Macmillan Publishers is a multinational publishing house and one of the English language publishing industry’s “Big Five.” There’s a strong likelihood that if you read, you’ve come across a Macmillan book. A number of classics, such as Kristin Hannah’s The Nightingale and Bill Martin’s Brown Bear, Brown Bear, what do you see?, were published by them, along with some more contemporary hits like Alex Michaelides’ The Silent Patient, Nora Roberts’ Identity, and S. A. Cosby’s Razorblade Tears. Therefore, it makes sense that Macmillan requires highly developed IBM Cognos Analytics and business intelligence (BI).
Analytics using data in the publishing sector
Macmillan Publishers has a long history of investing in technology that can source rich analytical information about sales, inventories, and market transportation of their titles because of their extensive global operations. The publisher has managed its operational reporting requirements, both internal and external, for over a decade using IBM Cognos Analytics. This includes their departments of finance, sales, supply chain, inventory control, and production. Additionally, the team has understood in recent years that there is a chance to go beyond centralized operational reporting in order to facilitate additional business growth. Users are more and more demanding faster access to reliable and accurate data as well as a method of doing so without depending as much on the already overworked Central Analytics Technology team.
The publishing sector makes extensive use of analytics and metrics, ranging from widely used ones like shipments, orders, revenue, point-of-sale, and costs to more specialized ones like pricing, inventory status, and a host of other ones. Departments across the whole organization use this data, which is vital to their day-to-day operations. These kinds of data are useful in making a number of critical business decisions, including how many books to print initially and in what format, how many more to publish later on, essential price considerations, and many more.
Data visibility and transparency declined as corporate processes became more intricate. Furthermore, data was not always kept in a fashion that made it easier to do the reporting that was necessary to come to wise business judgments. This increased our users’ need for additional analytics. Furthermore, over time, consumer demand for analytics increased naturally to the point where our IT staff could no longer keep up with the demand.
An innovative approach to business intelligence and data analytics
The Macmillan team finally realized that a fresh, “modernized” approach to data analytics and business intelligence was required as a result of factors such as these. The “self-service” concept at the heart of this strategy would enable users to find and exchange important data. The users were supposed to “fish for themselves” in the end.
Before the operating model change, whether a business user requested information on a title, an operational procedure, or any other generic analytics request, the Central Analytics team was responsible for producing a report or series of reports. These requests provide detailed, labor-intensive reports that frequently delve deeply into individual data points from several sources. Eventually, Macmillan’s data analysts and report producers were unable to keep up with the company’s growing need for more insights. The team chose to carry out their plan department by department when the transformation first started. Sales, Operations, Production, Inventory Management, Finance, Editorial, and HR were among the departments that eventually embraced the new paradigm.
The team concentrated on implementing the significantly improved self-service Cognos Analytics capabilities to support the existing and effective operational reporting platform, building on this new strategic approach. This required utilizing a lot of the more recent features found in IBM Cognos Analytics’ most recent releases. The group decided to move all on-premises Cognos Analytics operations to IBM’s hosted and managed SaaS platform as part of this endeavor. This spared Macmillan from having to host, maintain, and pay for an on-premises data center for business intelligence, allowing them to concentrate on what mattered most: supporting data-related activities.
The group brought in-house Cognos Analytics end-user training to further increase value. The objective was to expedite the time to market for all BI deliverables, demonstrate and educate customers “how to fish for themselves,” and enable them to make decisions more quickly. This instruction covered subjects like:
⦁ How to utilize the newest Cognos Analytics capabilities
⦁ How to create reports instantly
⦁ How to distribute reports simply and securely
⦁ How to quickly identify relevant data within Cognos Analytics using enhanced searches
Building a model of user-driven data analytics
The team collaborated with IBM’s technology partner, Sterling Technology Group, to complete numerous tasks. Together, they helped to rethink the platform with an emphasis on a model driven by users. Sterling persisted in being proactive and unwavering, providing services and advice to make sure Macmillan finished the migration completely and successfully. This involved moving the team to the IBM Cognos Analytics SaaS environment and assisting them in making use of the newest capabilities available in Cognos Analytics to facilitate self-service. The Sterling team made sure that the transformation of legacy systems aided in achieving the project’s objectives, which included lowering administrative maintenance costs, accelerating cost reductions, and facilitating the switch to a new, more functional BI model.
The secret to corporate intelligence programs that succeed
It is crucial to remember that the first step in completing any business intelligence project successfully is to comprehend the data issues that an organization may face, including those related to data volume and complexity. The Macmillan team understood that in addition to updating its BI strategy, it also needed to expand its data culture. It is challenging to demonstrate success in a self-service data and analytics effort without a strong data culture. Here, a few of Cognos Analytics’ most recent features were crucial to the changeover.
The Cognos Analytics Data Modules, for instance, enable users to establish connections with various data sources, manage rapid modeling, implement business rules, and incorporate custom groups and computations. With the help of a simple drag and drop interface, all of this is completed quickly and without error. Users may now create customized reports with only one deployment to monitor order status, delivery history, and market performance of any published title.
The Macmillan team also recognized the necessity for internal “report champions” who can assist specific business lines from the standpoint of end-user support. More training was given to champions so they could handle a lot of the older tasks that IT used to support. These super users now respond to inquiries from their teams and delegate tasks that would otherwise take up too much time for IT.
The group has observed a 50% decrease in administrative expenses and a 100% decrease in hardware expenses overall. They observed a 40% decrease in all administrative upkeep duties and endeavors. Most crucial, though, is that the team has witnessed the comprehensive benefits of a well-designed analytics platform, which can facilitate more accurate and fast decision-making at all organizational levels.
The future of Macmillan with Cognos Analytics
Currently, Cognos Analytics data is used by almost a thousand Macmillan workers from various business departments. The analytics team at Macmillan is seeking input from as many people as they can, both favorable and negative, regarding how well the system is serving their needs. More feedback from end users will help them succeed as they develop and improve their model. The number of users who are creating their own reports has already increased by 20%, according to the Macmillan analytics team. The team plans to implement Dashboarding on a larger scale in a later phase and anticipates significant benefits. Because they were awaiting further developments with our complementary endeavor to migrate the underlying data warehouse to Snowflake, dashboarding was originally restricted. In addition to dashboarding, they hope to benefit different user groups with AI-powered Cognos Analytics features, such as subscriptions, AI-powered explorations, and the utilization of the platform’s Natural Language Processing (NLP) features.
Richard Babicz, Senior Manager & Architect for Business Intelligence at Macmillan, suggests starting small to pinpoint the main problems and pain points if you’re unsure about moving to the cloud or experimenting with many of these new AI-based Cognos Analytics features. Create a prototype in a sandbox setting on-site, then test it using real, internal application data. Set project goals and an overarching plan of action with your team in collaboration with important BI sponsors and supporters in the company. This cooperative approach will produce fresh concepts and a thorough comprehension of what your business community requires for success.
