2 items tagged "workflow"

  • Digital Marketing Hubs Are the Center of the Digital Marketer’s Universe

    Suggesting that the digital marketing landscape has grown complex and fragmented is like saying that the universe is vast beyond description. Nobody (save for the astronomer, anyway) will argue with you. Truth is, sometimes considering the range of available digital marketing technologies can feel a bit like appraising infinite stars against an inky midnight sky. It can be overwhelming in its magnitude.

    What you really need is a map:


    HubBut while the map may bring order to the madness, you still need a center of gravity, not unlike a galaxy or a solar system. In this more earth-bound context, the center of gravity is the digital marketing hub:

     Think of the digital marketing hub as an interstellar junction, the connection point between content, data, rules and insights that inform the next best offer or experience delivered across virtually any channel.

    “Digital marketing hub” is the term that Gartner has adopted with the hope of disambiguating what so many vendors had come to ambiguously call “marketing clouds.” (Cloud, in our view, is a delivery option and an implementation detail, a hazy collection of droplets without any discernable form or focus. That’s hardly a description of any set of marketing applications or capabilities and, thus, hardly helpful as a naming convention. I’ve yet to hear anyone disagree, including the vendors themselves).

    The digital marketing hub is the gravitational center to a constellation of digital marketing tools and applications. Specifically, the digital marketing hub converges around four fundamental capabilities:

    1. A master audience profile—which unifies first- and third-party customer and audience data for targeting the right experiences to known customers and anonymous
    2. Workflow and collaboration—which facilitates and streamlines the creation and onboarding of content, data, plans and other digital marketing assets and artifacts that feed the beast.
    3. Intelligent orchestration—which informs the timing, targeting and coordination of content, offers and experiences across channels through a combination of business rules and algorithms.
    4. Measurement and optimization—which traces the thread between marketing investments and business outcomes and allows marketers to optimize their budgets and efforts to highest yield.

    This is all easier said than done, of course. Which is why we believe, more often than not, a digital marketing hub is a design pattern rather than something you buy off the shelf. The digital marketing hub, too, has its own center of gravity (see four capabilities), but the realization of its full promise relies upon a diverse array of constituent parts, many of which are likely nonnative to any specific vendor stack.

    Which is why, when we evaluate these digital marketing hub vendors, we consider both native capabilities and extensibility. Why? Because, while a digital marketing hub may invite a certain gravitational attraction, it depends on the considerable energy of its satellites. Beware of hubs that purport to do it all, for the digital marketing universe is vast and even the most capable vendor is infinitely smaller by comparison.

  • Four Drivers of Successful Business Intelligence

    BICompanies across industries face some very common scenarios when it comes to getting the most value out of data. The life science industry is no exception. Sometimes a company sets out to improve business intelligence (BI) for a brand, division or functional area. It spends many months or years and millions of dollars to aggregate all of the data it thinks it needs to better measure performance and make smart business decisions only to yield more data. In another familiar scenario, a team identifies critical questions the BI system can't answer. Again, months and millions go into development. But by the time the system goes live, market and/or company conditions have changed so much that the questions are no longer relevant.

    Building Better Business Intelligence Systems
    Today's challenges cannot be met by throwing more dollars into the marketing budget or by building more, or bigger, data warehouses. Ultimately, navigating today's complexities and generating greater value from data isn't about more, it's about better. The good news is that other industries have demonstrated the power and practicality of analytics at scale. Technology has evolved to overcome fragmented data and systems. We are now observing a real push in life sciences for a BI capability that's smarter and simpler.

    So how do we build better business intelligence platforms? In working with life sciences companies around the globe, IMS Health has observed a recurring journey with three horizons of business intelligence maturity: alignment of existing KPIs, generation of superior insights and customer-centric execution (see Figure 1).

    What does it take to advance in business intelligence maturity?
    No matter where a company currently stands, there are four fundamental steps that drive BI success: the ability to align business and information management strategy, improving information management systems integration and workflow, engineering BI systems to derive more value and insights from data, and making the most of new cloud computing technologies and Software-as-a-Service (SaaS) models for delivery.

    Step 1: Align Business and Information Management Strategy
    Many IT and business leaders recognize that the traditional "build it and they will come" mentality can no longer sustain future growth in agile and cost-efficient ways. To be successful, companies need to focus upfront on developing an information management strategy that begins with the business in mind. Through a top-down and upfront focus on critical business goals, drivers and pain points, companies can ensure that key insights are captured to drive development of commercial information management strategies that align with prioritized business needs. Leading organizations have achieved success via pilot-and-prove approaches that focus on business value at each step of the journey. To be successful, the approach must be considered in the context of the business and operational strategies.

    Step 2: Improving Information Management Systems Integration and Workflow
    Although technology systems and applications have proliferated within many organizations, they often remained siloed and sub-optimized. Interoperability is now a key priority and a vehicle for optimizing commercial organizations-improving workflow speed, eliminating conflicting views of the truth across departments and paring down vendor teams managing manual data handoffs. Information and master data management systems must be integrated to deliver an integrated view of the customer. When optimized, these systems can enable advanced BI capabilities ranging from improved account management and evolved customer interactions (i.e. account-based selling and management, insights on healthcare networks and relationships with influencers and KOLs) to harnessing the power of big data and demonstrating value to all healthcare stakeholders.

    Step 3: Engineering BI Systems to Derive More Value and Insights from Data
    Life sciences companies compete on the quality of their BI systems and their ability to take action in the marketplace. Yet existing analytics systems often fail to deliver value to end users. Confusing visualizations, poorly designed data queries and gaps in underlying data are major contributors in a BI solution's inability to deliver needed insights.

    By effectively redesigning BI applications, organizations can gain new insights and build deeper relationships with customers while maximizing performance. Effective BI tools can also help to optimize interventions and the use of healthcare resources. They can drive post-marketing research by unearthing early signals of value for investigation, help companies better engage and deliver value to their customers and contribute to improve patient outcomes. This information can advance the understanding of how medicine is practiced in the real world-from disease prevention through diagnosis, treatment and monitoring.

    Step 4: Making the Most of New Cloud Computing Technologies and Software-as-a-Service (SaaS) Models for Delivery
    Chief information officers (CIOs) are increasingly looking to adopt cloud technologies in order to bring the promise of technology to commercialization and business intelligence activities. They see the potential value of storing large, complex data sets, including electronic medical records and other real-world data, in the cloud. What's more, cloud companies have taken greater responsibility for maintaining government-compliant environments for health information.

    New cloud-based BI applications are fueling opportunities for life sciences companies to improve delivery of commercial applications, including performance management, advanced analytics, sales force automation, master data management and the handling of large unstructured data streams. As companies continue their journey toward BI maturity, getting the most from new technologies will remain a high priority. Leveraging cloud-based information management and business intelligence platforms will bring tremendous benefits to companies as approaches are revised amidst changing customer demands and an urgent need for efficiency.

    The Way Forward
    While each organization's journey will be unique, advancing in business intelligence maturity-and getting more value from data - can be achieved by all with these four steps. It's time for BI that's smarter and simpler and that realizes greater value from data. With focus and precision-and the support of business and technology experts-companies can hone in on the key indicators and critical questions that measure, predict and enhance performance.

    Source: ExecutiveInsight

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