How can a data lake enable an omnichannel architecture?

How can a data lake enable an omnichannel architecture?

Every day over 2.5 billion gigabytes of new data is created worldwide through business transactions, social media content, smartphone usage, online activities, and the Internet of Things (IoT).

What’s more, 90% of the world’s cumulative data throughout history has been generated in just the last two years. Due to the advent of technology, humans are not just generating larger quantities of data more quickly. The data itself has become far more complex in nature over the course of a few years.

Data is the most valuable commodity

Data has now been recognized as critically important to businesses, both operationally and strategically. Companies that cannot effectively analyze data to derive business insights and inform decision making are unlikely to survive in the long term. Traditional data gathering methods such as focus group research, surveys, or mere reliance on gut instincts are no longer viable because of the subjective results they tend to generate.

Why big data?

Savvy businesses have sought to invest in big data to reduce operational costs, optimize operational efficiency, and identify gaps in the market for product and service innovations. Big data has been recognized by experts and business leaders to be the next frontier for innovation and competition. The businesses that are succeeding in the digital age are those that are leveraging multiple sources of data to locate problems, opportunities, and solutions.

However, with so much big data being generated at any moment, it can simply be too overwhelming to collect, aggregate, process, and translate all the information into insights without the support of suitable tools.

How is all this data stored and used?

Virtually every business today relies upon some kind of database to capture operational data. Databases host data in its most elementary form such as transactional data related to appointments, airline bookings, sales data, and purchases. Data warehouses, in contrast, host multiple sources of complex data. They can be used to provide high-level reporting and analysis for more informed business decisions.

For example, a data warehouse would typically be used to carry out data mining of multiple large databases to analyze user behavior and thus inform complex business decisions—such as in the case of omnichannel marketing automation.

However, databases and data warehouses require data to be written on a schema, that is, in a structured data format in order for it to be stored and retrieved. It is estimated that 80% of the world’s data is unstructured. Machine learning cannot easily identify unstructured data as it does not conform to a standard database or spreadsheet. Unstructured data can take the form of photos, videos, or other human-generated content such as text messages, blogs, and other forms of written content.

For unstructured data to be stored in a data warehouse, it needs to be analyzed and quantified into relational databases on a schema so storage and retrieval become possible—a process that is time-consuming and potentially very expensive.

How does a data lake solve the issue of unstructured data?

Data lakes provide a storage solution for all types of data. They are much more scalable than data warehouses since they don’t require a rigid structure, which makes them ideal for big data.

However, to be able to make all the unstructured data in the data lake usable, the business needs to set up a query engine capable of replicating the way a data warehouse behaves by organizing data in a way that is conducive to analysis. A data analytics engine is required on top of the data lake as it runs between the query generated from the business intelligence platform and the huge mass of data in the lake. While data mining software can ingest raw data, advanced analytics tools can transform it into useful insights.

What’s the difference between data lakes and data warehouses?

Both data lakes and data warehouses help businesses store and process data. However, data lakes work better for organizations that utilize cloud-based data warehouses. Research from ESG indicates that approximately 35-34% of organizations are actively considering cloud-based warehouse applications because their scalability and reliability lead to increased performance and capacity.

Legacy systems have their limitations

Although databases, data warehouses, and BI applications have become the norm for businesses, they tend to rely upon legacy architecture that has a number of shortcomings such as rigid design, limited scalability, performance gaps, accessibility restrictions, and high costs. Data warehouses require specialists from a data team to execute data access, which not only slows down information retrieval and analysis but drives up maintenance costs.

Data lakes, however, are able to overcome many of these issues. Cheap and simple to implement, they lack a rigid structure, optimizing their scalability and ability to house vast quantities of raw unstructured data. Since data lakes support the usage of metadata—data that provides information on existing information in a data lake—businesses are able to gain insights into the data much more quickly.

Data lakes can play an important role in omnichannel marketing

To run effective omnichannel marketing campaigns, businesses need a platform or tech stack powered by big data.

While big data is unwieldy on its own, an omnichannel platform, powered by a robust data analytics engine at its core, is able to utilize machine learning and artificial intelligence to isolate patterns, reveal trends, and identify customer preferences from vast amounts of information. It can help the business achieve a single, real-time customer view to fuel sophisticated segmentation and individualization.

Data lakes, if architected well, can become the sole feeding point to the customer data platform, which enables the front end of customer engagement. A data lake can also be a conduit for gathering response data from interactions and communications across integrated touch points—a feature that is obviously conducive to the creation and enrichment of the aforementioned 360-degree customer view.

Omnichannel marketing hinges on a deep, evolving understanding of the individual customer, one that can be translated into relevant experiences and seamless journeys that are optimized in real-time and across channels. Data lakes provide a powerful new approach to distilling insights from untapped data, greatly boosting the agility of omnichannel marketing solutions along with the CDP. It helps brands break down the silos between customer interactions and data sources, empowering them to make better business decisions—in less time than ever.


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