Data Overload: Why are CMOs struggling to find actionable insights?
The data deluge and dilemma in the marketing fraternity seems to be a problem with no concrete solution yet, despite the barrage of martech tools and CDPs that have come into the picture. On one hand, the vast streams of information promise insights that could unlock massive levels of engagement and customer understanding. On the other hand, the sheer volume of data can be overwhelming, leading to the dilemma – distinguishing high-quality, actionable insights from the noise. According to a HubSpot survey, 82% of marketers acknowledge the critical importance of having high-quality data about their target audience for their success. Yet, more than half admit they are missing crucial information, highlighting a significant gap between data abundance and data utility. This conundrum not only challenges marketers' strategies but also calls into question the efficacy of their efforts in an era defined by digital saturation. What is ‘good quality’ data? Before delving into why the problem still persists, it is important to understand when data can actually be classified as ‘good quality data’. According to Magicbricks’ Prasun Kumar a good quality data needs to be relevant, reliable, consistent, accurate and complete. “Relevance is about whether the data gives you what was asked from it or not, does it justify itself for the purpose. Reliability is of course about the source of data and the correctness of the methodology with which it has been collected. Consistency is being seamless, not broken, empirically sound. Accuracy is about data conforming to norms of statistical deviations. Completeness is covering the entire spectrum of requirements,” Kumar explains. This holistic approach to data quality is crucial for marketers striving to make informed decisions. Yet, the path to achieving such data quality is fraught with challenges. Sajal Gupta, Chief Executive, Kiaos Marketing echoes a similar voice and also adds the aspect of privacy compliance. He says, “The data needs to be privacy compliant, given the regulatory concerns on privacy, to ensure consent to use the data has been procured and stored safely for easy retrieval.” Privacy compliance is becoming increasingly important in the digital landscape, as regulatory frameworks such as GDPR and CCPA, and also India’s Digital Personal Data Protection Act, 2023 place stringent requirements on how data is collected, stored, and used. Marketers need to constantly navigate these regulations carefully to maintain consumer trust and avoid legal pitfalls. Why is it still a top challenge for marketers? However, Jacob Joseph, VP - Data Science, CleverTap agrees that one of the biggest challenges for marketers today around data are quality, accuracy and fragmentation or the data disconnection crisis. “Inconsistent data quality often arises from disparate sources or siloed data, and manual processes for data collection and management. This challenge is exacerbated as businesses scale and accumulate more data from various touchpoints and channels,” Joseph mentioned. He added that additionally with Big Data becoming an important part of an organisation's strategy, businesses today have even more data and this data resides in disparate and legacy systems, making it difficult for them to harness the full potential of this asset. According to Joseph, without reliable data, marketers risk making misguided decisions and missing out on opportunities to engage effectively with their audience. Sumeet Singh, Group CMO, Infoedge too says that in a lot of cases if data is lying in a silo within the organisation, then there can be a problem with the data quality. Gupta added that while there are many data management tools and many powered by advanced AI, their success depends on the quality of data that is inputted to study the market scenario. “If the input is inaccurate and not representative of the market the output will also not be accurate and not truly representing the market. You can have the best of tools. The results it will give you will totally depend on the quality of data input,” he said. This also highlights a critical aspect of data management – the garbage in, garbage out (GIGO) principle. No matter how advanced the tools and technologies at a marketer's disposal, the outputs will only be as good as the inputs. Experts also mentioned that if one is looking at data regularly, s/he can find out the inconsistencies. “But sometimes you look at data only in a snapshot and therefore you feel that whatever data is showing you is true, but if you’re in a practice of looking at data regularly and consistently, you will be able to weed out the noise on the outliers, and that way data becomes more reliable,” Singh pointed out. What do CMOs need to do? According to her, there needs to be good cross functional collaboration between marketing, IT, data science, business intelligence or analytics depending on the structures and the way different organisations have organised themselves, because if there is good cross functional collaboration, then each of the teams is looking at data. Cross functionally data gets validated and there are lesser chances of it being of inferior quality. Both Kumar and Singh point out that the first step to ensure good quality data is for CMOs to have clarity about what is needed. “Often the requirements themselves remain vague leading to poor inputs coming from partners. A sharp requirement brief can often result in curbing the waste quite a lot,” said Kumar. Along with clarity, experts feel it is crucial to choose the right external partners and tools to help the organisation further. Gupta says, “Organisational data should also be further enriched with external partners to get additional attributes on the users which are not currently available within the organisation.” He also feels that the CMO’s must build processes where data quality is maintained across the organisation. “Use data validation tools to audits to identify inaccuracies and inconsistencies and are updated timely.” Some tools that are helping marketers to achieve the desired results and navigate through this data deluge, according to Joseph, are data integration platforms that automate data movement between platforms, reducing manual data entry errors and ensuring consistency; data quality management tools offer functionalities for data profiling, deduplication, cleansing, and standardisation and data visualisation tools to help visualise data quality metrics, identifying trends and areas needing improvement. For more updates, be socially connected with us on
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