Content marketing is a marketing strategy used to attract, engage, and retain an audience by creating and sharing relevant articles, videos, podcasts, and other media.
This approach establishes expertise, promotes brand awareness, and keeps your business top of mind when it’s time to buy what you sell
Statistics, miserable statistics, and falsehoods.
This succinct phrase encapsulates the majority of people’s attitudes toward data.
It’s either untrustworthy, untrustworthy, or simply uninteresting.
However, in the proper hands, data may be transformed into tales.
Stories that receive connections from prestigious publications.
This post will look at where to obtain fantastic data online, how to utilize that data to inspire an idea, and, probably most importantly, how to avoid making mistakes while analyzing data.
We’ll also take a quick look at data visualization approaches, but it needs its own article.
Someone with greater graphic design expertise, most likely.
There is a lot of information available on the internet.
It is believed that Google, Amazon, Microsoft, and Facebook together hold at least 1.2 million gigabytes.
However, this is only the tip of the iceberg.
Consider watching a Netflix movie of this magnitude to get a sense of how much information this contains.
This would take 2,535 years at a broadband speed of 100 Gbps.
That is a large amount of popcorn.
As a result, locating the appropriate data is difficult.
Fortunately, a brilliant marketer has put up a handbook for you.
For the majority of us, the initial step is most likely Google.
Many datasets, particularly those from governments or public organizations, may be discovered in this manner.
When searching for information on the internet, keep the following points in mind:
When looking for older data, use the term “historical.”
In your search, type “data.”
Keep your initial search wide (crime data London) to see what’s available, then go further into the various sources (data.london.gov, Met Police, ONS, etc.).
I like to do this in various tabs while having the main search results page open as well.
Once you’ve determined what you’re searching for, conduct an advanced Google search for.xls or.xlsx files, or visit government websites filetype:xls / xlsx / pdf, and so on.
Google also offers a special search engine dedicated to datasets, which is also worth a look at.
Finally, not all data, particularly the more esoteric ones, is easily accessible in this manner.
For this reason, Jeremy Singer-Vine, the statistics editor of Buzzfeed News, publishes a weekly email.
The greatest thing is that you can access the archive of all the datasets at any time, sparing you the hassle of searching through your inbox for the email that specified where walruses prefer to hang out.
We’ve previously discussed ideation.
This method, which I call data-driven ideation, is a little different.
Whereas Tom’s strategy is coming up with a notion and then hunting for references to back it up, I like to flip it on its head.
I hunt for datasets or data-driven papers that are relevant to my client’s specialty, and then I consider what datasets these queries may answer.
For example, we discovered a database of every publicly owned painting in the United Kingdom.
There are other questions this might address, but we concentrated on one: how many sculptures are created by women?
There are two things to bear in mind while thinking about questions to answer.
First, has the question already been answered?
If it hasn’t, that’s fantastic.
If so, would your response offer anything to the discussion?
Once you’ve found your data source, you’ll almost certainly need to manipulate it to get it to behave the way you want it.
Governments, in particular, appear to like the ugly Excel document prepared in unexpected ways.
For example, utilizing columns when rows would be more appropriate…
The modest average is the measure we’re most likely to work with as content marketers/data analysts.
But did you realize there are many kinds of averages?
When most people talk about the average, they are referring to the mean.
It is computed by putting all of the numbers together and dividing by the total number of possible possibilities.
Alternatively, you may use the AVERAGE calculation in Sheets or Excel.
It doesn’t handle outliers well, so if your data is skewed, please proceed.
The median of a dataset is the middle value, which is computed by sorting the numbers and determining the middle value.
You may also use the MEDIAN formula in your preferred spreadsheet program.
This metric is better suited to skewed datasets.
Assume I’m searching for a property in London and want to know what the typical price is.
All the oligarchs purchasing penthouses will distort the mean so much that it will depress me.
