We all know that data is the bread and butter to AI. Without it, Machine Learning models are less accurate, and perhaps unusable to some extent. As a solutions leader and engineer, I live and breathe the world of data.
My teams and I are always trying to find meaningful ways to help our clients succeed. But one of the biggest roadblocks we encounter working with clients is data readiness. However, to date, data readiness has been oversimplified. Within most organizations, data exists in large quantities, but knowledge on how to leverage data to achieve business growth needs to evolve.
One of the most common issues I notice in companies that struggle with “data readiness” is that their data is often restricted, and divided into different departments, wherein each department often acts like “data gatekeepers.” So, why is our data divided by departments? And how can we grant data access to more team members, so that they can get a more holistic picture of the business? These are the questions that should be at the forefront of our conversations around data, especially if Machine Learning is a priority.
Most people haven’t heard the term “data ecosystem” before, and that’s ok. What leaders everywhere need to start doing is viewing data as a living and breathing story — just like the business itself. In other words, data ecosystems evolve and change over time, impacted by trends, growth, tech infrastructures, software, markets, and so on. Of all the definitions out there, I think Deloitte captures the meaning of data ecosystems best: “A network of actors that directly or indirectly consume, produce, or provide data and other related resources.” In other words, a huge, interconnected network where information is stored, shared, released, and so on. To add to this complexity, you have employees, suppliers, partners, and clients each playing a part in the building, maintaining, and sharing of a data ecosystems, whether they are aware of it or not.
There are two types of data ecosystems – internal and external. Yet, the differences between the two remain strikingly similar. Both function exclusively within themselves, with very little sharing and communication, ultimately cementing themselves each into their own silo.
What’s the difference between internal data ecosystems and external data ecosystems?
Internal data ecosystems refers to a company-wide data set that is divided into silos and examined solely by the department the data pertains to. As an example, look at a typical customer journey from on-boarding to payment. Throughout this process, the client has left a data trail that has been collected and reviewed by each department that has processed it, in the aims of better understanding the customer’s storyline and to gather lessons to improve their practices for the future.
External data ecosystems are similar to internal data ecosystems in their siloed effect. By opening a productive dialogue with suppliers, clients and partners, the sharing of information can help maximize the potential of AI. It is no secret that companies are wary of sharing too much information between their clients and suppliers, but once we break down these barriers and begin working collectively, the potential for AI and a better working culture grows exponentially.
In both types of ecosystems, if we were to take a step back and examine the data throughout the entire story, we would begin to see a more holistic view of the business, with a complete storyline. What’s more, AI’s true potential goes beyond departmental challenges. In order to make the most of AI, we need to work with the complete storyline of the client from beginning to end, to be able to pinpoint particular moments throughout the journey that can be improved.
Democratizing your data: Removing data gatekeepers and saying goodbye to bottlenecks
Forbes recently defined data democratization as the process to which internal and external parties all have equal access to data and there are no gatekeepers that create a bottleneck at the gateway. Alongside granting access to the data, we are subject to providing an easy way for the parties to also understand the data so that they can use it to accelerate decision-making and uncover opportunities.
I want to connect this to my argument about tying data trails together and unifying the client data journey, and insist that we ensure that everyone has the same access to the data at hand. Here are just some of the advantages of data democratization:
- Unlock the ability to analyze more hidden datasets.
- Empower employees to find nuances and develop their own conclusions.
- Encourage a data-driven culture.
This is starting to feel like deja vu
Data will continue to be a crucial component in the business decision-making process, but we need to paint a bigger picture to make smart, accurate conclusions.
As we continue to advance in the age of digital transformation, we are exposed to more and more of our customers’ use of our products and services which in turn provide us with their data trails. Companies can create data ecosystems to capture and analyze data trails so that product teams, for instance, can determine what their users like, don’t like, and what they respond well to.
Businesses now consider cloud computing and services as an integral component to their organization that is necessary for storing and managing information. It’s fascinating to think that many challenges that the AI solutions market is facing are the same challenges cloud service providers underwent over a decade ago.
Leaders know and understand that the latest AI-driven technology plays an integral role in the advancement of their organization, yet many are wary of being the first adopters. We need those pioneering companies to jump in feet first. With that said, we can safely assume that in ten years, there will be another technology with the same challenges we face today.
Here are a few key steps that can help you evolve your data story:
- Launching a company-wide data literacy program
- Igniting the “democratization of data” conversation
- Granting access of data to both internal and external stakeholders
- Finding a partner that will work with your teams throughout the data evolution and AI adoption process