Data has rapidly turned into a key economic driver in the US and around the entire world. As companies deal with unprecedented demands from their customers, the need for product innovation, and rising concerns around cybersecurity and fraud, many companies are relying on data to empower their strategic decisions that could resolve these challenges. As a result, data is no longer considered as a resource; instead, data is treated as a very critical asset.
Despite the value that data deliver to many companies, many companies are still struggling to put data to good use. The ability to take strategic decisions to minimize risks, and to create and distribute into the market requires good data that is highly trustworthy. Do you have good data? If yes, then do you trust your data?
In today’s growing complex marketplace, access to quality data can break or make a company. Now, without data sales wouldn’t happen and campaigns that drive results wouldn’t exist.
What is data quality and why it is important?
Data quality is an assessment of the fitness of data that can serve its purpose for a given context. Data quality is determined by factors like relevance, reliability, accuracy, completeness, and how up to date it is. Since data has become highly complex linked with various operations of companies, the importance of data quality has gained huge attention.
Bad data is usually considered as the primary source of inaccurate reporting and ill-conceived strategies in any companies and some companies have tried to quantify the damage caused by bad data.
Data quality is very important as without good data you cannot truly understand or stay in touch with your prospects and customers.
In this data-driven economy, it is quite easy to find information about your prospects and customers. And this data about your prospects and customers enables you to market more efficiently and at the same time build meaningful relationships for a long time.
Determining data quality:
The aspects that are critical to data quality are the following;
Current:
As your contact and company data decays, it doesn’t just lose its value, but it can seriously harm your business. And as a result, many organization business objectives fail because of poor quality data.
The reality is that the majority of sales and marketing tools depend on up-to-date contact and company data.
Correct:
Even though it may appear as a repetition of the previous point, the idea of correctness should be applied to new incoming data as well as the existing data.
Just think about how many time you have encountered a new lead coming in with a major typo error. All your great subject lines and eye-catching banner graphics won’t matter without correct contact and company information; your emails will never reach the inbox of your subscribers.
Consistent:
Inconsistent data can harm your lead scoring, lead routing, and reporting considering the technical standpoints. While considering the marketing standpoint, consistency in your data can be the core difference between a highly effective and highly ineffective attempt at personalization.
For example, a lead comes into your CRM as “maRK”. Now, without proper data normalization capabilities, the automated follow-up sent to this lead will read, “Hi maRK” or something like “Dear maRK”. Clearly, not the best first impression you could have made.
Coverage:
In the B2B industry, buying committees are quite common. According to CEB now known as Gartner, averages of 5.4 individuals are involved in today’s B2B buying decisions.
Getting one individual to show interest in your solution is easy when you have to sell your solution to companies having an effective buying committee, you can’t effectively sell. Even if you want to identify your total addressable market (TAM), cross-selling to different departments, or enter a new market, you will need more coverage.
Complete:
It is a well-known fact that short forms outperform long forms in terms of their conversion rates. Unfortunately, lower the number of form fields the data collected from the form will be less
So, run a test to find a suitable number of form field for your website and landing pages. And then work with a B2B data provider (like Infotanks Media) to fill in the cracks.
The benefits of data quality:
There are many benefits of data quality, but the primary purpose of accurate, actionable data is to make informed decisions in order to make businesses more efficient and profitable. Following are the top benefits of data quality;
More effective marketing:
Data quality is of paramount importance in marketing. Earlier, companies could market only to broad audiences, wasting resources and money on targeting prospects and customers who were highly unlikely to be interested in the product or service being offered, because of the lack of demographic and other critical information about prospects and customers.
The abundance of demographic data available today, enable marketing teams to create a highly focused campaign and are more likely to achieve their desired results.
Lower mailing costs:
Data quality ensures that you have accurate contact data of your prospects and customers which ensures that more emails get delivered to your subscribers, which saves money as each undelivered email cost you money and when your email campaign has a higher number of delivered emails it further help you to achieve higher delivery rate for your future email campaigns which will further save you money.
Improved customer relations:
It makes complete sense that quality data improve your relation with your customers. Quality data enables you to truly understand your prospects and customers and helps you to avoid sending emails that they want. Quality data about your prospects and customers helps you to anticipate and deliver on their requirements and interest.
These create goodwill among your prospects and customers and is another benefits of data quality which otherwise would not have been possible.
Never miss an opportunity:
Poor quality data close the door to many opportunities and results in uninformed business decisions. Many bad business decisions translate directly into missed opportunities. Poor quality data compromises business strategies, and it also results in missed opportunities down the line.
A thorough understanding of about the spending behavior of your prospects and customers is very critical companies. Many marketers explore the opportunities from these data based on three main categories; current income, total net worth, and past buying behavior.
Without quality data about prospects and customers, companies cannot achieve their revenue goals. It also affects your ability to reach your prospects and customers effectively and deliver according to their specific requirements and interest. It further harms the efforts to maintain proper customer data, including their buying behavior that could lead to missed sales opportunities.
Increases productivity:
Data quality goes beyond dollars and cents. Lack of quality data slows down employees and they usually face performance anxiety. The poor quality data that is being used in an organization may have many errors, and when facing critical deadlines many professionals just make the change or correction on their own to finish the task at their hand. Many data analyst spends significant time on vetting and validating their data before their data can be used for making strategic and informed decisions.
The core of the problem is that when companies grow, their data becomes fragmented. When these changes happen, the data becomes inconsistent. And anyone cannot truly know which of their applications has the most recent and valid data. It reduces productivity and compels your employees to engage in more manual work.
