Data Quality Management

Data Quality Management for The Internet of Things (IoT)

Pradeep Bhanot   /     |   No comment

The explosion of data generated by Machine-to-Machine (M2M) systems introduces new sources of automated, sensor-based data and creates opportunities to gain new insights about the world around us. However, there is price to pay. New systems and approaches have to be employed to capture, verify, normalize and assimilate the torrents of data associated with the Internet-of-Things (IoT).

 

In this post, I’ll focus on the segment of IoT data that directly impacts IT Asset Management (ITAM), since the convergence of IoT and ITAM looks inevitable. Below are some examples:

 

  • Using GPS based geo-location to track where an asset is
  • Using geo-fencing to allocate IT capacity in proportion to inbound IT assets on demand
  • Locking down devices when they leave a secured network that is geo-fenced

 

Fortunately, the technology to effectively manage streamed data is commonplace. For example, big data technologies such as  Cassandra  are able to ingest multiple streams of data in parallel. Advanced machine-learning algorithms can filter, normalize and apply data quality to provide accurate data and insights in near real-time.

 

Maintaining security is one of the biggest concerns for organizations as increasing number of devices continuously enter the IT ecosystem. Bring Your Own Device (BOYD) presented a similar challenge that security technology eventually evolved to overcome. Hopefully the lessons learned with integrating and securing BYOD will apply to the expansion of IoT.

 

There are, of course, significant differences. IoT generates so much raw data that it often makes no sense to store it in that form. Data has to be condensed by processing the raw data, and assigning context and meaning to it before storing it as the most useful and accurate data in a system of record.

 

While IT continues to see IoT as something to manage, forward-looking executives are seeing the potential to leverage these new sources of data. Machine data is free of human error, improving the quality of decision making based on it. IT operations can allocate resources with greater precision and agility. By being more responsive to their customers, organizations can more easily improve services without increasing overhead. In a municipal context, imagine government buildings that provision services such as, heating, air-conditioning and lighting proportionally to areas that occupied or about to be occupied.

 

IoT is a core theme of this year’s National Association of State Technology Directors (NASTD) annual conference, where Blazent will be showcasing how they stepped up to the challenge of delivering our data quality service to the growing number of streams of asset data our state government customers provide.  You can learn about Blazent’s Data Quality Management platform that uses a big data processing engine at the core to handle the demands of IoT data here, and join our CEO Charlie Piper as he participates in a panel discussion on Wednesday the 24th titled “Are you ready for how the Internet of Things will impact service Integration and Management?”

 

 

No Comments

Sorry, the comment form is closed at this time.

Request Demo