Exponential growth in data and information at hand is forcing organizations to rethink their business models
Exponential growth in data and information at hand is forcing organizations to rethink their business models. With the rise of digital channels (both as sales channel and marketing channel) the business environment is becoming more complicated and challenging, and the competition is intensifying. Firms are responding by setting up dedicated analytics (or information science or insights) divisions, or repositioning existing teams, to enable a culture of data-driven decision making.
There has been significant momentum in recent years with rampant hiring of analytics talent. However, business need to assess if the investment has paid off by achieving the intended objective, or showing signs of positive ROI. Here are SIX steps for businesses in their journey to maximize analytics ROI:
1. Set up effective communication and active partnership between analytics and business units
Analytics has the potential to be a game changer for business in driving competitive advantage, not just an enabling/supporting function. Active partnership between the business units and analytics, and collective decision making can better harness the potential of analytics. Progressive organizations are increasingly leaning towards data-driven as opposed to data-enabled business KPIs.
2. Align analytics success with organizational KPIs
Ensure that the targets for analytics divisions are aligned with the broader organizational KPIs and reflect quantifiable influence on revenues/costs on the rest of the organization. Even a virtual P&L for the analytics division, with incentives linked to the impact to overall business, empowers the analytics team, and also promotes the culture of data-driven decision making.
3. Effectively operationalize analytics for repeated consumption
Transition from having a series of ad hoc analyses supporting business needs to a tightly integrated operations with day-to-day decision making. It is critical to realize the long-term benefits and the long shelf-life of analytics outputs to maximize the ROI. Successful organizations are building solutions to embed analytics as part of decision systems to drive real-time decision making.
4. Define a clear approach to vendor selection and partnership
Define a 3 to 5 year analytics roadmap, assessing internal capabilities and identifying the gaps that business is looking to fill. Identify the key strategic requirements around data management, exploratory analytics, modelling expertise, operationalizing solutions, product expertise, short-term resourcing, etc. The priorities change as the analytics maturity grows within the organization but the decision around choice of vendors, timing of engagement, duration of partnership and nature of RFP will have to be closely aligned with the roadmap.
5. Balance the top-down and bottom-up approach to analytics
While the growing influence of analytics warrants a top-down pan-organizational view of business priorities, a fine balance of “top down” and “bottom up” can strengthen stakeholder engagement at multiple levels and expedite the journey of institutionalizing analytics. After all, analytics has existed in some shape or form in the past, possibly de-centralized and distributed across multiple divisions. Building on past achievements and actively engaging the respective teams can build quick credibility and promote partnership.
6. Make the organizational structure agile and responsive
Consumers are expecting near real-time information; competitors are constantly rethinking their business models; an agile and responsive organizational structure can promote consumption of analytics within the organization. Further, as the analytics organization and/or strategy evolves, the rest of the organization need to show the agility to quickly adopt to the evolved processes.