The 2014 EMC Digital Universe Study, with research and analysis by IDC, predicts that by 2020 the digital universe will contain nearly as many digital bits as there are stars in the universe.
According to the study, digital growth “is doubling in size every two years and by 2020, the digital universe—the data we create and copy annually—will reach 44 zettabytes, or 44 trillion gigabytes.”
As companies brace for this data tsunami, they are challenged to identify the next business opportunity, improve risk management, customer engagement and sustainability. They will need to become “predictive enterprises” which leverage their data to define their future focus and how to get there. Sifting massive amounts of data to find relevant insights for business will be a continuous process, constantly evolving and adapting to business climate. IT departments need to have a robust framework to manage their organizations’ ambitions and goals.
I strongly believe for an organization to be a predictive, analytic-driven enterprise, there are four key pillars which are critical for success. It is not all about technology.
1. Business Value
When it comes to predictive analytics, the business should drive the process. Any analytics-based project, as well as an organization’s analytics approach overall, needs to focus on the specific business value that such analytics will bring to the enterprise.
Analytics is a business-led initiative supported by IT to maintain its competitive edge in the business place and constantly improve its operations.
Too often, business users pursue analytics projects without proper focus on targeted results and outcomes, and end up wasting resources on multiple projects that don’t yield any business value. Results may help keep the lights on, but they don’t help move the needle toward improving business performance by improving operational excellence, increasing revenues, increasing customer base or serving customers better.
Organizations should align analytics projects to specific strategic business goals with a clear objective and projected return on investment (ROI). For example, salespeople may want to use data analytics to help better target which customers are most likely to renew product contracts. The value is that salespeople gain insight on which accounts to focus on for prospecting and revenue opportunities.
Sponsorship—garnering leadership support for analytical projects is critical to furthering business objectives.
The business prioritization needs to consider cost (complexity, resources, and platform) as well as business value which can encompass both hard and soft benefits. The business and IT need to determine the addressable market for the business opportunity and best/worst case scenarios on potential benefits when the analytics initiative is embraced by business users.
Prioritizing analytics projects by business value and costs will help your organization develop a comprehensive value-based roadmap which should have value tracking and attainment to close the loop.
Governance is a mighty word but often brings lot of skepticism. It is the fabric that promotes collaboration, provides transparency and accountability within the different parts of the organization. And it is an essential component in shaping the predictive enterprise.
At a practical level, governance has two key aspects — information governance and financial governance. It involves stakeholders from both the business and IT.
Analytics project/program funding needs to be disciplined and aligned tightly to the business value thereby meeting the broad stakeholder expectations. If your organization has $5 million to spend on analytics projects, the governance helps identify priorities and corresponding funding.
Information governance seeks to ensure that data required to execute the project/solution has the necessary constructs for primary data; data quality; technical metadata; business glossary; data stewards; security privileges; information catalog; data lineage and so forth. What do we have to do to enable people to do their jobs? Do we collect this data? Do we not collect that data?
Governance is also the driver for business enablement of analytics, laying groundwork for Change Management for a successful adoption of analytics within the business community. This is one of the most critical aspects to turn expected/potential savings to real savings.
3. Operating Model
The operating model is about the people who will lead your analytics project. It defines who will participate in the effort from business and IT. The question is how do we find the path of least resistance to execute the project in the best possible way.
Business sponsorship plays an important role in this pillar by supporting the needed technical resources and getting people on board. Sometimes an operating model is completely IT driven, in other cases it is run and funded by the business. But it may also involve a collaboration of resources from both IT and the business.
More and more a hybrid model is required to focus on effective utilization of all resources, business or IT, and adhering to Enterprise IT architecture standards and processes of executing at the speed of business. The business and IT need to collaborate to accelerate the delivery and improve business enablement. Analytical solutions should be capability driven to deliver a minimum viable product in short sprint cycles.
Technology is the vehicle to create and consume the data insights—hindsight, insight and foresight—that the business is seeking.
Once IT has determined what the business customer needs from the project, the IT support team puts together the best tools to design, architect, develop, deploy and support the analytics project. These include the technology platforms and infrastructure, databases, data ingestion, data transformation, data access layer, security layer, data quality tools, reporting and visualization tools.
These four pillars need to be manned and supported by folks with the right energy; and like simple architecture, all four pillars must participate for successful support and sustainability. Through this framework, the business and IT can collaboration to truly leverage organizational data assets. It can use predictive analytics to drive successful business strategies for the future.