We’re now well into the Fourth Industrial Revolution. The first Industrial Revolution was about steam and railroads, the second about electricity and the third was brought about by the internet. Artificial intelligence and the data it produces, the basis of the Fourth Industrial Revolution, will completely change the way business is done and companies are run in the next five to 10 years, just as the internet has done in the past 10. The transformation will be bigger than any previous revolution has brought about.
Even if you feel ready to turn your organization into a data- and model-driven enterprise, you may be unsure where to start. The following six steps are derived from my work with enterprises across various industries that have transformed successfully, and can guide you in your own transformation journey.
1. Set a data strategy.
You already sit on a lot of hidden information about your customers, clients and business that can help you transform your organization and take it to the next level if—and only if—you treat your data as a strategic asset informing all your business decisions.
When I mention this concept to business leaders, their immediate response is often, “Hey, this means I’ll have to realign the entire organization. How would that work? How can I align all my 100,000 people with a single data strategy?” But setting data strategy is different from goal-setting. With goal-setting, we start at the top. Everything must orient to the goals top executives have set for the entire organization for the year. Data strategy, however, can be different for each sub-team and still contribute to solving your top business problems. These different strategies don’t need to involve a single set of constraints.
2. Democratize your data.
The second step involves democratizing your data throughout the organization. This is important because everyone, from the barista to the CEO, makes business decisions on a daily basis. We know that data-driven decisions are better decisions, so why wouldn’t you choose to provide people with access to the data they need to make better decisions?
Let’s be practical, however. We live in a world of constraints and regulations. Not all organizations can completely democratize their data, particularly in industries such as banking, insurance, and healthcare. For privacy reasons, data leakage in these cases would be catastrophic. It would introduce direct business risk and liability. You also don’t want to share all your data with the entire organization in case proprietary information leaks out and costs you your competitive advantage.
So how can we democratize data intelligently? The answer is to figure out how to provide relevant data to relevant decision-makers so they can enhance their decision-making. Look at people’s roles, identify what decisions they make on a daily basis, and then provide them with the data that will support these decisions. Providing the right data to the right people will enhance their capacity to make the right decisions at the right time.
3. Build a data-driven culture.
Step three is about creating a data science and analytics culture within your organization. Leaders must create incentives for employees to cultivate the habit of looking at data whenever they make decisions, which I call “the point of action.” This is tightly linked to the corporate culture you build. I often suggest that executives get creative and set up competitions and rewards for employees who champion data.
A second component of this principle requires you to bridge the gap between technical and non-technical teams, so they can work seamlessly together to realize and operationalize machine intelligence. This is a key tool for increasing ROI. Currently, these teams don’t understand each other or know how to work together. This is a major problem that must be faced and overcome.
One of the remedies is educating both teams about each other’s roles and functions. The second is a smart, highly collaborative, embedded organizational work structure that requires the two teams to interact during the normal course of business. The third is creating a semi-technical role for a middleman between the two sides of the business.
4. Accelerate speed to insight.
The idea behind this principle is to democratize information and insight about your business throughout the organization. If you provide high-speed, dynamic insight to decision-makers, they will get into the habit of making data-driven decisions. The definition of a data-driven organization is an organization that cultivates a culture of looking at data to make all business decisions. To do that, it’s important to use your data to generate as much insight as possible.
One of the simplest and best ways to unleash insight throughout the organization is to use dynamic dashboard tools that provide insight into and beyond the data. Many organizations do not emphasize the importance and usefulness of such solutions. Static summaries and reports are no longer dynamic enough to inform decision-making.
5. Measure the value of data science.
The fifth step of data-driven transformation is about taking action. You must measure the value and impact of data science and machine learning on your business and make this metric one of your key performance indicators (KPIs). In doing this, prioritize data science investments with the highest potential ROI. A typical chief information or chief data officer at a Fortune 50 or Fortune 200 company receives between 2,000 and 2,500 requests a year for different data products. People within the organization think they should act upon all these, which is rarely feasible.
How should you prioritize? Look at an investment’s feasibility and impact. Feasibility refers to whether you have the data or not. Is the data clean and labeled? Do you have the talent, resources, and processes to get the project started? Impact refers to financial contribution. If you’re going to invest in this project, will it genuinely revolutionize your business over time? Will it add millions of dollars, or will it add US$10,000?
Think about these two dimensions before you submit a request to your CIO for a project you think might be a good use case. Particularly when starting the journey, you don’t want everyone to submit hundreds of use cases. You want to grab one with high feasibility and impact that will be able to transform your organization quickly.
Start by piloting a project. If you see that a magnitude of change is reasonable, pour more money into it: Invest more and hire more. Then operationalize it throughout the organization.
6. Implement a data governance framework.
This final step is all about the environment in which your data sits. Your data assets must be secure and private. This is a priority, and all large corporations should have thoroughly established data governance, security??and privacy by this time. By my standards, however, many of the companies I work with are still quite far behind the curve. While the importance of safeguards should go without saying, it still needs to be said: Many organizations haven’t yet instituted them.
Organizations must start by gaining high visibility into their data flows, from the point source to the end destination within the enterprise. This entails visualizing and quantifying the various data routes, understanding the different data types, and tools that these data interact with. Only then can organizations securely apply the necessary policies that ensure governance from the outset. Approaching governance and security in this way helps organizations not only effectively manage their data, but also capitalize on it with confidence, knowing quality and security is not compromised.
Move toward data-driven maturity
Initially, applying these six steps may appear daunting. No doubt it will take you a while to start thinking about maximizing the use and protection of data in every decision you make. Nonetheless, it can be done.
As an executive, positive transformations start with you and trickle throughout the organization. Before long, you will begin to see more people understanding and living by these principles. Then, your organization will be on its way to data-driven maturity.