Why data & analytics is 80% about people

It’s a misconception that data & analytics projects involve mainly specialized technical expertise. No matter how skilled your data team is or how well your models and tools work, these investments are wasted if your employees do not use them to take action or make operational decisions.

First things first: what exactly is data & analytics? Does the term refer solely to raw information and the highly technical process of interpreting it? In reality, it’s a bit more complex than that. The term ‘data & analytics’ encompasses everything an organization does to get its data act together.

Data & analytics is the collection of paradigms, patterns, and best practices aimed at managing and growing data assets and data streams and extracting more value from data. It’s the act of gaining a comprehensive overview of all the data that are relevant to an organization, with the explicit goal of understanding what has happened, what is happening, and what is likely to happen.

Adding value means getting everybody on board

When considering a data project, the first thing that comes to mind is knowledge of tools and data-related approaches and technologies.

However, we argue that the ability to convince other people in the organization of the importance of data – particularly senior management – is just as crucial. Achieving real value from a data project requires building enthusiasm and educating all employees across functional areas about the hidden potential, opportunities and potential pitfalls presented by data. They must understand what’s in it for them, and what is required of them in order to be successful.

Only after the entire organization firmly supports your data journey, specialized people should jump in to channel the energy, set priorities, develop a concrete strategy, and do the actual data project.

People-related challenges in data & analytics projects

Employees understand data differently based on their roles and functions. In order to communicate clearly about the value of data, you have to first make sure that everybody is speaking the same language.

Here’s an example. Suppose you’re about to launch a data project, and a consultant asks your business manager to define what a “customer” is. If the manager responds by dismissing the question as too simple, it’s clear that your organization still has a long way to go on the road to data maturity. Why? Because the question "What is a customer?" can be very difficult to answer, and data-aware managers know this. They know that sales and finance teams often disagree on the definition of a customer, and they understand that these differences of opinion matter. Data-literate organizations are fully aware that questions about data are never simple, and understand that getting the answer right is paramount.

How to establish a data culture on multiple levels

Getting everybody on the same page doesn’t mean training your IT people to work with new tools. It means providing people on the business side with enough insights to understand the challenges of working with data, as well as the roles they play in overcoming them.

‘Starting small’ also applies to establishing a data culture in your enterprise, but C-level support is essential. To involve the leadership team in the process, it’s vital to educate them about the basics of data management and teach them how to think about data.

To establish a data mindset among the broader workforce, the best approach is to set up agile pilot projects. These are small, low-risk business cases or challenges that you can tackle with data. By demonstrating how data & analytics can quickly generate value, you persuade people to participate in the movement in the long term. Even more, such projects can be inexpensive, and they always offer valuable learning opportunities – even if they fail.

Practical tips: prepping for your data & analytics journey

The cold hard truth is that 80% of the work involved in data & analytics lies in persuading people to commit to the data-driven journey. Follow these tips to ensure that every stakeholder has your back.

• Establish a collaborative culture in which ideas can be freely explored and shared. Empower your users, offer them a safe space for innovation, and reward them when their projects add value.
• Create a data-driven culture in which decisions are structurally made based on – or backed up by – data insights, not on instinct. Commit to finding relevant data, analyzing it and deciding based on what those numbers tell you, not on what your gut says.
• Offer training not just on tools, but also in how to analyze and assess business situations. This includes information modeling, thinking about business problems from a data perspective and basic critical thinking. This has to be a regular, ongoing initiative – one day a year won’t cut it.
• Set up coach-to-coach and ask the expert sessions where employees can freely discuss pain points, challenges and hurdles.
• Share success stories from inside the organizations to create ambassadors, generate enthusiasm and inspire others to take action.
• Clearly define roles. Power users are capable of creating complex data models, but who also have strong business knowledge. Key users can use models and create support. Information stewards guard data quality, data architects maintain a company-wide overview of data initiatives, and project managers responsible for driving multiple parallel data projects.