Jan 17, 2019

Why Data Quality Matters for IT and Business Stakeholders

data_quality_integration Data quality is a major factor in delivering personalized experiences to today’s customers. If your data is not up to par, hyper-personalization at scale runs the serious risk of all sorts of problems, from annoyed customers receiving an irrelevant offer to decreased marketing ROI. Poor data quality is a barrier to recognizing a customer in real time, which makes a personalized customer experience almost impossible.

Yet according to a study by Blazent and 451 Research, only 40 percent of C-level executives are “very confident in the quality of their organization’s data.” This lack of quality – characterized by data redundancy, missing data, out-of-date data, among other issues – impacts every area of today’s business from strategic decision making to the delivery of the customer experience.

Given the strategic importance of data in empowering businesses to deliver customer experiences and analytics teams to deliver new insights, it is now clear that substandard data is simply unacceptable. Less clear for many enterprises is how to resolve these issues, through combined efforts of IT and business line managers. Both are key stakeholders involved in ensuring data quality and they view the requirements of a customer data platform (CDP) and other data consolidation tools very differently.

The CDP’s Role in Data Quality

To create differentiated customer experiences – ones that are dynamic, hyper-personalized, multi-stage and omnichannel, brands need to implement technology like CDPs to pull data out of silos and into a single customer view. A robust CDP is able to link all knowable customer data to enable an always-on, always-processing view of a customer, which provides a unified and complete “golden record” and makes that data available wherever and whenever it’s needed.

A CDP ingests all sources and types of customer data, including batch and streaming, internal and external, structured and unstructured, transactional and demographic, to present the unified view of each customer. An optimal CDP should operate in real time, offering a golden record that is continually available at low latency to all applications and users who need access.

CDPs play an important role in ensuring data quality by automating data transformation features such as contextual matching, standardization, normalization, deterministic and probabilistic matching, merging, purging, householding, de-duplication, and other profiling tasks to quickly and dramatically improve data quality.

Enhanced confidence in data quality, and marketing access to that data enable companies to achieve performance gains from artificial intelligence (AI) and machine learning, which can support advanced real-time personalization and next-best-action recommendations. Feeding high quality data into advanced AI and machine learning-supported algorithms is the brass ring for marketing personalization in that it generates relevant next best action recommendations in the proper cadence at every stage of the customer journey.

Importantly, this is accomplished without the usual pratfalls that keep marketers up at night when faced with insufficient data quality, such as damage to the brand, lost revenue, and inaccurate insights that hamper strategic decision-making. Automating data transformation also frees marketers and IT to focus on value-added activities instead of manual cleansing. An IDC study revealed that businesses spend 75 percent of their time with data gathering and cleansing, leaving just 25 percent to analytics. Advanced technology enables organizations to flip that ratio, so that 75 to 80 percent of the time is spent on value-added analytics and delivering competitive customer experiences.

Line of Business (LOB) Executives: Utilizers of Data

As marketers increasingly design data-driven customer journeys, and customer experience solutions increasingly incorporate data science, AI, and machine learning, many marketers fear an over-reliance on IT to access and ensure data quality. In reality, if the technology is in place to ensure quality data in a single view, new solutions like the Redpoint Customer Engagement Hub™ can be used by marketers to access data, predictive analytics and insights while also personalizing omnichannel experiences that meet the expectations of today’s consumer – with little or no intervention from IT.

LOB execs now need to be responsive to consumers that increasingly engage in real-time and expect a degree of personalization this is consistent across enterprise touchpoints. This requires a responsive IT organization that makes high quality data accessible in real-time and in ways that span traditional silos.

Many enterprises have yet to create these bridges, leading to disconnect with the IT department or long wait times for prioritization of the requests from LOB. This disconnect is a reason that many future-forward organizations are appointing CDOs (Chief Data Officers and/or Chief Digital Officers) that have one foot in each area of the business.

IT Executives: Protectors of Data

Data quality is not only important for line of business executives and marketers. For IT, data quality has an important role in maintaining enterprise reputation and integrity. IT executives and teams are many times viewed as the guardians of data for the enterprise. Errors, omissions, inconsistencies, old data or other issues can reflect poorly on the organization. Mitigating risk with data quality can have a positive impact on the bottom line.

As we’ve written before, data quality can often cover not only the cleanliness of the data in the system but also the governance of that data. Compliance and privacy are at the forefront of data quality today, with increasing pressure from consumers and government to protect sensitive data. The emergence of new regulations such as the General Data Protection Regulation (GDPR) and California Privacy Act governing the use of data related to individuals has added pressure on organizations to safeguard the information of individuals and to be more accountable when it comes to how the data is used and by whom.

Ensuring data quality helps IT reduce the need for personnel to address data errors or go through multiple steps to access data and interpret insights. Many IT departments face a shortage of resources, especially when it comes to data scientists. Finding ways to ensure data integrity and access for marketers and other business line executives without manual IT intervention is of interest to most IT departments.

A Unified Data Quality Strategy

While there may be different perspectives on the ways to improve data quality, both technology and business executives understand that poor data quality can lead to all kinds of problems related to customer experience. Poor data quality is a serious obstacle to a superior customer experience, which many companies are showing can be a competitive differentiator.

To deliver on a quality experience, it is imperative that the business, IT, and marketing are unified in defining their customer strategy and executing that strategy with data that protects the customer’s privacy and the enterprise reputation while at the same time helps create dynamic, personalized customer experience.