you are planning to collect? How can you minimize the personal or sensitive data collected? 

Integrity - Professionalism, Transparency,Ethics

• Voluntary participation (opting out)
• Redress mechanisms for complaints
• Using simple language (communicating data rights, terms and conditions, survey objectives)
• Ethics apply to both passive and active data collection
• Approval from any institutional review boards or ethics committees.
• Impartiality, professionalism, scientific approach in the compilation of statistics.

Data protection vs open government

• Open government is an inclusive and participatory approach to governance that allows citizens to be involved in the formulation and eventual implementation of public policies. 
• Recognized by the OECD as a catalyst for democracy and inclusivity in sustainable growth

Data Protection

• Privacy and data protection are two interrelated
• Privacy is usually defined as the right of individuals to control their own personal information - the right to know and the right to say no!
• Privacy will often apply to a personal sphere. 
• Data protection is the legal mechanism that ensures privacy.

Responsible open data and data sharing

● ´There are a number of scenarios you might share data internally within government or externally
○ ´Third parties that you are contracting such as evaluation firms, survey companies, technology providers, or data processors
○ ´You might also be asking partners or third parties to share data that they have collected with you
● ´There is need to assess data privacy and security in these cases and develop data sharing agreements or data processing agreements in cases where personal, sensitive or confidential information would be exchanged or shared.