The arrival of interactive personalised marketing and digital business via the web, email, mobile and social media has generated a huge demand for collecting, integrating, analysing and using data, according to Nigel Turner, vice president of information management strategy for Trillium Software, who here asks how trustworthy all that data might actually be. This article is copyright 2013 The Best Customer Guide.

A recent Economist Intelligence Unit report commissioned by Capgemini noted that 75% of business leaders believe their organisations are now data-driven. Marketing is clearly at the forefront of this. Now so reliant upon data to create customer experiences and build engagement, as well as for campaign execution, management and measurement, marketers must ask themselves a fundamental question, Turner warns: "Can I trust my marketing data?"

But why should we question our data? According to Gartner Group, 50% of all IT spending outside of the CTO's department, is carried out under the budget of the chief marketing officer (CMO). With marketing (and sales) departments spending millions of pounds on customer relationship management systems (CRM), marketing automation, social, mobile, analytics and other technologies, and using them to run key customer-focused and revenue critical processes, then they'd better be certain the data on which they run is accurate, complete, consistent and accessible.

The truth might come as a shock. A DemandGen Report survey found that more than 62% of organisations rely on marketing/prospect data that is 20 to 40% incomplete or inaccurate. Additionally, almost 85% of businesses said they are operating CRM and/or sales force automation (SFA) databases with between 10 to 40% bad records.

Additionally, when taken across organisations as a whole (and not just marketing), one analyst group's research suggests 33% of organisations rate the quality of their data as 'poor at best.' An Aberdeen Group research suggests that even in 'best in class organisations' only 48% of firms are satisfied with their data quality.

The impact of poor data
Inaccurate data undermines the potential effectiveness of technology-driven processes. Poorly targeted, conflicting and tardy messages hit return on investment from marketing activities, perhaps even turning customers off your brand. Spurious data undermines the reliability of analytics and insight-based marketing decisions.

  • According to one well-respected analyst firm, 50% of companies are not satisfied with their current CRM programmes due to a lack of data integrity.
  • Research by independent analyst firm Ovum, suggests that across the enterprise, bad data is costing businesses on average around 30% of turnover.

Closing the gap
One of the biggest challenges to ensuring data is of high quality is that it now enters organisations at an ever-increasing pace, in ever-increasing quantities and formats. Before this flood of data can be woven by marketing's technology platforms into anything useful and meaningful, it must be cleansed, standardised and then matched with other relevant data from across channels, as well as with existing legacy data for that customer.

Perhaps one of the reasons poor data costs organisations so much money is that all too often nobody's responsible for ensuring key data is fit for business purpose. Business managers sometimes think of data as a technical problem and dump it on the IT department to resolve. But this is a mistake as data is a business asset, generated by business operations. Those in IT often feel their own competencies are to capture, store and secure it, and make it accessible. They cannot bear full responsibility for its quality.

The good news is that CMOs are starting to realise their department's role in data management and data quality. They are recognising that it's marketing managers who need to 'own' marketing data and marketing managers who must bring passion and determination to improving and managing its quality. CMOs need to work with CIOs. Together, marketing managers can talk about what data really matters to them and where the real challenges are, while IT can advise on the data quality systems and solutions to help support improvements.

Three step process
The key to ensuring good data for marketing applications is to create a data quality compliance process. This means ensuring that the data entering corporate and marketing systems and processes meets required standards for cleanliness, relevance, and timeliness. There are three main data compliance steps:

  1. Assess the data's quality
    Assess the quality of existing data and its degree of reliability and consistency. There is no point in embarking on an expensive marketing systems implementation only to find that your data isn't of good quality, doesn't reconcile, and doesn't provide a reliable customer view. Data profiling enables you to fully understand the issues in your data and determine what steps need to be taken to remedy them. Specialist data quality software automates this process, enabling you to incorporate your own rules, so the data is not only validated for quality, but also for relevance to your specific marketing needs.
  2. Correct and standardise the data
    Convert these rules into processes that transform and correct the data into a common format. A standardised and corrected customer record ensures it will match associated data coming through other channels and legacy systems of data collection. This ensures that associated customer, financial, product, and historical data is linked to the correct person, and that any external data can be appended.
  3. Validate at the point of capture
    The same process used in step 2 can also be embedded into your marketing systems to automate the validation and correction of data at the point of capture. Marketing systems, supporting teams, processes and users will all have a high level of data consistency, quality, and reliability serving their specific business requirements.

Overall, coherent and integrated attempts to improve data quality in organisations could reap massive rewards. In 2013, John Lewis Partnership (JLP) announced that it was implementing an enterprise-wide data quality assurance programme. The investment will enable JLP to maintain high levels of confidence in its customer knowledge, through ensuring that before use, data captured from across the partnership's complex multichannel retail environment is standardised and made consistent, and is complete, timely and accurate. The platform enables JLP to automatically match each customer's data to any existing record, presenting a comprehensive and reliable single customer view.

So "an I trust my marketing data?" is a question every CMO needs to ask - and answer. But when you do answer it, work from solid fact, not from supposition; measure and assess your data; and undertake a full scale data quality assessment first.