IBM Cognos Analytics helped Macmillan Publishers achieve

One of the “Big Five” English-language publishers and a worldwide publishing house is Macmillan Publishers. There’s a strong possibility that if you read books, one of them was published by Macmillan. The Nightingale by Kristin Hannah, Brown Bear, Brown Bear, What Do You See? by Bill Martin, and some more contemporary hits like The Silent Patient by Alex Michaelides, Identity by Nora Roberts, and Razorblade Tears by S. A. Cosby were among the numerous classics they published. The requirement for advanced business intelligence (BI) and data analytics at Macmillan is thus not surprising.

The publishing sector is using data analytics

Macmillan Publishers has a long history of investing in technology that can gather in-depth analytical information on sales, inventory, and transportation of its titles in the market due to their extensive worldwide business. The publisher has been managing its internal and external operational reporting requirements using IBM Cognos Analytics for more than ten years.

This includes their departments for finance, sales, supply chain, inventory control, and manufacturing. Additionally, the team just discovered there was a chance to go beyond centralized operational reporting in order to support additional company development. Users are clamoring for faster access to reliable and accurate data and a method to do it without heavily relying on the Central Analytics Technology team’s hectic schedule.

The publishing sector makes extensive use of analytics and a wide range of metrics, from more specialized metrics like pricing and inventory status to more widely used metrics like shipments, orders, revenue, point-of-sale, and expenses. All organizational departments make use of this data, which is crucial to their business operations. Such information helps in making crucial price choices, a variety of other crucial commercial decisions, including judgments on how many books to print initially and in what format.

Data openness and visibility degraded as corporate processes became more complicated. Additionally, data wasn’t always housed in a manner that made it easy to generate the reports needed to make sensible business choices. This resulted in our users’ needing additional analytics. Additionally, as time went on, the consumers’ need for more and more analytics rose naturally, greatly outpacing our IT team’s capacity to handle it on their own.    

A novel approach to corporate intelligence and data analysis

The Macmillan team eventually came to the conclusion that data analytics and business intelligence required a new, “modernized,” approach. This strategy would be based on a “self-service” concept that would enable users to find and exchange important data. In the end, users were supposed to “fish for themselves.”

Prior to the change in working paradigm, the Central Analytics team was responsible for producing a report or group of reports whenever a business user required data about a title, an operational procedure, or any other general analytics request. These requests lead to detailed, labor-intensive reports that often drill down on precise data from several sources.

In the end, Macmillan’s data analysts and report producers were unable to keep up with the company’s growing need for additional insights. The team chose to carry out their strategy department by department at the beginning of the change. Sales, Operations, Production, Inventory Management, Finance, Editorial, and HR were among the departments that eventually adopted the new paradigm.

The team concentrated on implementing the much improved self-service Cognos Analytics capabilities to complement the present and successful operational reporting platform, building on this new strategic approach. This required turning to several of the more recent capabilities included in IBM Cognos Analytics’ most recent updates.

The team decided strategically to move all on-premises Cognos Analytics activities to IBM’s hosted and managed SaaS platform as part of this endeavor. This freed Macmillan from having to host, fund, and maintain an on-premises data center for business intelligence, allowing it to concentrate on what was most crucial supporting data-related operations.

The team took Cognos Analytics end-user training in-house to give even more value. The goal was to shorten the time to market for all BI deliverables and help customers make quicker choices by demonstrating and teaching them “how to fish for themselves.” This instruction included subjects like:

• How to use improved searches in Cognos Analytics to quickly identify important data
• How to create reports instantly
• How to easily and securely distribute reports
• Using the most recent Cognos Analytics features

Constructing a data analytics model driven by users

The team collaborated with IBM’s technology partner Sterling Technology Group to complete several of these tasks. Together, they helped develop the platform with a user-driven paradigm in mind. Sterling stayed steady and aggressive, providing advice and other services to make sure Macmillan successfully completed the conversion.

This involved moving to the IBM Cognos Analytics SaaS environment and assisting the team in using Cognos Analytics’ most recent self-service-supporting capabilities. The Sterling team made sure that the modernization of legacy systems aided in achieving project objectives, such as generating cost savings, reducing administrative upkeep, and assisting in the switch to a new, more efficient operational BI model.

The secret to business intelligence project success

It is crucial to remember that the first step in completing any BI project successfully is to comprehend the potential data difficulties that a business may be facing, such as data complexity or volume. The Macmillan team understood that their updated BI approach required a data culture expansion. It is difficult to demonstrate success in a self-service data and analytics effort without a strong data culture. Here, several of Cognos Analytics’ most recent features were crucial to the move.

Users may connect to many data sources, conduct fast modeling, apply business rules, embed computations, and create custom groups, for instance, using Cognos Analytics Data Modules. An intuitive drag and drop interface allows for the smooth and simultaneous completion of all of these tasks. Users may now create individualized reports via one deployment that show order status, the condition of every book that has been published, shipment data, and market performance.

The Macmillan team also saw the need for internal “report champions” who can help certain lines of business from the standpoint of end-user support. In order to carry out many of the legacy duties IT traditionally handled, Champions had extra training. These power users now respond to inquiries from their teams and delegate tasks that would otherwise take too much time for IT. 

The group has reduced hardware expenses by 100% and administrative expenditures by 50% overall. Their entire administrative maintenance chores and efforts were reduced by 40%. But perhaps most significantly, the team has seen the comprehensive benefits that a well-designed analytics platform can provide, facilitating more prompt and accurate decision-making at all organizational levels.

Future of Macmillan with Cognos Analytics

Currently, Cognos Analytics is used by around 1000 Macmillan employees spread throughout the company’s business groups. The Macmillan analytics team is interested in hearing from as many users as possible, both good and negative, on how well the system is serving their requirements. As they advance and improve their model, having more end-user feedback will increase their success. The percentage of customers creating their own reports has already increased by 20%, according to the Macmillan analytics team.

The team plans to expand the use of Dashboarding as a future step since they believe it will be very beneficial. Because they were waiting for additional advancement with our complementary endeavor to migrate the underlying data warehouse to Snowflake, dashboarding was originally restricted. Along with dashboarding, they want to improve the experience of different user groups using AI-based Cognos Analytics capabilities, such as subscriptions, AI-based explorations, and the utilization of the platform’s Natural Language Processing (NLP) tools.

Richard Babicz, Business Intelligence Senior Manager & Architect at Macmillan, advises beginning small to identify the major difficulties and pain-points if you’re on the fence about transitioning to the cloud or attempting many of these new AI-based Cognos Analytics capabilities. Create a prototype in a sandbox environment on-premises and test it using data from an internal production application. Invite influential BI supporters and sponsors to collaborate with your team in developing the project’s objectives and overall plan of action. This collaborative approach will provide fresh concepts and a thorough understanding of the requirements for the development of your business community.