Macmillan Publishers is a global publishing company and one of the “Big Five” English language publishers. If you’re a reader, chances are good you’ve read a book from Macmillan. They published many perennial favorites including Kristin Hannah’s The Nightingale, Bill Martin’s Brown Bear, Brown Bear, what do you see? and some of the more recent bestsellers such as The Silent Patient by Alex Michaelides, Identity by Nora Roberts and Razorblade Tears by S. A. Cosby. It’s no wonder then that Macmillan needs sophisticated business intelligence (BI) and data analytics.
Data analytics in the publishing industry
With such a widespread global operation, Macmillan Publishers has a long history of investing in technology that can source deep analytical information about sales, inventory and transportation of their titles in the market. For more than 10 years, the publisher has used IBM Cognos Analytics to wrangle its internal and external operational reporting needs. This encompasses their finance, sales, supply chain, inventory management and production areas. And in the last few years, the team realized there was an opportunity to expand beyond centralized operational reporting to enable further business growth. Users have become increasingly hungry for quicker access to trusted and timely data, and a way to access that data with less reliance on the busy Central Analytics Technology team.
The publishing industry is a heavy user of analytics and a broad use of metrics, from commonly used measures such as shipments, orders, revenue, point-of-sales and costs to more focused metrics such as pricing, inventory status and countless others. This data is leveraged by departments throughout the organization and is essential to their business operations. Such data helps decide how many books to print initially and in which format, how many to print in the future, key pricing decisions and a host of other important business decisions.
As business processes grew more complex, the data transparency and visibility suffered. What’s more, data was not always stored in a way that facilitated the reporting process required to make informed business decisions. This contributed to the need for more analytics by our users. Additionally, the users’ demand for more and more analytics grew organically over time, to the point where it far exceeded our technology team’s bandwidth to support it on their own.
A new strategy for data analytics and business intelligence
Factors like this ultimately led the Macmillan team to realize that a new “modernized” approach around data analytics and business intelligence was needed. This approach would center on a “self-service” model, empowering users to source and share key data. Ultimately, the goal was for the users to “fish for themselves.”
Prior to the operating model shift, the Central Analytics team was tasked with creating a report or set of reports when a business user needed information about a title, an operational process, or any general analytics request. These requests result in specific, time-consuming reports, that often drill down on granular data from multiple sources. Ultimately, the demand for more analytics across the company grew beyond the capacity of Macmillan’s data analysts and report developers. At the onset of the transformation, the team decided to implement their plan on a department-by-department basis. Over time, adopters to the new model included Sales, Operations, Production, Inventory Management, Finance, Editorial and HR.
Building on this new strategic direction, the team focused on deploying the greatly enhanced self-service Cognos Analytics capabilities to supplement the current and successful operational reporting platform. This meant gravitating to many of the newer features included in the latest releases of IBM Cognos Analytics. As part of this effort, the team made the strategic decision to migrate all on-premises Cognos Analytics operations to IBM’s hosted and managed SaaS platform. This allowed Macmillan to focus on what was most important — supporting data-related activities — without having to host, pay for and support an on-premises data center for business intelligence.
To further add value, the team brought Cognos Analytics end-user training in-house. The aim was to show and teach users “how to fish for themselves”, speed the time to market for all BI deliverables and make more timely decisions. This training focused on topics such as:
How to easily find relevant data within Cognos Analytics through enhanced searches
How to build reports on the fly
How to share reports simply and securely
How to leverage the newest Cognos Analytics features
Creating a user-driven data analytics model
To accomplish many of these tasks, the team also worked with IBM’s technology partner, Sterling Technology Group. Together they assisted in redesigning the platform with the focus on a user-driven model. Sterling remained steadfast and proactive, offering consulting and services to ensure Macmillan completed the migration thoroughly and successfully. This included the migration to the IBM Cognos Analytics SaaS environment and helping the team leverage the latest features within Cognos Analytics that support self-service. The Sterling team ensured the transformation of legacy systems supported the project goals including driving cost savings, reduced administrative maintenance and helping with the transition to a new, more effective operating BI model.
The key to successful business intelligence projects
It is important to note that the key to being successful in any BI project is first understanding the data challenges that may exist within an organization, whether its data complexity or the volume of data. The Macmillan team realized the need to grow its data culture alongside its revamped BI strategy. Without a robust data culture, seeing success in a self-service data and analytics initiative is difficult. This is where some of the latest features of Cognos Analytics played an integral part in the transition.
For example, Cognos Analytics Data Modules allow users to connect to multiple data sources, handle quick modeling, apply business rules, and imbed calculations and custom groups. All of this is done seamlessly and simultaneously via an easy-to-use drag and drop interface. One deployment now allows users to generate customizable reports to track orders, view the health of any published title, see shipping history and see how a title is performing in-market.
From an end-user support perspective, the Macmillan team also realized the need for internal “report champions” who can support individual lines of business. Champions were provided additional training to perform many of the legacy functions IT once supported. These power-users now answer questions for their teams and offload work that would otherwise become too time-consuming for IT.
Overall, the team has seen a 50% reduction in administrative costs and a 100% reduction in hardware costs. They realized a 40% reduction in overall administrative maintenance tasks and efforts. But most importantly, the team has seen the holistic value that a well-designed analytics platform can bring, enabling more timely and accurate decision-making at all levels of the organization.
Macmillan’s future with Cognos Analytics
Currently, around one thousand Macmillan employees across Macmillan’s business units consume data from Cognos Analytics. The Macmillan analytics team wants as many users as possible to provide feedback — positive and negative — on how the system is meeting their needs. More end-user input will lead to better success as they move forward and refine their model. The Macmillan analytics team has already seen a 20% growth in the number of users authoring their own reports. In another next step, the team is looking to roll out Dashboarding on a broader scale and expect it to add great value. Dashboarding was limited initially because they have been waiting on more progress with our complimentary initiative of migrating the underlying data warehouse to Snowflake. Along with Dashboarding, they are looking to add value with AI-based Cognos Analytics features to various user groups, including subscriptions, AI-based explorations, and the use of the Natural Language Processing (NLP) features within the platform.
If you’re on the fence about switching to the cloud or trying many of these new AI-based Cognos Analytics features, Richard Babicz, Business Intelligence Senior Manager & Architect at Macmillan, recommends starting small to identify the key issues and pain-points. Build a prototype in an on-premises sandbox environment and evaluate with production in-house application data. Invoke key BI supporters and sponsors within the organization to work together with your team to set up project goals and overall plan of action. This collaborative approach will lead to new ideas and a true understanding of what your business community needs to be successful.
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