Three analysts from McKinsey recently said it best: “Chemical manufacturers have already invested in IT systems and infrastructure that generate enormous volumes of data, but many have failed so far to take advantage of this mountain of potential intelligence.”
Mountains can be scaled. You just need the right tools and expertise. Data analytics tools have become exponentially more powerful and easier to use. Computational power is cheap. The most innovative data analysts are putting their mountains of process, product, and other types of data to work in the chemical industry to help navigate the significant changes and volatility that are the new normal.
Once, pulling in data from across and outside of a large-enterprise chemical company to power analytics was easier said than done. Now it’s happening.
Next practices
Based on our global experience working with the leading, most innovative chemical companies, here are three “next practices”– capabilities and outcomes to help chemical companies utilize data and analytics on a grand scale.
Integrate diverse data sources. Data is scattered. It’s in multiple applications, files, data warehouses, data lakes, and public and private clouds. Each silo walls off the data with proprietary rules and complexity. You need visibility into that data. Without it, you have a disjointed picture of the business. With it, you can do things like model the impact of geopolitical and environmental disruptions within hours. Run ad hoc simulations to determine the financial impact of sales and operations scenarios. Calculate differentiated prices based on bundling, value, and fully landed costs. And much more.
Next practice #1: Integrate your data by combining data sets – including Big Data, process data, product data, analytical data, etc. – as needed into a single data universe for much greater visibility.
Make data more useful. Your data comes to you structured, semi-structured, and unstructured. It may be spatial, chart, numeric, geographic, time-series, relational, JavaScript Object Notation (JSON), etc. Integrating all these different types of data is extremely complex. But without it, your company is at a competitive disadvantage, squandering available resources.
Next practice #2: Integrate your data sources using orchestration and governance solutions. Go from raw feed to intelligence with real-time analysis of vast data sets. How? With solutions to understand, integrate, cleanse, manage, associate, and archive data to optimize business processes and analytical insights.
Simplify your data landscape. Centralized. Easy to use. Automated. That’s what you want from your data analytics platform. And those features have been a challenge because of all the different databases, apps, and clouds in your IT environment. But now a centralized data management solution is available that manages all facets of an enterprise chemical company’s data universe. Represented visually, the architecture is easy to share and understand. Stakeholders assigned to an architecture team within your company can collaborate through a user-friendly Web application in the planning, design, and governance of the architecture.
Next practice #3: Create and maintain a complete landscape architecture that is easy to share and understand. Open this landscape to an array of company employees and managers to jointly manage your data environment as an agile, strategic tool.
A growing number of data analytics use cases for chemical companies
Data analytics is a vital tool for innovating faster than the competition, creating new markets and products, and attracting and retaining customers.
Chemical companies are using data analytics fed by an increasing array of datasets to analyze the cause and effects of complaints and manufacturing deviations and to scan through thousands or millions of documents to identify intellectual property and determine the return on investment of R&D and acquisitions. They are using it to ensure compliance of new formulations through data analysis, to institute condition-based maintenance to minimize risks and reduce costs and environmental impacts, to forge IoT strategy, and to do more accurate economic forecasting. The list goes on.
These are just some of the many, quickly evolving, creative ways that larger and diverse data sets are being put to work to guide chemical companies today. Some use cases are relevant to every type of organization within the chemical industry. Others are more suited to different types of businesses, geographies, markets, and other unique characteristics.
Learn how and start now to become a data-driven chemical company!
And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.