We’re now well into the fourth industrial revolution. The first was about steam and railroads, the second about electricity, and the third brought about by the internet. Artificial intelligence, 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. The transformation will be bigger than what any previous revolution has brought about.
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We're now well into the fourth industrial revolution.
The first was about steam and railroads, the second about electricity, and the third brought about by the internet. Artificial intelligence, 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. The transformation will be bigger than what 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.
1. Set a data strategy
According to Ginni Rometty, CEO and chairman of IBM, only “20 percent of data is searchable.” The rest is behind the firewall. This is your proprietary data and your competitive advantage.
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.
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 health care. For privacy reasons, data leakage in these cases would be catastrophic.
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.
3. Build a data-driven culture
Step three is about creating a data science and analytics culture within your organization. Leaders must incentivize employees to cultivate the habit of looking at data whenever they make decisions, which I call “the point of action.” I often suggest that executives get creative and set up competitions and rewards for employees who champion data.
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.
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.
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.
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.
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 apply the necessary policies that ensure governance from the outset.
Nir Kaldero is the head of data science at Galvanize Inc., which has technology and co-working campuses across the country.
