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Your IoT Strategy: Data’s Critical Role

RobPattersonIn this special guest feature, Rob Patterson, Vice President, Product Marketing at PTC, posits that as manufacturing organizations hone in on their IoT strategy, data’s critical role within that strategy should be top of mind and central to decision making around how the IoT will affect their business going forward. Rob is a results-driven technology executive focused on building highly effective corporate strategies and marketing organizations. Proven and documented success working in senior marketing, strategy, and management roles at technology companies such as PTC, ColdLight, Qlik and Microsoft. He holds a BS in Food Marketing at St. Joseph’s University.

The role of data is taking center stage in the world of smart, connected products. With the future of competitive advantage hanging in the balance, data and analytics are dramatically changing the landscape of manufacturing organizations transforming to adapt to a more connected world.

The wealth of data coming from connected products presents countless prospects for organizations to create new value opportunities for their business and customers. This makes it no surprise that data has significantly increased in stature to be ranked alongside people, technology and capital as one of an organization’s core assets. A recent Harvard Business Review article, “How Smart, Connected Products are Transforming Companies” says “data in many businesses is perhaps becoming the decisive asset.”

It is a decisive asset as well as the key to future competitive advantage. Aberdeen Research on data-driven manufacturing finds “today’s manufacturers no longer view data-driven insights as a welcome bonus, but rather, a critical underpinning of their fight to stay competitive” and “are 3 times more likely to use machine/sensor-generated data than all other industries.”

As manufacturing organizations hone in on their IoT strategy, data’s critical role within that strategy should be top of mind and central to decision making around how the IoT will affect their business going forward.

Here’s a look at the top three priorities for data that manufacturers should consider when developing their IoT strategy.

Prioritizing data within the overall IoT strategy

Most organizations are not prepared to handle the volume, velocity and variety of data coming from smart, connected products. They often struggle to effectively collect, manage and analyze the data across their IoT ecosystem. In order to capitalize on the tremendous insight that can be gleaned from all of this data, first and foremost organizations must make sure that the issue of utilizing and leveraging this data is addressed fully in their IoT strategy. This includes deciding what they want to see out of their data, what data to capture, secure and analyze as well as what the organization needs to do to manage the complexity of analyzing the vast amounts of data.

Making data a key organizational structure

The Harvard Business Review article reveals that “the nature of smart, connected products continues to dramatically change the work of virtually every function within the manufacturing firm and while core functions adapt and redefine themselves entirely new functions are emerging including those to manage the staggering quantities of data now available.”

Currently, most organizations do not have a dedicated internal structure in place to efficiently and successfully manage the depth of data coming from connected products. Many organizations are making substantial investments in this area. These investments include:

  • The designation of a unified data organization – An increasing number of organizations are creating dedicated data units that are led by a C-level executive or in some cases, a Chief Data or Analytics Officer who reports to the CEO, CFO or COO. By creating a dedicated data organization you have a centralized function that is tasked with consolidating and analyzing all of the data collected; managing the issues of data security; as well as communicating the insights from the data across the applicable functions and business units it impacts. This strategy ensures that there is a unified vision and strategy for data across the organization.
  • Investing in new talent. – There is a definite data analysis skills gap in most organizations. The Harvard Business Review article says “the business or data analyst of the past is evolving into a new type of professional, who must possess both technical and business acumen as well as the ability to communicate insights from analytics to business and IT leaders.” This has led to a demand for Data Scientists who are “capable of building and running the automated analytics that help translate data into action.”

Investing in technology infrastructure for data analytics and security

Analyzing data has always presented challenges and with smart, connected products, typical methods of data analysis fall short. To fully unlock the potential of the volume of data coming from connected products, organizations must become better storytellers and strengthen their analytical capabilities not only by hiring Data Scientist talent but by investing in technology. With more insight into the data that is now available manufacturers cannot only utilize the data for optimization and prediction but also for anomaly detection and control. And with machine learning technology that automates big data analytics, developers can build solutions into their IoT platforms that include automated advanced and predictive analytics.

The Harvard Business Review article highlights the need for a new supporting technology infrastructure for handling the new volumes of data and says that smart, connected products require an entirely new supporting technology infrastructure. “This “technology stack” provides a gateway for data exchange between the product and the user and integrates data from business systems, external sources, and other related products. The technology stack serves as the platform for data storage and analytics, runs applications, and safeguards access to products and the data flowing to and from them.

As the digital and physical worlds continue to collide the role of data within an organization’s overall business strategy will continue to grow. It is an imperative that manufacturers take steps now to prioritize the way they will manage it to capitalize on its potential for competitive advantage and future growth opportunities.

 

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Comments

  1. Ilya Geller says:

    As I wrote many times there is no era of Big Data and analytics!

    Oracle already structures unstructured data:
    1. Oracle obtains statistics on queries and data from the data itself, internally.
    3. Oracle gets 100% patterns from data.
    4. Oracle uses synonyms searching.
    5. Oracle indexes data by common dictionary.
    6. Oracle killed SQL, where SQL either does not use statistics at all, or uses manually assigned one, or (at Internet) uses ‘popularity’.
    See Oracle ATG Search Administration Guide – https://docs (dot) oracle (dot)
    com/cd/E24152_01/Search.10-1/ATGSearchAdmin/html/s1007understandingtermweights01.html

    So, all data is soon structured and easily searched: no need in parasites (so called ‘data scientists’) help – analytics.

  2. Jeff Rutherford says:

    The IoT and new IoT clouds built specifically to handle the volume of data generated by IoT sensors will dramatically change the IoT infrastructure of many companies.

    This will eventually lead to new algorithmic business — systems taking autonomous control of business processes and making decisions based upon a myriad of inputs from big data analysis and IoT sensors.

    Equinix recently wrote about the future of algorithmic business: http://bit.ly/1NP6rvn

    Jeff Rutherford
    commenting on behalf of IDG and Equinix

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