If you’re starting a business or already halfway there, you need to listen to what your business data has to say. Data is more than numbers, graphs, pies, or percentages. They are essential information pieces to your business. Also, they offer insights into the status of different systems, departments, and locations.
Apart from that, they are also critical parts of systems advocacy. If you wish to change or improve something in the system, you need data. You can present facts and figures to support your claim. The same goes when you’re asking for more funding or lobbying for reforms.
There’s no guessing game with data. It doesn’t matter if your output is what you expected. With data, you can justify your business decisions and strategies. Yet, raw data won’t be of so much help.
With more than 2.5 quintillion bytes of data received per day, data shortage is not the issue. Instead, it’s how humans make sense of them. This is where analytics comes in.
What is Data Analytics?
The process of examining, cleaning up, and transforming data is what makes up data analytics. It also includes posturing data to discover insightful information. This is where businesses could use it to draw conclusions and support decision-making. With data analytics, you stop guessing. Instead, you start sticking to facts when making business decisions.
Must-Read Books on Data Analysis
If you’re unsure how to leverage data analytics for your business, you need to understand how it works. Below are the top five books you can start reading. With these, you’ll understand data analytics. You can also get ideas that can impact your business.
1. Wayne L. Winston’s Microsoft Excel Data Analysis and Business Modeling
If you want to learn Excel from the ground up, this is the perfect book for you. Excel has been every beginner’s favorite for statistical analysis within the last 35 years. If you master this, you can launch a career in analytics. More so, you can help your business grow.
Many consider this book as one of the best in data analysis. Why? Because it uses Excel for probability analysis and basic statistics. Aside from that, the author also filled it with many practical applications. These applications are for technical topics like forecasting, multiple regression, and more. The content is also extensive because it can help you become an expert in the topic, with its many exercises.
2. Phil Simon’s Too Big to Ignore: The Business Case for Big Data
If you’re still unsure whether big data is helpful for businesses, then you have to read this book. The author showed how institutions leverage data. They used the government, private sector, and big corporations to explain this point.
Phil Simons also included several lessons from big data experts. Also, he added case studies from across the globe. Anyone who wants to dabble in data analytics must read this book. It can give valuable insight on how to turn data into intelligence. It would also teach you how to turn intelligence into actionable information.
3. Cole Nussbaumer Knaflic’s Storytelling with Data: A Data Visualization Guide for Business Professionals
If you want to learn how to communicate efficiently with data, you need to read this book. This shows you how to leverage the knowledge of visualization for your business. The book also offers pragmatic guidance to business analytics experts. Through this, they can present data in a more palatable and understandable manner.
You can master data analytic skills with this book. It shared insights and information to achieve this. The book challenges you to go beyond your comfort zone. You can do this by using conventional data visualization tools. As such, you can create a more compelling, informative, and engaging story.
4. Gohar Khan’s Creating Value With Social Media Analytics
This book will assist in your learning further about data analytics. It will also teach you how to apply big data to various social media strategies. Through this, you can drive engagement and value. You need this if you want to improve your conversion rate and increase market traction.
If you delve deeper into the principles shared in this book, you will better understand them. It discusses resources, techniques, strategies, concepts, and theories to enjoy social media. With this, you can increase website traffic and generate high-quality leads. You can also improve buyer patronage and make better business decisions.
5. Andriy Burkov’s The Hundred-Page Machine Learning Book
This book is best for data science beginners. If you want to get acquainted with machine learning, read this book. It talks about technicalities and mathematical concepts in simple terms. As such, it doesn’t sound intimidating or overwhelming to novice data analysts.
The value of big data and big data analysis for a business is undeniable. Regardless of their size and experience, companies should learn how to leverage data. With this, they can improve their working methods and increase customer satisfaction. They can also improve their business bottom line.
Read these books and jumpstart your business’ data analytics journey. With the valuable data analytics insights in these books, you could never go wrong. And, if you’re ready to scale your business, contact Thematic. They can help you make sense of your big business data.
I have two points for which I occasionally try to be persecuted:
- If you don’t know which tool to choose, then choose any tool. And this is most likely Google Optimize.
