As data analytics started getting more relevant to the everyday operations of any organisation, the vacancies for skilled data analysts also soared sky high. Today, practically all the organisations dealing with big data are on a lookout for a upskilled data analysts almost all the time. However, as the need grew, so did the myths associated with it.
Like, for instance, people confuse data analytics with mathematics – a big LOL to them. Little do they know, heh!
Most of these myths spring up because people aren’t aware of the different domains involved in Data Science and often end up confusing one for the other. So, before we get to busting the prevalent myths, let’s first talk a bit about Data Analysts and see how are they different from Data Scientists.
The role of Data Analyst is similar to that of a Data Scientist for the most part. The only difference being that a Data Scientist comes into the picture when an organisation’s data volume and velocity exceeds a certain level that requires more robust skills for sorting through a rolling sea of unstructured data (big data) to identify questions and extract critical information.
Hence, the day-to-day tasks of a Data Analysts slightly resemble that of a data scientist – just at a comparatively smaller scale. Let’s look at the significant responsibilities that surround a data analyst:
Collecting data and setting up infrastructure
The most technical aspect of a data analyst’s job is correctly collecting the relevant data. Data collection often involves them collaborating with web developers or application developers to optimise the data collection. One of the critical functions of a data analyst is to streamline the data collection and develop automated and reusable routines. Analysts keep a handful of specialized software and tools in their toolkit to help them accomplish this.
Spotting patterns
Once relevant data is in place, the analyst now aims to derive trends and patterns from the heap of data. Successful data analysts always know how to create narratives with data, and spotting patterns are one way to get started with it. To make better sense of the data, an analyst first needs to observe essential patterns in the data.
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Producing reports
The trends and patterns found in step 2 need to be communicated to the rest of the team. For that, an analyst is required to create and maintain reports – both internal as well as client-facing. These reports eventually provide the management with the insights about new trends as well as the improvement areas for the organisation. The reports have to be understood by the next decision-maker, so it’s essential that the analysts weave a story around his report – so that it’s easier to understand and analyze.
Collaborating with others
Although the word “analyst” might make you think of someone who works in isolation from the rest of the company, that’s far from the truth. Being responsible for making sense of data and conveying the results to the stakeholders, these data analysts are also responsible for working in close collaboration with the rest of the teams. From the business unit to understand business requirements to the tech team to monitor the type of data being collected, there’s not one domain that’s devoid of the expertise of a data analyst.
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Let’s take a step forward and bust some myths revolving around the lives of a data analyst:
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Myth #1: Data analysts are the masters of Mathematics.
This might have been true at one point of time, but now with much more sophisticated tools entering the market, there are more opportunities than ever for people who don’t have a math background to learn about analytics.
There are numerous analytics tools to help you get started. These tools can make it data acquiring more comfortable for you, leaving you with the heavyweight work of data analytics. Further, there are also many resources that can teach you the art of data analytics. All of this requires a logical mindset and not expertise in mathematics.
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Myth #2: Analytics takes a lot of time.
Most of the organisations opt against data analytics thinking it’ll take too much of time and they’ll be left with little time to do the actual work. However, that’s seldom the case. Once you figure out the metrics, you should be keeping an eye on, and how to track them in your tools, it’s pretty quick to measure those metrics. You will know precisely where to pull those and how to make changes to your operations based off of those metrics.
Although, when you’re trying to answer a new question, you might find that analyzing your data can take a while longer. However, it’ll still be way less than what you expected.
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Top Data Science Skills to Learn to upskill
SL. No
Top Data Science Skills to Learn
1
Data Analysis Online Courses
Inferential Statistics Online Courses
2
Hypothesis Testing Online Courses
Logistic Regression Online Courses
3
Linear Regression Courses
Linear Algebra for Analysis Online Courses
Myth #3: Analytics won’t tell you anything you don’t already know.
Just because you think you can guess the fate of your campaign does mean it’s necessarily true. Every time your organisation runs any campaign, the data analyst analyzes the type of content and channels that are performing well. Your conversion rates on email marketing might be different than social media marketing. These metrics vary from campaign to campaign, and the only way to be sure of any campaign’s fate is actually to sit down and measure the metrics.
Myth #4: Your company isn’t big enough to need any analyses.
Any company, however big or small, can overhaul their operations using data analytics. Especially for smaller organisations, data analytics can come in extremely handy to understand how to grow — and to find out whether you are growing in the desired direction. It also helps you track visitor-to-lead conversion rates and lead-to-customer conversion rates; which helps in understanding if there’s an area of the funnel that is not working and helps you decide what to focus on. Keeping track of your organisation’s performance on various social media channels will also let you know which promotional channels/campaigns are working for you.
If you’re tight on budget, let’s tell you that you don’t need expensive tools to get started. Using a Google Spreadsheet or Excel to keep track of your progress will also work seamlessly.
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Myth #5: You must report on every single metric.
Well, this is entirely up to you. If you want, you can spend 100% of your time reporting on every single metric you can find. There is an endless amount of data, and you can go on creating metrics for analyzing – it’ll take you to an infinite loop.
However, that’s not how things pan out in reality. Before conducting any analysis, the analyst first sits down with the business teams to understand the exact requirements that help in deciding the critical metrics for the task at hand. They don’t need to report on every single metric, but they do need to know the essential metrics to measure for a particular problem statement.
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Is Data Analytics needed only by large companies with big data?
Big data analytics may be used to better drive business success regardless of the size of an organisation. Analytical insights can assist a company discover issue areas while also offering an accurate picture of what customers want. It’s a common misconception that data analytics is only possible when dealing with massive amounts of data, sometimes known as 'big data.' There’s no reason it couldn’t be used on smaller data sets, too. In truth, the quality of data, not the quantity, defines the type of business insights provided and whether or not they assist decision-making.
Is the cost of Data Analytics exorbitant?
The goal of using a technology solution like data analytics is to reap concrete benefits from the project. Concrete benefits here primarily refer to money.
But most small and medium sized companies believe that investing in Data Analytics is extremely costly. However, not all data analytics projects require a large investment. In reality, the cost is relative and is determined by the sort of solution chosen by the company. When it comes to receiving a tangible benefit, data analytics allows businesses to make better-informed decisions, resulting in increased ROI.
Companies can avoid growing data analytics expenditures by making smarter infrastructure decisions by adopting new, cloud-based technologies and big data architecture.
What are the challenges that a Data Analyst has to face?
A Data Analyst may have to face many challenges such as:
1. Data comprehension and Domain knowledge - As a data analyst, you should be well-versed in data fields. You should be able to retrieve the relevant information from a data base that has many tables and hundreds of fields/columns.
2. Data security - As a Data Analyst you must verify that only those who are authorised to view the data have the necessary permissions and access.
3. Senior management resistance
4. interference - As a data analyst, you must interact directly with the organization’s top management, and it can be challenging to communicate your message or the objective of your results.
