In a world so dependent on data that it has become the pinnacle corporate asset, hundreds of companies are deploying dozens of initiatives around data: ERP, shared services, CRM, contact centers, supply chain, RFID, SFA, etc. With the launch of these new technologies, Gartner, in a ground breaking report, predicted that poor data quality would be one of the top inhibitors to the success of any data-related technology implementation. They also predicted that ROI from these initiatives can only be realized when customer-facing technologies are combined with analytic techniques such as collaborative filtering, predictive modeling and business rules. In other words, customer intelligence, blended with historical data via predictive science is required to predict future customer purchase behaviors. Additionally, the information resulting from all the data manipulation is necessary to shape the decisions about how companies innovate products, go to market, and determine customer focus.
Critical to making data-oriented strategies and technology solutions transform customer data into actionable intelligence is access to accurate, reliable, and consistent real-time data. But most companies do not have a master database where information is consolidated and maintained. This means that customer data, often collected from multiple disparate sources, is incomplete and of poor quality. And if the data is not cleansed, updated and integrated, errors, redundancies and delays can be expected. In the end, money spent on all the new data-related initiatives and software packages may be in vain because the data-based applications are only as good as the data that runs them. Garbage in, garbage out.
Analyst Findings on Corporate Data Quality
A number of analyst groups have published alarming statistics about company data quality. Forrester published a study reporting that 37 percent of companies cite duplicate and overlapping files as significant data-management problems. Gartner’s new studies on data show that contact data typically deteriorates at a rate of 33 percent per year. In addition, they found that more than 25 percent of the critical data used in large corporations is flawed due to human data-entry errors, data becoming outdated (i.e., from customer‘s moving) and a lack of proper corporate data standards. Gartner also predicts that through 2007, more than 50 percent of data-warehouse projects will experience limited acceptance, if not outright failure, because companies are not proactively addressing the data-quality issues.
In another report on CRM Data Cleansing, Gartner found that more than 40 percent of all companies take on CRM or similar projects without understanding their existing data quality problems. And, at least 60 percent underestimate the resources required to perform data quality clean-up. Without proper attention, the data will inevitably become incorrect, unusable and ultimately untrustworthy. So, the very thing that corporate strategies and tactical plans are being based on is in itself unsound.
Business Reasons for Creating a Single Customer View
With the strategic focus on data, one of the most significant challenges facing CIOs today is wrestling with the issues to make data valuable to the corporation. One of the most important aspects of making data valuable is being able to create a single customer view (SCV) with the data. A single customer view means that across all applications, databases and customer touch points, a company has a single accurate, consistent and complete view of their customers and their data.
The business reasons to create a SCV are numerous and span all departments of a company. They include the ability to:
- Better target products and services to current customers to increase revenue
- Identify the company’s most valuable customers
- Increase forecast accuracy
- Comply with federal regulations with a complete picture of customers and their transactions
- Provide great customer service, resulting in higher customer retention
- Anticipate customer needs to develop better products
- Better focus marketing initiatives towards customers interested in the products and services
- Assess and then leverage a merger or acquisition by getting a quick, reliable view of the combined customer base and
- Sell more effectively through channel partners
The financial, operational, customer satisfaction and regulatory affects of unreliable data are overwhelming. Examples of negative outcomes include:
1. CRM Investments
Companies cannot reap the rewards promised in part because the customer data they are storing and managing is inaccurate, outdated or redundant. Lost revenue and dissatisfied customers also result from improperly addressed customer shipments and invoices. More emphasis should be placed on buying a CRM package and implementing it than the data itself. Information quality has been taken for granted, ignored or given second priority to the deployment of a CRM system. Mergers and acquisition are executed many times to leverage the additional customer base. But, without a good view of the customer, the point of the merger may be lost.
Sending mailers to undeliverable addresses or duplicate promotional materials because customers are duplicated in the database result in ineffective use of budget. Opportunity costs increase when companies do not send marketing materials to the right prospects because the segmentation data is flawed, and unnecessary printing, postage and staffing costs result. A company’s credibility with customers and suppliers erodes by sending them things they are not interested in.
The sales force calls unreachable phone numbers and the process of integrating internal data with partner or channel data that has different levels of quality, completeness and tags risks violating financial reporting and privacy legislations.
4. Government Regulations
Government-instituted regulatory compliance laws, including consumer privacy regulations or tracking events.
Businesses that can answer the following types of questions have accurate, reliable data and gain return on their investments:
- How can we sell more to our top accounts?
- How effective are our channel marketing programs?
- How well do I understand my customer relationships across departments, lines of business, and geographies?
- What is our financial exposure to customers?
- How can we effectively respond to the needs of our customer?
Having answers to those kinds of questions provides the strategic advantage over competitors required to succeed in this fast-paced, global marketplace. The real bottom line questions are: What is the cost of a lost customer? What is the true cost of bad data?
The Cost of Bad Customer Data
In the past, the cost of “poor data quality” and the severity of data quality problems were not applied to the bottom line. However, with increasing awareness of the strategic importance of data, a number of firms and organizations are beginning to evaluate the financial impact of bad data. A recent study by the Data Warehousing Institute found that poor-quality data costs U.S. businesses $600 billion a year.
Additionally, a senior analyst at Yankee Group, Kosin Huang, reported that $40 billion of that can be attributed to the consumer packaged goods industry and retail supply chain alone. The study quoted experts who found that customer data becomes obsolete at the rate of 2 percent a month because people move, get married, divorced or die. Nearly half of the companies surveyed had no plans to improve data quality. To put this static into perspective, assume that your company has 1,000,000 customers and prospects. If two (2) percent of your records becomes obsolete in one month that equates to 20,000 records per month or 240,000 records per year. In several years, about half of the records will be unusable if left unchecked.
A study published in Information Week sited that when 413 manufacturers shared their data with their retail customers they found 2,784 data errors. These errors included bad data about products, quantities and brands. Forty-four of the supplier’s data was so bad that it could sabotage the supply chain, resulting in millions of dollars in lost revenue due to poor merchandising decisions.
An AT Kearny study showed that 30 percent of data reported by grocers was erroneous. They also estimated the consumer packaged goods industry is losing $40 billion in annual sales due to bad product data. This study found 43 percent of invoices included errors leading to unnecessary price reductions.
Yet another study by Automotive Aftermarket Industry Association (AAIA) found that trading partners sharing product data were forced to manually enter it. The AAIA estimated the cost of manual rework like this, combined with the lost sales, costs the automotive industry $2 billion a year. Dirty data also resulted in excess inventory, invoice payment deductions and delays in new product launches.
The Unsolved Customer Data Challenge
The issue of a poor data quality and the lack of a single view of a customer is caused by:
- Inaccurate data entry at the source
- Inconsistent definitions of customers across different systems
- Poor data design in legacy systems
- Lack of a data steward department and point person
- Lack of a data management plan and
- Lack of single customer view strategy and tactical plan
While each of these issues can cause problems within single systems, creating a single customer view increases the problems exponentially as a result of connecting two or more databases. The challenge is to gain control over a problem that, if left unsolved, will make many other data initiatives pointless.
Source : www.hitachiconsulting.com