High Quality Data : Holy Grail Or Achilles Heel?

Marketers today are navigating in an era of ongoing technological disruption. Digital transformation and therefore the influx of smart technologies have evolved customer expectations and led to an unprecedented demand for fast gratification and high-quality, personalised experiences across all channels.

At an equivalent time, there's a growing need for transparency, accountability and ethics in marketing, especially as additional states still introduce new data privacy laws. These rising standards bring a replacement level of complexity to the marketing landscape and it’s now more important than ever for marketers to verify the standard of their data.

In the age of empowered consumers, competitive advantage hinges on an organisation’s ability to harness the info needed to enhance customer understanding and engagement. But poor data quality remains marketers’ Achilles heel, hindering insight while draining valuable resources.

A new report from Domo and Censuswide highlights the most important challenges that marketing leaders face around data and therefore the pressure of maintaining with the newest industry changes. Almost half marketers say the abundance of knowledge channels and sources makes it harder to plan for his or her long-term strategy, and 83% of marketers at large enterprise organisations admit the increase of latest technologies and techniques means it’s now harder to remain on top of everything.

Despite these pain points, data remains the backbone of decision-making in business — consistent with a recent CMO survey, reliance on marketing analytics to form decisions has increased from 30% to 42% within the past five years. Yet, many organisations still struggle to optimise the standard of their data and, in turn, the standard of insights they generate. So, what’s the disconnect?

To put it simply: data is merely as valuable because the insights it produces, the actions it influences, and therefore the results it fosters. Without the power to make sure data integrity, marketers are relinquishing their most powerful tool — and a huge competitive advantage.

The reality is marketers believe high-quality data for fueling successful marketing initiatives, yet they struggle to realize it. In fact, Forrester Consulting polled 409 US-based midsize and enterprise organisations on behalf of selling Evolution for its report, “Why Marketers Can’t Ignore Data Quality,” and located that while 82% of companies place a high priority on refining data quality, quite 1 / 4 of all marketing campaigns were hurt by substandard data within the last 12 months.

Marketers cannot afford to form data quality considerations an afterthought. Survey respondents reported variety of negative consequences thanks to poor data quality, like inaccurate targeting, lost customers and wasted media spend (21 cents of each media dollar spent by organisations within the last year was wasted as a results of poor data quality). The findings uncovered several barriers that marketers face, including the necessity to manage a good range of knowledge sources, soaring data volumes, integration issues, also as privacy concerns and regulatory complications.

While poor data quality remains marketers’ Achilles heel, high-quality data is undoubtedly marketers’ Holy Grail. Equipped with higher quality data, marketing leaders are ready to more successfully satisfy and have interaction with their customers, elevate brand awareness, and drive sales conversions. So as to enhance data quality and maximise its insight potential, the Forrester report identifies seven quality dimensions that marketers should align their data across for best results:

• Timeliness: Timely data comes from sources that are up so far . Access to faster data enables relevant insights that meet business objectives

• Completeness: Complete data records are ones where all expected attributes are provided. A complete customer and marketing data set ensures that each one behaviors, intentions, permissions, and sentiments are captured for robust analysis, like understanding channel halo effects or how customers feel about your brand

• Consistency: Consistent data references a standard taxonomy across platforms, channels, and campaigns. Having consistent data for things like campaign codes and customer identifiers can help marketers speed up the information collection process and analyse trends over time, without fear about data being labeled correctly

• Relevance: Relevant data directly relates to the analysis being performed. Adding a slew of knowledge into the system won’t help solve the business problem if it’s not relevant. Relevant data helps answer marketing business problems, address customer behavior questions, and make day-to-day decisions

• Transparency: Transparent data refers to data whose sources are easy to trace and identify. Marketers who understand the data nuances from first-party and media sources, like ad servers, are going to be ready to determine if specific streams of knowledge are necessary for his or her marketing performance analysis

• Accuracy: The adage “garbage in, garbage out” has never been more relevant in today’s data-rich world. Only accurate data can reflect true actions

• Representativeness: a crucial a part of targeting, representativeness ensures data collected and leveraged for insights accurately reflects the marketplace or an advertiser’s audience

Tackling data quality improvements and boosting marketers' trust in their data proves a worthwhile effort — because the key to achieving business success hinges on an organisation's ability to harness high-quality, reliable data. Firms with above-average confidence across these seven quality dimensions report improved understanding of campaign performance, faster decision-making, reduced waste in media spend, better customer experiences and customer targeting, and improved trust from both employees and customers as a results of greater data reliability.

When it involves marketing analytics, delivering trusted data is significant . High-quality data is that the foundation of any high-quality brand — empowering marketers to effectively understand and influence shoppers at the proper moment, while also enabling marketers to demonstrate accountability for consumer spend. Looking ahead, those that don't prioritise data quality, run the danger of being left behind.

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