Why you can (or cannot) trust your reports

According to Harvard Business Review, 90% of business leaders believe data literacy is crucial for company success. However, 84% of marketers have difficulty accessing data and insights. Dashboards and reports don’t display the real picture and cannot be blindly trusted.

Why is this the case? Poor data quality and data errors are among the top reasons why you cannot trust your marketing reports. According to a Gartner survey, “the average financial impact of poor data quality on the organization is estimated to be $15 million per year.”

If you don’t trust your dashboards and the data on which they are built, then management decisions won’t be made based on actual numbers. And for businesses, that’s terrible.

In this article, we discuss all possible data errors and stages where those errors may occur. Then we provide you with specific advice on how to avoid them. Here’s your checklist for saying no to bad data quality!

If you want your company to succeed, you have to empower business users with high-quality data insights. With all available information at hand, marketers can easily explain why a decision worked, what affected the success or failure of advertising campaigns, what can be done better, etc.

Moreover, with the rapidly growing digital media landscape, customers are using more and more media channels and expect a personalized approach backed by data so they can get precise recommendations. It’s unreasonable to ignore the capabilities of marketing analytics, especially considering that more than 80% of marketing budgets rely on digital channels for attracting customers. It’s as if you had a powerful Ferrari in your garage and were afraid to drive it!

However, implementing this logical and straightforward approach is complicated: for executives and marketing specialists to apply the data gathered, they have to trust it. And therein lies the root of all problems.

What can be done to improve the quality of data collected from all channels marketers work with and increase confidence in data findings? Prevent mistakes from appearing!

There are three stages in which data errors usually emerge:

1. Measurement planning
2. Data collection and preparation (normalization)
3. Report creation

Note! At each of these stages, data can be mishandled due to human factors.

Let’s look at what can be done to stop errors from appearing. After all, it’s much easier to prevent mistakes than to look for errors and data discrepancies throughout the whole reporting system.

The first step in working with data seems pretty easy — you have to plan the collection of all the marketing data you need. However, data collection is often planned only for a specific task, so you may not have enough data for new tasks you undertake or new projects you start. Therefore, skipping the planning step is unreasonable. The OWOX team always does an express analysis before they start working on a project. Why? To identify possible bottlenecks in data collection. Sometimes the team even develops entire systems of metrics and enumerates all possible parameters.

The challenge is to collect all fragmented data from all data sources (different advertising platforms and services you work with) and make this data work for you.

The absence of this planning stage, or an unstructured approach, leads to reconfiguring the transfer of identifiers while historical data isn’t available. If you don’t have all the data, your decisions and actions will be based on incorrect or incomplete information. To avoid this, you need to factor in all customer touchpoints by collecting:

• User behavior data from your website and/or application
• Cost data from advertising platforms
• Call tracking, chatbot, and email data
• Actual sales data from your CRM/ERP systems, etc.

The main errors that can appear at this stage are the result of:

• Getting aggregated, sampled data. Sampled data is generalized data that occurs when only part of the data is analyzed and used for reporting. Let’s imagine you have a kilo of apples and your task is to decide whether these apples are good or rotten. If you use sampled data (two or three apples from the whole bunch), you can decide that this kilo is of excellent quality when in fact all the other apples you didn’t sample are rotten.

Why is this happening? Since processing massive data arrays in the shortest possible time is a complicated and resource-intensive task, systems process data quickly by using data sampling, aggregation, and filtering to generate reports as fast as possible.

How does it affect data and report quality? Obviously, badly. Smart decisions cannot be made based on just a small data sample. For example, you may not see a profitable advertising campaign and may turn it off due to distorted data in a report.

How can you identify this problem? In the Google Analytics interface, you’ll see a message at the top of the report that says This report is based on N% of sessions.

If you want to have reports you can trust, it’s a must to get raw unsampled data. So after deciding on all data sources, you need to preferably use connectors that automatically collect raw data and regularly check data completeness.

• Getting incomplete and incorrect data from an ad service’s API. Why does this happen? Advertising services collect a lot of data about users and their behavior; however, while transmitting data, errors such as data doubles, data loss, or discrepancies during a retrospective update may appear. How does this affect the quality of your data and reports? These errors are transferred into reports, and, as a result, it’s simply impossible to make the right decisions due to inaccurate reporting.

How to fix this problem? As it’s impossible to directly control code when working with APIs, it can be difficult to interact with services. We strongly recommend using data import tools that support API changes. In case data is unavailable, these tools can show existing data gaps and download data afterwards.

Where do I store all this information?

If you want to store every byte of data on your own servers, it’ll cost you a fortune. We recommend using cloud solutions, as they will save your resources and provide access to data globally. Without question, the best option on the market that considers the needs of marketers is Google BigQuery. You can use this cloud service to store raw data from websites, CRM systems, advertising platforms, etc.