- You don’t need to know the under-the-hood math of A/B testing. Look at the tool’s “Win/Lose” messages.
So why do I think that?
- If you don’t know which tool to choose, chances are you have no or very little experience in A/B testing. To get that same experience, it’s important to start as soon as possible.
- If you don’t know which tool to choose, it means you have no requirements for the tool. If there were real requirements, a simple comparison of functionality would quickly solve the problem. And if there are no requirements, then anything will do.
- Knowing the under-the-hood math will help if you write your tool from scratch. In other cases, it’s practically useless: you can’t change the Google Optimize algorithm(s) and/or other tools.
It seems like picking the best tool and figuring out the under-the-hood math is very, very right. The devil lies in the definition of “right.” I insist that speed of motion (start early) is more important in most cases. In that time, you will have time to do more and get better results than that gain from a better tool and knowledge of math.
Run experiments whose wins will be reported to you by accounting, not by the analytics system.
Data collection for most qualitative research relies primarily on face-to-face interaction. It is due to the nature of the data itself, taking the form of concepts, words, and ideas.
But pandemic restrictions have affected all aspects of our lives, including research pursuits. This has significantly affected data collection for qualitative studies. Due to this, many researchers have resorted to remote methods of data collection. This shift brings up some new challenges unique to remote interactions.
We’ll go through some popular remote data collection methods for qualitative research. What are their advantages and characteristics? What factors do we have to consider with the use of online/electronic means for data collection?
Remote Data Collection Methods
Popular pre-pandemic methods of data collection were face-to-face interviews and focus group discussions. Aside from transcribing the words of the participants, researchers also made field notes. These notes consisted of the researchers’ observations on the environment and nonverbal cues, among others.
However, you cannot access all this extra information through remote interactions. Alongside other factors, most people are more comfortable with in-person conversations.
Researchers have decided to make do and glean whatever information is available. Remote methods also present some unique advantages.
Video conferencing experienced a surge in popularity during this pandemic. Platforms such as Zoom and Google Meet became popular choices for communication.
Video conferencing is a popular alternative due to it being an audiovisual experience. It’s the closest we can get to face-to-face interaction without having to be near each other.
Depending on factors such as internet speed and access, discussions can flow naturally.
Most video conferencing platforms also have built-in recording tools. These leave you with a video file you can review and transcribe when needed.
Phone calls are also a popular remote interview choice. They are relatively inexpensive and accessible, especially for respondents in more remote areas.
Phone interviews are an excellent way to collect verbal testimonies and interviews. It may also help respondents feel more comfortable since they won’t feel as observed. Since you cannot see each other, there is less pressure to ‘perform’.
However, this lack of visual cues can also be a disadvantage. It makes building a rapport more of a challenge. It also makes it more challenging to pick up on non-verbal cues like body language.
Organizing a group discussion over a phone call could also be quite a challenge. Some older phone models might not support the feature, and it would be harder to keep track of each speaker.
You can also conduct interviews or discussions via text message or online chat. There are a lot of messaging apps available right now, with both mobile and desktop versions.
Depending on your research design, these discussions can be synchronous or asynchronous. You can send your questions and have the respondents answer them. You can also have everyone go online at the same time and have a live discussion.
Like phone calls, respondents could feel more comfortable communicating via chat. Without visual cues, they can feel more at ease with expressing themselves. It’s also easier to record conversations since the medium is text-based.
Its weakness, however, is the lack of visual and auditory cues. The medium limits you to communicating via words on a screen. This risks misinterpretation. You can use emojis, slang, or tone indicators, but it is not the same as face-to-face interaction.
Remote data gathering is not a new phenomenon, but it hasn’t been the norm until recently. Due to its differences with more traditional methods, it also poses unique considerations.
Most remote data gathering methods need to use third-party programs or applications. You may reassure them that you will keep their data confidential, but these apps may not.
It’s best to be open about this to your respondents. For some, it might not be a huge concern, but it’s best to be safe. Better yet, you can find messaging or conferencing programs that guarantee your privacy.
This concern can depend on who you are writing for or your research question itself.
