Jan
16

By CommenceCRM

How Bad Data Can Negatively Influence Your Decision Making Process 

Data Cleaning

Data is one of the most valuable resources any business could have, whether it’s for your marketing or sales teams. However, data is only useful when it is of high quality. Bad data that is incorrect, irrelevant, or missing can create issues for you and your company. At best, these problems may be inconsequential but at worst, your company could end up making mistakes or bad choices.

So how does bad business data influence your decision making process? Businesses rely on data to determine what to do with their stocks, marketing campaigns, and resource allocation — to name a few. Inaccurate insights can lead to the wrong business strategy because they don’t present what is going on in reality, causing leaders to make decisions blindly. 

What Is “Bad” Data?

Data quality is the foundation of effective business intelligence. Unfortunately, poor data quality costs the US economy $3.1 trillion per year. No industry is immune to bad data and organizations struggle because they lack confidence in the information that is supposed to guide their day-to-day decisions. 

To be more specific, bad data is any piece of information that is erroneous, misleading, or confusing in its format. It could be unintelligible handwriting from your sales reps’ notes or a cluttered database you can barely understand. In short, bad data is simply data that isn’t useful and prevents your data collection efforts from paying off. 

There are several types of bad data that could be plaguing your database: 

Missing data 

Incomplete or empty fields are like a missing puzzle piece; you won’t have a complete picture of your leads. More data points in your records will lead to more insights. Imagine running a campaign targeting prospects who are in a managerial position but not everyone on your list indicated their job title. This could lead you to missing opportunities or segmenting potential consumers inefficiently. 

Inaccurate data 

Inaccurate data could mean many things. It could be fake information, like a false email address. Or it could be inaccurately recorded information, like one incorrect digit on a phone number. Sometimes, a field may also store improper data such as a text field containing a numerical value. Inaccuracies can make your data impossible to filter and use, leading to issues like poor targeting and segmentation, irrelevant messaging, or nonexistent email delivery. 

Outdated data 

Outdated data is information that has expired. In a modern digital ecosystem, changes happen rapidly that many databases don’t get updated on time. Even if the information looks promising and accurate, it may no longer be relevant. Individuals change their roles and companies while organizations are rebranded, get acquired, or close down. However, you need to trust that your information is up-to-date and fresh for smart decision-making and analytics. 

Duplicate data 

Duplicate data are multiple copies of information in one database. This often happens due to customer relationships management (CRM) software integrations, manual entries, or batch imports. Duplicate information can slow down your storage, skew your metrics, and create inefficient workflows. It can make the works of sales reps more difficult if the duplicates are inconsistent like recording two different numbers they would have to verify before using. 

Unformatted data 

Poorly formatted data is the most common type of bad data. These are misspells, typos, inconsistent abbreviations, variations in spelling, and formatting. They might not cause a lot of damage to your decision-making process but these errors can be time-consuming. Imagine looking at the title field and trying to filter out “CEO” and “chief executive officer” because of formatting issues. 

Compared to these types of poor-quality information, here are some characteristics of good data: 

Qualities of Good Data 
Completeness  There are no gaps in the data; everything that was supposed to be collected was done so successfully. 
Relevancy The information is actually useful for your initiatives, campaigns, and decisions. Even if the data is high quality, it’s useless if it’s irrelevant to your goals.
Accuracy The data you have describes real world conditions that help you form the correct conclusions. 
Consistency Each data item is uniform in content and format with their counterparts across multiple datasets and databases. 
Timeliness Data reflects current reality and recorded immediately after the real world event. 

How Poor Data Quality Cripples Your Organization 

In the era of the digitally-engaged customer, the quality of the customer experience is what defines a business’s success. Since a substantial portion of customer engagement happens online, it’s imperative that your company has a 360-view of your customer to gain a competitive edge. A 360-view is acquired through the customer data collected at various digital touchpoints. 

The reason why poor data can affect your organization’s performance drastically lies in the purpose of data itself. Whenever a business decides to collect information, they do so with the intention of making decisions based on what they compile. This holds true for major initiatives like a merchandise launch or a new investment but can also affect simple actions like the number of products shipped to a company branch. 

The short-term and long-term decisions you plan to make is tied to the quality of business data you have. Data even helps you form internal policies and processes so false, irrelevant, and incomplete information can be a waste of your time and resources. As a company’s resources are finite, bad data can cripple your operations in several ways including: 

A wrong business strategy 

The primary role of business data is to help you make better decisions and give your plans a better chance to succeed. Inaccurate data works against this goal because it leads you to wrong conclusions and an ineffective strategy. 

Although business data is not a crystal ball that can foretell all the answers you need, it is certainly better to have it than to work blind without information. With quality data, you will recognize which business moves will create more value and estimate how much success you can expect to achieve. If you’re misled by your data sources or you have no confidence in the available information you have, you take on more risks and are less likely to have positive results. 

A damaged reputation 

Organizations that make assumptions about the accuracy of their data are more prone to inefficiencies, subpar customer support, compliance issues, and reduced productivity — all of which are directly related to customer satisfaction and reflect upon your company’s reputation. An unsatisfied customer will express their negative opinion about you on social media and review websites, as well as to the people they know. 

