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    Data as currency, power, and opportunity

    5 ways that collecting data through connected systems can make your business better

    In today's data-intensive economy, data is currency, power, and opportunity. And so it's key that organizations possess the right connected systems — systems that will collect and aggregate the raw information that any organization increasingly needs.


    Needs for what? The areas in which Big Data can be crucial are proliferating fast. Here are five of the most important of them:

    1. Efficiency

    The Big Data age will be a far more efficient one. Time-and-motion studies of how workers circulate through workspaces have already changed office layouts and shop floor designs, making them more comfortable environments for the human body (and mind) — and bigger transformations await.

    The tracking of discrete machine parts through production lines has improved production processes, tightening up assembly line slack and indicating where exactly bottlenecks form. Connected sensor tech can also tell planners compelling stories about what assets are underutilized at a given time. Facilities managers can generate easy savings with the aid of data that indicates which sections of a building are overheated, overcooled, or overlit.

    2. Product design

    Insights into how consumers are using a product translate into iterative improvements of that product — and inspire breakthroughs into fundamentally new products. Decode the data that a product's user interface provides, and you can divine how customers wish your product would work, setting the stage for your next success story. Where are users moving their cursors as they use your software? To which spot do their fingers gravitate on the control panel of a connected appliance?

    Here, new data technology offers a combination of real-time feedback and focus group insights, without interrupting the customer.

    Aggregating data that testifies to product defects and failures can tell designers where the big-picture problems lurk, and clue in parts suppliers to flaws with their own pieces of the puzzle.

    Connected data can also enable expansion into lucrative pay-per or anything-as-a-serviceopportunities of the sort that are becoming standard. (These days, you can lease even heavy mining equipment on a pay-for-tonnage-moved basis instead of buying it outright.) To work reliably for vendors, such business models require data from multiple sources. These sources have to testify to how much, how often, and how intensively a vendor's product is being used, and by whom, and under what conditions. That data also needs to be tamper-proof, to avoid the digital-era equivalent of rolling back a car's odometer.

    On the other side of the equation, customers will appreciate the use of multiple sources of good data, so they can feel confident that vendors are billing them fairly.

    3. The monetization of data itself

    Boston Consulting Group recently proposed that every company describe itself in terms of its technology and its data – thus making that company's connected systems a direct measure of value as well as a competitive advantage.

    Such self-definition is only part of the process, of course. Next you need to decide how data will create value in specific ways that will convince a CFO. Will data lead to cost-saving upgrades? Help you refine your product mix? Improve product targeting?

    The mere quality of an organization's data itself will, however, increasingly function as a differentiator.

    4. Complex change modeling

    Having crunched the data to detect roadblocks that chip away at efficiency and retard progress, data scientists can then draft predictive models. Those models can help them choose a course of action, from loosening controls, to speeding up approval processes, to other measures.

    5. Risk management and mitigation

    Numerous systems and devices already track attempts to hack them, modify them, or otherwise illicitly access them.

    But not all risks are equal, and not all breaches are equally damaging. Connected systems let you go beyond basic IT security by helping you map and track threats and damage on a wider scale. Instead of focusing on noisy but irrelevant local attacks with little chance of success or impact on operations, the risk manager can make judgments about threats that really matter.

    How to promote an internal data culture

    That's just a glimpse into the value of the data that connected systems produce. It's in an organization's interest to seed a thriving internal data culture. Here are some steps an organization can take towards making that a reality.

    Start with a comprehensive connected platform. The growing numbers of smart devices and sensors in enterprise networks need the right platform: one that's flexible, capable of both edge data collection and edge processing, designed to promote efficiency, and sufficiently scalable to absorb big spikes in data quantities fast. Amsterdam, a leader in the smart city space, integrates 12,000 data sources into its connected platform. An enterprise of any size could use even more.

    Support data science teams and give them room. Give data scientists the time they say they need to study your data. And provide them with the influence and clout they need to put their analyses and impulses into action with pilot programs that can go mainstream quickly if they show promise. Lack of support for data science, on the other hand, can be a show-stopper. As McKinsey describes in an inventory of ten red flags surrounding data culture, without a clear vision and a standard, one that recognizes success and demands that success continue past the experimentation stage, projects will flounder and fail.

    Be deliberate about structure.
    Some organizations are more comfortable keeping data scientists in an autonomous business unit. Others prefer fully embedding them in teams, which is useful in overcoming the "last-mile problem" that can bedevil corporate data programs. Still others take a blended approach.


    Wherever you land, make the choice a conscious one, and be sure that data scientists and their co-workers understand how they should share information and give or receive strategic input. As predictive analytics expert Eric Siegel recommends in the Harvard Business Review, you can better structure your data science teams if you clearly define their roles and business objectives.


    “As fashionable as it is, 'data science' is not a business objective or a learning objective in and of itself," writes Siegel.

    Empower data scientists to acquire clean data and keep it clean.
    Data scientists should be able to stand by the quality of their proposals because they know they can stand by the quality of their inputs. And data scientists should promote data literacy, being empowered as they are to help people avoid poor decisions based on questionable data or faulty conclusions.

    Some experts go even further and say there needs to be a data dictator who wields broad power over how every stakeholder collects, analyzes, and stores data.

    Data culture, connected systems, and the data scientists who preside over them are changing the way business is being done, and are driving broad changes in our economic and social lives. This article touches on only a fraction of the benefits that data and connected systems can provide an organization—so long as your internal data culture is mediated by the right platform and the right people.

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