President and CEO of CLOUDSUFI. Innovator and angel investor. Global leader in creating antifragile enterprises.
What has intrigued me the most about this notion of “data dancing” is some of the trends that Gartner is predicting over the next four years. By the end of 2024, it expects that “75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”
Think about it: There’s an estimated 2.5 quintillion bytes of data produced every day, and every person on the planet generates 1.7 megabytes of data every second. If you can wrap your head around it, that amount of data is overwhelming.
What’s important to remember is that we have to humanize this data. We have to make it real and meaningful for organizations. We have to bring it to life and find ways for these organizations to engage with the data in new, creative and powerful ways. That leads me to the three strategies that are critical to making enterprise data dance.
1. Get To The Heart Of The Business Problem
Most times, when it comes to data, organizations look to collect data from a multitude of internal and external sources without thinking about the value the data may add toward solving a specific problem or how valuable the problem may be.MORE FOR YOU15 Key Qualities That Define An ‘Agile’ LeaderAntifragility On Steroids: Embracing Disruption As The New NormUsing VR To Step Inside Your Data: VR Or AR-Enabled Analytics
To make the data dance for you, you first have to cut through the noise and get to the core of the problem. You have to ask four fundamental questions to understand the problem:
1. What is the process?
2. Who is impacted?
3. What is the impact?
4. What is the impact contribution toward the key performance indicators?
Based on the value each of these questions carry, you can prioritize and select the problems to solve. Then, take a look at the possible reasons for the problem. Create a hypothesis, and list the data that will help you evaluate the hypothesis.
To get a clearer picture, go a step further to wireframe your solution and use a “day in the life” approach to validate the solution. For this to work, you have to understand that continuous learning is then applied to build a robust and automated model to define or refine the process to solve the problem and make life better for the impacted stakeholders.
2. Create A Robust Data Supply Chain
Data is oxygen. No organization can survive without it. Yet most organizations struggle to make it flow. Many fail to realize that the data supply chain consists of transforming raw data into useful insights. There are four critical components that companies must apply to develop a data supply chain that drives results. Let’s take a look at these four components.
First, data acquisition is at the heart of it all. On any given day, organizations have to find effective ways to capture huge amounts of data at every moment of the day from a wide variety of sources. This data could be coming from your employees, your customers or anything that is happening in the world that impacts the market you cater to.
The second component is data transformation, enrichment and storage. Raw data is not helpful, so the organization needs to create a system where it can be stored in a primary location, allowing its team members to sort, analyze, convert and augment what can be used for a specific function or actionable task.
The third element required is statistical and heuristic modeling. Organizations have to create models using algorithms to identify the best-case circumstance for the business. Models are not static. They must be constantly reviewed and revised to adapt to the current market conditions.
Lastly, you need to have a visualization of outcomes. This is an effective way to clearly define the impact of your models and identify other potential solutions.
3. Establish Effective Strategies To Convert Data Into Dollars
If you don’t monetize your data, it’s worthless. Blending the principles of Agile in product engineering and time value in economics leads to an interesting phenomenon in the data monetization process: time value of data. Here you iteratively evolve to meet the monetization objectives. You start with an assessment of existing datasets. You have to look at what you have, assessing all data sources and how the data was gathered. You also have to use all methods and tools available to you to ascertain how valuable the data is and if you need to dig deeper, finding new datasets to aid you in creating value. Next, you must establish the buyers — both internal and external. For example, you are likely to realize that the best customer for your data may be another division within your organization.
After assessing the datasets and establishing the potential buyers, it is essential to enable the technology. As the saying goes, time is money, and in this case, organizations need to develop methods that automate all these processes, including storing, collecting, analyzing and converting the data into a form that translates into dollars. You’ll also need to ensure governance for compliance with data privacy and security best practice. It is critically important to remember that you have to protect your data so that it is not easily identifiable and complies with all laws within your industry, state and country.
When organizations embrace the power and possibility of making enterprise data dance, they will transform into companies that better understand the marketplace and better serve their clients and customers.