The new generation of analytics tools make it easy to produce visual analytics from data without technical skills. What is important now in analytics is the business and data understanding to create actionable insights.

The top three things to consider when setting up your analytics would be:

  • Aligning business priorities with analytics KPIs
  • Getting the right data for analytics
  • Designing analytics to maximize impact

Aligning business priorities with analytics KPIs

Top management will have already set out the business priorities. They define the competitive strategy of the firm, its strategic positioning, core competencies, operating model, and value chain. They set out what success looks like and decide what matters most. Your job is to fully understand this and to design for it. You will need to talk to the firm’s subject matter experts.

Getting the right data for analytics

Getting the data right begins with choosing from many data sources. These have grown in recent years. Look internally and externally. Consider open, and proprietary, free, and paid-for data. Cast your net wide to find relevant data. Once you have it, put governance, support, and maintenance in place for it as part of your data strategy.

Designing analytics to maximize impact

Design analytics around what matters most to the business and model the data that is most relevant. Make the most of what is possible with the latest analytics tools and techniques.

You match the priorities with the data to create the right design. So, these three things depend on each other.

Some examples of how industries set up successful analytics

The business priorities will reflect the competitive strategy. In the past, military strategy was about picking ground which gave you a positional advantage, concentrating your forces against an opponent’s weak points to give you superiority. As the battle became more chaotic, you would depend on dashing field commanders to exploit tactical opportunities as they arose.

These principles still apply. For example, some businesses seek positional advantage in a brand, others in IP, such as a patent, or copyright. In CPG businesses, where a strong brand is an advantage, a priority will be strategic marketing campaigns. These compete for the attention of consumers and customers in direct competition with rival firms.

Companies that have online presence will use data-driven marketing strategy, marketing analytics, and consumer analytics to out-smart and out-gun the competition. Marketing managers will use tactical initiatives to exploit churn in the market.

In CPG firms predictive analytics are used within sales and operations planning (S&OP) to align supply with demand. In the military, predictive analytics are used to give early warning of an attack. Predictive analytics are used in promotions planning and analysis to help to boost returns. Predictive analytics are used in supply chain planning and analysis to make the supply chain more robust.

Execution and Implementation

Getting the analytics right requires iterations to make successive improvements and get the best result.

Work with users and subject matter experts (SMEs) from across the business – people from both back-room and front-line roles. Aim to co-create the design with them.

  • Talk to SMEs to decide how best to translate the business priorities to dimensions, measures and KPIs.
  • Talk to the people who will use the analytics on the choice of tools. Stimulate their thinking by showing them what’s possible. Listen to their concerns and ideas. Generate enthusiasm.
  • Iterate the design with sprints that create something tangible. This gives SMEs and users something to react to, and to comment on. This will help them to contribute.