Some studies have also questioned the validity of qualitative interviews. They cite that the researcher’s personal biases could influence their line of questioning. The additional barriers innate in remote methods could add to the method’s limitations.
Either way, it is best to consult with fellow researchers or your higher-ups. Getting feedback helps you ensure the clarity of your methods and the validity of your data.
Unique circumstances require unique solutions. There is no question that researchers will continually adapt and overcome. Pandemic or not, we remain committed to the pursuit of knowledge to help our institutions.
Once you have your data together, you’re ready to move on to analysis. Thematic has a quick yet comprehensive guide to help you through the process.
While it is true that online dating has the edge over traditional dating, there is a downside that involves sharing your intimate information with the matchmaking services you use. Not all, but most Matching & dating sites and apps rely on the information you provide to run their business. Some use it to suggest the best matches, while others use it for targeted advertising and to make money. Ultimately, the choice of the dating site will matter a lot in determining how safe and secure your data is.
Which Groups of Daters Are Most Concerned about Collecting Data?
Everyone seems worried about sharing their location and intimate personal details with online dating sites, but married people looking for affair dating are usually more concerned about how their information is stored and protected. Similarly, mature singles who are returning to the dating scene are often skeptical about dating collection by these online services. That is why platforms targeting affair dating keep a close eye on their privacy policies so that users can feel their data is secure. As most sites are now moving away from advertising and relying more on paid upgrades and subscription revenue, wives seeking married dating or single parents returning to dating should know their data is safer than it used to be.
How Much Data Dating Services Collect about Their Users?
It usually depends on the quality and reputation of a dating site, but most of them collect a variety of highly personal data and often retain it indefinitely. It may include text conversations with other members, photos, videos, and info on sexual orientation, gender, religion, political affiliation, location, ethnicity, body type, desire to have children, and beyond. Some platforms also collect data related to preferences in a partner – they achieve it with the help of filters or by utilizing powerful algorithms that keep an eye on users’ every swipe.
Today, an increasingly large number of dating sites encourage you to join through Facebook, Instagram, or other social media sites. This option allows those sites to access thousands of additional data points, including who your friends are, what you like online, and what kind of content you have been viewing. Speaking of sensitive information, the top on the list is your location. Dating sites collect your location data because they claim to need it for recommending relevant matches nearby.
This means that whether you use an app-based platform or you are on a website-based service, know that dating services will have a bunch of your data. Plus, a website data tracker can help reveal the URLs you visit while exploring a dating site. The information is then used for a positive result from the matching service, which is harmless as long as the site that is used has a high trustworthy reputation.
How Dating Sites Use the Collected Data?
Providing better services for finding partners is a key point, exactly for which the entire data collection is started. That is why choosing a reliable, authentic, and reputable dating site becomes even more important. An authentic site would use it for the following purposes:
- To Improve Customer Experience
A good dating site uses your info to improve customer experience. For instance, they access your location data to help you find someone in your local area. How precise the data tracking is, varies from site to site, with the opportunity for members to share or indicate themselves their current city and country.
Some dating apps would show even more granular location info – they allow you to find out users who may be only a few feet away from where you are. It means you can find people in the same town or even on the same floor of your apartment building. And with data about your preferences in a partner, such matches will inevitably lead to a real date.
- To Update Algorithms
Dating sites work on algorithms to fetch you the most relevant matches, and the data you share will have a huge impact. They constantly upgrade algorithms considering your personal information as well as who has liked you on the platform and how you use the service. The algorithm is updated considering why your profile is “Liked” or “Not.”
They also consider preferences you share with them, which allow the sites to introduce new filters and help you find accurate matches. That is why some sites allow you to filter results based on body type, ethnicity, and religious background, and others do not.
- To Secure Users
Security is an important concern for online daters, and these sites and apps augment using your data. For instance, they use your photos and screen your data using AI to ensure better security. Some sites preemptively screen images and block everything that might be considered lewd. Such steps help increase customer satisfaction and allow them to browse a site with confidence.
Profile identification, implementing fraud and spammer tracking systems, blocking inappropriate content – all this is possible thanks to preliminary data collection and previous user experience.
