There’s no doubt that data is the preeminent buzz word these days, and for a good reason. Companies who master the art of sorting, collating, and analyzing data can more easily convert prospects than their rivals who don’t use the same procedures.
The trouble is, traditionally, that managing data and data quality have been challenging. Not many people outside of data administrators had the tools needed to do the job right.
Defining Data Quality
Data is only as valid as to its completeness and accuracy. Missing or incomplete records are virtually useless and are at the low end of quality. Ultimately, businesses judge data quality on the information the database contains and whether it helps them make informed decisions.
At the highest level of the quality-spectrum resides data records that are up to date, accurate, and helpful for completing business objectives. An example would be a thoroughly precise customer list. The marketing department, when using this type of record, will be able to achieve its business objective of increasing sales.
Data Matters Because It Informs Decision-Making
More companies than ever are data-driven, meaning they use meaningful data analysis to aid their decision-making process. That fair use of data allows them to be less emotional and more analytical before plunging forward with business plans.
The danger comes when the data is of poor quality. If decisions derive from the data and if it’s no good, the outcome will also be of low value. That’s what’s pushing businesses to implement ways to ensure that their database is clean and efficient. Bad data quality muddies the waters and may spoil the entire process of forming strategies.
How Poor Quality Data Hurts an Organization
Data-driven decision-making goes downhill fast with a low-quality database. Consider some of the adverse effects that result from incomplete data.
- Marketing initiatives become annoying or ineffective. Without proper personalization and historical record-keeping, much of your marketing budget may miss the mark.
- Errors in supply chain data could result in slower shipment times, reducing customer experience.
- Wrong data can negatively impact financial reporting, causing a host of problems.
Everywhere in the organization, bad data quality is likely to cause problems. All these potential problems mean it’s time to put someone in charge of data quality management.
Data Quality Management Is Essential for Success
Accountability is the primary way to ensure that someone in the company is responsible for improving and maintaining data quality. This person will need to set clear benchmarks for performance and will need to implement strict routines to ensure quality.
The responsibilities of data quality managers vary from industry to industry. However, a few characteristics remain the same across verticals. The manager needs to establish safety and quality standards and must come up with ways to enforce compliance. Failing to do that could introduce areas of failure that could harm the program.
That means they’ll need to understand the source of data and all subsequent internal handling. Data quality management is an ongoing responsibility that requires reviews and reporting. It’s not enough to set a high standard at first, only to neglect is over time. Instead, the manager will ensure a complete set of processes that maintain the highest levels of integrity throughout the program. For most modern enterprises, there’s no end in sight for managing data. Most are adding more all the time, making the job even more challenging.
Managing Data Is a Full-time Job
Unsurprisingly, the role of data managers is growing, and so is the amount of people in the position. Even sales and marketing companies are now in the game, thanks to the importance of marketing automation and CRM. The job is lucrative, with an average annual salary of $102,000.
It’s up to companies to decide whether to handle the responsibilities in-house or to have a third-party handle it for them. The benefits of internally doing it are privacy and control. The advantage of outsourcing is it will cut costs substantially. Each firm will need to make that decision. However, few companies are going to go without some data quality initiative. It’s painfully apparent to all that keeping clean and accurate data helps improve nearly all business processes.
What Qualities Makeup High-Quality Data
Managers analyze data for seven key characteristics.
- Relevance
- Uniqueness
- Timeliness
- Precision
- Consistency
- Conformity
- Completeness
That way, they can score the records or database quickly. Using these elements as a way to compare each entry, empowers them to make crucial decisions.
Records that fail the quality check will be omitted from the master file, improving the quality.
Many companies that have existed for decades have multiple records that refer to the same customers. De-duplicating and merging these files is a significant step forward for maintaining an up-to-date, accurate, and productive database.
After taking some of the distinct steps, most companies will tailor their data programs to their needs. Most will want to achieve particular business objectives so that they will slant their application in that direction. After that, the entire process becomes one of monitoring and then tweaking to ensure the best results. With enough work, most firms end up with quality data that is vastly improved