Data-directed working – how is it done

13 September 2018

What can public organisations learn from the successes and lessons learned from data-directed working in commercial organisations?

We are seeing more and more public sector organisations (municipalities, provincial governments, healthcare institutions, safety regions etc.) make inroads into data-directed working. Taking data as the starting point to factually support, explain and implement policy, service provision and activities. Rather than just relying on employees’ gut instincts and years of experience to determine the direction that an organisation should follow. The rapid increase in data available is particularly playing a more and more important role.

In the future, will course be determined by data, insights and statistical models alone? No, at some point there has to be some healthy interplay between the subject knowledge and experience of the employees on the one hand and the insights and analysis of the data on the other.

Data-directed working and structural anchoring

Data-directed working sounds easier than it is. It’s a new way of working for an organisation and its employees: a transformation, an organisational change. It’s not something that can be achieved overnight. It calls for a structured approach.

We have had the opportunity to advise many organisations so far as they make their first steps towards becoming a data-driven organisation. Structural anchoring remains the most challenging part of data-driven working for many organisations. Initiatives based on a data lab, a pilot to produce a prediction model (e.g. for predicting early school leavers) or setting up a dashboard (with insight into the use of social support provisions in various neighbourhoods) are terrific examples of first steps. But how do you make this a reality at a tactical level within the organisation and how can you scale it up further? In other words, how do you become a modern, data-driven organisation?

Start with a target in sight

Ultimately what you want is to create value from data. But what form does that added value take and when are you successful? As you do this, it is important for everyone in the organisation to have the same vision of what data-directed working means. Are staff sufficiently aware of the need to really put it into action and thereby bring about a change in the organisation? If everybody really is approaching from a shared starting point then they are likely to provide sufficient support for it. And that is precisely the starting point for change.

In order to really get value out of the data, you start by setting a target, a shared starting point. This could be one target per department, division or even a lower level for each topic (youth welfare, safety, enforcement, environmental legislation, subversion, etc.). Also, if you are not working towards a clear target (such as cost-cutting, citizen participation, customer satisfaction, quality of life, etc.) it is difficult for you to demonstrate the final outcome. Having that shared target for the organisation or department provides direction, clarity and prioritisation for how to use resources and how to apply data.

The Target to Data model

You can this model, the Target to Data model, to further consolidate your data strategy. The model creates a correlation between data, insights, applications and targets for the organisation etc. This can be a starting point for data-directed working.

The model can also be expanded into a roadmap and growth model. The Davenport maturity model can be a good tool to use for this.

Maturity Model

In a growth model, you take a “snapshot” of where you are now as an organisation, where you want to get to, and how that translates into concrete steps for each aspect of the organisation. The most important aspects, or building blocks, for a data-driven organisation are people, resources, processes and organisation.

Commercial domain vs. public domain

In the marketplace we often hear about public sector organisations falling behind in terms of their service provision to citizens and companies compared to commercial organisations. They are said to have a stronger focus on the internal organisation rather than on the citizens (their customers) and companies.

The commercial domain certainly can’t be compared like-for-like with the public domain. However, the approach of moving towards data-directed working actually comprises the same steps. Commercial organisations do have a completely different organisation-customer relationship, where the consumer has choice. The targets and motives of the organisation are consequently very different too: more turnover and customers, a higher NPS score, securing loyalty of customers for longer, etc.
But in the public sector, residents are dependent on the arrangements in their own municipality or province (e.g. for applying for a passport or a permit). However, both sectors do want to focus on “The Customer”.

For some time now, marketing departments in commercial organisations can no longer get away with acting on the basis of market experience. They had to prove to the market with facts (data) what their added value was.
In commercial organisations, you see the front office (sales, customer service) and the back office (IT, intelligence) working together continuously. Customers are central and their behaviour is the starting point. They create an integrated view of customers based on data. The integrated view of customers makes it possible for service provision to ensure relevance. This is a good example of data-driven and customer-driven working.

Public sector organisations often have many different systems for recording information and therefore data. By creating an integrated view of customers with status information you get a better grasp of the provision of information. Municipalities can connect to their customers (citizens and companies) better and provide real added value with relevant service provisions by working across departments. This makes innovation in the field of information management possible. For example, say you go to register a child with the municipality and so you will be immediately notified at the same time that another family member’s passport is about to expire. Or providing targeted (proactive) information about maintenance work in an area following complaints from individual citizens.

Citizens are increasingly the focus and targets such as citizen participation, customer (citizen) satisfaction and increasing public value are increasingly being taken as a shared starting point. By running through a customer journey, within your organisation, you can then learn how citizens and companies experience the service provision and how it is can be best arranged. Customer (citizen) data combined with the subject knowledge of experts in their field: this is what ultimately produces more customer-driven service provision. Including in a variety of topics and tasks.

Lessons Learned

What can organisations in the public sector learn from commercial organisations? There are a number of experiences that are of interest to consider when developing towards a data-driven organisation.

• Start with a target in sight
  Don’t think in terms of resources (tooling/systems) and solutions, but in terms of a target that is widely supported across the organisation.
  Immediately filling every corner of the whole city with sensors to collect data is very interesting. But then what do you do with it? What problem are you solving and what question are you answering for the organisation so that you can apply a different policy in the future. First you should set a shared target. That enables you to determine the right priorities and provides direction for using resources and applying available data.
• Getting the basics in order
  In a world of Big Data and Data Science, it can be very tempting to jump straight into new developments like machine learning, the Internet of Things or blockchain. But, to really get to grips with using data, the basics needs to be in order first. You can spend a lot of time unlocking the right data. Especially when it is kept in multiple different systems, if you are reliant on supplier or if data has to be taken from several departments.
• Focus on the customer
  Don’t start by analysing the data, your starting point should be the customer and their objectives.
• Descriptive analyses provide good insights too
  Predictive analyses aren’t always the logical first step. First you should look back and describe the situation at present (with reference to reports and dashboards) – this often provides good insights too. Based on these insights you can take subsequent steps and make policy decisions.