If you’re completely new to data analysis, this article is a great place to start. While it’s not an exhaustive look at data analysis and its many benefits, it does introduce the importance of this emerging specialization.
Organizations large and small have acknowledged the almost incalculable value of data, and it’s data analysts who put all that data to work. With the help of an expert, we’re going to talk about how that happens and how it helps.
At the center of data analysis
Ravindra Patel is a bona fide data analysis expert, with years of experience in analysis and research.
In addition to serving as a highly-skilled professional data analyst for companies including Metro One Plus, Inc., he is a vocal proponent of data analysis and its many applications.
There’s a great deal to talk about under the data analysis umbrella, but while assisting us with this article, Patel focused on the most crucial lessons relevant to data analysis and its positive impact.
Here, he summarizes the biggest takeaway of them all:
“The most important lesson for organizations to grasp regarding data analysis is that it can help them to understand problems and explore data in a meaningful way. Data analysts help to interpret and structure data into useful information that provides context.”
With that background in mind, let’s jump right in.
To kick things off, we wanted to cover some common misconceptions about data analysis, both from the perspective of new data analysts and business leaders.
First, from the perspective of an analyst just starting their career, there can be an inaccurate assumption that all employers will have sufficient data to draw from.
“When I first started working as a data analyst, I had many misconceptions about what the job would be like. A common misconception is that you’ll always have the data you need to do your analysis. That isn’t the case.”
In situations like this, it’s better to explain this problem to the organization’s leaders than to treat the subpar available data as adequate.
Collecting sufficient amounts of data takes time, especially for a smaller organization, and analyzing a small sample size carries significant risks. Most importantly, these issues could result in an inaccurate understanding of the collected data and lead to inaccurate conclusions about the organization’s customers.
This leads right into one of the most common misconceptions that business leaders have about data analysis, which is that data analysts are wizards who can get valuable information out of absolutely any data.
As Patel just touched on, analysts don’t always have a workable amount of data. Also, the quality of the collected data has a direct impact on the information that can be drawn from it.
Moving along, let’s talk about the advantages that can come about when data is plentiful and reliable.
Data analysis = better business
Whether you’re a business leader or someone who’s hoping to work in data analysis, something you absolutely need to understand is that data analysis can have multi-pronged benefits for just about any organization, not just tech giants and Big Data firms.
Even small, locally-owned businesses can benefit enormously from data collection and subsequent data analysis.
Methods for collecting and storing data is a topic for another day, but suffice it to say that, for small businesses, the most basic form of data collection is customer feedback.
Even massive tech companies are deeply interested in customer feedback and customer browsing and shopping habits.
Though there are many other types of data to be considered, especially for organizations operating in industries that are not customer-facing, in our examples we’ll focus on customer-facing organizations for the sake of simplicity.
Now, we’d like to walk through three of the most significant ways in which data analysis can result in better business outcomes, with Patel’s help, of course.
First, let’s discuss how data analysis can aid in intelligent decision-making.
“Data analysis allows you to make informed decisions. The more information, the better. We can’t predict the future, but analysis can make forecasting much more accurate and viable.”
For a customer-facing organization, it’s simple to see how data analysis can help leaders make better decisions.
Not just collecting but understanding how customers interact with your organization can help determine what the organization should focus on.
For example, if data analysis has made it clear that a customer base responds well to special offers, leaders and relevant department heads can plan strategic timing for these offers to generate higher engagement numbers or website traffic.
For obvious reasons, decision-making naturally spills over into customer satisfaction, our next topic.
Direct customer feedback collected through various survey types and reviews can be incredibly useful to a business, but not every customer is going to provide feedback. That’s just the reality of the situation.
So while direct feedback should definitely be requested (providing incentives in exchange for feedback can definitely boost participation in most cases), there are many other data points available to organizations if they’re willing to find ways to collect and record them.
Tracking how much time users spend on an official website, for example, can be useful. Far less complex to capture are simple sales numbers for specific products and the timing of purchases.
Handing a data analyst months or years’ worth of purchase data can provide valuable insight into key operational and sales metrics.
“Customer base data helps companies learn more about their trends and patterns, which will ensure that the company can deliver exactly what its customers want.”
Giving customers what they want is a win-win scenario. Customers will be happier with their experiences interacting with the company, and the company itself will likely see higher sales numbers and an increase in repeat customers.
The last major advantage of conducting data analysis on a regular basis and making it part of a business model is that it can help increase profits, on top of all the other advantages.
This benefit is actually a direct result of the other advantages we’ve already detailed.
When organizations have not just data but useful, relevant information, continued success and growth are more likely.
Having lots of happy customers means positive word-of-mouth, which can bring in new customers.
Higher profits mean more resources are available for various internal departments, including the marketing department.
Higher marketing budgets translate to increased brand awareness, and if intelligent, informed decision-making has resulted in a smooth, pleasant experience for customers, then future transactions feed right back into this positive cycle.
Data analysis can be the catalyst for all this and more. But how do data analysts communicate relevant information to organization leaders?
Advantages of data visualization
While data analysts might understand what data is communicating after some intensive study and, of course, analysis, another very important stage in the process is communicating those concepts to others within the presiding organization.
In this way, data analysts act almost as interpreters, listening to the lessons and ideas that the data is communicating and translating those lessons so that other members of the organization who don’t have a deep understanding of data analysis can glean important concepts and use them to inform their decisions and workflow.
In pursuit of this goal, Patel is a big supporter of data visualization.
So as we’ve already talked about, data analysis takes raw data and puts it into context. Data visualization can do an excellent job of placing that data in context, visually.
Charts, graphs, and more complex forms of data visualization can make an analyst’s key points stand out more clearly.
“I think data visualization can be extremely helpful. It helps to tell stories by curating data into an easy-to-understand form, which can provide useful information to the organization without intimidating people.”
Please keep in mind that not every type of data visualization makes sense for every type of data.
A very famous example of unsuccessful data visualization is a Powerpoint slide from a meeting of military and government officials regarding US military strategy in Afghanistan, circa 2009.
This slide illustrates an important lesson about data visualization, not through its content, but via its overall impression.
Technically, it does visualize data and puts it into context, but the sheer amount of data being displayed and the complexity of its interconnections, as well as how those interconnections are represented visually, render this data/information nearly useless.
Though it’s unlikely that the average data analyst will need to visualize this amount of data simultaneously, it’s vital for analysts to recognize when visualization has gone too far.
If the average person doesn’t get clear takeaways from your visualization, it’s time to try another approach.
Thank you for joining us for this data analysis discussion, and special thanks to Ravindra Patel for providing his expert insights.
To summarize, quality data analysis requires large amounts of accurate data, and expert-level data analysis can have a large number of practical benefits for organizations.
Data and data analysis simply can’t be ignored here in the modern era, and for organizations willing to put in the time and money, this can lead to a competitive advantage with stunning rewards.