Resolving Data Integration Challenges in Internet of things

Resolving Data Integration Challenges in Internet of things
4 mn read

As the dimensions of the Internet of Things (IoT) grow, the number and types of applications increase. A new reality that translates into large amounts of data of all kinds that we have to learn to manage and analyze to make the most of the opportunities offered by the IoT connection. Among others, we can analyze patterns to maximize performance, prevent problems, make predictions, and, in short, make better decisions at an operational and managerial level.

The cases of use of the IoT are almost endless but, apart from the casuistry, they are all based on data generated by applications and both can be of very different types. Therefore, it is necessary to take into account the integration of each other in order to exploit the data in order to obtain competitive advantages.

Achieving the integration of data with other systems, in effect, is essential to analyze the information offered by the devices. Therefore, in order to successfully implement integration systems in the dispar IoT, despite the fact that it is an ideal technology for this scenario, it must necessarily evolve.

Analysis and data integration, a double challenge

The integration of IoT technologies, including devices, data, connections, and processes between them and globally with the IT environment poses important challenges. Only through an adaptation that respects the new requirements for data integration technology will it be possible to feed business processes based on sensor data. According to David Linthicum, senior vice president of Cloud Technology Partners and a cloud computing expert, being prepared to face it means addressing three aspects:

  • The exponential increase in data volume and transmission speed, almost in real-time. In this regard, Linthicum warns that the old approaches to integrate data based on the message may not work well.
  • The quality of the data and its governability are fundamental aspects for the IoT information to be valuable. Data quality controls are mandatory. IoT has changed the way we collect and treat data in real-time. For example, the quality of the data must be checked in the device, both at rest and in flight, and also when entering a data warehouse, where it can be analyzed immediately.
  • The integration should make it possible to combine data in real-time, for example, the ability to assign the ranking from a specific database, using data collected in real-time, usually in IT cloud or hybrid environments.

On the one hand, it is undeniable that IoT is synonymous with an improvement that represents a change in the revolutionary technological paradigm that opens up enormous opportunities for organizations. However, on the other, achieving the desired efficiency when managing company information requires including the IoT devices in our data integration strategy. Only then can we include them in the main business processes efficiently.

Besides being able to carry out an integration with other systems such as CRM or, for example, ERP planning systems, thanks to an integrative approach, taking full advantage of the data generated by IoT machines means managing heterogeneous data from different devices to obtain data. critics. Thanks to them, it will be possible to make better decisions, solve problems proactively, and, among other benefits, have a better knowledge of the machines.

In the same way that integration makes it easier for us to address IoT projects, without an ad hoc data integration strategy and technology it will be unfeasible to perform analyzes that really represent a great opportunity for innovation and growth.

Solution

Data integration is among the three main strategic technologies used by companies. But the concern is that companies are not getting the most for their investment in data integration technology. Maybe it’s time to look at both the best basic practices that have existed for years, as well as other new best practices that many companies do not know about, and also review the errors that should be avoided regarding data integration. Let’s see 3 best practices and 3 data integration errors.

Companies must start searching for the right talent before starting their journey to a well-integrated company. Those who try to find the right people at the last moment will realize that this approach does not work.

Although we have already commented on the first best practice, we return to indicate it as an error due to its importance. Although it seems obvious, most of the most important data integration errors go back to the failures around the understanding of the existing data in the systems of origin and destination. There could be data stored in data objects, relational databases, and even in proprietary data stores. The data must be defined in terms of physical storage, as well as structure or lack of structure, if applicable. From there, it is necessary to determine which approach is best for data integration, including the transformation and translation of live data, as well as whether the structure should be applied before the data is used by the data integration engine.

Another common error is to assume that the data integration technology has no latency. That is never the case. If you consume a large amount of data from many source systems, the processes with this data will determine the performance of the data integration solution. If the processing is intensive or complex input, things will be slow. If there is little processing, then things will accelerate. The only way to deal with performance is to understand the data integration technology as well as the use cases that we plan to integrate. Not understanding those parts means that the performance will be difficult to predict and could end up failing, just because the solution is too slow during production. This is a difficult problem to solve after it occurs.

Governance, especially data management, is also important. In the same way that we need to understand the data, we must also make sure that we control how that data changes over time, as well as restrict who can change and access the data through policies. In the same way, and although we have also commented on the best practice number 2, security must be systematic to the solution of data integration. We also have to deal with compliance issues. There are often many laws that determine how data should be handled.

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