The ability to store, analyze and act on big data is quickly becoming a must-have in the hyper-competitive world of retail. It’s an essential capability for effectively targeting consumer segments, strategizing product offerings, maintaining costs and innovating competitive advantage. From the largest retailers seeking cost savings on big data programs to smaller organizations implementing a big data solution for the first time, Kurt Salmon works alongside retailers to assess their big data needs and craft a roadmap to the ideal big data target state, all in 60 days or less.
DATA THAT DELIVERS CUSTOMER INSIGHTS AND SUPPORTS CRITICAL DECISIONS
As retail systems increasingly move to a SaaS model and transition to cloud-based platforms, data pools—both structured, internal datasets and external, unstructured data—are growing exponentially. To leverage this data for everything from proactive merchandising and inventory management to more effective marketing strategies and optimized supply chains, retailers must understand the information available, have the proper technology to handle the data and have the right teams in place to conduct analysis that provides actionable insights.
Yet retailers are typically held back by these common challenges:
- Outdated systems that cannot process and store available data
- Lack of expertise on staff or inability to identify existing expertise and skill sets
- Organizational structures that are not optimized for big data
- Limited time to address expanding or changing demands
Our approach to big data implementation addresses each of these issues, beginning with an assessment of the data available to our clients and determining what—and whom—it will take to access and analyze it. We then examine how clients’ data can best be leveraged and monetized and apply our proven human capital assessment tools to generate recommendations for big data analytics team roles, responsibilities and organizational structures that support critical business goals. We enable our clients to identify and capitalize on specific big data opportunities, increase revenues, reduce costs and efficiently meet ever-changing consumer demands.
Our key differentiators are our:
- Focus on human capital—Optimizing human capital is the most important part of any big data endeavor. We have an exclusive partnership with Battalia Winston, an established, national firm with 50 years of experience in executive search and a dedicated practice in recruiting big data talent.
- Holistic approach—Our accelerated methodology helps you understand all data present across your enterprise so you can find or create efficiencies in data storing, sharing and analysis.
- Agile and rigorous timeline—With our agile framework, we typically produce an end-to-end big data value proposition within 60 days.
- Participatory nature—Our clients are involved in every step and are encouraged to provide feedback.
Kurt Salmon’s clients will know within 60 days how to approach a big data initiative. We present a clear picture of what the organization can accomplish once its resources are strategically aligned toward a comprehensive big data initiative. Our methodology also identifies the cost and time involved for individual big data business cases, including an assessment of quick wins and priority ranking of longer-term projects.
From setting up a real-time monitoring system for trends in consumer behavior that is linked to proactive advertising and merchandising efforts to developing big data systems knowledge centers to feed improved decision-making capabilities, Kurt Salmon’s approach scales to fit our clients’ needs.
- Big data decision matrix: outlining two or three big data business cases, highlighting requirements, ranking, weighted factors and different options.
- Human capital and resource plan: including clearly defined roles, responsibilities, hiring and change management transitional plans.
- Vision scope document: outlining overall requirements, scope and immediate actions.
Using the accelerated big data methodology gives retailers a holistic picture of their opportunities, assets and weaknesses so they can approach their big data efforts correctly the first time.