White Paper
Data Quality:
Introduction
Information and data are an organization’s strategic assets. The ability to harness and mine one’s business data is critical for solid decision-making. According to a report by The Data Warehousing Institute, “Intellectual capital and know-how are more important assets than physical infrastructure and equipment.” In regards to IT, enterprises spend months and even years determining which computer hardware and software solutions will help them grow their business. However, some organizations fail to devote equal attention to the quality of data that will support their investments in these systems.
TDWI claims that information is the currency of the new economy and data is the critical raw mate-rial needed for success. Without this input, businesses are crippled. The needle cannot be moved if companies are plagued by bad or “dirty” data.
Dirty data refers to information that can be misleading, incorrect, and without generalized format-ting. Unfortunately, no industry or organization is immune to it. In addition, dirty data affects
com-panies of all sizes. If not identified and corrected early on, defective data can pose serious threats.
High Quality Data = Highly Valuable
Data is paramount. Therefore, data quality should be a key initiative on your company’s radar. Why? Here are a few reasons on how high quality data can improve businesses:
• Efficient operations
• Enhanced customer experience • Increased revenue/cost reduction
According to Forrester Research, “Business drivers for data quality implementation are plentiful and often differ based on industry. No matter the industry, many organizations build their business case for data quality investments to increase revenue through improved direct marketing and
ac-count management, reduce costs through improvements to operational efficiencies, and mitigate and control regulatory and financial risk.”
High quality data allows greater confidence in analytic systems and decreases the time spent
reconciling data. It enables a more uniform version of the truth, allowing stakeholders the ability to identify and implement necessary changes. This in turn causes companies to cut costs and in-crease ROI.
Customer Data is Key
In a recent report, companies identified these as the main types of data prone to quality problems:
• Sales contact data 27% • Data from ERP systems 25% • Employee data 16%
• International data across multinational companies 12% • Other 10%
The study showed that customer data was significantly more flawed with errors and inconsisten
-cies, as compared to other data types. Customer contact data is notoriously volatile and difficult to
maintain at high accuracy levels. Experts estimate that 2% of records in a CRM database become obsolete each month due to customers dying, divorcing, marrying or moving.
To put this statistic into perspective, assume that a company has 500,000 customers or leads in its CRM database. This means each month 10,000 customer records become obsolete, culminating in 120,000 out-of-date records every year. If no action is taken then within two years about half of all the records will become outdated.
While other data is important, we will be focusing on customer contact data, since it is the key data type that companies are struggling to monitor and maintain. As you can tell, these data elements are the most problematic because its quality quickly degenerates over time.
The Many Facets of Data Quality
Since bad data is a multi-faceted and costly problem, companies rely on a variety of solutions and
processes aimed at improving data quality. First, let’s take a step back by defining what data qual -ity entails. Data qual-ity takes into account the following:
• Existence: whether the organization has the data
• Validity: whether the data values fall within an acceptable range or domain
• Consistency: whether the same piece of data stored in multiple locations contains the same values
• Integrity: completeness of relationships between data elements and across data sets • Accuracy: whether the data describes the properties of the object it is meant to model • Relevance: whether the data is the appropriate data to support the business objectives In short, data quality solutions and processes are aimed at improving the accuracy and complete-ness of the information your organization receives. This involves cleansing and transforming data by removing inaccuracies and standardizing on common values.
In regards to customer contact data, data quality practices enable companies to communicate and
accurately profile customers. In addition to marketing effectiveness, high quality customer contacts
means increased sales performance, operational excellence, customer satisfaction, and cost sav-ings.
There is no such thing as perfect or defect-free data. Nevertheless, your company should not dis-credit the importance of trying to improve data quality to the best of its ability.
Cost of Bad, “Dirty” Data
If left alone, defective data can contaminate systems and information assets. The end-result is a myriad of problems from high costs, jeopardized customer relationships, imprecise forecasts and poor business decisions.
According to Gartner, “Fortune 1000 enterprises will lose more money in operational inefficiency
due to data quality issues than they will spend on data warehouse and customer relationship man-agement (CRM) initiatives.”
Dirty data is a costly problem that affects all verticals. The SiriusDecisions 1-10-100 Rule helps demonstrate just how costly defective data can be. It posits that it takes $1 to verify a record as it is being entered (and cloud-based data quality solutions have decreased this cost substantially). If it is cleansed and de-duped later then it will cost $10. If a company decides to do nothing, it will
incur a cost of $100 as the ramifications of the mistake are repeatedly felt.
As you can imagine, even a low data error rate can add up. According to TDWI, bad customer data costs U.S. businesses more than $611 billion each year. They explain that this happens because “most organizations overestimate the quality of their data and underestimate the impact that errors and inconsistencies can have on their bottom line.”
Lather, Rinse, and Repeat
Now that you understand the critical importance of data quality, let’s discuss ways to start cleaning your customer contact records.
The first step is to build a team. There is too often a divide present between IT and business stake -holders. Much to the detriment of organizations, departments exist as silos. This causes a barrier between trusted data (typically driven by IT) and process transformation initiatives (typically driven by business leaders).
The effective collaboration between business process and data management professionals is the key to success for data quality. Business processes will break down if they do not use trusted data, and data quality initiatives will fail to deliver business value if the data does not support your orga-nization’s most critical business processes and decisions.
Stay Clean from the Get-Go
However, one of the most important data quality lessons to learn is that you need to focus on
pre-venting errors at their source, not in finding and fixing them as they crop up. As the saying goes,
“Garbage in, garbage out.” If you put bad data into your database then expect poor results. For this reason, it is important to rely on data quality solutions that work in real-time (i.e. cloud-based), as compared to on-premise database vendors. Validating and managing data in the ear-liest stages of collection can lead to better lead scoring and lift conversion rates by about 25%
between the customer inquiry stage and the point where marketing/sales qualifies the leads.
Data entry errors can be prevented by using validation routines that check data as it is entered into the Web, client/server, or terminal-host systems. Think about all the sources of how you obtain and enter data into a database. Figure out ways to implement data quality solutions at each point-of-capture.
Enhance Your Database
In addition to verifying and validating data at all sources, it is important to rely on an append solu-tion that can enhance your contact records. Even if you cleanse the data you currently have avail-able, there is a wealth of knowledge that can be gained from the information you do not have at your disposal. Append solutions bridge the gap between “what you know” and “what you’d like to know” about customers. The businesses that do the best job of closing this chasm will be the most successful.
Conclusion
There is no better time to recognize the huge costs of dirty data. As discussed, ignoring the
prob-lem and letting it go unchecked only magnifies the issue. Build a team that will help work to come
up with successful data quality policies and programs. After all, data will only continue to grow and businesses and institutions will further rely on it.
Ultimately, the goal for companies is to manage the quality of data with the same attention devoted
to other critical resources. Once a company values data as a significant raw material, it will see the
natural progression to making a corporate commitment to manage data quality. This commitment demands establishing a program that organizes processes, systems, and data quality tools to achieve a common goal of high quality data.
About StrikeIron
strikeiron.com
StrikeIron is the cloud leader with the most mature and reliable Data-as-a-Service (DaaS) platform on the market. We are passion-ate about innovation in data quality and data communications. We empower businesses by providing access to valid, accurate, and actionable data – when and where they need it. StrikeIron’s cloud services also enable businesses to communicate with consumers in the medium of their choice. StrikeIron’s solutions are delivered as SOAP and REST Web Services that can be easily integrated into web forms, CRM systems, websites, and other business applications.
