We keep hearing about how we’re living in the age of Big Data. While it’s certainly true that we can now access more data than ever, this comes with some challenges. Many companies are now suffering from data overload. They have more data than they know what to do with. They aren’t sure which data is useful and how to turn it into an actionable plan. Let’s take a look at the problem of data overload and how businesses can get past this and actually benefit from data.
Data Overload: What is it and Why is it a Problem?
Data overload is a symptom of the modern world, where everyone is bombarded with information throughout the day. Even people who aren’t in business and don’t deal with hard data experience information overload in the form of phone calls, emails, news stories, ads, media broadcasts, and other communications. When you’re dealing with business-related data, the problem is more specific. You’re accumulating numbers and information that are relevant to your business. While such data is essential, it can easily get out of control. According to one estimate, there will be a more than 4,000% increase in annual data by 2020.
Many Types of Data
You can accumulate data in a number of ways:
- Research. Much data, either free or at a price, is available from Google, Statista, Gallup, Pew Research, and other companies.
- From consumers directly. You can get data simply by asking for it or by getting permission from your customers and website visitors to access behavior such as browsing history through cookies.
- Analytics tools and services. Tools such as Google Analytics, Facebook Analytics, as well as many paid software tools and services provide all kinds of data
- Data purchased from consumer data companies. Data farming is now a huge industry and there are many companies that sell all types of information.
The kind of data you acquire may include:
- Sales data. Companies can access their own records and track which products and services customers have bought as well as return rates, repeat business, and upsells.
- Market research data. This includes demographic data about your customers such as age, gender, income, geography, education, etc. Data collected from surveys and focus groups also fit into this category.
- Email and social media analytics. Email open rates and conversions, social media views, followers, shares, engagement.
- Website analytics. Traffic, bounce rate, opt-ins, sales. More detailed information can be derived by tools such as cookies and heat maps that measure specific visitor behavior on web pages.
Why Data Overload?
Data overload is a subjective term that doesn’t refer only to how much data you have but your ability to make use of it. What’s perfectly useful for one person or company may be overwhelming to another. You may experience data overload for a number of reasons.
- There’s more data all the time. Simply put, data is outpacing our ability to process it.
- Not all data is useful. You have to sift through it to find what’s relevant and actionable. Some data is outdated, not relevant to your industry or target audience.
- Inconsistencies. You sometimes get contradictory data. For example, if you do keyword research using different tools you may get different results. Conflicting data is confusing and makes you wonder what to believe.
- Difficulty interpreting it. Turning raw data into useful information can be challenging at times.
- Not knowing how to turn data into stories. While data is useful for research, consumers prefer stories.
Now let’s explore ways to prevent and overcome data overload so you can start making good use of your data!
Make the Best Use of Data
There are several aspects to avoiding data overload and actually benefiting from your data.
Identify Your Goals
Be discerning at what kind of data you collect and make sure it’s relevant to your goals and KPIs. For example, you may want to collect data to:
- Increase productivity. Observing benchmarks lets you identify the practices, policies, and tools that deliver the best results.
- See better ROI. Looking at analytics for advertising, email, or social media campaigns show you what is and isn’t working.
- Observe market trends. Identify new products to release and the best customers to target.
- Consumer preferences. Surveys, focus groups, and measuring customer behavior tells you what your customers prefer.
When you know your objectives, you can more readily decide which data is useful and which is superfluous or not relevant.
Practice Data Filtering
When you have lots of data coming in, it’s helpful to filter it. This means focusing on a subset of all the data that’s available. For example, you may have information about consumers from all over the world when you only need to focus on one country or even one city. To use a simple example, you can set up filters when using a program such as Excel Spreadsheets that lets you reduce the number of rows or columns to exclude anything that’s not relevant.
While data filtering works with spreadsheets, the principle is actually is much broader and should be applied to any case where you have redundant or irrelevant data. For example, if you purchase data from an external source (e.g. a data broker) you can request that they filter it before sending it to you. You can set filters on analytics programs such as Google Analytics. Often, simply excluding extraneous data from your field of vision clarifies matters.
Educate Employees on the Importance of Data Insights
Collecting and analyzing data is no longer the specialized field it once was. Every department in an organization can contribute to the accumulation and understanding of data. Not everyone has to be an expert in data management. They can, however, be trained to recognize relevant trends and information so they can pass it along to the appropriate people within your company.
