How three organizations learned to think differently and successfully monetized their data
A major chemical multinational, despite having a state-of-the-art business information platform, had a data problem. The company had hundreds of dashboards and thousands of reports, but none of that information was driving better decisions.
It’s not uncommon for organizations to keep investing in systems even when they don’t deliver the benefits they promised, but this company decided to take a step back and re-examine the potential of its platforms.
There was a first stage of review of the metrics that were in use and these were applied to identify and solve the unresolved obstacles of the users. The result was an increase in the use of the platform of 25% from 2015 to 2018, during which time the commercial value multiplied by 4.2.
It is not enough simply to have the data. The value of data comes from the insights it creates, the processes it optimizes, and its ability to enable improved decision-making. The reality is that despite the hype and expectations of data and analytics, most companies are not successfully monetizing their data.
Data and analytics can be a valuable business asset that will improve decision making, drive digital business transformation, and generate new revenue for the organization. But to get it right, false assumptions about data monetization must be left behind and the cultural, structural, and procedural barriers that cause many companies to fail must be addressed.
Use of data for business optimization
Companies that realize the promise of BI and analytics platforms and act to optimize them across the enterprise will find real value and will recognize opportunities that were previously not apparent.
The chemical company understood how to create value by optimizing business processes. First, they identified which teams were using which parts of the BI and for what purposes. If that team was getting a lot of value from an underutilized solution, they were being asked to share their victories and stories with other parts of the business. If there was a part of the business that was looking for a particular solution, the team guided them towards the most effective option. The constant feedback loop and iterative solutions enabled substantial revenue growth.
Using data for business challenges
One of the biggest challenges with data is that it can exist in remote chunks and silos. In many cases they have individualized configurations and collect their own data for their purposes, but companies often lack a coherent overall narrative. This makes it difficult to use the data for anything in the real world.
This was exactly the case of an AI platform provider, unable to bridge the gap between data and real-world problem solving. The company’s solution was a flexible chart analysis framework. This meant that data throughout the company was organized at a level of abstraction, with each data point representing a person, object, location, or event. This easy-to-understand framework was used as a common language to explore business issues in their contextual and structural richness.
Using data to collect the best data
A common mistake that companies make when it comes to monetizing data is looking for opportunities in readily available existing data. This is an understandable mistake for businesses that have been led to believe that data itself is inherently valuable. However, a global technology company decided that an counterintuitive approach might make more sense. Rather than looking at the data they already had, the organization selected the markets to target and closely examined what kind of data would create value for that market.
Then they realized that the data that they already had, and indeed most companies have, offered limited value, as it is often common themes and is optimized for internal use. Data monetization requires unique data that companies don’t yet own.
Internal or indirect methods of data monetization include using data to make measurable improvements in business performance and communicate decisions. External or direct methods include exchanging data to obtain beneficial terms or conditions from business partners, exchanging information or selling data directly (through a data broker or independently) or offering products and information services (for example, including information as a added-value component) of an existing offering.
At Data Innovation we are dedicated to helping Marketing & CRM teams increase traffic, sales and new activations and optimize their KPIs. As a data, CRM and optimization consultant, we work with companies that send more than 10MM emails, agencies, eCommerce or retailers, and ESPs. And as partners, we rely on Tableau, Sparkpost, Netcore, Litmus, although we adapt to the technological environment of each client. The objective of our service is for your Marketing & CRM team to have visibility and perspective, apply expert knowledge, learn and put into practice pragmatic and innovative growth tactics, integrate the most advanced tools with a focus on the final results: more sales, more active users, more ROI.
