Macmillan Publishers is a global publishing company (operating in over 70 countries) and one of the “Big Five” English language publishers. If you’re a reader, chances are good that you’ve read a book from Macmillan. They are the publishers of the influential Wheel of Time fantasy series, Oliver Sacks’ extraordinary The Man Who Mistook His Wife for a Hat, and Sunday Times’ business book of the year, Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World.
With such a widespread global operation, Macmillan has long invested in platforms to source deep analytical information about sales, inventory, and transportation of their titles in the market. The publisher has used IBM Cognos Analytics for more than ten years to wrangle its internal and external operational reporting needs. This usage encompasses their supply chain, inventory management, and production.
The publishing industry is a heavy user of metrics. For example, when an editor is interested in acquiring a new book, the standard operating procedure is to investigate historical sales figures of related books to judge how a similar title would do in the market. Using metrics is essential to their business operations, and data assists them in making all kinds of business decisions. Data helps decide how long to keep a book in print, when to move it to the backlist, or delist it entirely.
However, due to outdated fulfillment systems, the quality of that data was not as high as it should be, causing the analytics and reporting process to become overly complex and burdensome. In addition, transactional data was stored in a way that did not support the deep analysis required to make the best future-looking decisions for the business.
Publishing leaders need access to operational data like internal sales figures and external vendor sales data to make the best decisions. They may also need to understand how well a title performed in the market and how much physical inventory exists. The operational side of the business cannot function without access to these granular metrics.
The operational side is editorial, production, inventory management, sales and finance, and HR teams. Until recently, when a team member needed operational information about a title, they had to request a report from an analytics team member. Because a requester only needs to see information on their titles and not the business’s overall health or another editorial group’s book list, these analytics requests are specific, time-consuming, and drill down on granular data.
The Macmillan analytics team wanted to make it easier for anyone in the company to collect the answers they’re looking for themselves. So, the group kicked off an initiative to standardize reporting and analytical objectives across departments. In going through that process, the analytics team discovered that the most strategic way to implement self-service was to move their business intelligence operations to the cloud. Moving to the cloud would ensure their data remained organized, clean, and connected.
Working alongside its IBM technology partner, Sterling Technology Group, Macmillan redesigned their reporting tool and user-driven modeling. Sterling remained steadfast and proactive, offering consulting and services to ensure Macmillan completed the migration thoroughly and successfully. Above all, the Sterling team ensured that the transformation of legacy systems supported the business goals of driving cost savings and reduced administrative maintenance. Overall, the analytics group can now spend less time maintaining and optimizing servers and more time adding value to business operations.
Since they already had a trusted relationship with IBM Cognos, the Macmillan analytics team decided to keep the partnership going and upgraded to IBM Cognos Analytics with Watson. The upgraded platform improved their data warehouse processes, business intelligence models, and reporting.
“Cognos was the easy part.”
– Richard Babicz, Business Intelligence Manager & Architect at Macmillan
The next important step in the journey for the Macmillan analytics team is to grow its data culture. Without a robust data culture, seeing success in a self-service data and analytics initiative is difficult. The granular data teams need to understand is very complex. The data requires business rules and logic applied, requiring a higher skill level than most editors or sales associates have. The Macmillan analytics team uses a two-part strategy to grow their data culture: Data modules and champions.
Data modules are a data modeling feature inherent to IBM Cognos Analytics. A data module allows the analytics team to set up containers that describe rules for accessing data, and they can fine-tune them for each team’s use. The result is an easy-to-use drag and drop interface. So, users can now generate customizable reports to track orders, view the health of any title published, see its shipping history, and how the title is performing in-market.
On the other hand, champions are individual team members from across the business. A senior analyst trains them to help scale training and further self-service adoption. By using the pre-fine-tuned data within the data module, champions are their team’s primary point of contact for analytics and reporting. They are both answering questions for their team and training their team on how they can get the most out of the platform.
The goal is to empower users companywide to quickly and easily pull whatever information they need about their titles. The solution also allows users to derive more insights from financial reports like accounts payable and royalties. In addition, the finance team has automated a once manual process using Cognos. They deploy Cognos to scrape information from Amazon on chargebacks and returns so that they can have a complete picture of a title’s lifecycle.
Other benefits the analytics team has seen include a 50% reduction in administrative costs and a 100% reduction in hardware costs. They have also realized a 40% reduction in overall administrative maintenance tasks and efforts. In addition, several key users can access data via data modules and merge it with external sources using the new UI and improved search functions that facilitate content discovery.
Around one thousand users across Macmillan’s business units and teams currently consume this data. The Macmillan analytics team wants as many people using the platform as possible to uncover issues with the system. More users give them more opportunities to solve problems they aren’t yet aware of. The Macmillan analytics team predicts that the combined strategy of data modules and champions will help self-service usage grow by 20%.
If you’re on the fence about switching to the cloud, Richard Babicz, Business Intelligence Manager & Architect at Macmillan, advises creating an on-premises sandbox prototype. Then, migrate a representative subset of your content into that sandbox and see if it solves the problems you’re looking to solve. A local on-premises sandbox environment will ensure the switch to the cloud has no impact on your query modes.
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