Translating Data into Better Decision-Making: Organization & Process

Many businesses aspiring to become data-driven organizations embark on a digital transformation or embrace smart technology such as artificial intelligence or machine learning. They are right about that given 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.

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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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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, such as making sure your customer service agents know who to call today so the company keeps attracting and retaining customers. While part of digitalization is about IT, the data-driven part is all about getting smarter.

To think in terms of value, not data
Many businesses want to switch to a data-driven approach overnight, but they tend to fall into the trap of thinking too much from a data perspective. That may feel novel up to a point, but it’s never really productive to simply hand over data to data experts and tell them to "dig around."

I advise to decide how you would like to use that data, which means putting the business and its interests first. You also need to identify the challenges and opportunities, and what kind of market you are operating in. Instead of starting with the data, think of the value that data can add. For some businesses, one "to do" item is to clean up their data. This process can take many years. Take the value-driven approach and start with the data you need tomorrow.

It all begins with the question: Why does your organization aim to be data driven? This is also important for 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 types of questions 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 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, ask why you want to pursue this option. 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. Employees and management will need to understand that it takes more than technology to best serve the customer. It requires knowledge of the business because without it, the technology will not deliver on its expectations. 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 are needed to ensure your organization implements your plans? Then start with an action plan that tackles each aspect through a common approach.

Learn fast versus fail fast
The first step in any digitalization process should be to overhaul the company’s overall strategy rather than just its digital strategy. Look at the market, identify trends and translate these plans for each department. The plans have been recalibrated to team plans, which will lead you to the data you need. Think about the innovations needed to implement the business strategy, after which you need to make sure that those innovations materialize. Show that the strategy pays off, why you’re making this team effort, and what opportunities are available. Convince managers it will help achieve their targets.

Also, don’t focus on one idea that needs to be an overnight, slam-dunk success. Instead, 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. Instead, 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 on the organization and its culture. You can get innovative by launching 100% new ideas of which 10 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. You need the people who will use the software – including the decision-makers and those who come up with the ideas – to understand what you’re doing. The entire organization needs to have a specific mindset, skillset and toolset to effectively do their work.

Involve the entire supply chain
The ultimate goal is for analytics to be an integral part of the organization. An example of this is the Spotify model: Organizations used to adopt a functional approach in structuring their organizations. They now want to do so based on the product or service they offer. 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.

Bringing together the various links of that supply chain requires 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. Though to group on a product-oriented level, roles need to be fully mature. If a company is only just starting to get traction in data analysis, it 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 in the IT department. The idea is to have all data analysts temporarily work in the same location because the focus should be on sharing knowledge, building expertise, and establishing standards. Next, reassign those data analysts 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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Conclusion

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.