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How to Break Data Silos and Drive a Customer 360 Strategy

To compete in the COVID era, enterprises need effective, sustainable customer 360 strategies that distil clear signals from noisy markets. They must unify views, consolidate tools and govern their data. Winning enterprises take the additional step of fusing data management, AI and cloud technologies to improve agility, efficiency and governance.

So what is a customer 360 strategy? It is a strategy that builds unified, consistent and governed data views across all relevant sources, to analyze customer needs, influences and behavior. Executed well, it generates insights that drive targeted marketing and sales programs, and guides product innovation and strategic planning.

Customer 360 strategies vaulted to the top of corporate agendas in the aftermath of COVID-19. Massive behavior shifts, ranging from toilet paper hoarding to home-cooking binges, force enterprises to engage customers more proactively. They need to segment targets, describe intentions, predict actions and prescribe next steps—all faster and more accurately than ever before. To achieve this, they must break longstanding data silos.

But how? Here are three primary tenets of an effective customer 360 strategy.

  • Unify customer viewsMost marketing and sales programs today fall short of a full customer 360 strategy because they fail to reconcile customer records across multiple data silos. Customer Relationship Management (CRM), marketing automation and business intelligence (BI) applications often have different views of the same individual. Enterprises must fix the problem by integrating and governing all this data. They must unify, cleanse, match and merge records—then provision the resulting high-quality data to various applications for consumption.
  • Consolidate tools. A number of overlapping data platforms and tools contribute to customer 360 strategies. While each tool yields benefits in isolation, a mature customer 360 strategy must rationalize how they work together, and seamlessly exchange data in just a few clicks. A customer data platform, armed with master data management and data quality features, creates a natural center of gravity by enabling the unified customer views described above. For example, this week Informatica unveiled a Customer 360 SaaS solution that merges several offerings—most notably data integration, master data management and data quality—into a cloud-native platform, to simplify deployment, usage and updates.
  • Govern data. Fragmented data, tools and processes pose increasingly unacceptable risks. Predictions and recommendations based on inaccurate data can kill deals, drive away customers or even invite regulatory scrutiny. Marketing managers, BI analysts and other customer 360 stakeholders must collaborate with data stewards to establish sustainable, enforceable and measurable governance processes.

Enterprises also have the opportunity to fuse data management, AI and cloud technologies so that they reinforce one another. There are three aspects to this virtuous cycle.

  • Cloud makes data management and AI more agile. Elastic cloud resources and a microservices architecture enable data teams to flexibly deploy, scale, configure and adapt their customer 360 environments. They can rapidly spin up a sandbox, then use APIs to add, remove or change ML/AI models that test advanced analytics approaches. They can easily accommodate growth and still integrate with on-premises, hybrid and multi-cloud data sources.
  • AI improves data management processes. AI features can automatically learn and execute data integration tasks. For example, ML algorithms can infer data similarities, discover relationships and match schemas to source data. Business users can review data sets and actions that are automatically recommended to them, and use intuitive automatic prompts to help data teams label data.
  • AI improves governance. Data stewards can use ML to intelligently sort and control metadata. They can automatically infer data relationships and create metadata models that track data lineage and apply data quality rules. BI analysts and business managers can match and merge a sample set of customer records to help train ML models that learn and automatically apply those harmonization techniques to high volume data sets.

As with any technology endeavor, customer 360 strategies risk meltdown if you do not synchronize the forces at work. But if you comprehensively assess, plan and execute on your customer 360 strategy, you can achieve that fusion. You can create unified and governed customer datasets that help decipher customers and drive revenue.

Kevin Petrie

Kevin is the VP of Research at BARC US, where he writes and speaks about the intersection of AI, analytics, and data management. For nearly three decades Kevin has deciphered...

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