We live now in the zettabyte era. A zettabyte (ZB) is a measurement unit of data, and 1 ZB equals 1021 bytes. To understand the magnitude of just 1 ZB, consider this: according to Cisco, in 2016, we passed 1 ZB total annual Internet traffic. Thus, 1 ZB is an enormous number to fathom. And the global data quantum has been growing exponentially. IDC predicts that the collective sum of the world’s data will grow to 175ZB by 2025. You can well imagine the phenomenal momentum (mass * velocity) at which data is growing and the massive business transformation that will result.
This provides business leaders a significant opportunity for designing their company strategy around big data in this new zettabyte era. Data strategy is no longer a standalone technology topic for the IT organization. How a company uses its enterprise data can bring in significant business value. As an example, per McKinsey, data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain those customers, and 19 times as likely to be profitable.
Organizations that are able to harness this explosive data growth and make it operational will create significant business differentiators with respect to their competition. Think about scenarios like:
- Improving employee engagement and retention and helping them achieve their goals by recommending relevant learning experiences based on their existing talent, your company’s strategy, and their emotional experience at the workplace
- Reducing the bullwhip effect in your supply chain and increasing your inventory turns by getting real-time, data-driven visibility of your entire demand and supply chain with predictive insights
- Leveraging big data to drive the entire lifecycle of order management: from generating interest to driving purchase behavior, from order processing and fulfillment to completing downstream processes like logistics, finance, and service
You will also need to harness the powerful synergy of your operational and experience data. The coming together of the operational and experience economy opens amazing growth potential for all organizations.
Enterprise data strategy model driven by business outcomes
Consider this framework below. You need to blend in these three critical themes to design your data strategy:
Business value drivers
Your key business value drivers will have to influence your enterprise data strategy. Business leaders have to ask themselves: How can we leverage data to fulfill our business plan? While designing your business value drivers, think about these key themes:
- How can you increase both customer lifetime value and also operational efficiency?
- How can you blend in the emotional insights and experience data and use that information to drive operational excellence?
- How can you design your business strategy using data to drive business outcomes?
Big data management
Many organizations over the last few years have been reactive in this space. As an example, you might be now living in an enterprise landscape where you have various technologies supporting some of these key data-management focus areas, but you are executing them in an ad-hoc, disconnected, and non-strategic manner:
- Data ingestion, replication, and extraction-transfer-load
- Data federation
- Data cataloging
- Master data governance and data quality
- Data pipelining and orchestration
- Distributed big data processing
- Big data databases and data storage
- Cloud platform-as-a-service
- Machine learning and data science
As we progress rapidly in this zettabyte era, it’s critical to build a data intelligence strategy to start shaping a coherent big-data management platform that is sufficiently scalable, flexible, and powerful to take on this new world of big data. Here are some examples of what that would mean:
- Business application transformation: Streamline innovation initiatives around business applications to support enterprise transformation programs.
- IoT ingestion, orchestration, and robotic process automation: Transform IoT event streams into enterprise-ready data and derive actionable insights and then automate the process by leveraging intelligent robotic process automation.
- Connected data warehousing and predictive analytics: Experience the power of analytics when it is consumed at the moment of experience. Build a multifaceted data warehouse across diverse and distributed data assets and connect that with your applications with live connections.
Business process integration
We live in a world where you now have access to very powerful technology. Having said that, sometimes this results in individual lines of business (for example, supply chain, marketing, sales, etc.) or individual business units deciding on their data strategy and technology enablers in silos. At the surface, this approach may seem nimble, agile, and fast. However, you will soon realize that this strategy is not scalable. By operating in silos, you are not just sacrificing your operating margin by losing efficiencies of scale; more importantly, you are not harnessing the power of one common enterprise-wide big data platform to power your business process.
Business process integration is thus not just about joining two APIs; it’s no longer only an IT topic. It’s also not throwing data into one giant data lake, making it virtually impossible to make that data actionable in real time at the point of experience. Business process integration is a key business-driven strategy causing a synergy that results in a massive exponential gain for your organization.
Business outcome-driven common data-value model
As your next steps, it then becomes imperative to create a common data-value model for your business. As per Harvard Business Review, common data-value models help facilitate communication between business leaders and data experts. The success of your enterprise in this zettabyte era will depend heavily on how your entire organization comes together to create a common enterprise data strategy driven by your business outcomes.