What is Data Quality?
Data quality is defined as the conformance of data to business rules. Data of good quality is suitable for its intended business purpose. In other words, it is subject to the business user or department using the data. An organization has many departments and each department has its own business rules. So, data which is of high quality to one business user or a specific project may not fit into the needs of a second user especially if the business rules are considerably different. But at a master data level, it is a different ballgame and there should be some level of agreement around each business entity like customer, product, employee etc. on what constitutes good quality at the organization level.
Why Your Organization Needs Good Quality Data
Now why is data quality so important to the success of an organization? An organization makes key business decisions and establishes future strategy based on its data. Reporting based on incorrect data could lead to incorrect assessment of the demand for its products. This could lead to production outpacing demand and may cause the organization to sell its products at reduced prices or write off the inventory and take a huge loss. Perhaps the organization could be producing less than the actual market demand leading to resources and employees being kept idle and end up losing market share to its competitors. Recently, I was working on a consulting assignment on a business initiative for a leading US restaurant chain. The business was trying to launch a new product and was trying to anticipate the demand based on the data in the data warehouse. They were trying to estimate the inventory for the launch. But unfortunately, their data warehouse did not have data from the west zone and this restaurant chain recorded more than 70% of their sales coming from the east and west zone for the previous years. They ended up lower in their estimates and were not able to service the demand for the new product.
Organizations spend a lot on operations research, streamlining their operations and reducing costs. Lack of high quality data could lead to spending a lot of money on redundant operations, improper work forecasting and scheduling of resources. This could cause expenses to soar and dent profits. Sometimes, it could cost less in the long run to purchase a piece of machinery rather than to lease it. Analysis based on good quality data could point this out along with many other cost saving measures.
Customer Data Quality
Customer is king for an organization. Having high quality customer data can lead to accurate invoicing and even bundling all services in a single statement. Having redundant customer data could lead to promotions or catalogues being sent multiple times to the same customer thereby leading to a loss of advertising dollars. Having the correct customer address and contact information can ensure timely communication on new product launches and promotions and help provide excellent customer service leading to repeat business. Also, it would lead to an improved demographic analysis which would help the organization to develop a very effective marketing strategy and marketing plan.
There are many advantages of having high quality data and I have outlined just a few in this article. Data is an asset to an organization if used in the right way. Data quality initiative costs are difficult to justify upfront but it’s a long term investment with unlimited returns and you could see a lot of cost savings with improved productivity, increased revenues and profits.
Most businesses assume they have good quality data and they start building on top of it. It’s only after encountering a major problem down the line and spending a lot of project budget and hours, they realize their data is not accurate, unreliable and does not address their goals. So it is highly imperative to address the data quality issues at the beginning of a new business initiative. Data considered reliable today may not be accurate after a few months as organizations keep on adding more and more data. So there are two key steps to be taken whenever a new data related business initiative is undertaken. First, assess and understand your data, see if it meets the business goals and have a project plan to correct any inconsistencies at the beginning of an initiative. This can save a lot of cost. Secondly, make sure the batch data loads are of high quality too.
PR3 Systems has helped a lot of companies assess their data and correct it to meet their business goals. For more information on how we can help your organization please contact email@example.com.