6 Steps to Maximize ROI On Big Data

by Eric Liang

ROI is a term from the investment world, the operational mantra of which is risk management.  Maximizing the likelihood of positive return on any investment requires discipline throughout all stages of the investment cycle.  Investments into Big Data are no exception1. Take the starting point, for instance.  An astute investor would not be investing in all the ‘good’ opportunities in front of him at once, even if they all pass the initial sniff tests.  You must be selective — to align with your investment style and risk tolerance.  Like dabbling in any new arena, you would likely start out small and incrementally up your bet if the first forays prove fruitful. During execution of the project, you may well acquire new staff (skills), establish new controls (processes), and buy new tools (technology) to monitor it, which calls for rigorous requirements definition.  As the project matures and grows, the number of stakeholders, curious onlookers and potential ‘second-wave’ investors increases, so a governance structure would be needed to manage demand and trial usage of the project’s outputs and that should be part of the plan at the get-go, not an afterthought. Finally, since all the skills2, infrastructure and technology most appropriate for capturing the investment’s benefits are not likely to be resident in-house long-term, some sourcing strategy alongside the investment plan should be in place as well.

In short, a Big Data investment cycle should cover the following steps:
  1. Prioritize Big Data Analytics (BDA) opportunities
    • Identify specific target use cases
    • Quantify benefits and risks
    • Identify executive sponsors
  2. Assess readiness of the people to be involved
    • Definition of roles
    • Skills requirements
    • Organizational placement
  3. Define the end-to-end processes for the Big Data use case
    • How multiple departments collaborate
    • Data storage/retention practices
    • Data security considerations
    • Controls (e.g. HIPPAA, PCI)
  4. Clarify the needed technology
    • Software
    • Infrastructure
    • Hosting options
  5. Establish governance early on
    • Demand management
    • Data usage
  6. Select sourcing channels and partners
    • Focused skills
    • Infrastructure
    • Software
    • Hosting

With the rapidly advancing ubiquity of Big Data use cases, most organizations would soon feel compelled to develop some level of BDA capability.  If BDA were to become the next must-have competence for most IT groups, as it is poised to be, a strategy to build up such competence is required.  After all, there is no other way to learn a complex skill than trying-failing (partially)-learning-and trying again.


According to an Infochimps Inc. survey, 44% of Big Data initiatives got scrapped, apparently due to poor planning
The McKinsey Global Institute estimated that data analysts demand will outstrip supply by 50-60% by 2018
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