The median, on the other hand, takes into consideration a significantly higher number of slightly more moderately priced residences, resulting in a presumably less gloomy figure.
Finally, we get at the mode.
In a dataset, this is the most common value.
You may use the MODE formula if your dataset is numerical.
If you’re looking for the most common text, the method is a little more complicated but still simple.
Outliers are outcomes that are much greater or smaller than what would be expected based on the rest of the data.
If you’re not cautious, they may wreak havoc on your analysis.
There is a measurement or data input error, you should repair it as soon as feasible.
If you are unable to amend it, eliminate the observation since you are aware that it is inaccurate.
You can exclude the outlier if it is not a member of the population you are researching (due to exceptional traits or circumstances).
You should not eliminate it since it is a natural component of the population you are investigating.
A lot of data-driven content includes categorizing locations based on one or more parameters, such as pint costs or spider occurrences.
It’s a great method to receive attention in a variety of local newspapers.
However, there are dangers when evaluating locations based on criteria that may be influenced by population size, as this frequently quoted XKCD comic illustrates.
We employ per capita measures to solve this problem.
This is just a fancy Latin way of saying divide your metric by the population of the area to which it applies, yielding the metric per person.
Unless you’re dealing with ridiculous quantities like GDP or national debt, this will yield a modest number in most circumstances.
As a result, the norm is to multiply by 100,000 to make it more comprehensible.
This provides you with the measure per 100,000 persons in that location.
And, voilà, the larger place no longer always wins.
There are several ways to be incorrect, but only one way to be correct.
In this section, we’ll look at some of the most typical data analysis blunders and how to prevent them.
Choosing a data range that supports a certain point of view while neglecting the bigger trend
Making generalizations about a broader population based on a small sample size
Using percentage change for tiny quantities is deceptive.
Correlation might not always imply causality.
Even if we don’t claim something is causing something else, juxtaposing two patterns leads readers to reach that conclusion.
Avoid excessive precision:
Taking numbers beyond the decimal point might be misleading if one of the values in the computation is an approximation.
Don’t get the percentage point difference (40 percent – 30 percent = 10 percentage points) mixed up with the percentage change (40 percent to 30 percent is a 25 percent decrease)
In general, when employing percentage change with variables that are already percentages, use caution.
This may result in more mistakes.
Keep a detailed record of your steps, noting where you obtained the data.
Websites’ hidden areas might be difficult to locate.
Check that you’re dividing by the correct number when using percentages or division.
Dates should be standardized, including being broken down into days/months/year if appropriate.
When feasible, avoid typing and instead rely on formulae.
Data entry by hand increases errors.
After making significant changes, double-check your data.
Data-driven content must be thorough and precise.
Journalists will not cover, much alone link to, something where the data have been manipulated.
The easiest approach to avoid this is to be as cautious as possible.
One of the most crucial aspects of the process is deciding how to visualize the data you’ve painstakingly collected and analyzed.
After all, most people dislike spreadsheets.
Making your data more visually appealing is another step in achieving the coverage you seek.
What chart you use is primarily determined by the story you wish to convey.
A bar chart, for example, is great for presenting the number of items in each category, but a line chart is best for demonstrating how the data has evolved over time.
If you want to be more daring, there are additional, fancier charts available.
Choropleth charts employ color to depict values over a geographic region, whereas Sankey diagrams depict the movement of something (energy, money, etc.) from one location to another.
The preceding strategy will assist you in transforming dull old data into an interesting content campaign.
To summarise the procedure, first, pick a fantastic dataset library that you can return to again and time again.
Then, while seeking ideas, check for related datasets and see if they generate any ideas or problems that need to be answered.
Preparing the data for analysis is usually the most tedious, but most necessary, portion of the entire process.
Use all of your Excel/Sheets techniques to analyze as quickly and simple as feasible.
Finally, select a good visualization style and wait for the links to come in.
You’ll have to reach out to it first, but that’s a story for another day.