As you refine your approach and learn the best type of data to collect and how to filter it, pass this knowledge to others in your organization. It’s helpful to have at least one person in a department who’s data savvy. For example, customer service managers can identify key metrics in areas such as customer satisfaction.
Transform Data Into Stories
Many business leaders complain that they’re inundated with data when they actually need stories to make sense of it all. Data, while often valuable, isn’t an end in itself. Out of context, it’s hard to make sense of it. Here are some guidelines that can help you or people in your organization turn data into actionable stories.
Gain Clarity by Simplifying Data
Suppose customers answer a survey that gives them 5 choices to express how happy they were with a customer service experience. The choices might be:
- Extremely Satisfied
- Satisfied
- Neither Satisfied nor Dissatisfied
- Dissatisfied
- Extremely Dissatisfied
These results may be indicated by the above phrases, stars, or perhaps visual depictions of smiling or frowning faces. While 5 choices may not seem like a lot, it’s still enough to create complexity when compiling results.
Obviously, your overall goal is to improve customer satisfaction. You might set a KPI to increase the number of customers who are either satisfied or extremely satisfied to at least 90%. For this purpose, it’s simpler to look at it as a binary such as simply satisfied vs. less than satisfied rather than looking at all 5 possibilities.
This approach is often clearer if you’re summarizing results in a report or in a marketing campaign. If you boast that “Surveys show that 58% of our customers are extremely satisfied with our customer service, 28% are satisfied…” your audience will get bored or confused with all these numbers. It’s better to simply say “88% of our customers are satisfied or extremely satisfied with our service.” This tells a story with a clear message rather than a jumble of statistics.
It’s a similar case with any data that can be quantified into multiple categories such as income (e.g. percentage of customers earning under or over $50,000/year), taste tests, color or style preferences, etc. Simplifying makes for a better and more understandable story.
Identify the Most Useful Information
Stories are about people and actions. In a novel or movie, not all characters and events are of equal importance. There are major heroes and villains and supporting characters. Numbers, statistics, and other data are similar. Numbers can support stories but can be dry and even confusing on their own. Suppose you’ve just gotten the results of a customer survey on a software product and you want to create a useful report based on this data. The results include these findings:
83% agree that the overall functionality of the product is good.
73% agree with the statement “I wish it was faster.”
91% find the dashboard intuitive and easy to use.
15% experienced at least one glitch while testing the software.
You need to interpret these numbers and extract the most useful information from them. For example, while it’s a positive sign that 83% gave the software a good overall rating, this has to be considered within the context of the other results. Almost three-quarters of testers finding the software slow may be a red flag if speed is an important consideration.
Similarly, at first glance, it might be reassuring that “only” 15% experienced a glitch, but this is actually a significant number if the problem is serious. Such a situation could result in many refund requests and negative reviews.
When you get information, whether from surveys, market research, or analytics tools, you need to hone in on the most relevant information. It’s often best to concentrate on one variable at a time. If you try to address too many issues at once, you can lose focus.
Use Data to Tell Stories About the Future
One useful way to use data is to create a story about the future. This is known as predictive modeling, a non-traditional yet often valuable type of market research. One company that makes predictions in a variety of fields is Microsoft with Bing Predicts. Using search engine data, the Microsoft-owned company makes predictions on everything from sports results to the Academy Awards. Bing’s high-profile picks are always entertaining to read even when they turn out to be wrong (e.g. Bing predicted Roma would win Best Picture at the 2019 Oscars while the actual winner was Green Book).
While predictive modeling is, by its very nature, based on probabilities and estimates, it lets you take numbers and turn them into a story that’s readily understandable. For example, the Bureau of Labor Statistics publishes data on the fastest growing occupations. While taken from current findings, these are all predictive as they project likely growth over 10 years. The BLS projection that physician’s assistants will grow 37% between 2016-2016 is a statistic. However, colleges, guidance counselors, city planners, and others use this type of data to tell stories that serve the needs of their audiences.
Make Your Data Work For You
When properly used, data helps you understand the world and make better decisions. The challenge is to not get bogged down in excessive, redundant, or irrelevant data. Aside from the guidelines we’ve shared, aim to maintain a conscious relationship with data. Rather than accepting everything at face value, be discerning about what data is useful and for what reason. And don’t forget to turn your data into actionable plans and stories people can relate to.
Imagine If is a progressive full-service agency that can help you make better use of your data. We’re obsessed with research actionability and positive business impact. To find out more about our services, contact us.