Matching customer data across a disjointed tech stack

How many platforms is your organization using to collect customer data? If it’s over a dozen, you aren’t alone. Today’s challenges are no longer about having enough data; they’re about being able to use that data effectively.

Enterprise data is increasing at a rate of 42.9% annually. But when data is collected across a disjointed tech stack, it becomes impenetrable. Modern organizations struggle with duplicated, incorrect, and disjointed customer data.

Luckily, there’s an answer. Let’s look at how organizations can leverage machine learning, AI, and data warehousing solutions to manage their customer data even across the most disjointed tech stacks.

Matching customer data across multiple data sources

It’s understandable how a tech stack can become unwieldy. Organizations have a tendency to grow organically. New tools are added at the fringes as new demands arise. Unfortunately, this leads to a tech stack that may not be fully integrated or fully controlled.

Valuable data is everywhere. Customer data is everywhere. But to actually acquire meaningful analysis from this customer data, technology and processes need to be put into place to verify that the data is correct and useful.

In today’s multi-channel world, customer data comes to us across a multitude of platforms. From our purchasing platforms to our advertising platforms, we need to be able to normalize this disparate data. Consider that when advertising, the same metric—such as visits—could mean vastly different things based on the platform that collected the data. Across various platforms, “visits” could refer to discrete sessions, individual hits, unique customers, or something else altogether.

Disparate data, of course, makes it extraordinarily difficult to glean actionable insights from data sets. If you cannot compare metrics apples-to-apples, it becomes impossible to make intelligent decisions. Pull all the data directly into a single consolidated interface, and you could think that you’re looking at 12,000 discrete customers when you’re really looking at 6,000 customers and 6,000 duplicate records.

Data normalization is the solution. But it has only recently become feasible through machine learning/artificial intelligence suites—which, in turn, rely upon data consolidation.

Data warehousing and data lakes

Before data can be normalized, it has to be consolidated. Today, organizations are rapidly moving toward data warehousing and data lakes, otherwise known as ETL solutions (Extract, Transform, and Load).

Data warehousing solutions import and store data, generally on a cloud-based system (although on-premise data centers may be utilized). Data lakes don’t store the data; rather, they interface with existing data storage solutions. ETL solutions are becoming an essential component of an organization’s technology stack, as they are what enables the consolidation and analysis of discrete data silos.

Both data warehouses and data lakes interface with multiple data sources at the same time. But while this makes it possible to create a single source of truth, it doesn’t mean that this data is valuable—not yet. The data still has to be transformed and normalized before it can be properly analyzed.

Normalizing and transforming disparate data through machine learning

Once data has been consolidated, machine learning algorithms process the data together—eliminating redundancies and normalizing the data across the board, ensuring apples-to-apples comparisons even amongst varied platforms. This type of data transformation is absolutely critical before any true analysis can occur.

As with all machine learning solutions, data normalization gets better over time. Platforms must be trained on the data sets being used to understand how to normalize, compare, and analyze them in comparison to each other. Thus, when choosing an ETL solution, organizations must look for ETL solutions that support the type of data that they are trying to consolidate and analyze.

The importance of having consistency across your tech stack

Data drives decisions. Without the right data in hand, no organization can even hope to understand its consumer base. Today, it’s unavoidable that organizations will need to interact with customer data from across an extraordinary array of platforms. Without consolidating and normalizing this data, it’s impossible to get a big picture view of your organization and its customer relationships.

And it isn’t just about getting the right data—although that’s obviously the end goal. ETL solutions reduce administrative time, as employees are no longer bouncing from platform to platform and attempting to garner insights from multiple sources of data. Fewer mistakes are made and less time is wasted.

Start transforming your customer data today with Lytics

The Lytics customer data platform leverages machine learning and AI to enable businesses to better leverage their cloud data warehousing, eliminate data silos, and achieve better data governance. With Lytics, you can consolidate and analyze your data for hyper-personalized, intent-driven marketing campaigns. Contact Lytics today to find out more.