The internet of things (IoT) is increasingly becoming a key component of many companies\u2019 data-driven transformation strategies. Indeed, organizations that have embraced IoT are already seeing benefits such as improved operational processes, better inventory management, and enhanced equipment maintenance \u2014 to name a few.\nBut a successful IoT strategy is more than just connecting a bunch of devices and sensors to the internet and gathering data from these \u201cthings.\u201d IT must establish the ability to effectively analyze the vast amounts of data IoT creates in order to make sense of it and gain real business insights.\n[ Learn how to develop an internet of things strategy and follow the advice in these 10 internet of things success stories. | Get the latest insights by signing up for our CIO daily newsletter. ]\nThat\u2019s why an analytics strategy for IoT should be a top priority for any company looking to get the most out of all the connectivity.\nOrganizations can enjoy a number of advantages in leveraging the IoT data they gather, says Carlton Sapp, senior director and research and advisory leader at Gartner.\n\nTech Spotlight: The internet of things\n\nHow IoT is becoming the pulse of healthcare (Computerworld)\nHow IoT changes your threat model: 4 key considerations (CSO)\nIoT analytics: Reaping value from IoT data (CIO)\nIoT down on the farm: Drones and sensors for better yields (Network World)\nHow to choose a cloud IoT platform (InfoWorld)\n\n\nThese include contextual awareness of equipment and systems; improved decision-making, optimization and supervisory control of equipment and resources; reduced costs associated with data management; proactive, predictive and prescriptive management of equipment; and environmental compliance.\nThese opportunities are pervasive in use cases such as fleet optimization and management, asset management, financial risk management, and smart cities, Sapp says.\nBut they require a sound, streamlined approach to the data end of IoT. Here are several tips for dealing with IoT data, and getting the most out of these resources.\nBuild an IoT analytics organization and infrastructure\nOnce an organization has an idea of its IoT analytics business goals, it needs to identify the key stakeholders who will be involved, says Stacy Crook, research director for IoT at IDC, and ascertain whether or those stakeholders require additional skills to make the project successful.\n\u201cIt is a well-known fact that data science skills are in short supply in the industry, but these are essential for IoT analytics projects,\u201d Crook says. \u201cSo the project may require hiring new employees, or outsourcing certain parts of the project to third\u00a0parties,\u201d if in-house data science skills are thin.\nOrganizations should also consider appointing a chief data officer (CDO) to champion IoT data analytics efforts and lead the data governance strategy, Crook says.\nBecause IoT is essentially a big data problem, IDC suggests organizations consider how their existing infrastructure could also serve IoT use cases.\u00a0\u201cAlthough older big data architectures might have been focused on batch-oriented workloads, increasingly there are tools available to run real-time workloads over this same backbone,\u201d Crook says.\nLeveraging the same infrastructure for various IoT workloads can have benefits in terms of preventing data siloes and providing the ability to more easily run cross-functional data analysis across those workloads, Crook says.\u00a0\u201cIt can also provide data governance and security benefits,\u201d she says.\nDeploy an architecture that supports IoT data growth\nCompanies need to start with the right IoT data architecture and understand how to manage\u00a0IoT data at various locations.\n\u201cData emanating from IoT endpoints offers new and unique challenges, such as unreliable network access and combining devices that may be distributed over large distances and generate data in multiple formats over multiple protocols,\u201d Sapp says.\nToday, most IoT data is telemetry data, but endpoints are increasingly emitting image and audio data that should be handled by persistent data stores, Sapp says. \u201cStart with an appropriate IoT data architecture that will support the expected growth in the volume of IoT,\u201d he says.\nOrganizations often fail to effectively manage IoT data, due to a lack of a flexible\/elastic data architecture.\u00a0\u201cData will continue to grow, so design an architecture that leverages analytics and data mining techniques that identify critical information that can be used to improve processes, improve decision-making, or reduce costs,\u201d Sapp says.\nFor example, telecommunications companies are successful at reducing the cost of moving data over a network by taking advantage of IoT analytics at the network edge that reduces "noisy data."\u00a0\n\u201cThose organizations focus on scalable edge-centric data architectures that are designed for rapid knowledge discovery in IoT data,\u201d Sapp says.\nDeliver analytics across data pipelines\nThe IoT data architecture should also support analytics across data pipelines (via streaming) and in local data stores to take advantage of faster decision-making and reduced costs, Sapp says.\nOrganizations can do this by\u00a0focusing on data-centric design patterns when creating and deploying IoT analytics, including the use of event-driven architectures.\n\u201cStart by distributing analytics at the edge, on streaming pipelines, on the platform, and in the enterprise,\u201d Sapp says.\u00a0Organizations should take advantage of streaming IoT data pipelines as a source to deploy analytics to improve latency and reduce costs and security vulnerabilities, he says.\nFor example, the U.S. Department of Defense often performs analytics over streaming data pipelines to reduce the throughput of data over a network, Sapp says.\u00a0It also leverages IoT edge analytics to avoid sending any data over a network, using operational analytics closer to the source of data.\nThere will most likely be multiple analytical environments deployed to support disparate analytics, Sapp says. \u201cEnvironments may range from operating systems to embedded analytics software,\u201d he says.