Translating Data into Better Decision-Making: Organization & Process

Many businesses aspiring to become data-driven organizations embark on a digital transformation or embrace smart technologies such as data analysis, artificial intelligence, or machine learning. And they’re right about that given that the benefits of a digital transformation are manifold. Yet not all companies are equally successful in their attempt to use data to generate business value. “The key to success is the awareness that digital transformation is as much about organizational change as it is about technology,” says Rianne Langenberg, Managing Consultant at ORTEC.

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“The key to success is the awareness that digital transformation is as much about organizational change as it is about technology.”

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Langenberg: “Digitalization is not just about improving efficiency, so you could reduce the size of your customer service team, for example. It’s mainly about becoming smarter: by for instance making sure your customer service agents know who to call today to ensure the company keeps attracting and retaining customers. While part of digitalization is about information technology (IT), the data-driven part is all about getting smarter.”

To think in terms of value, not data
Many businesses are eager to switch to a data-driven approach overnight, but Langenberg says they tend to fall into the trap of thinking too much from a data perspective. “Sure, that may feel novel up to a point, but it’s never really productive to simply hand over a bunch of data to some data experts and tell them to ‘have a dig around.’ It’s advisable to firstly decide how you’d like to use that data, which means you need to put the business and its interests first. You also need to identify the challenges and opportunities out there, and what kind of market you’re operating in. Instead of starting with the data, you need to think of the value that data can add. For some businesses, the first step toward a data-driven approach is cleaning up all their data: a process that can take many years. That’s why it’s better to start with the data you need tomorrow.”

It all begins with the question: ‘Why does your organization aim to be data driven?’ This question is also important in terms of getting the rest of the organization on board. You need to tell people exactly what they can expect, because ‘shifting to a data-driven approach’ is a very wide-ranging goal, with all kinds of implications.

Listed below are the types of questions you need to answer first. At this stage, you need to create a basic plan for change: why, what, who and how. There’s no one-size-fits-all solution that works for every organization, but there are plenty of best practices to guide you.

• Do you intend to become a disruptor? Are you looking to catch up with your market competitors? Do you want to be an industry leader?
• What time frame have you set for achieving that goal?
• What is the biggest change you intend to achieve?
• What is your strategy? Will it be a strategic RvB-trajectory, a cultural program, or will you set up a center of excellence first?

The road to success depends on your people
When creating a strategy for a data-driven organization, you need to first ask why you want to pursue this option. As covered in the first article of this series on Customers & Innovation, the guiding principle is the added value for the customer. Based on this goal, you can then start looking at what it takes to achieve that objective. This generally requires getting the entire organization on board. It takes more than technology alone to serve the customer effectively; it also requires knowledge of the business, because without it the technology will not deliver on its expectations. Both employees and management will need to embrace the transformation, and operating processes may need to be changed as to facilitate technological innovation. These processes need to be aligned, as all components are interdependent.

What is your organization’s current position, and what changes are necessary in terms of People & Culture, Organization & Process, and Data & Technology? What measures do you need to take to ensure your organization can implement your plans? Once you have figured that out, you can start creating an action plan that tackles all these separate aspects through a common approach.

Learn fast versus fail fast
As Langenberg explains, the first step in any digitalization process should ideally be overhauling the company’s overall strategy rather than just its digital strategy. “You also need to look at the market, identify trends, and translate these plans for each individual department. The plans have been recalibrated to team plans, which will automatically lead you to the data you need. You need to start by thinking about the innovations needed to implement the business strategy, after which you need to make sure that those innovations actually materialize. Above all, you need to show that the strategy pays off, why you’re making this team effort, and what opportunities are available. You need to convince managers that it will help them to achieve their targets, just to keep people on board. You should also avoid doing anything that has little or no impact. You don’t need only one idea which needs to be an overnight, slam-dunk success: what you need to do is abide by the ‘fail fast’ principle. Instead of just soldiering on for two years to try turning a low-potential use case into a success, you’re better off dropping it and starting something else after one month. This sometimes requires a cultural change, if the belief that ‘failure is not an option’ has always been part of the company’s value system.

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“You don’t need only one idea which needs to be an overnight, slam-dunk success: what you need to do is abide by the ‘fail fast’ principle.”

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It’s actually better to think in terms of ‘learn fast’ rather than ‘fail fast,’ because what matters is that a business can quickly identify what does and doesn’t add value. Also, be sure to celebrate small wins: people will have a very hard time committing to a change program for three years if there’s only something to celebrate at the very end.”

Everyone needs to understand
Businesses need to approach a digital transformation not just from a technology point of view, but also based on the organization and its culture. Langenberg: “You can get innovative by launching one hundred new ideas of which ten might turn out to be a success, but that doesn’t work in an organization that expects every idea to be equally successful. You can innovate by attracting people from outside the company, but then you need to accept that a data-driven approach will not be embedded in your organization. You need more than just external advisors and data scientists; the people who will be using the software – including the decision-makers and the people who come up with the ideas – also need to understand what you’re doing. The entire organization needs to have a specific mindset, skillset and toolset to be able to do their work effectively.”

The entire supply chain needs to be involved
The ultimate goal is for analytics to be an integral part of the organization rather than being restricted to a single location. An example of this is the Spotify model: whereas organizations used to adopt a functional approach in structuring their organizations, they now want to do so based on the product or service they’re offering. This means the various functional expertise fields need to work much more closely together – which is exactly what a data-driven approach is all about. “You can get a lot more mileage out of your customer journey by looking at the journey as a whole. Digitalization has made all that a lot easier: you can only set your price points once you’ve mapped out the entire customer journey. The very nature of analytics often requires use of the supply chain as a whole.”

Langenberg explains that bringing together the various links of that supply chain requires some strategic thinking, particularly if a company is organized along traditional, functional lines. “If you group people together based on their role in the organization, it makes sense that you would put all the analysts together. However, if you are organized in a product-oriented way, then all analysts will be spread among various business teams. A secondary functional organization is important then, at least in the form of a community for analysts. But in order to group on a product-oriented level, roles need to be fully mature. If a company is not currently involved in data analysis, that company should not hire five analysts and assign them to five different sections of the organization; those analysts first need to learn to work together and apply the same set of standards. We therefore advise customers to start with one core team, for example in the form of a center of excellence, and do so in a central location rather than within the IT department. The idea is to have all data analysts temporarily work in the same location: the focus needs to be on sharing knowledge, building expertise, and establishing standards. Next, those data analysts need to be reassigned within the organization, leaving just an expertise center with a limited number of roles.”

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“It’s about the complete package: technology is useless if people are unwilling or unable to use it, while at the same time even the most brilliant people achieve less without the right technology.”

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At the end of the day, it’s all about the combination of people and technology: technology is useless if people are unwilling or unable to use it, while at the same time even the most brilliant people achieve less without the right technology. In the next installments of this series, we will be looking at issues around People & Culture and Data & Technology.