Most organizations understand that in order to compete in business, you need to understand your customer and provide the personalized experience that they have come to expect from brands. Brands are no longer simply competing based on products or services, they are directly competing on customer experience.
Many of these organizations choose to utilize data lakes for their broad variety and large volumes of raw customer data, but one of the greatest issues in adopting data lakes is that brands fail to ensure the quality of data that is amassed within these lakes. While the ability for organizations to store high volumes of structured and unstructured data has become more accessible, there are few brands that have been able to avoid these data lakes from turning into data swamps. That is, the quality and mass of the data becomes too large and are ultimately inaccessible to the end users, making it nearly impossible for organizations to fully realize the value of their data lakes.
For brands to fully capitalize on the advantages of their data lakes, they must implement automated processes to manage the data to produce accurate customer profiles for analytics and engagement.
Here are the three things you need to have in place to get the most out of a data lake:
Data lakes are repositories that house a broad variety of data – structured or unstructured. While it is a wonderfully easy solution for housing a variety of customer data, in order to actually put the data to use, it needs to be sorted and easily accessible across the organization. This requires a tool that can natively handle all these different data source formats so that they can become easily accessible for the business user. This includes multiple NoSQL and document formats such as MongoDB, Avro, and Parquet.
Siloed customer data is often a major barrier to creating consistent customer experiences because the data is stored separately. Data lakes solve this problem by ingesting data from all the customer engagement points into a single location. The end result is a unified customer profile, or “golden record,” that collects all that is knowable about each individual customer
The quality of the customer experience is entirely dependent on the quality of the data. Without correct or timely data, brands often run into the issue of irrelevant or repetitive offers. Customer data platforms (CDPs) are one of the best solutions for ensuring the quality of the data.
A true CDP should be able to handle cross-source data extraction, name and address normalization, tuned deterministic and probabilistic matching, along with workflows for resolution auditing and compliance. Only a customer data platform can provide the insight into consumer preferences and analytics functionality that forward-thinking brands need to drive average revenue per user higher and succeed in the long term.
In order for brands to provide a personalized experience for each customer, they need to ensure that the data is timely. Recent research from Dun & Bradstreet found that organizations who actively maintain their data have a 66 percent higher conversion rate than their peers. If the customer has moved homes, changed jobs, or already purchased a product, it will ultimately affect the customer journey. In the age of the smartphone, the entire path to purchase has been significantly shortened, and the ability to engage with the customer in a timely manner has become critical to attracting and retaining customers. This requires model and performance testing as well as product capability that minimizes latency. With the right tools, a company can handle the variety, velocity, and volume of data lake information to effectively utilize their data lake to meet their customer experience goals.
Effective utilization of data lakes can enable companies to succeed in today’s fast-paced marketplace, but it is even more important to implement the right tool for your data lake to ensure the ability to deliver a seamless and personalized customer experience throughout the customer journey on a richer and more personalized basis.