Adopt a Data Unification strategy to stay ahead of your competition

Businesses should stitch together data — regardless of its source — to unlock value from it. That means investing in an infrastructure that provides a fortified, compliant, and an automated data pipeline.

So, where should one start? The answer lies in putting together a well-thought data roadmap that synchronizes well with the organization’s business strategy.

Step 1 — Develop a framework for Data Unification

Regardless of size, every business has a pool of data — much of it in disparate silos or repositories like spreadsheets, source systems like CRM, Events Data Store, Relational & Non Relational databases, and more. The first step in the data integration roadmap is understanding what data you have, how much it is retrievable, and its cost.

Most important among the different types of data is Customer Data, which is often prioritized by

The three pillars of a sound Customer Data Infrastructure are

  • Data integration to connect, unify and augment your first-party data
  • Data governance to ensure your data is accurate, updated, trusted, and democratized across teams
  • Audience management to deliver a differentiated and superior experience to customers

Data integration involves many considerations, including data volume, width, velocity, structure/schema, and integration requirements of different data sources. The role of pre-built connectors, integrations, and APIs is pivotal for this. The right data platform can orchestrate the flow of data basis the needs of the organization. Lately, Customer Data Platforms (CDPs) have become crucial in data integration at scale. You can find out more about the role of CDPs today over here.

As part of data governance, one needs to ensure that data in your downstream applications is accurate, consistent, and complies with internal and external privacy and security policies.

Audience management orchestrates data from various data sources seamlessly into multiple use cases like micro-segmentation, look-alike modeling, campaigns, revenue management, etc. We at propellor.ai are developing the next-generation audience management platform. Fully equipped to stitch first party, second party, and third party data together into dynamic, actionable cohorts, propellor.ai makes it easy for business users to build several audience cohorts based on more than 1000 attributes.

Step 2 — Define goals, roles, and use cases carefully

Defining goals is as simple as deciding which destination does one wants to travel.

Often, in a hurry to deliver results to the stakeholders, businesses fail to define the objectives of their data journey and end up with a concoction of poorly stitched data models. It’s imperative to take an outside-in approach to define goals which means taking customer input is critical. The outside-in approach ensures that the data model is market-driven and evolves as the market evolves.

We all have heard the typical statement in the boardroom “Too much data, confusing insights and no story.” Today, there is an emergence of cutting-edge data platforms that guide the user to set definitions, e.g., “Active Users,” “Churn,” “CLTV.” These platforms also help you define critical metrics, frequency of data refresh, archival policies, etc. Users across the organization benefit from a superior data environment and start developing their data stories.

One of our eCommerce clients in Bengaluru had a single standard churn definition across 2000 odd products. With such a generic definition of churn, their retention teams would often keep going in circles, unable to get to the causality of churn.

Modern Customer Data Platforms help users in defining critical business metrics. This helps in better tracking, real-time alerts when something changes in the business and also also helps in gaining a better understanding of the user base.

Step 3 — Find the right Customer Data Platforms

Once an organization understands its data well and puts methods to leverage more than 75–80% of its data, it’s time to start putting some of the defined use cases to test.

As the use cases develop, the data requirements will also evolve. Hence, one must invest in a modern technology stack and, to a large extent, future-proof. Platforms that have embraced a serverless architecture are best suited to run alongside a fast-changing, ever-evolving data organization.

Next-generation data platforms will automatically enable organizations to discover data types, adapt to different data schemas, and dynamically apply granular access controls for downstream data consumers.

The platform of choice should provide fortified infrastructure, governance and manages real-time updation.

With the emergence of cloud platforms, many opportunities have opened up for the next-generation data platforms that can do the job seamlessly for your organization and unlock the value needed to stay ahead.

To learn more about data unification, follow us on propellor.ai

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A compilation of TBB-generated and curated reads from the domains of digital analytics, enterprise analytics. For more: https://www.propellor.ai