Dow Chemical had a data problem despite a cutting-edge business insights platform. The company generated hundreds of dashboards and thousands of reports, but none of that information enabled better decision-making.
The Dow Chemical D&A team stepped back and looked at what the platform could accomplish and how to achieve them. Typically, organizations continue to invest in systems even if they don’t produce the benefits they promised.
Using usage metrics, Dow identified and solved some unsolved user obstacles. The result? The platform’s consumption increased by 25% from 2015 to 2018, and Dow’s business value with enterprise analytics and BI solutions grew by 4.2 times during this period.
Insights result from data, and when they are used toward better decision-making, data is beneficial. However, despite all the hype around data and analytics, most organizations are not successfully utilizing the available opportunities.
According to Shelly Thackston, Principal Specialist at Gartner, “Data and analytics can enable better business decisions, drive transformational change in your organization and generate revenue.” The key to monetizing data effectively is to let go of false assumptions about how it can be used and to break down the barriers, which are cultural, structural, and procedural.
Nevertheless, Dow Chemical has rethought its entire data strategy to demonstrate the value of using data to monetize successfully. In contrast, many organizations have struggled to monetize data.
Usage of data to optimize the business:
Analytics and BI platforms carry immense promise for organizations that invest in them and optimize them across the business. This will allow them to discover actual value and identify opportunities they hadn’t previously recognized.
The company built value by optimizing business processes by identifying which departments used different parts of the BI for which purposes. It was requested that if a team benefited greatly from underutilized solutions, they share their wins and stories with other company parts. Each team member guided a part of the business toward the most effective solution when they needed it. Each team member headed a part of the business toward the most effective solution when they needed it.
Usage of data to address business challenges:
An essential factor to consider in Problems with data is that it Often it exists as isolated silos and fragments. Companies often lack a cohesive overarching narrative because they have individualized structures and collect data for their goals. Consequently, the data can’t be used for real-life applications.
A Nordic AI platform provider, Turku City Data, found itself unable to bridge the gap between data and real-world applications to solve business difficulties. This company came up with a flexible framework for graph analytics. A level of abstraction was applied to data from across the organization, resulting in each data point representing a person, object, location or event. By using such an approach, Turku City Data expressed and explored business problems in their contexts and structures from a common point of view.
Usage of data to gather better data:
Having a narrow focus on only readily available existing data is the standard mistake organizations make when monetizing data. Data itself is not inherently valuable, and organizations who believe that it is, are making an understandable error.
In contrast, technology giant ZF Group opted to take a counterintuitive approach. The organization examined the type of data that would generate value in a target market rather than just analyzing the information they already had.
It was discovered that the data the organization already had – and indeed many organizations possess – had little value since it was often about familiar subjects and designed for internal purposes. Organizations need unique data to monetize data.
As stated by the company, the challenge is to find the remaining 20% of data that will make a new product truly valuable. They typically have 80% of the data they need to build a new product, but not all of it. Using IoT sensors, the organization sells ball joints that produce data that algorithms can use for pre-emptive maintenance. After that, users can access consumer-friendly analytics and visualizations that can enable predictive maintenance. Therefore, they are constantly seeking opportunities to develop new types of data that may not even exist yet, making those data valuable for others.