\u00a0\u201cBe prepared to deploy IoT analytics across a landscape that spreads from the network edge to the corporate enterprise. For example, utility organizations leverage distributing IoT analytics across various infrastructures to support fleet management.\u201d\nLeverage artificial intelligence\nOrganizations should enhance what they can do with IoT data by taking advantage of AI, Sapp says.\n\u201cEdge intelligence is an emerging field that uses AI as an analytic method deployed at the network edge, to develop intelligent applications from IoT data,\u201d Sapp says.\nThese intelligent applications range from video surveillance to intelligent supervisory control and data acquisition (SCADA) systems.\u00a0For example, environmental organizations use IoT data to build intelligence control systems to maintain environmental compliance.\nAdding AI to the IoT architecture Is becoming an operational imperative, Sapp says. IoT systems, including endpoint devices, must become smarter and more autonomous in order to deal with the ever-increasing magnitude of data. To make these systems smarter, organizations need to deploy AI and machine learning.\nBe a cloud native\nGiven the huge volumes of data generated by IoT applications, for many organizations the cloud will be the only answer for getting a hold on data management, including analytics.\n\u201cIt\u2019s not worth it to build the scale and speed needed to really manage this volume in real time,\u201d says Greg Meyers, group CIO and chief digital officer at Syngenta, a company that produces agrochemicals and seeds.\n\u201cTrying to manage it yourself in your own data center or on your own infrastructure is hugely self-defeating,\u201d Meyers says.\nIoT gives Syngenta the ability to manage its customers\u2019 farms and fields, which are usually arbitrarily aggregated into small micro segments. \u201cHumans are great at managing averages, but computers are better at managing variability,\u201d Meyers says. \u201cIoT lets us understand why things that are happening in one area are different than things that are happening maybe 100 meters away.\u201d\nLeading public cloud vendors are offering services to help companies with IoT analytics. For example, Amazon Web Services (AWS) offers IoT Analytics, a managed service that enables companies to run and operationalize sophisticated analytics on massive volumes of IoT data, without having to worry about the cost and complexity typically required to build an IoT analytics platform.\nMicrosoft offers Azure IoT, which includes a data analytics service called Azure IoT Central to provide analytics capabilities to examine historical trends and correlate various telemetries from connected devices. And Google provides Cloud IoT, a set of tools to connect, process, store, and analyze data both at the network edge and in the cloud.\nPrioritize data governance, security, and privacy\nOrganizations need to ensure they have governance, security, and privacy mechanisms in place for IoT data analytics processes. Much of the data generated by IoT will be sensitive or have competitive value \u2014 and needs to be carefully managed and protected.\n\u201cReassess current data governance practices [to] include machine data,\u201d says Nicholas Colisto, vice president and CIO at Avery Dennison, a manufacturer and distributor of adhesive materials, apparel branding labels, and tags.\n\u201cFrom my experience, IoT governance is an immature area,\u201d Colisto says.\u00a0\u201cIn a previous company, I faced a situation where a business unit deployed an IoT system without seeking IT involvement, and simple operational tasks and tools to audit devices and apply firmware were not considered.\u201dCompanies need to consider IoT data risks based on confidentiality, privacy, and retention requirements, Colisto says. \u201cFor example, if you are working with personal data, consider the issues that can arise from algorithmic bias or inability to comply with regulations such as GDPR [General Data Protection Regulation], which can lead to legal action and damage your company's reputation,\u201d he says.\nLeverage IoT data for new revenue opportunities\nData generated from IoT can be valuable both inside and outside the company.\nChemical manufacturing company Texmark Chemicals launched an effort to modernize operations at its plant by deploying sensor-enabled pumps. Using technology from Hewlett Packard Enterprise and Aruba Networks, the company gathers operational data from pump sensors that measure temperature, pressure, vibrations, flow, and power. This data is analyzed to predict equipment failures before they happen.\nThrough a \u201cworkshopping\u201d process, Texmark realized that having sensor-enabled equipment not only helps the company monitor its assets and processes, but has opened the possibility to new business models, says Doug Smith, CEO.\nThe use of IoT becomes an additional selling factor prior to contract negotiations, Smith says. \u201cClients are beginning to realize the value of having access to data coming off contractor assets,\u201d such as industrial pumps, he says. Clients then ask Texmark to add sensors to their pumps and provide them with the data.\n\u201cIn essence, we are developing a library of historic performance attributes that can be catalogued and shared with other companies using similar equipment,\u201d Smith says.\u00a0\u201cWhen deploying machine learning analytical models, the more data acquired, the greater the accuracy in the analytical prediction.\u201d\nBy sharing IoT data with pump manufacturers or fellow suppliers, \u201cwe could prove the new business model, as long as the documentation is clear and precise,\u201d Smith says.\u00a0\u201cMeanwhile, customers are impressed we have deployed instrumentation and software analytics to capture, analyze, and report on such data \u2014 allowing for more cost-effective decisions.\u201d\nThis new data-as-a-service offering enabled by IoT can distinguish Texmark from competitors, Smith says, and creates a stronger bond with customers while empowering employees to achieve more from their work.