The Seven Dimensions Of An Outcome-Focused Industrial IoT Strategy

Shelly Dutton

The allure and potential of data-intense digital initiatives such as the IIoT are undeniable. Manufacturers that adopt such digital strategies are the ones that are developing new markets, crossing over to different industries, and capturing new revenue streams with incredible ease, foresight, and swiftness.

How do capture the right information and turn it into insight and action so quickly?

According to the Enterprise Management Associates (EMA) infobrief, “Handling Too Much Data and Finding Too Little Information,” sponsored by SAP, organizations that know how to accomplish such a feat are typically mindful of seven fundamentals of managing large volumes of data.

1. Information chaos

Increasing the number of sensors, devices, and machines in an IIoT network inevitably brings more “noise” that can keep vital insights hidden in a mass of data, unless the manufacturer controls this information chaos. By uniting natural silos between data sets as well as traditional and remotes data sources, organizations can knit the information together into a centralized core of intelligence.

Decision-makers can access the data itself and the defining metadata to organize and translate a wide array of scattered data into a coherent information strategy. Furthermore, automation of this capability through intelligent technologies, such as machine learning, can help scale collection and connection and help identify potential actions.

2. Data platform bloat

The more information sources included in the IIoT ecosystem, the more data platforms used to store, manage, and process the data. By understanding this growing diversity of the data landscape, manufacturers can link the processing power of each platform to optimize their ability to manage data on premise, in the cloud, with a cloud provider, or within a hybrid of the two.

3. Data privacy

The privacy of information – from customer data to intellectual property – is of great concern. In fact, EMA revealed that more than one-third of survey respondents indicate that data privacy and masking techniques of are great importance. But once a flood of data is generated by an IIoT network and enters the business system, the ability to keep that intelligence secure becomes more difficult.

With a keen understanding where personally identifiable information and operational data resides and who accesses them, organizations coordinate data masking and access protocols across the data landscape – ensuring protection and verification processes are documented and evaluated and adjusted when necessary.

4. Data security

Everyone like to believe that those accessing data and data platforms are trusted decision-makers from the business. But as the number of data breaches rises year after year, we are, unfortunately, reminded that this is not the case.

To safeguard IIoT-generated data as well as the rest of the information landscape, analyzing and tracking all access points to understand when and where security attacks take place is an excellent first step. Next, manufacturers should consider adopting a single layer for data access and security, similar to that of large-scale cloud providers, is critical to tamp down complexity and focus configuration, monitoring, and management efforts on the prevention of security breaches.

5. Data governance

Data is no longer a commodity that provides a record of past success and failure. With the introduction of forward-looking, predictive analytics tools, manufacturers are quickly realizing that data’s value as an asset should be evaluated and managed strategically.

Providing board- and executive-level visibility and access to data assets is just the first step to establishing a strategic approach to data governance. A more long-term strategy is needed across the entire ecosystem – edge to edge – wherever the information is used. From back-office functions to customer care, fulfillment, warehouses, partners and suppliers, empowering each of these groups to provide feedback on semantic definitions, data quality, and schema configuration help scale how data can and should be leveraged.

6. Governmental regulation

When IIoT-enabled devices and machines are located on customer sites, it’s easy to cross the line from value-add and cool to intrusive and creepy. Many government entities are addressing such concerns by creating and enforcing data regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPPA).

Manufacturers, first and foremost, should consider completing a thorough evaluation of their compliance with all relevant data protection mandates across the data landscape. Doing so not only avoid costly fines and detects and handles future exposure proactively, but also secures customer trust.

7. Landscape coordination

Manufacturers need to match their deployment strategies with the pace of the business – otherwise, they risk escalating costs and limited value of their data-driven projects. This requirement calls for a more agile, repeatable methodology that encapsulates core technical complexities and allows the flexibility to adopt new technologies without a complete renovation of the IT architecture.

DevOps strategies for data-driven initiatives can lower labor costs and speed time to value by accelerating the implementation and testing of data applications. Then, organizations should establish technology environments that allow fast configuration and deployment of data applications with the tools and environments developers and data scientists prefer. By adopting such an automated environment, companies can leverage operational metadata from the deployment platform to monitor, manage, and optimize business performance.

Data management: Turning good IIoT plans into phenomenal outcomes

Device automation and integration, intelligent robotics systems, and monitoring of machine health – all of these IIoT-enabled capabilities are fast-becoming mainstream requirements. But once manufacturers get a better handle of their data, they can further optimize the outcomes of their IIoT plans in ways that keep them ahead of their competition.

For more on IIoT, see “The Industrial Internet of Things And The Platform Decision.”