Aside from looking unprofessional, bad data could also be preventing you from doing effective audience targeting. Instead of creating a personalized and efficient customer journey, you may end up reaching out to a broad consumer market and customers may think your brand won’t suit them. 

An increase in financial costs 

In any industry, bad information can cause mistakes and inconveniences that cost your organization money. Bad data leads to bad business and you can lose out on revenue when you commit error after error. Aside from poor decisions and problems, bad data can also cause a dip in your productivity. 

Everyone uses the same data, from managers to customer support, marketing, and sales. If the data quality is substandard, productivity suffers because all employees have to double-check and correct the information instead of being able to implement it immediately. These inefficiencies waste company time and your business lose more money. 

A number of missed opportunities 

Every wrong business decision is a good opportunity missed. Knowledge about your customers’ spending power, interests, and behaviors is essential for establishing your business strategy. If your data is inaccurate, your plans won’t be as effective and you might miss out on potential prospects. 

Even worse, you won’t even realize what the problem is because your data won’t tell you that it’s wrong. You won’t be able to solve the problem or optimize your strategy. You might also lose your advantages over competitors because you were unable to anticipate the needs of your prospects. 

Techniques To Improve Business Data Quality

What can you do to avoid data error and improve the quality of your business data? How do you transform information into your organization’s greatest asset? Data governance is the answer to these questions.

Data governance is the process that ensures each data item is managed properly so you can trust and rely on the information they present. Governance over your data is like quality control over the information because you assign people who can be made accountable to the data. You have to put people in charge of fixing and preventing data-related issues. Some techniques in data governance include: 

Going straight to the source

Finding the source of bad data is the key to solving the problem. It’s not unusual for data quality to be poor if the source is inaccurate. If you want to use data for reviewing internal policy, you have to collect data from and do research with relevant parties. 

Your current data extraction techniques should also be correct; you might be running into problems because you’re compiling qualitative data instead of quantitative data. Each small error and irrelevant factors can skew your results and affect data quality negatively. 

Refining your data collection process

Even if your source and extraction methods are fine, your organization could be suffering because you aren’t collecting data you actually need. Consider doing research on other data types and comparing the results with what you have. You can also finetune your process by double-checking with sources and having third parties review the information after collection and compilation.

Managing your database regularly

Quality data is a result of an ongoing effort; it won’t work with just a one-time cleanse. All the information you have should be properly collected, processed, and managed especially as your business expands. Your needs may change and your data types could grow irrelevant. Regular data cleansing should be performed by one group of people following a set of rules for consistency.

Practicing Effective Decision-Making With Good Data 

Once you have recognized the importance of keeping your information organized and updated, you can now apply your learning to decision-making. How does one make a data-driven decision?

Making a data-driven decision is a process where you collect data and extract patterns, facts, or insights from these to make inferences that guide your final decision. Your choices and strategies as an organization are based on actual information, rather than intuition or haphazard observations. Here are the basic steps for practicing effective decision-making using good data:

Step 1: Identify your goals.  Every data analyst should know the business well and have a good understanding of what the organization needs. This knowledge is gathered through research and introspection on the problems affecting your industry and competitors. You will need this foundational knowledge to establish better inferences later on while your goals can streamline data collection and guide the interpretation. 
Step 2: Find reliable data sources. You will draw sources from several channels: web feedback forms, social media accounts, and various databases. Putting together a list of sources can make it easier to coordinate information, although finding common variables within each dataset can still be tricky. It is also a good idea to present the data in a way that it can still be accessible with other scenarios or goals. 
Step 3: Clean and organize data. It is impossible to interpret data if it isn’t clean and orderly. Data cleaning is the process of preparing raw data for analysis by removing or correcting all corrupted information. In fact, most analysts spend their time on cleaning over analytics. 
Step 4: Perform statistical analysis. You will need to build a statistical model for your analytics. Models like random forest regressions or decision trees are among the model types that can test your data and answer business questions. Some companies even try models created using a machine learning algorithm so it really depends on how you want to present the information to answer your question. 
Step 5: Draw conclusions from the information.  The last step in the decision making process is to draw your conclusions from any information or insights you learned from the model. A good place to start is to ask yourself questions you think you already know the answer to. Many companies make assumptions such as “X type of people are our best customers” or “consumers want X product”. By challenging these assumptions, you can either confirm them with data or correct them with your new learning. 

The conclusions you draw from a data-driven decision making process can assist you in making more informed decisions and driving more successful strategies forward. Although it can be time consuming to maintain data health and make decisions like this, it will benefit your business in the long-run. 

Improve Your Data Quality with Commence Cloud CRM 

A CRM system is a great way to manage and improve your data quality. With a cleaner, more comprehensive picture of your customers, you can make smarter decisions for your business and develop strategic relationships with all your stakeholders.

Commence Cloud CRM offers an award-winning solution for small and mid-sized businesses who want to maximize customer information. Sign up with Commence CRM today. 

 

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