Home Data Engineering Data News How to Ensure Data Consistency and Quality

How to Ensure Data Consistency and Quality

Your company needs data to thrive. Quality data leads to key insights for businesses, and factors heavily into their decision-making.

But where do you find quality data? While much of your company’s data will come from internal sources such as CRM and ERP software, even more of it will come externally from the web. In fact, the web is the largest data repository out there.

The overall volume of data in the digital universe has grown significantly, and shows no signs of slowing down. Experts say it’s doubling in size every two years, growing from a mere 4.4 zettabytes in 2013 to a predicted 44 zettabytes (or 44 trillion GB) in 2020.

However, this data is unstructured, unorganized, and lacks consistency. To fully capitalize on it and glean its highly-valuable insights, you must efficiently extract, prepare, and integrate data so it can be consumed at scale.

Not only that, it needs to be clean, reliable data. To help with that, you need a trusted platform that treats external data with the same quality and control as internal data sets.

Here are some strategies to ensure data consistency and quality across the board to benefit your business.

Develop Guidelines for Your Sales Teams

Sales teams have access to a massive amount of data. But a lack of consistency in data handling amongst team members can quickly lead to quality issues, so it’s critical to make sure everyone is on the same page.

While most of your data will be gathered and input automatically, team members should also be instructed on how to input data manually.

For example, they should follow best practices, complete all necessary fields, and use consistent formats for names and contact information. They should also recognize that some information will be more valuable than other information. For instance, having a lead’s phone number may be preferable to having their email address; so it’s smart to train your team on how to prioritize data-gathering efforts.

Team members should also regularly check for data accuracy. Old data can quickly become irrelevant to your sales and marketing initiatives and should be eliminated to prevent tainting your campaigns and disrupting analytics. Data should be consistently reviewed by splitting it into groups and making sure the data in each group is thorough and accurate.

It’s also important to develop a data recovery strategy. Accidents happen, and any major loss of data could have devastating consequences. Having a formal strategy in place ensures team members know how to respond in a worst-case scenario to minimize the damage.

This might include backing up information on a cloud platform, having a chain of command for employees to follow, and implementing a formalized procedure for reporting an incident.

Consolidate Data from Disparate Web Sources

It’s common for old systems to be updated or replaced. Unfortunately, this can create gaps where old systems don’t line up with new ones, which can compromise data quality.

Database consolidation is a solution that keeps data clean and prevents it from overlapping.

This allows you to create a database that houses data from distinctly different sources and mix it with internal data for comparison. In turn, you’re able to synthesize data so that it’s easy to digest and has a level of homogeneity.

Using a standardized operating system is a critical first step. Pick a single platform and ensure all the software and apps you use support it.

“The workload should also be tested for compatibility to run in a single, unified database,” explains Hosting.com. “Make sure the hardware infrastructure is actually able to handle the consolidated database workload. Considerations encompass requirements for storage I/O, memory and processing, among other parameters.”

If you develop your own applications in-house, they should be designed so they can be deployed on your OS.

Also, ensure all admins receive proper training on OS procedures.

Normalize Data

Collecting data from different sources can result in formatting and spelling differences. This confuses CRMs and ERMs, creates redundancies, makes it more difficult to segment leads, and generally pollutes your data quality.

Normalizing data standardizes it, which ensures the level of consistency needed for lead scoring, segmentation, and more.

For example, say you’re getting product data from several different sites in different countries and different currencies. Normalizing data would allow you to put it all in a single currency. Or say you’re dealing with bookings and availability where sites have different calendar formats. You could put all of it into a single date format to simplify things dramatically.

The basics of the process involve developing normal forms numbered from lowest to highest (e.g. 1NF, 2NF, 3NF, and so on). Each form follows set rules, which are intended to organize your database and cleanse your data.

Check out this resource to learn the fundamentals of data normalization and how your organization can apply it.

