Optimizing the cost of insight
Based on our work helping leading enterprises make faster, better, and more cost-effective data-driven decisions, we've identified four ways to optimize the cost of insight.
1. Data discovery: harness the power of cloud technology
With modern, cloud-based data architectures, organizations can store and manage large volumes of data at a reduced cost – usually saving 15–20% when compared to traditional storage methods. Cloud also allows businesses to structure, query, and process data at speed and scale. With a cloud strategy that combines deep industry, technology, data, and process expertise with the right partner ecosystem, you can significantly reduce the cost of insight.
With cloud, the sky's the limit: A leading investment and asset management firm was struggling to scale its existing on-premise technology platform to handle advanced analytics. By building a scalable cloud-based data hub and integrating data from various sources to reduce processing time, this solution reduced infrastructure costs by almost 50%. In our increasingly virtual world, every business must accelerate cloud adoption to realize greater business agility and innovation.
2. Data ingestion: connect data use to business priorities
You don't need to build the perfect data lake; you need to focus on the most valuable data. When working with large enterprises, we've seen that 80% of data-driven decisions made use just 20–30% of available data. Focus on data use cases that solve the most critical business problems to reduce overall data costs by 5–10%.
Focusing on what matters most: A medical devices company was struggling to establish a standardized view of their KPIs. To deliver reports to business leaders, employees would trawl through masses of data and pull metrics from disparate systems. By adopting a more targeted approach to data ingestion, the team delivered a fully automated dashboard visualizing 350+ KPIs within four months and saved the business 40% of its previous data ingestion costs.
3. Data management: carefully design a data management solution
Without a robust data management strategy, you risk compromising compliance, speed to market, and customer experience. According to Harvard Business Review, “More than 70% of employees have access to data they should not, and 80% of analysts' time is spent simply discovering and preparing data." 
So, how do you avoid this? When designing and implementing a data management solution, make sure it has the following features:
- Near real-time, traceable information with potential to reduce employee intervention by up to 10%
- A future-proof, trustworthy data foundation to prevent rework and accelerate the delivery of insights
- Reusable data and analytics assets to support future data and analytics solutions
- Short cycle times with the ability to experiment, which has potential to reduce forecasting and planning cycle times by up to 30%
Small changes lead to big benefits: A manufacturing company was struggling with data proliferation and an inefficient purchase requisition to purchase order process. By developing a new data management solution, it increased productivity and sourcing savings and reduced excess inventory. Overall, the company realized a 30% productivity increase in master data management operations and a 90% overall reduction in vendor cycle times.
4. Data consumption: create your analytics pod
Your ability to uncover insights at speed and scale is directly linked to the cost of insight – the faster and more useful the insight, the more justifiable the cost. Therefore, many enterprises are creating analytics pods, or teams, to increase the speed and scale of analytical insights.
If you're unfamiliar with analytics pods, here are some of their key components:
- A self-organizing team that can speak the languages of technology and business processes and in which every member takes responsibility for turning data hypotheses into actionable insights
- A collection of cross-functional skills necessary to create new analytical solutions
- Close collaboration with the business, including IT and end users, during solution development
- Focused on knowledge sharing across multiple pods so every area of the business can benefit
The power of the pod: A large health insurance company established an analytics pod to overcome its siloed analytics strategy. In just 11 months, eight employees with bilingual talent worked as analytics pods to develop eight new analytics solutions. These solutions cut medical costs and leakage and boosted care quality, with a total bottom-line impact of $150 million.