Today, people do not mind mining dating sites and apps for love, and they even do not mind becoming a premium member to enjoy additional matchmaking services. Over 30% of US adults are using those online platforms and would continue to do so until they find a partner. Being on an authentic dating platform would help keep things safe and secure with the most positive outcomes possible.
Data-driven storytelling is based on the great allure of stories. With this in mind, more and more businesses are adopting a narrative approach to internal and external communication in the effort to convey abstract data in vivid ways.
In times of big data: Presenting complex information in an understandable way
Business intelligence tools, CRM software and the use of artificial intelligence all give marketing and sales departments a wide range of options for data collection and analysis to choose from. But the crux actually lies in the wealth of information available and in the complexity of that information: The mere generation of numbers and data is largely pointless if it does not succeed in communicating the meaning of these numbers and data and putting them in a context people can understand.
Data-driven storytelling, on the other hand, prepares naked figures in such a way that stakeholders and customers experience them as understandable, interesting and appealing.
“Sometimes reality is too complex. Stories give it form.”Jean-Luc Godard
Storytelling with data as a communication strategy
Basically, data storytelling is not new. For instance, a trend toward data-driven journalism has been emerging for several years now. The term describes not only a certain type of information acquisition but also a particular form of presentation. These aspects are also true of data-driven storytelling in in-house corporate communications and in marketing.
In essence, data-driven storytelling comprises three areas:
- the analysis of the data
- the narrative
- the visualization of the data
Thanks to narrative as well as visual and interactive elements, abstract data sets take shape, and this contributes to greater reach.
Data processing: Here’s how to turn data into a good story
But how do you proceed if you want to illustrate the latest sales figures, or user interactions in the last quarter? First of all, there should be careful consideration of which topic is to be prepared for whom, and on the basis of which data:
- What point do my data illustrate? How meaningful and representative are they?
- What target group do I want to address?
- Which aspects of my data evaluation should be conveyed to the target group?
- What prior knowledge does the target group have?
- What misconceptions does the target group possibly assume?
7 typical storylines in data-driven storytelling
Only once the above aspects have been isolated does it make sense to think about what the narrative should look like. First of all, a storyline should be considered that is appropriate to the question at hand and the existing pool of data. According to marketing manager Ben Jones, 7 basic types can be distinguished here:
- Change over time: A story is told about a process or transformation.
- Drill down: The narrative begins with an overall view and leads to a concrete example.
- Zoom out: Over the course of the narrative, a tiny focus is extended to taken in the big picture.
- Contrast: Different protagonists, data or issues are compared.
- Intersection: At the heart of the narrative lies a crossroads where two or more questions or data points intersect.
- Dissection of factors: Data and storylines are interrogated for correlations and causalities. Unclear records are “dissected,” so to speak.
- Profile of outliers: The story is dedicated to special cases and statistical outliers.
A meaningful structure of a data story
Like any good story, a data story should captivate its readers or listeners. Taking a cue from the dramaturgy of classic feature films, a structure in at least three parts is recommended:
- Exposition: Presentation of the topic and context of the data analysis; what is the occasion for broaching the question?
- Confrontation: Presentation of the central question and the challenges involved; what are interesting observations and problems?
- Resolution: Concluding wrap-up with recommendation for action; what insights does the data analysis yield, and what things might need to be changed?
Data visualization: Preparing numbers for visual effect
In addition to the actual narration, visual elements also play a decisive role in data-driven storytelling. Infographics, diagrams, animation and highlights make the world of numbers tangible, even for the untrained beholder. The presentation should be as clear and simple as it is precise. The accompanying narrative can pick up on and explain any relationships that cannot be conveyed visually. Combining textual and visual elements makes data stories easy to understand and internalize. This not only bolsters in-house communication processes but can also contribute to improved customer loyalty.
About the author: Cora Eißfeller
Cora Eißfeller works as an online editor at content marketing agency textbest in Berlin. After working for several years in publishing, the literary scholar now devotes herself entirely to digital marketing. Her focuses are e-commerce, new work, and urbanisation trends.