Basically, the best option to avoid errors in data collection is to never ever collect data manually. Today, there are tons of marketing software solutions such as OWOX BI that automatically collect data into a data warehouse (or data lake) from different services and websites.

For many years, marketing depended on third-party data. But now big influential companies such as Google and Apple are changing the way they use personal data. Soon, the world will abandon third-party cookies. For marketers, this means complicated data gathering and losing tons of valuable data on user activities at different touchpoints. What can be done to maintain your performance? First and foremost, you have to focus on collecting first-party and second-party data. Secondly, you should be prepared to use data lakes, as only data stored in such lakes is owned and controlled by you (and not by advertising platforms). Due to updates in Google products (Google Analytics 4, the Privacy Sandbox initiative), we recommend using Google BigQuery, as the new version of Google Analytics has native integration with GBQ and provides full data exports.

The second step after collecting all data is utilizing it. However, you cannot do that right away. Data is impossible to work with until it’s prepared (normalized). Why is that so? Data from different advertising platforms/services has different structures and is in different formats and currencies. To be able to build reports, you need your data to be structured, updated, and complete.

While trying to normalize data, you can encounter challenges related to:

• Different data formats, structures, and levels of detail. Why is this the case? Different services use different schemes for uploading data. For example, one advertising service may have a column named Product name, whereas another has a column named Product category.

How does this affect the quality of data and reports? It’s simply impossible to build reports if data is in different structures.

How to fix this problem? Before analyzing data, it must be converted to a single format; otherwise, nothing good will come out of your analysis. For example, you should merge user session data with advertising cost data to measure the impact of each particular traffic source or marketing channel and to see which advertising campaigns bring you more revenue.

• Different currencies. Different advertising services use different currencies, and to get the correct numbers in your reports, you should always check which currency is used and convert all currencies into a single base currency.
• Insertion, updating, and deletion dependencies. While eliminating unstructured data to be perfectly uniform across all records and fields, various undesirable side effects may appear.

How do these dependencies affect the quality of data and reports? The most common result of such errors is that data is discarded by the system and isn’t taken into account when creating reports, making the reports themselves erroneous. For example, say we have a sessions object and an advertisements object. In sessions, we have data for days 10 to 20, and in advertisements there’s data from days 10 to 15 (for some reason there is no cost data for days 16 to 20). Accordingly, either we lose data from advertisements for days 16 to 20 or data from sessions will only be available for days 10 to 15.

How to fix this problem? If the user doesn’t know the peculiarities of merging data and doesn’t verify the data they work with, the probability of making an error is very high. Therefore, the solution is to check your data before using it.

Note! Data normalization is a manual and routine “monkey job” that isn’t very inspiring and prevents analysts from extracting insights. Normalization difficulties usually take up to 50% of an analyst’s work time. And let’s be honest: it’s quite frustrating. To prevent this from happening, use automation tools!

The perfect way out is to apply automated solutions that can collect, clean, normalize, and monitor the quality of your data so it’s business-ready. An even better variant is if your chosen data connector can do it all for you, as the all-in-one OWOX BI platform does. With the help of OWOX BI, you can easily face all the challenges that await marketers and analysts and get business-ready data you can trust.

Data collection and normalization errors will never disturb you with OWOX BI. This service frees up your valuable time and handles:

• Data collection. Get all the data you need from Google Analytics, advertising services, your website, your offline store, call tracking services, and CRM systems into Google BigQuery. OWOX BI allows you to obtain reports without sampling on any available parameters. The service gathers raw data and warns you in case of mistakes in an API’s data transfer. All you have to do is provide access to data sources and choose what data you want to gather.
• Data monitoring. You can always see where you have data discrepancies, during what period, and how critical they are with the help of OWOX BI. It compares the amount of data in your BigQuery project daily by hits, sessions, users, and transactions with Google Analytics and signals significant discrepancies.
• Data normalization. OWOX BI helps you with cleaning, deduplicating, and updating data as well as converting costs into one base currency and monitoring the relevance of data. Also, OWOX BI can define mistakes in UTM tags such as unsupported dynamic parameters, syntax errors, or an outright lack of obligatory UTM tags.

With OWOX BI, you can collect marketing data for reports of any complexity in Google’s secure BigQuery cloud storage, which is the best choice for marketing needs.

According to surveys, 84% of marketers have difficulty accessing data and insights, whereas 86% think they need better tools for getting them. Simply put, marketers want to have a report as soon as they think about it. The business benefits when the marketing team is implementing insights whereas the analytics team is extracting them. And with more and more user behavior data gathered by marketers, it should be easy to get useful insights. However, in practice, things often work in quite the opposite way.

