Strategic analytics for competitive advantage

In stage 2 of data application, we focus on ensuring your data solutions are useful. This may include working more efficiently – perhaps by automating manual work and so freeing up costly time, or rationalising expensive staff departments and reorganizing processes more efficiently.

The question is whether this is all you can do? The answer is no, of course. Strategic analytics takes your data to a next level. They are investments in holistic insights that are not possible without data, but require a strong understanding of the business and the human factor. How could Uber, Airbnb of booking.com be so successful without a peerless platform in which data and algorithms and understanding of human behaviour play crucial roles? There’s the age-old question of whether traders work best on intuition or with the assistance of high-frequency-trading algorithms? Our opinion is clear.

Let’s address an issue. Unfortunately, in the new field of analytics, there are now lots of storytellers. Riding the analytics hype, they impress with Orwellian tales of terror or flavoursome futuristic tales of triumph. However impressive these stories may be, they’re not going to help your organisation take the next steps in the right direction. Equally, technical tricks and gadgets are dead ends. It takes a little more than the ability to put together an algorithm.

Like said, you at least need to be looking at useful analytics. Strategic analytics takes it to another level.

The essence of our times is that organisations have increasing volumes of data at their fingertips – quantitative certainly, and qualitative often. The only question is how do you use it, and what for?

One of the key features of algorithms is that they let you make quicker decisions. With reference to Uber and booking.com, it’s this lightning speed decision-making that has unequivocally changed their industries.

But they go a step further. The key difference is that they take into account one other critical factor: the business. They understand the interrelationships between their internal processes, and they understand their customers. These factors are included in their change processes, which further increases their competitive advantage. Of course, these are smart yet simple business models with little regulation that are taking advantage of the new market conditions. Multinationals need far deeper insight, empathy and understanding.

Speed of decision-making is therefore essential to keep up and create competitive advantage. In many sectors, especially when they touch essential services for individuals, the semi-automatic decisions need to remain understandable for clients and staff. It is the semi-automatic nature of the decisions that need to be stressed. As the strategic level in decisions increases, the human factor must increase too to ensure alignment with organisational processes and acceptance by the employees.

Strategic analytics demand an in-depth understanding of both data and the people factor. Below you will find a number of scenarios that indicate strategic analytics may be of use. To find out the range of solutions we provide, click here.

Once a decision has been taken that you will look at how to use data in determining strategy, there are two key steps:

Steps to understand the situation

• Identify the operational processes where acceleration is needed
• Use data and interviews to discover the bottlenecks for introducing a new way of working
• Share results, communicate and explain so that a sense of urgency arises.

 
Steps for implementing the change

• Rough definition of the instruments needed
• Create effective, working prototype for a segregated business component
• Rolling out the solution throughout the organisation, including full change communications support.

 
So, if you are interested in developing a strategy that lasts, takes organisational wisdom into account, and has a significantly higher rate of acceptance by the organisation and customers, get in touch. We’ll be delighted to help you take the next step with your data.

Private Banking Case
After the credit crisis, the quality of tens of thousands of client portfolios and managed by hundreds of investment consultants turned out to be less god than believed. Through an innovative measurement system based on a clear investment framework, quality ranking was assigned to clients, banks’ branch offices and the bank as a whole. As a result, the behaviour of clients was measurable and steerable. Our role: project manager reporting directly to the steering committee of the directorate.

CBR Case
Use of process mining to reveal inefficiencies in the processing of assigning driving licenses. Business case to allow computers to assign licenses instead of manual planning and self-direction was found not to be enough. Considering the commotion afterwards, this may have been the wrong decision. Our role: data researcher and initiator of the solution.

Freight Forwarding Case
An international was looking for a solution for the following. Three-thousand operators regularly forgot to bill for work they had done. Using an advanced tool with 20,000 automatically generated mathematical models we managed to generate USD6m in returns. Our role: project manager and content-driver for the project.