Taking Data Centricity from Lofty Concept to Notable Achievement

An architectural design that supports and encourages data centricity includes three critical building blocks

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From the time data was first proclaimed as “the new oil,” most enterprises have come to consider data as a strategic asset and the means for deriving meaningful insights.  However, unlike oil, the path to profits from data is not always clear. Building a data-centric enterprise provides many opportunities to overcome organizational inertia and realize success with data.

“Data centricity” refers to the thought process and culture of building infrastructure, applications, and processes around data, and creating pathways for converting data into insights and foresights across business processes and functions. Many cloud services providers offer a means for distilling data into knowledge, enabling organizations to enhance their data centricity. Before experimenting with cloud-based data analytics, however, we advise businesses to consider the following organizational and cultural issues:

  • Data is not a disposable asset. Once data is created, its lifetime is infinite. Data lives longer than infrastructure and applications, which must be replaced frequently.  Creating a culture where every bit of data is stored and harnessed promotes strong data awareness within the organization.
  • Data democratization is mandatory. To encourage innovation and deeper insights, data should be accessible across the organization, with proper governance frameworks in place.
  • Self-service isn’t a nice-to-have – it’s indispensable. Employees need to be able to access data for insights without bureaucratic restrictions and delays.
  • The right investment in tools and infrastructure can enhance data’s value. Data management is an investment, not a cost. To reap the benefits of data, organizations need to continuously invest in tools such as cloud-based data management solutions and other data science advancements.

Data-Centric Design Essentials

From there, organizations should embrace an architectural design that supports and encourages data centricity. The following three building blocks are critical:

  • Establish a data lake: This should be the first step of any data journey. A data lake is where all data created by the enterprise is centralized and stored. As such, data lakes help break down organizational silos and the politics associated with data ownership. Data from corporate databases and line-of-business applications – including mainframe and legacy systems, real-time data from websites, mobile applications, Internet of Things devices and sensors, social media channels, and ERP systems – can be ingested into the data lake in raw format. According to an AWS-Aberdeen study, organizations that implement a data lake outperform similar companies by achieving 9% greater organic revenue growth.

  • Get a handle on data security and governance: A secure and well-governed enterprise data architecture takes into consideration issues like data masking (e.g., anonymized employee names in HR data), compliance, federated authentication, and role-based access and data protection (encryption at rest and in transit).

  • Use a data catalog: A data catalog provides a single source of truth about the assets in the data lake and serves as an interface for data analysts to query these assets.

Finding the Path to Value

The recent general availability of AWS Lake Formation will accelerate the adoption of enterprise data lakes. In our assessment, this service will reduce the barrier to entry for organizations to quickly build and manage enterprise data lakes, as it addresses the design elements above and automates many of the manual and complex tasks of loading, storing, and preparing data.

For example, we worked with a global automobile manufacturer to improve the effectiveness of its marketing campaigns. The first move was to migrate the company’s structured and unstructured customer data to set up a secure data lake using AWS Lake Formation. The structured data included vehicle sensor data, CRM systems, after-sales data, financial services, and marketing campaigns. Unstructured data included social media data, video images, voice and agent observations, email, and click trails. 

Using the AWS Redshift cloud-based data warehouse, the manufacturer created a 360-degree view of the customer, which improved its ability to target its marketing campaigns. Immediate business benefits included more than a 250% improvement in the click-through rates of its campaigns.

This is just one example of how organizations can profit from a well-designed and executed data management strategy. It’s no longer enough to just appreciate the importance of data – the building blocks are here to shorten the path to realizing its full value.  

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