9TECH OUTLOOKNovember 2015every part of the organization readily, but it will also help to ensure that we are capturing the full value of our data by leveraging it across organizational boundaries. Second, IT has the great access to the data that exists across the company, which is an essential asset when building data warehouses. Third, we know how to get the most out of our information through the use of technology, which certainly doesn't hurt in this space.These characteristics on their own don't ensure success. For examplewe needed some boundaries. Analytics could never, and should never, be an IT-owned initiative. That would all but guarantee our failure. For Merck to really capture the true power of analytics, it must be owned by the entire company and catalyzed, empowered by IT. So that's what we set out to do.The Business Analytics Practice Empowered by ITAs a pharmaceutical company, Merck consumes scientific data to advance our medicines as well as business data to drive operations. Since Merck's R&D organization has been successfully embedding scientific analytics into its standard practices for many years, we chose to focus first on business performance analytics and established a cross-functional Business Analytics Practice. The team is comprised of individuals representing every region and divisions across the company, and through it we determined three key areas, or stacks, where IT will play a pivotal role.Stack #1: Data Liberation & IntegrationEvery time an employee logs onto the network, a customer or patient calls into a call center, someone clicks into the website or a product enters or leaves inventorydata is created. In the past, this data sat in silos, either inaccessible across the company or simply not speaking in the same `language' from division to division or region to region. IT, with its end-to-end visibility across the enterprise, is the ideal owner of the data integration process and the best function to liberate it through a data store so it is easily accessible to anyone in the organization. Stack #2: Analytics Libraries, Models, Algorithms, Indexes, and Self-serviceWith all of the data integrated and accessible in a big data store, we needed a way for business colleaguesnot all of whom have a data science backgroundto access it, read it and analyze it in a way that could lead to valuable insights.In this case, IT's role is to not only provide the models and algorithms that will turn raw data into meaningful information, but also to do it in a way that is easy to use, digestible and repeatable for the everyday user. Facebook, for example, calls this the democratization of datamaking it easily consumable so that it can be provided in a self-service way to a diverse group of teams who may or may not have any data background.Stack #3: Analytics CatalystIt doesn't matter how integrated and liberated your data is, or how advanced your algorithms areif you don't have business managers who are aware of (and open to) data-driven decision making through analytics, the program will fail. At the end of the day, our business colleagues own the outcomenot IT. However, IT can act as a catalyst to help teams not only understand what analytics can do for them, but also guide them to ask the big tough questions that will allow data analytics to perform its `magic' and achieve the highest impact. Delivering ValueMerck's Business Analytics Practice realized more than $700 million in value in 2014, but that's only the beginning. We are getting smarter and more efficient as we partner with various groups across the organization. In fact, IT has a better understanding of business drivers than at any point in our recent history, affording us a seat at the table for many strategic business discussions. Most of all, though, our work with our colleagues is empowering Merck with the information and data-driven decisions that could save and improve more patients' lives around the world. For Merck to really capture the true power of analytics, it must be owned by the entire company and catalyzed, empowered by IT"
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