And as a result, data scientists have to spend their 50% to 80% of time doing manual work of collecting and preparing unreliable data so that it can increase the productivity of your entire company.
Increases reputation:
Again, poor quality data costs in more than just dollars and cents. Bad data quality data is quite expensive and may harm your reputation. Measuring the true value of data quality, companies make assumptions (usually incorrect) about the state of their data and as a result, they continue to face excessive cost, inefficiencies, compliance risks, unsatisfied customers. Meanwhile, the quality of their data goes without proper management.
Unsatisfied prospects and customers result in loss of company reputation.
Whenever any of your prospect and customer have a bad experience and are unsatisfied with your company, they may share their experience on social media channels. When data inconsistencies are left unchecked, even your own employees can raise concerns about the validity of your data. This may result in your employees asking your prospects and customers to validate customer data, product or service during an interaction, increasing the time of each interaction and gradually reducing trust.
Greater confidence in analytical systems:
Some companies still use data in which they have either very little or no trust at all. Surprisingly data, even when they are flawed is treated as better than working without data when the deadlines start to come closer. There are several data quality tools that can ensure that only quality data is being used for making informed decisions which further increase confidence in those analytics tools.
Increased customer satisfaction:
When you only use quality data, it leaves you with highly satisfied customers. Quality data enables you to deliver on your prospects expectations by proving more value to them based on their requirement and interest throughout their customer journey.
Quality data helps you to keep your customers for a longer period by providing after sales services like technical support, providing information that increases the productivity of your solution and helps them to use your solution easily and more effectively.
More consistent data:
Bigger companies that offer multiple entry points to their prospects and customers usually face problems as they get more inconsistent data. Inconsistent data leads to many problems like duplicate contact information, and it holds a company to effectively reach their prospects and customers.
Inconsistent data is the host of many problems that surface because of the lack of quality data. Data quality helps you to bring all your teams on the same page to achieve better alignments between your teams and be more effective in achieving the company goals with join efforts.
Data quality challenges:
There are interesting but quite strange aspects of data cleansing efforts of a company. Many companies are striving to have quality data. And based on the current state of the data, companies plan their data cleansing activities. In order to efficiently resolve bad quality data issues, you should start by reviewing the common sources of data pollution.
Almost every organization deals with the issue of duplication for names and addresses. For an individual person, several data records can exist in multiple systems. And even within a system, several data records can exist for an individual person.
You should consolidate every activity of each individual person from multiple duplicate data records that exist for that person in multiple systems.
As we move towards a more data-driven world, it is very important for companies to have quality data, in the correct format, at the right time so that it can be used for insight and making informed decisions. However, many companies end up spending their significant time and resource on their data quality.
Following are the data quality challenges;
Data duplication:
Several copies of the same data record may result in incomplete or incorrect insight and also takes a toll on computation and storage. One of the primary issues can be human errors (someone entering the data multiple times by accident) or it may arise due to an algorithm that has gone wrong.
Incomplete Data:
When the data does not get entered into the system correctly or when some data files get corrupted, then the data left has many missing data fields. Example, when the data field for address does not have zip code at all, then the remaining data fields can be of less value, as the geographic aspect of data will be very hard to determine.
Inconsistent formats:
When the data is stored in inconsistent formats then the systems used to store and analyze the data may not correctly interpret it.
Example, if a company store and maintains the database of their prospects and customers, then the format for storing the data should be pre-defined. Like name (first name + last name), date of birth (MM/DD/YYYY), phone number without country code (754-3010) or phone number with country code (+1-541-754-3010), etc, should be stored in the exact same format. As if a company that does not follow this practice to store and maintain their database, they may have to invest significant resources and time to just unravel the several version of the data stored.
Data accessibility:
The data which most companies use to create theorize, evaluate, and predict the end product or the result usually gets lost. The way data flows down to analysts in bigger companies, starting from departments, sub-division, branches, and ultimately to the teams who are actually working on data. It leaves data for which the next user may or may not have complete access to.
This process of sharing providing access to the data in a highly effective way for all the employees in a company is the foundation of sharing data.
System upgrades:
Every time your data management systems get an upgrade or even when the hardware of your data management system gets upgraded, the chances of your data getting corrupted or being lost are very high. In order to avoid these situations, you should create several backups of your data and upgrade your systems only through trusted sources.
Data storage and purging:
With different management levels in a company, the chances of the locally stored data getting deleted are quite significant. So, saving the data in a better way and sharing just a copy among your employees is critical.
As companies grow and become more complex, their employees get frustrated when they are unable to get the answer they need, they may stop waiting and act blindly without quality data.
On the other hand, they may even go rogue and use their own analytics tool (individually or team-wise) to get the data they need, which results in conflicting insight and truth. Either way, your data will lose its potency.
Fixing data quality issues:
With GDPR and the risk of data breaches in the picture, many companies are in a situation where they have to fix issues of their data quality.
And the first step towards resolving the quality of data requires identifying all the problems related to your data. You can use many tools that can perform the quality assessment on your data to verify whether the data sources are accurate. Find out how much data is there and the potential impact of resulting from a data breach.
From there, you can create a data quality program, getting help from data protection officers, data stewards, and other data management experts. These data management professionals will assist you in implementing a business process that can ensure that data collection in the future and its use follows the regulatory guidelines and at the same time provides the value that you expect from the data being collected.