After coping with obstacles at the data collection and data preparation levels, there are still some other difficulties with report creation such as:

• Deciding which attribution model to use. There’s no right answer, as different models are required for different tasks. Also, you should keep in mind the peculiarities of your business. A detailed overview of all attribution models on the market can help you choose the one that’s best for you.
• Deciding which report building service to use. Choose a service that can easily provide understandable data visualizations and can update reports automatically. Please note that while data visualization services such as Google Data Studio can work with more than two data sources, it’s still not possible to use them for merging and transforming data. If you want to create a report based on many data sources, you have to first collect all the necessary data into a data lake (e.g. Google BigQuery).

The more complicated the entire reporting ecosystem becomes (especially for enterprise businesses) and the more reports and SQL queries are built, the more easily the system breaks. Apart from factual data errors, there may also appear various difficulties that can lead to even more errors, broken SQL queries, or misunderstanding and misuse of collected data.

• Too many edits to reports (and/or SQL queries) in a short amount of time. Why is this happening? In the classic reporting architecture, there was a dataset under each report built using SQL queries and nothing changed. But today, these SQL queries are changed and edited every now and then. How does this affect the quality of data and reports? There are so many changes that it’s easy to forget what changes were made and when. This means that edits at the level of one dataset aren’t applied to another dataset.
• Requirements are constantly changing (and the transition to a new version of Google Analytics 4 and data usage limitations don’t make it any easier). Why is this happening? Whereas the need for different reports is growing, analysts need to create datasets, normalize data, and write SQL queries for each report. How does it affect the quality of data and reports? When an object’s meaning is changed — for example, what is understood as a conversion changes to a website visit instead of making an order — it’s a challenge to remember what needs to be changed so that data remains precise and correct in every report.
• A long reporting process. Why is this happening? Analysts are always overloaded and marketers have to wait for reports. Moreover, according to studies, creating even a single dashboard takes an average of 4.5 days, a minimum of three iterations, and most importantly, about $18,000. How does this affect the quality of data and reports? Marketers don’t have the opportunity to find immediate answers to all kinds of what, why, when, and where questions the moment they arise. As a result, decisions are made based on intuition or incomplete and incorrect data.
• Difficulty in understanding data. Why is this happening? Different people work with the same reports, and there’s not always the same meaning behind a particular metric and a particular parameter. For example, within various reports the user metric can mean a registered user with no purchases or a returning customer. How does this affect the quality of data and reports? When you make a decision, you define a user one way, but there’s no guarantee that the report you’re referencing defines a user in the same way.

All these factors can be illustrated by one example: playing a game of whisper down the lane. Your input data seems to be correct and relevant, but the result is still entirely not what it was supposed to be.

It’s quite a task to make data and reports trustworthy and applicable. Among the major pains and obstacles in achieving this goal are the following:

• Having access to up-to-date, complete, high-quality business-ready data to free analysts’ time to look for risk and growth zones and find new approaches to working with data.
• Being able to generate marketing reports within minutes so that marketers can test hypotheses fast and search for insights.

Fortunately, modern cloud analytics solutions provide marketers and analysts with reports and data they can trust. If you want to create reports you can trust — reports that don’t break and don’t take days to be prepared — you should try using OWOX BI. It’s a service designed specifically for marketing reporting needs based on complete, high-quality data. With its thoughtful approach, analysts can test hypotheses and find insights five times faster.

OWOX BI democratizes access to data for all users, regardless of their technical background. The service provides you with automated processes for collecting and preparing business-ready data, allowing you to create and edit reports in minutes. Besides, you can stop worrying about errors in reports when you make changes, since with OWOX BI, your reports never break!

OWOX BI makes it easier than ever to unlock insights trapped in your data and create reports you can trust. Book a free demo to see how else OWOX BI guarantees data quality and how you can benefit from fully automated data management today!

A company gets the most value from its data when that data is high-quality and when decision-makers can quickly act on data findings.

An analyst is most valuable when they work with business-ready data, can quickly answer business questions with the help of data, and can bring actionable insights. An analyst’s work is no longer about spending time collecting, preparing, and harmonizing data and writing SQL queries.

A company needs to use automation tools with modeled business-ready data to grow faster than competitors using marketing insights. High-quality data is the basis on which every report and management decision should be built. While there can be many obstacles to getting high-quality data that can be trusted, starting from the data collection stage and going all the way up to the report creation stage, doing so is still possible.

What can be done to overcome these problems?

• Prevent errors when collecting data, as it’s easier and cheaper to stop errors from happening in the first place than it is to mitigate their consequences.
• Avoid the very possibility of an error by using as much automation as possible in workflows. Not to mention that automating manual and repeatable tasks enables specialists to focus on more value-added activities such as developing actionable data insights.