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HOW I FELL IN LOVE WITH DATA ANALYTICS

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Discovering a passion for data analytics isn’t just a career choice; it’s a love story in itself. In our conversation with Joseph Eriwha, Head of Business Development at Maison Atlantic,  we explore how he found himself captivated by the power of data analytics. His story of how data analytics became a vital tool in reviving businesses is a tale worth sharing.

ABOUT JOSEPH ERHIWHA

Holding a bachelor’s degree from Middlesex University, majoring in Information Technology and Business Management Systems, I would classify myself as a seasoned business development expert with a wealth of experience in converting strategic plans into achievable business goals. In over 8 in business development, I have explored various industries and sub-sectors in the technology, power, logistics and hospitality space.

Connect with Joseph Erihwa on LinkedIn.

Was it love at first sight with Data Analytics?

For short, the answer is yes. So I’ve been in business development for eight years in various sectors from financial technology, health technology, logistics technology, information technology and the like. 

And the first time I saw Data Analytics, I saw the influence it has on internal business processes. It has a lot of focus on internal business processes, more than the conventional tools that we use as business development officers which include SWOT analysis, Porter 5 forces, PESTEL and the like.  It takes into account external factors that we an organisation do not have control over such as your competitors, and force measure. So yeah, I would really say with Data Analytics it was love at first sight.

What sparked your interest in Data Analytics initially?

So I used to be the sales manager for what I will call the biggest telemedicine platform in Nigeria and we saw that we were not really making a lot of growth in terms of customer base. So we had to crunch our numbers to also see the types of people that we have (types of enrollees) on our platform. We saw that the majority of them were working class, and we were able to switch our business model because we utilised Data Analytics.

We were able to switch our business model from B to C to B to B, and easily forecast our financial numbers,  also the enrolees base we plan to have within the year, which was easier to do with B2B. So that streamlined our sales activities to achieve individual organizational goals.

Can you share a memorable project or experience that solidified your passion for Data Analytics?

Yes, let me use my most recent place of work, somewhere I currently consult for. It’s in the logistics technology sector and I’ve been able to utilise Data to understand our high-traffic areas, high-traffic customers, the form of demographic and also the psychographic data on why they purchase our products. That way we’ve been able to push our energy to areas that we have a lot of others coming from.

So we are going to have better customer service in terms of speed, pricing and pick up time. It has affected affected our business positively. So instead of just using our numbers or our efforts to capture the whole of Lagos, we try to streamline it to various geographic locations.

In our case, we are using local governments as a basis to look at how to grow our numbers and that is working for us instead of just having scattered sales activities around Lagos.

How has your love for Data Analytics influenced your career and personal growth?

Well, to be honest, I will say it has affected me. In the past, in some of my past activities, we’ve utilised the services of what we call data analysts consultants, people who are not under the payroll but are at our beck and call, based on request.

Now having that skill at hand gives me an edge in the market because most business development officers I know have no knowledge about data. So it gives me an edge because of that skill set. Being able to analyse businesses, the strengths and weaknesses of businesses based on the data they’ve gathered. So informed decisions are made better.

What is your favourite thing about being an Analyst?

I would say it’s something people shy away from, but data cleaning is very important. From my personal experience, just to give you a rough estimate might not be the exact numbers, but roughly about 90% of the time I spend with data analysis is on data cleaning.

You have to have clean data to have accurate results. So spending a lot of time on data cleaning, it’s it’s I think has been my core strength. It’s something I’m still developing because there’s room for growth, but it’s my core strength currently as a Data Analyst. Utilising Excel and PowerBi, I’m currently working on that for SQL and it’s going pretty great so yeah!

We hope you enjoyed reading this article as much as we enjoyed writing it. Our discussion was truly enlightening, emphasizing the incredible potential of data. It’s amazing to see how data can change lives and revolutionize business practices.

If you’re eager to share your own experiences with data analytics, send us a message! We’d love to hear from you.

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Free Datasets for your next project

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Whether you are adding a data project to your portfolio or you’re starting your first project as a paid Data Analyst, your first task will be to find a suitable data set. In this article, we’ll go over the basics of what a data set is, how to find them, and ways to determine their quality.

So, what is a Data Set?

A data set is simply a collection of information. Usually, datasets are not always organized in a way that is immediately useful, and it will need a bit of work on your part to make it usable. Datasets can come in various forms, such as spreadsheets, databases, JSON files, or even text files. They serve as the foundation for Data Analytics and visualisation.

So where do you find them?

In this article, we’ll highlight a few repositories where you can find data on everything from business to finance and even crime.

Kaggle

Kaggle is one of the largest platforms for data science and machine learning competitions. It hosts a vast repository of datasets across a wide range of topics, including healthcare, finance, social sciences, and more.

   – Type of data: Various types of datasets, including structured, unstructured, and time-series data, covering topics such as healthcare, finance, natural language processing, computer vision, and more.

  – Access: Users can access datasets through the Kaggle platform by creating a free account and browsing the dataset repository. Some datasets may require participation in competitions or adherence to specific terms and conditions.

UCI Machine Learning Repository

The UCI Machine Learning Repository is a collection of databases, domain theories, and datasets widely used by the machine learning community.

   – Type of data: Datasets suitable for machine learning research and experimentation, including classification, regression, clustering, and recommendation systems.

   – Access: Datasets are freely available through the UCI Machine Learning Repository website. Users can browse datasets by category, view dataset descriptions, and download data files in various formats.

Data.gov

Data.gov is the official open data portal of the United States government, providing access to a vast array of datasets from federal agencies.

   – Type of data: Governmental datasets from federal agencies covering diverse topics such as demographics, economics, healthcare, environment, public safety, and more.

    – Access: Datasets are accessible through the Data.gov portal, where users can search, browse, and download datasets for free. Data.gov promotes transparency and collaboration by providing open access to government data.

Google Dataset Search

Google Dataset Search is a specialized search engine developed by Google to help users discover datasets across the web.

-Type of data: A wide range of datasets from various sources, including academic repositories, data providers, government agencies, and research institutions.

   – Access: Users can search for datasets using keywords, topics, or data formats through the Google Dataset Search website. The search results provide direct links to the source repositories or websites hosting the datasets.

GitHub

GitHub is a popular platform for software development, collaboration, and version control.

   – Type of data: Datasets shared by researchers, data scientists, organizations, and communities on the GitHub platform, covering diverse domains and topics.

   – Access: Datasets are accessible through GitHub repositories tagged with “dataset” or by searching for specific datasets using keywords. Users can explore repositories, view dataset files, and download data for free.

Are these quality datasets?

Determining the quality of a dataset is crucial for ensuring the reliability and validity of your analysis. Here are some factors to consider when evaluating dataset quality:

  1. Research the source of the data and the methods used for its collection.
  2. Confirm whether the dataset contains all the necessary variables and observations required for your analysis. Incomplete datasets may lead to biased or inaccurate results.
  3. Verify the accuracy of the data by cross-referencing it with other reliable sources or conducting validation checks.
  4. Ensure consistency in data formatting, units of measurement, and coding schemes across the dataset.
  5. Evaluate the relevance of the dataset to your specific research question or analytical objectives.

After choosing a suitable dataset, the next step is to clean, visualise and generate insights. You can learn how to do that by registering for our next training here.

Comment if you want a part 2 with more sites to check out!