You Don’t Have To Put Up With Bad Data

November 9, 2017 Kevin Larsen

All areas of a business rely on data to enable operations and decision making. Sales and Marketing, Human Resources, Customer Support, Product Development, and more are all driven by data. And increasingly, these business units are tapping new sources of valuable data such as from 3rd party datasets, sensors embedded in buildings, equipment and products, and that sourced directly from customers and suppliers.

But every business shares a common problem: much of its data is of poor quality or just plain bad - consisting of invalid, duplicated, inconsistent, or missing value.

Bad data leads to bad decisions and delayed or broken business processes. Consider the following scenarios:

  • Order fulfillments are delayed because ship-to addresses are inaccurate or missing.
  • Excessive raw material is ordered because nobody knows exactly how much is already on hand.
  • Personalized marketing campaigns target the wrong customers because a complete and accurate view of personal preferences isn’t available.
  • Customer satisfaction declines because inventories of the items customers desire aren’t accurate.
  • Employees receive poor performance evaluations due to incomplete information on accomplishments and skills.

These undesirable business outcomes, and many more like them, are caused by bad data. The obvious solution is to correct, or remediate, all bad data, but this can be expensive and time-consuming. For example, business users must first locate the specific systems and their owners that store bad data. Unfortunately, businesses have data everywhere - spread out across many different systems, both on-premises and increasingly in the cloud. Many of them are outside of the oversight of the IT department. Organizational standards for data, if they exist, must be located and understood so that a change to bad data isn’t made worse. The impact of corrected data on other data must be understood as well so that dependent data isn’t rendered invalid. Once all of this is understood, a request can be made to the overworked system owner to correct the data.

Since most businesses have a lot of bad data, and new data enters systems at a high velocity, many businesses conclude that it makes more sense to just work around it.

But the status quo isn’t good enough for businesses that aspire to create positive outcomes: to develop new sources of competitive advantage, to accelerate innovation, to showcase compliance with regulations, and to quickly fend off market threats. These businesses view data as a critical business asset and make sure that it is of the highest quality so that it can best serve the business.

Our data quality solution finds and fixes your bad data – before it impacts your business processes. It will:

  • Define all of your rules for data quality in a single location t, and orchestrate their enforcement within any system, process, and stewardship platform, including our Data Stewardship Platform™

  • Monitor data across all business systems, including those in the cloud, to find violations of these rules. 
  • Notify data stewards or appropriate users when non-compliant data is encountered, who can then initiate workflows that send email to business users responsible for correcting the data.

  • Allow business users to quickly and easily fix data, either through a consistent web interface or an uploadable spreadsheet, with no technical knowledge of the underlying system required. Changes can be made to individual data items or en masse.

  • Perform quality checks at the the point of correction to ensure accurate changes, and provide an optional approval process to ensures data is business ready before it is posted to business systems.

These features help to keep your business moving forward by reporting on and quickly correcting conditions that could disrupt business process or impact the business in some way.

Our solution can further create visual representations that highlight the volume of monitored data that conforms to industry standard dimensions of quality. Quality levels are based on percentage or sigma calculations.

As areas of the business have varying quality requirements, you can set thresholds on business objects that alert users when their quality approaches and falls below desired levels. 

Our data quality software provides for more than just error correction. You can use prepackaged reports or create your own to generate operational insights based on business performance data. You could be alerted to business conditions where process interruptions may not occur yet should be addressed. For example, vendors who have had no purchase orders or disbursements in the last 180 days. You can further measure against your KPIs or you can report on virtually anything, such as when the number of invoices with NET 30 day payment terms that are open is higher than permitted.

> Check out this demo to learn more about our data quality software: http://bit.ly/2zKbbr2

 

About the Author

Kevin Larsen

Kevin Larsen is the Senior Product Marketing Manager at BackOffice Associates. With over a 25+ year career, he has held positions in Software Development, Business Development, and Product Marketing. Kevin holds a B.A. in Mathematics, an M.S. in Software Engineering, and an M.B.A.

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