How to Get Started with Data Democratization & Monetization

As companies across the globe accelerate their digital transformation, the question often arises: can the creation of data and analytics products significantly boost our business strategy?

Our previous exploration in “Data Democratization and Monetization, Explained” laid the groundwork by showcasing the intrinsic value of making data accessible and actionable.

As we delve deeper, this article will guide you through determining your readiness for such an endeavor and outline the strategic steps your company can take to harness the full potential of data and analytics products.

Creating Data and Analytics Products

It’s understandable that you might hesitate to move forward if you sense your company isn’t ready to make the most of your data and analytics opportunities. But the return on your investment to build and maintain those data and analytic products may be more accessible than you think. 

Are you ready for data democratization? 

Data democratization gives your internal organization ubiquitous and properly governed access to your data, enabling more effective performance analysis on their respective functional areas. Its value is more indirect, as opposed to monetization, which directly generates revenue.

The bottom line? If you’re pulling data into Excel and then mashing it up to make it ready for a particular analytic data point, you’re ready. Because if there’s one data analyst doing this in one functional area, there are probably 50 or 60 of them throughout the company doing the same thing — across marketing, product engineering, sales, delivery support, warranty management, finance and more. The efficiency and productivity of those activities is extremely low. The solution is publishing curated data for internal consumption — a.k.a., data democratization. 

The good news is that anyone can help their business owners and analysts make better decisions with data. If you don’t have the internal resources to do it yourself, you can consider partnering with a trusted advisor.

Types of data that can be monetized

If you’re a service provider in a particular industry and you’re sitting on data from multiple customers, you have industry data that could help your customers and suppliers understand where they are within their peer groups, where they are relative to the industry, and more.

Some well-known examples include IDC, the global market intelligence firm, and the former IHS-Markit automotive industry reports. Vertex Inc. publishes datasets that help companies understand the specific tax regulations in the locations where they sell their goods and services.

A company in the property and casualty insurance industry works with insurance carriers like State Farm, Allstate, Geico, and others to analyze the predicted loss costs of their policies, based on where they do business across the country. This helps their clients understand what it would cost to provide insurance based on a prediction of loss. Their clients use that analysis to determine the margins they need in order to make a profit based on their premiums. 

Are you ready for data monetization?

While just about any company can benefit from data democratization, monetization is a bit trickier. You have to determine whether there’s a market for your data. Will people pay for it? How much would they be willing to pay? What will they realize by using it? 

Could you pilot a data and analytics product and offer it as a value-added service from the beginning? Could you pilot it and offer it as a value-added service without charging for it until you get critical mass and the product’s value is established? 

One global B2B enterprise sold equipment to the oil and gas industries, including catalytic converters and heat exchangers, etc. for refineries, nuclear reactors, and more. They also had process monitoring software that would give the operators of Shell or Mobil the ability to monitor the performance of all the equipment across all their oil fields. Those sensors would alert operators when maintenance or repairs were needed.

The company’s goal was to move beyond reporting of events having happened to predicting the likelihood of equipment needing repairs in a more controlled manner. They started offering insight on how and when to take a piece of equipment down for maintenance — which could mean millions of dollars a day in the case of nuclear reactors. They rolled out that set of analytics as a value-added service until they saw traction. Soon they realized there may be a way to license a predictive maintenance module.

One young entrepreneurial startup processes point-of-sale transaction data on behalf of multiple convenience stores and gas stations and shares the analytics. Their customers can see, for example, that similar retail outlets are selling more Frito Lay products than they are — which allows them to adjust their marketing accordingly.

Older, more established global companies usually haven’t created a business model around data. One large printer manufacturer, for example, had loads of data on their printers and copiers. Their customers were concerned with keeping their office equipment up and running. There was a natural market for analytics that could help them do that. 

Years ago (pre-cloud), one high-tech computer manufacturer provided reports to their customers showing them how well their servers were maintained versus their peer groups. The security was in place for customers to log into the manufacturer’s data warehouse and see where they stood.

So there’s always an opportunity for monetization if your company supports B2B customers, especially when it comes to letting them know where they stand within their industry peer group. Those data and insights could be valuable enough that customers will pay for them.

Steps to Monetization

Once you decide to monetize data through sales, licensing, or subscription, there are several factors you’ll want to consider. 

Understand your customers

Conduct market research to understand your customers’ requirements, pain points, and expectations of data and analytics products. Determine your specific value proposition and know how it differentiates you from your competitors. Look at market demand, customer willingness to pay, and popular pricing models.

Get a handle on your costs

Know what it costs you to develop, maintain, and deliver data and analytics products. Consider data collection, storage, processing, analytics infrastructure, security, personnel, ongoing support, and any necessary external data sources or tools. Understanding your cost structure is crucial to ensuring profitability while remaining competitive.

Price based on value

Consider the potential income impact for your customers or suppliers as well as cost savings, revenue growth, operational efficiency improvements, or risk mitigation. Align your pricing with the perceived customer value while staying competitive. 

Create a product catalog or sales SKU

Clearly define the features, functionalities, and pricing of each of your products. Make sure that each product/SKU is easily understood and aligned with your pricing models. Use clear and concise descriptions of the value proposition and benefits of each.

Consider different pricing tiers or packages

Select the pricing model that aligns with your offer, target market, and customer preferences. You may include one-time purchases, subscription- or usage-based models, tiered pricing, or customized pricing based on customer requirements.

Cater to diverse needs with varying levels of functionality, data access, support, or additional capabilities or services. This allows customers to choose the best option for their requirements and budget while providing opportunities for bundling, up-selling, and cross-selling. Create a premium model that lets you monetize the more advanced features or exclusive insights.

Enabling Go-to-Market Actions

Getting ready to sell data and analytics products requires implementing several best practices. A few that you’ll want to consider include:

1. Providing your sales team with in-depth training on features, capabilities, and benefits. Ensure they understand how your data and analytics products address customer needs. Emphasize the value and how your offer differentiates you from your competitors. Be sure to also cover important technical aspects or integration requirements.

2. Create high-quality, targeted sales collateral that effectively communicates your value proposition. Include brochures, presentations, case studies, data sheets, and demo videos that highlight key features, use cases, and success stories. Tailor collateral to the different customer segments and industries you serve.

3. Train your sales team to effectively communicate the business value and return on investment customers can expect. Address specific pain points. Show how your products drive revenue growth, improve efficiency, reduce costs, and enable strategic decision making.

4. Encourage your teams to develop a deep understanding of the industries and domains that your customers operate in, including industry trends, regulations, and challenges.

5. Enable your sales teams to effectively identify and understand customer needs and pain points. Train them to ask probing questions. Foster strong relationships with customers and suppliers including regular communications, timely support, and being a trusted advisor. Leverage customer success stories and use cases and foster collaboration between sales and marketing teams to align messaging campaigns and lead generation efforts. Finally, continuously refine your sales processes.

Moving Forward with Data and Analytics Products

These examples are really just the tip of the iceberg, but hopefully they inspire you to think about the untapped value in your organization’s data and the insights you can derive from that data.

Intrigued? Reach out to our experts, who will be happy to do an evaluation for you.