What exactly is ‘Data’ and why do we need to be mindful of it?
It’s been quite some time now since we have been hearing this word ‘Data’ in our daily lives. Most of us might hear the mention of this word at least once a day. So, what exactly is it and why is it still a hot topic after quite some time now?
In the simplest of terms, Data is basically the bits of information which all of us living on this planet are producing every second. That sounded big, right? That is exactly what it is. According to a recent study conducted by Finances Online, the amount of data generated solely in 2022 will reach around 94 zettabytes by the end of this year. That is how big of a topic it is.
Why exactly should we be mindful of it?
The rule of success is to be mindful of all of the circumstances that can either hinder our success or pave a way clear for it. If we are aware of the situation, we can definitely make better decisions that can in turn help us grow. This is exactly the job of the data. It is produced by us, for us. It makes us aware of the situation and gives us exact facts and figures and makes us intelligent enough to potentially make better decisions.
How to be mindful of the data?
This is the step that requires our efforts. The data which is being generated every minute by all of us, in its raw form it isn’t much of a help. It is not understandable. In order to make that data useful and beneficial for us, we need to clean it and remove the ambiguities and abnormalities from it and convert it into the form which is meaningful for our studies and deductions. In other words, we need to work on the data and convert it to a form which would serve our purpose.
How to work on the data?
The data which is big in size and which serves a certain organization is stored in a proper manner and this proper store house of the data in technical terms is called as a database. All the working that we perform on this data is performed within that database. We have a number of tools and languages available to communicate with that data within the database and engineer and structure it according to our needs. The one that I am going to demystify in my blog today is SQL. So, let’s begin.
What is SQL?
SQL stands for “Structured Query Language”. This is the language with which we communicate with the data stored within the database and apply different operations on it. Using SQL, firstly, we can perform all the necessary cleaning required to make the data readable and secondly, we can enhance our data and add a lot of new values and features to our data. Data, in any organization is the most valuable asset. It can be a cause of a huge success of an organization if handled with intelligence as well as it can also be a cause of a downfall if not handled with proper privacy and security.
Aug 18, 2021 | Nikita Phavade
1. Hi Lindsay! Tell us more about your domain, Data Analytics. What does it mean to you?
Data analytics is about discovering insights from data that could bring values to the organization in a form of optimization. For example, improving effectiveness in customer acquisition efforts or reduction in operational costs. So, there are two parts of it; insights generation and value generation by taking action on the insights.
The first part includes digging deep into the data by asking micro questions that relate to the bigger objective. This requires technical and statistical skill. Technical skill is to plow through big datasets via SQL Query and Python. Statistical skill is to understand the behaviour behind numbers.
The second part of value generation involves different ways to communicate these findings to stakeholders. It could be in the form of self service analytics via dashboard or ad hoc analytics via presentations. This point requires soft skills from the analyst, specifically data storytelling. For dashboards, creativity is empirical to know how to present the data in a way that will be easy to understand. For presentations and bigger projects, influence and storytelling skill are more prominent to be able to convince stakeholders on testing out the recommendations.
2. What does a day look like for a Data Analyst ?
Well, it started with a cup of coffee, hoping that nothing breaks in the dataflows. LOL. There are no two similar days as a data analyst. Some days, I am challenged with new technical issues in dataflow. Some days, there will be meetings with business stakeholders trying to understand their needs for insights and analytics work that will enable them to make better decisions. Some days are spent in front of a dark screen coding away in Python to complete a Machine Learning project.
3. What are the best parts, and maybe some challenges involved in your work?
The best thing I enjoyed most about my work is the variety. You get to be technical one day and present your insights and recommendations the next day. I also enjoy the creativity of designing dashboards to showcase data. I am constantly challenged and stimulated intellectually with new problems, which makes it interesting.
The difficult part is influencing the stakeholders to take actions on the recommendations. All hard work on discovering data patterns is all gone to waste if it does not bring value to the organisation. Therefore, understanding how to work with different types of stakeholders is also key to bringing impact through data.
4. Data Mining is a buzzword today, but a lot of us are still trying to catch up. With your expertise, what are the upcoming trends you foresee in your domain?
Analytics on demand might be in the near future. We have seen increasingly that companies are automating Machine Learning processes. Similarly with analytics in general, with the tremendous advancement of NLP (Natural Language Processing) and GPT-3, we can enquire any questions about the data by letting the computer learn about all the tables in our database.
5. What message would you like to give someone trying to carve a path in the Data analytics industry?
I believe curiosity and inquisitiveness are the key qualities you should embrace in this industry. Sometimes the problem is so vague or novel that you might know the answer yet. Curiosity will enable you to probe further to understand the questions better. Willingness to learn will empower you with knowledge in new areas that could potentially solve the issue. In terms of technical skills, SQL and Python are two key skills that all aspiring analysts should be comfortable with. These are the enablers for dealing with big data which usually is not as straightforward to mold according to our needs. Being familiar with data structures and schemas will be important too.
6. Lastly, I want to thank you for all the amazing work you do at She Loves Data Community, how has your experience been?
I am currently part of the Data Integration team contributing as a data engineer. I am in charge of bringing in, cleaning, and transforming data from multiple sources so that it is ready for data visualisation. The team aims to understand how much lives we impact and to learn from the past events on what worked and did not work.
It has been a great experience so far. I am surrounded with smart and passionate people with the same goal in mind whom I always learn from. I am always upgrading and being challenged to go beyond the familiar. It has been fulfilling to be part of the journey of women empowerment in the data and analytics industry.
Our Origin Story
It all started with a simple question: Where were the women? When Jana Marlé-Zizková and Pavel Bulowski went to work meetings or events in the tech industry, the other attendees were often men. They discovered that many women had a lot of enthusiasm and interest in data analytics and related areas. Yet, not everyone knew how to get started or access community support. This inspired the first workshop on data analytics for women in 2016. In the face of the overwhelming response, one workshop led to another, and then to many more. She Loves Data was born.
As a community, we commit to the belief that women have many talents, virtues, and values to bring to the table. This bears out in our very own logo, which is based on the Coxcomb chart. Florence Nightingale famously used the chart to explain the preventable deaths of soldiers during the Crimean war. She later led the sanitation reform of British hospitals. Just as Nightingale used data to create change, so too can more women become active contributors to a data-driven world. Pam Ooms, our volunteer who designed the logo, felt that Nightingale’s story fitted very well with She Loves Data’s purpose and wanted to borrow design elements from the Coxcomb chart.
Today, 200 years from Florence Nightingale’s time, she continues to inspire as a role model. We also find ourselves in the midst of a crisis, where the need for data literacy and digital skills is more urgent. We are forced to adapt to disruptive changes and in many instances, shift from the physical to the virtual. One silver lining is that the transition has led She Loves Data to create our first webinars, and we’re able to reach more people. At the same time, our community focus remains core.
It’s important that women building skills in tech don’t feel like they are on this journey alone. We understand that it can be tricky to build this feeling of community and sense of belonging online. There is no easy solution, but we can continue to support each other with friendly exchanges in our Facebook and LinkedIn groups. She Loves Data also continues to expand. We’re building local tribes and partnerships in places from Armenia to Vietnam so that more women can benefit from our work. We hope you keep safe and stay strong.