“Data is the new oil”. As organizations are shifting towards customer-centric decisions, the ability to leverage available data has become one of the most critical aspects of business development.
With an increase in the number of customer engagement platforms, the accumulated customer data has taken a sharp rise too. By 2025, we’ll create 180 zettabytes of data annually, says market intelligence firm IDC.
From this huge amount of data, what matters to us is the handful of insights that are trapped inside. While this data just being stored in data warehouses, it will only pile up. To effectively use it, CDP comes to the rescue. CDP scans through all the behavioural, demographic, and transactional data about the customers to give out meaningful insights in the form of customer-specific data profiles. To read more about how it happens, click here. To read about factors to be considered in choosing the right CDP, click here.
Going back to the beginning, it all depends on the ‘data’ that is being served as a dataset to these algorithms in the CDP. This data should essentially be updated, consistent, and accurate for the insights to be helpful. But how do we ensure that? This is where Data Cleansing comes in.
What is Data Cleansing?
Data cleansing is a practice that involves the eradication of errors from the database to ensure the “cleanliness” of data. These errors may range from improper punctuations to misspellings. Factors causing dirty data include:
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Incomplete data:
Incomplete data leads to the portrayal of an incorrect picture of the real-time customer engagement scenario. Thus, paving the path for weakly founded decisions.
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Inaccurate data:
Worse than incomplete data, inaccuracy in data can lead to skewed decisions or unjust analysis of the situation in hand.
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Duplicate data:
Just filling up the storage, duplicate records can potentially confuse the data scientists, which would lead to delay in decision-making and finally in loss of both money and time.
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Inconsistent data:
Data collected from multiple locations come in multiple formats, which makes it difficult to accurately comprehend. Thus, leading to faulty interpretations.
“The average financial impact of poor data quality on organizations is $9.7 million per year.” – According to a global research and advisory firm Gartner.
Practices to Minimize Dirty Data:
Gone are the times when data cleansing ‘had’ to be done manually. Humans are prone to making errors, which may put the integrity of the data at risk. It is thus important that the data cleansing process be automated. Let’s see of some ways that this can be accomplished:
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Focusing on disengagement:
Data of uninterested customers should not fill up databases that would be used as the basis of decision making for interested customers. Thus, if an interactive Customer’s frequency of visiting the page suddenly drops, than it should automatically be placed in the disengaged category.
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Eradication of ‘unlikely to buy again’ customers:
For customers that do not respond to emails, open personalized ads, or cease to engage in business transactions should be deemed ‘unlikely to buy again’. By eradicating such customer’s data, more focus can be paid to potential customers. Thus, reducing the churn rate.
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Deletion of profiles:
Organizations can have a ‘set threshold’ of days for example 200 days. If a customer is inactive for 200 days, automatically the profile of that customer should be deleted. With this process automatized, data scientists can focus on analysis instead of cleansing.
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Standardize inputs:
A lack of standardizing constraints can prove to be a base for dirty data. Threshold constraints for user entered input data can include number limits, eradication of capitalizations, and normalization of acronyms. Such a consistent representation of data throughout channels will make data cleansing easier.
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Update data in real-time:
Customer data including their addresses, contact numbers, and job profile change from time to time. B2B data decays at a rate of 70% per year. Organizations should thus pay attention to updating data using third-party tools to avoid data decay.
How CDPs Leverage Technology to Clean Data:
Organizations that utilize multichannel marketing end up having a large customer database that includes useful details like product usage, heatmaps, cookies, and customer service interactions. But these siloes also store petty details like ad impressions, which sometimes prove out to be redundant data. With all this data in hand, accurate segregation can be a headache for Data Scientists.
Data scientists spend 80% of their time on collecting, cleaning, and organizing data, which leaves them with only 20% of the time for analysing that data.
– A survey by CrowdFlower, provider of a “data enrichment” platform for data scientists.
Is There a Solution?
Yes. For data scientists, bigger datasets equals a more time consuming and labour intensive process. In contrast to this, for Machine Learning algorithms, bigger datasets equals more precise insights. Thus, leading to accurate decisions. Let’s dive into how it really happens.
With a manually defined rule-based approach for Machine Learning algorithms, the data cleansing can be accomplished in 2 steps:
- Identifying the dirty data
- Eradicating the dirty data or replacing it with clean data.
For the first:
Data comes in through a plethora of channels which leads to inconsistency in the format of the data. So, first and foremost, ML algorithms need to put data in a stable arrangement from where comparatively patterns can be established for recognition of the type of data. Then going through that data, ML’s Natural Language Processing (NLP) capabilities can be put to work. Manually defined rule-based algorithms would thus spot inconsistent, duplicate and outdated data, which has to be substituted with clean data.
For the second:
Using statistical techniques AI can ensure that data like the missing values are reasonably estimated based on the existing neighbouring values. With scaling data, predictions and pattern recognition ability of the ML algorithms increase significantly, making it the aptest choice for organizations.
By utilizing ML algorithms for Business Intelligence in CDPs, organizations would also get predictive scoring, customer journey orchestration, intelligent insights, data visualization and a lot more, which would otherwise be difficult for teams to accomplish in the same amount of time.
Artificial Intelligence may not be ready to replace the efforts of data scientists but can surely lend a hand and thus increase productivity.
Conclusion:
In recent times, data cleansing has to be considered as one of the crucial elements of data management. Increased productivity, better customer experience, and improved decisions are some of the benefits of maintaining clean data .
Making data-driven decisions using analytical tools will only be beneficial as long as dirty data isn’t used as a basis for it. With data cleansing practices in place and high-quality customer-specific data in hand, organizations can make sure that the right content reaches the right customer at the right time. Thus, manifesting the end goal – Customer Experience.
