Home > Companies > Thought Leaders > Q&A with Sadiqah Musa, Co-Founder at Black In Data

Q&A with Sadiqah Musa, Co-Founder at Black In Data

Sadiqah Musa, Co-Founder at Black In Data, is also an experienced Senior Data Analyst at Guardian News and Media with a demonstrated history of working in the energy and publishing sectors. She is skilled in Advanced Excel, SQL, Python, data visualization, project management, and Data Analysis and has a strong professional background with a Master of Science (MSc) from The University of Manchester.

As people become more aware and concerned about their data, the need for a regulating body will emerge, and organizations’ accountability will be mandated.



MEDIA 7: Could you please tell us a little bit about yourself? What inspired you to pursue a career in Data Analytics?
SADIQAH MUSA: My name is Sadiqah Musa, I am a Co-Founder of Black in Data, a movement I started to push for inclusivity and diversity in the Data Analytics Industry. My current role is as a Senior Analyst at Guardian. Data Analytics found me as  I originally started my career as a geophysicist, focusing on the exploration of the oil and gas industry and analyzing seismic data. When people ask me, what inspired me to pursue a career change to Data Analytics, I always give an honest and real answer. I didn’t have a sort of epiphany moment where I realized Data Analytics was the new future and therefore I needed to be part of that game, instead, it was simple. At the time when I was considering other career options, there was a huge bust in the oil and gas industry, this is fairly standard and part of the ‘Boom Year, Bust Year’ cycle, however, I wanted a career to give me more stability. Therefore, Data Analytics was this new stable career field, which also allowed me to bring my technical skills of analyzing seismic data for 8 years and general comfort with the skillset I had already had.


M7: What impact do you think analytics & big data will have in the next 5 years?
SM: I think two major things will happen in the next 5 years: There will be a push in Machine Learning Algorithms and discussions around the Ethics of AI. In terms of Machine Learning Algorithms, this is something that is already happening, but not on a scalable platform/level. The platforms we currently use to create machine learning algorithms are open-source and free, but you will need additional expensive software to turn all of your algorithms into a real, tangible product. At the moment, I'm noticing a shift in this situation, with the software becoming more accessible. As a result, I believe there will be a greater shift in machine learning and natural language processing in the future, where the algorithms will recognize speech and perform functions independently. Discussions on the Ethics of AI are also being held, but as the AI industry continues to grow so will our concern about its morality. A particular area is the biases within the algorithms that people have created, this is an industry that is not being regulated. When we look at things like facial recognition data, we can see that millions of images of our data are being stored without our knowledge. As people become more aware and concerned about their data, the need for a regulating body will emerge, and organizations’ accountability will be mandated.


Some companies and sponsors appear as incredibly supportive and in alignment with our organization however, they fail to provide the financial support needed as well as offer their resources to push our organization further.



M7: What have been your two biggest challenges as a co-founder at Black in Data, and how did you tackle them?
SM: Our biggest challenge has been a reluctance to follow through with the discussions regarding implementing change and diversify the workplace. Whilst we have inspired a lot of initial interest and support regarding our movement, the difficulty lies within having our sponsors or facilitating companies commit to tangible change and action. However, we have been working to resolve this and openly having these discussions with our proposed sponsors. Also, another challenge we face, as many non-profit organizations must face, relates to our finances. Some companies and sponsors appear as incredibly supportive and in alignment with our organization however, they fail to provide the financial support needed as well as offer their resources to push our organization further.


M7: You have recently partnered with CodeUntapped, Frocentric_tech and Google Digital Garage. Could you please tell us about some of the developments that all of you at Black in Data are excited about?
SM: At Black, In Data we are most excited about our latest partnership with CodeUntapped. It is a partnership where we get people from BID, put them into a training scheme with CodeUntapped and then put them into Jobs with amazing Organisations. The training scheme lasts for 6 weeks, is completely paid and by the end of the training scheme, the candidates are placed into the physical role that they have been training for. Once contracted for roughly 9 to 12 months, training continues. The usual outcome is securing a permanent role with the company. With Frocentric, this is an exciting person of the color networking data-industry site. It strives to connect all the different tech sectors to one platform where we can all network and support each other. It ranges from Black Founder of Data Science, AI Companies and Data companies and bringing them together to break down the compartmentalized and fractured industry. Finally, Google Digital Garage is an incredible platform that enables us to provide soft skills training ranging from CV writing to Google Digital analytics.


Traditionally people used to consume their news via structured platforms, however social media is allowing you to get the raw content from the people that are being the most impacted.



M7: What do you see as the most noticeable change right now happening in the workforce, encouraged by the rise of social media and digital technologies?
SM: The most noticeable change happening in the workforce is down to the impact of social media being used as a tool for awareness and awakening. Traditionally people used to consume their news via structured platforms, however social media is allowing you to get the raw content from the people that are being the most impacted. This has been fantastic for people of color, trying to educate on their lived experiences, share them and finally bringing it to the fore so that organizations have to address it. Topics typically neglected as well as deemed taboo have now been brought to light, forcing companies to evaluate their workplace treatments and biases.


M7: What is your advice to the young women who are starting out their careers in Data Analytics?
SM: The most important thing to do is ensure that you have all of the necessary technical skills, such as being extremely proficient in Statistics, which is a critical component of Data Analytics. One thing I was never told about that I now realize is the significance of, is being able to do storytelling with Data. Usually, you might just think analyzing your data and presenting your findings is enough but you need to master how to explain your findings very well. It is also easy to second-guess yourself, so you need to be confident in your ability and trust yourself. Ultimately, I’d say be kind to yourself. Growing up, I was exceptionally scared of failure but it is all those mistakes that have shaped me into the analyst that I am today!

ABOUT BLACK IN DATA

Black in Data is a collaborative movement for people of colour, striving to promote equality of opportunity and representation within the data industry. Diversity and equality at work is crucial not only for social justice, but also for the development of a flourishing data and technology industry, able to adapt in a dynamic world. The face of the workforce is changing, and  every industry benefits from the greater breadth of knowledge, skills and personal experience that this greater diversity brings. Black in Data aims to energise and accelerate this pace of change.

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data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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