Automate Repetitive Tasks

Automating data collection not only saves time, it also eliminates many of the minor errors that can compromise consistency and quality. There are many repetitive tasks that can be automated, including:

  • User input
  • Data entry
  • Validation
  • Data field and mismatch updates

To automate user input and data entry, for example, use a UX-driven CRM that syncs with popular apps and email. This allows leads to quickly and conveniently import critical information like their name, company name, telephone, email, and so on in one fell swoop. It’s hassle-free for them, and your sales and marketing teams get the information they need to efficiently move prospects through the sales funnel.

An example of automating validation would be ensuring that information that’s incorrectly input into fields is caught. For instance, if a user accidentally enters their birthdate into an age field, they should receive an error message indicating that there’s an issue and telling them exactly what they need to change.

Processes like these ensure data integrity, where your team only receives accurate information.

Automating repetitive tasks is also important for internal training, and creates a unified framework where new employees are on the same page right from the start. There’s no guessing as to what format they should use.

Benefits of Automating Repetitive Tasks

There are some profound benefits to automation. First, it makes your customers’ and team members’ lives easier. Customers don’t have to meticulously enter form information and fill out fields one by one. Instead, software can grab key information, helping move them through the process more smoothly.

And your staff will spend less time completing redundant, lower-level tasks. Automation increases their efficiency and reduces many of the headaches that can come with sorting through mountains of data.

In turn, this can save your company money. When team members are able to spend less time on arduous, painstaking data-related tasks, they can focus on more pressing issues, maximizing your manpower.

It also improves communication both between technologies and your sales team, and between your sales team and customers. Automating repetitive tasks helps relay information seamlessly from software to team members, and eliminates much of the friction that comes along with manual input. It also helps your sales team better connect with customers, giving them a comprehensive overview of order status, shipping information, and so on.

Beyond that, automation is advantageous from a compliance standpoint. It ensures sensitive information is handled correctly and minimizes the chances of it being intercepted by unintended third parties. With regulations like the General Data Protection Regulation (GDPR) cracking down on mishandled data, this can be a huge asset.

Leveraging a WDI platform can create a tremendous advantage when you’re dealing with external data. Not only is it a massive timesaver and integral to communication, it’s important from a legal standpoint as well.

Employ a WDI Strategy

When data is acquired from the web, it can lead to valuable insights. But sifting through that data can be overwhelming.

Teams often struggle with the complexity of extracting and transforming data, maintaining and ensuring data quality, and reacting to growing demands from business users and data analysts.

High-quality web data integration (WDI) is a new approach to acquiring and managing web data that focuses on data quality and control. Using it enables the speedy and repeatable automation of website data capture and aggregation — something that’s essential for enterprises looking to employ data from the web at scale or for critical business functions.

So how would a WDI solution be used?

Say you’re looking to examine the competitive landscape. You want to see how top rivals are positioning themselves, and identify changing attitudes, sentiments, and interests early on. WDI uses robust extraction, allowing you to access a massive amount of web data, including displayed data, hidden data and derived data to gain a better understanding of what competitors are doing and how customers are responding.

Web data complements traditional enterprise data, helping you stay updated on competitive challenges. It allows you to synthesize detailed information about competitors to improve your company’s decision-making.

While absorbing a large volume of data like this would be difficult on its own, a WDI organizes it and packages it in a way that allows you to easily understand and manage it so you can extract its full value.

Conventional “web scraping” techniques that parse HTML documents can provide an enormous amount of data, but digesting the data is time-consuming and misses the big picture.

Using a robust WDI solution, however, helps you extract, prepare, integrate, and consume the data in a meaningful way. Not only do you have access to a large volume of data, it’s high-quality, relevant to your business, and easy to implement. It focuses on data quality and control, which can have a tremendous impact on operations and creates a huge competitive advantage.

Improving Data for Better Decision-Making

Poor data quality negatively impacts your company on many levels. Not only does it lead to bad decision-making, but it can be costly as well. According to Gartner research, “The average financial impact of poor data quality on organizations is $9.7 million per year.”

So do everything within your power to ensure data consistency and quality.

The specific points mentioned here should provide you with actionable ways to improve in these areas. Developing guidelines for your sales team, consolidating databases, normalizing data, and automating repetitive tasks focus on internal data, while employing a WDI strategy focuses on external data.

The end result is pure, consistent data for better decision-making and increased profitability.

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