Challenges of IoT Analytics Today

Across every industry, analytics and business intelligence are hot topics that promise greater insight, more efficiency, and a more competitive organization. And analytics can truly deliver on those promises, despite the myriad challenges that many organizations face in getting that value.

One of the most pervasive challenges today is siloed data. Data coming from assets may be used only at the edge, while business applications often have their own separate infrastructure, databases, and sets of information. Getting a complete picture of information across the organization is difficult—often impossible. Without the ability to bring data from disparate systems together in a way that enables fast, smart decision making, business outcomes are poor and costs are high.

Not only are data silos a common problem, but data generation itself can be a challenge, too. Many organizations have not been ready to take advantage of analytics capabilities, and don’t generate enough data to derive insights from. Other organizations may generate large volumes of data in the hope that they can derive some business value from it, but lack the filtering capabilities to make sense of data and put the right information in the hands of people who need it.

Good predictive abilities require good data gathering and filtering processes. By bringing together the right data from across the organization, whether it lives at the edge or in the data center, the results can be better business outcomes. The analytics capabilities of IoT product suite needs to address these challenges and deliver fast time-to-value, so that your outcomes are better, faster.

What are some of other IoT Analytics challenges are you seeing in this space?

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