Data Integration

Address Integration Challenges With the Proliferation of IoT Devices

Actian Corporation

July 16, 2019

During the past few years, IoT devices have evolved from being a consumer novelty to a core part of next-generation IT ecosystems for businesses of all sizes. IoT devices (such as smartwatches, fitbits, home sensors, RFID point-of-sale scanners, heartbeat monitors, etc) have become attractive and useful because of their inexpensive price point, ease of setup/administration and their diverse capabilities. IoT devices are everywhere and used in a variety of industries and use cases.

Now that business users are experiencing the value of IoT devices in their personal lives, they are understandably asking for similar capabilities at work. As companies embark on digital transformation journeys, they expect IoT to be one of the key technologies to serve as a source of new operational insights and performance gains. Small, inexpensive devices connected to a company’s network provide real-time telemetry and monitoring of business processes, operations, delivery logistics, facilities issues and much more.

The characteristics that make IoT so attractive to business users is also leading to a rather large headache for IT staff. In the personal environment, smartphone applications or a commercial digital assistant (such as Siri or Alexa) manage the connections to individual IoT devices, unify the interaction experience and aggregate the data from all of a user’s IoT devices.

In the business context, where there can be thousands (or more) IoT devices deployed and operating across the IT ecosystem, consistently managing connectivity and integration can be challenging. One of the biggest areas of concern for IT staff (and business users) is integrating the data from IoT devices into a company’s data ecosystem and providing real-time insights based on that data.

IoT Data Integration and Digital Transformation

Most companies have embarked on some sort of digital-transformation journey during the past few years – seeking to re-invent business processes to leverage modern technology at a deeper level. IoT has been critical to enabling digital transformation by providing a cost-effective set of solutions for monitoring end-to-end business and operations processes and controlling physical infrastructure.

Applications for IoT have included such diverse scenarios as monitoring manufacturing quality, optimizing power consumption in company facilities and tracking the flow of customers through retail stores. As more companies move towards digital business processes, IoT will be even more crucial to enabling centralized monitoring and control, and with real-time process optimization.

Leveraging IoT effectively within a business environment requires a scalable and effective means of collecting, connecting and delivering data to each of the deployed IoT devices. The issue with managing connectivity with IoT devices is the sheer volume of individual devices that are deployed as well as variety of data that is collected . Where many more sophisticated IT infrastructure devices (such as wireless access points) can be loaded with standardized configurations and administration profiles, IoT devices are often simple and lacking the robust and typical administrative features of IT environments. There is also very little API standardization across IoT vendors and as a result, a centralized service independent of the IoT devices must typically perform connection management and data aggregation.

The Value of an Integration Platform for IoT Solutions

In the consumer space, a smartphone or digital assistant may be all that is needed to manage integration across a handful of IoT devices and a few users. Businesses with hundreds or thousands of IoT devices and thousands of employees and the need to leverage them require an enterprise-scale set of integration capabilities to be successful. That is the reason organizations need a hybrid data-integration platform like Actian DataConnect, that supports both batch as well as real-time data integration capabilities required by IoT use cases. Real time data integration is crucial for IoT applications.

Driven by business use cases, IoT solutions may require data processing to be brought close to the edges — to the “things” where data is generated. So you need a data integration platform that integrates data at rest as well as data in motion. So you need an integration platform that really spans from the core of the network to the edge. They may also sometimes require aggregation of data in IoT platforms and other data stores, where it can be analyzed in bulk. New IoT data must be reconciled with traditional enterprise master data, for example. Interacting with and reusing data that is constantly in motion and available in various locations throughout highly distributed IoT infrastructures, and working with data within IoT operational flows, will push integration work to the edges.

Integration platforms built for business scale have been optimized for the central administration of large numbers of data endpoints. With many IoT devices, saving a couple of minutes of admin time on each device can add-up quickly. Add new devices quickly, monitor device connectivity status, orchestrate the flow of IoT data to data warehouses and analytics tools and manage who is using your IoT data in a central place. With Actian DataConnect, you don’t need to worry about IoT proliferation bloating your administrative costs. Instead, you can focus on deploying the IoT capabilities your business needs and be confident of your IT staff’s ability to support them.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

Data Intelligence

Data Stewardship and Governance: The Data Steward’s Multiple Facets

Actian Corporation

July 11, 2019

Where Stewardship refers to the taking care of and the supervision of a specific property or organization, Data Stewardship refers to data supervision. Initially, the idea was that a domain expert would be in charge of qualifying and documenting data from their professional standpoint. Data Stewards are those who work closest to where the data is collected; they are often those who best understand the different aspects of data and the standards to which they must adhere.

Data Stewardship and Governance: The Responsibilities

In practice, Data Stewardship covers a wide range of responsibilities, depending on the maturity level of the organization. We can organize these responsibilities in four broad categories:

Operational Supervision and Quality

This refers to monitoring and supervising the complete life cycle of a dataset.

More specifically, Data Stewards must define and implement the necessary processes for the acquisition, storage, and distribution of datasets.

They must also ensure that the data produced fulfills the quality criteria that were defined (values, gaps, completeness, freshness, etc.) and that the procedures are put into place to evaluate and correct potential quality problems.

Documentation

A Data Steward is in charge of defining and documenting data and creating a glossary of industry-specific terms.

They must ensure that each element of a dataset possesses a clear definition and a specific use.

The documentation constitutes a collection of technical and functional metadata according to a meta model in common principle.

Conformity and Risk Management

Data protection and the management of regulatory risks or ethics is one of the most challenging aspects of the Data Steward’s role.

The regulatory environment around data is more restrictive and shifting. It’s up to them to ensure that the proliferation of data is framed by a collection of protocols ensuring conformity with the applicable standards – especially regarding privacy protection.

Security and Access Control

Finally, Data Stewards must define the rules governing data access.

These include the different levels of confidentiality and procedures, allowing the authorization of a person or group to access data.

How Does Data Democracy Strengthen Agile Data Governance?

Orchestrated by a Data Management division, implemented by different types of Data Stewards, data governance must be deployed in an organization. To ensure this deployment, several operational models are conceivable in theory – decentralized, federated, centralized, etc. We think what distinguishes organizations is not the structure of their governance but the underlying culture of this organization. This culture has a name: Data Democracy.