Is poor data quality undermining your AI efforts?

Businesses’ potential to deliver results relies on data quality; but data accuracy, consistency, and validity continue to be a challenge for many organisations. Inconsistent data quality is holding back several departments including sales & marketing teams – a business function near & dear to my heart. These teams struggle to convert leads into sales, accurately tracking campaign performance, and taking on the larger challenges of optimising product mix, revenue forecasts.

The latest analytics, Account-Based Marketing (ABM), CRM, marketing automation, and lead scoring tools all provide real-time data capture and analysis. How the tools ensure consistent data quality directly impacts the quality of the AI and machine learning models the tools use.

Inconsistent data drives opportunities away

Sales & marketing teams can’t deliver on their goals with bad data quality. For example, inaccurate prospect data clogs sales pipelines by slowing down efforts to turn marketing qualified leads (MQLs) into sales qualified leads (SQLs).

• Two-thirds of sales leads don’t close because of bad data quality, and up to 25% of a typical organisation’s customer and prospect records have critical data errors jeopardising deals, Forrester said in a research brief earlier.
• Gartner’s 2020 Magic Quadrant for Data Quality Solutions says poor data quality costs the typical enterprise up to $12.9 million or more every year.
• Dun & Bradstreet’s study The Past, Present, and Future of Data says that 25% of businesses with over 500 employees have lost a customer due to incomplete or inaccurate information.

Problems with data quality increase the odds of failure for AI initiatives such as predictive audience offers and promotions, personalisation, AI-enabled chatbots for advanced service, and automated service recovery. A quarter of organisations attempting to adopt AI report an up to a 50% failure rate, IDC said earlier. The leading causes of inconsistent data quality in sales & marketing include problems with taxonomy and meta-tagging, lack of data governance & loss of productivity.

No data consistency

The most common reason AI and ML fail within the sales & marketing is that there’s little consistency to the data across all campaigns and strategies. Every campaign, initiative, and program have its unique meta-tags, taxonomies, and data structures. It’s common to find sales & marketing departments with 26 or more systems supporting 18 or more taxonomies, each created at one point in the department’s history to support specific campaigns. O’Reilly’s The State of Data Quality In 2020 survey found that over 60% of enterprises see their AI and machine learning projects fail due to too many data sources and inconsistent data. While the survey was at the organisation level, it would not be a stretch to assume the failure rate would be higher within individual departments, as it’s common to create unique taxonomies, databases, and meta-tags for each campaign in each region.

The larger, more globally based, and more fragmented any sales & marketing team is, the harder it is to achieve data governance. This survey found that just 20% of enterprises publish information about data provenance or data lineage, which are essential tools for diagnosing and resolving data quality issues. Creating greater consistency across taxonomies, data structures, data field definitions, and meta-tags would give data scientists a higher probability of succeeding with their ML models at scale.

Up to a third of a typical sales & marketing team’s time is spent dealing with data quality issues, which has a direct impact on productivity, according to Forrester’s Why Marketers Can’t Ignore Data Quality study. Inaccurate data makes tactical decisions harder to get right, which could impact revenues. It was found that 21 cents of every media dollar have been wasted over the last 12 months (as of 2019) due to poor data quality. Taking the time to improve data quality and consistency in sales & marketing would convert the lost productivity to revenue.

Start with change management & Data Piloting

Too often, sales / marketers and the IT teams supporting them rely on data teams to improve inconsistent data. It’s time-consuming, tedious work and can consume up to 80% or more of the data scientist’s time. It is no surprise that data scientists rate cleaning up data as their least-liked activity.

Instead of asking data scientists to solve the sales & marketing team’s data quality challenges, it would be far better to have the concerned teams to focus on creating a single, unified content data model. The department should consolidate diverse data requirement needs into a single, unified model with a taxonomy rigid enough to ensure consistency, yet adaptive enough to meet unique campaign needs. Change management makes the sales & marketer’s job easier and more productive because there is a single, common enterprise taxonomy.

Data Piloting is key to solving this problem, and sales & marketing leaders need to be able to explain how improving metadata consistency and content data models fits within the context of each team member’s role. After that, one should focus on standardising across all taxonomies and the systems supporting them.

The bottom line is that inconsistent data quality in sales & marketing impacts the team by jeopardising new sales cycles & creates confusion in client relationships. The ability to get AI and ML pilots into production and provide insights valuable enough to change a company’s strategic direction depends on reliable data. Companies will find their campaigns’ future contributions to growth are defined by how the team improves data quality today.

Vokse Self-Service Data Piloting platform automates data quality and management in real-time, allowing you to consistently take the right decisions based upon reliable data.

We uniquely enable AI & Data projects to deliver business value, increase revenue, and improve customer experience; whilst accelerating the business & technology transformations needed to become more data-driven.

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