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The most recent articles & research from Devo
There’s a lot of talk about the ability of AI and machine learning to augment digital transformation journeys by creating better customer experiences and empowering employees to make decisions using data. However, IT and business leaders can sometimes face analysis paralysis when confronted with this topic because it means something different for every business – and it means shifting an entire company culture towards a new way of working. One key shift is making use of machine data. Machine data has the potential to increase productivity for employees with the rise in instrumented enterprises to work smarter, not harder.
As data analytics capabilities have grown, so have customer expectations. IT and business leaders increasingly want to make use of AI, ML, and other cognitive technologies to improve their businesses by automating actions, anticipating their needs and behaviors, and creating a fast, reliable experience online and IRL. This expectation also goes beyond business units or other structures in place – the expectation is that this great experience will translate across an entire organization, seamlessly. But how can businesses deliver these experiences without an equally digitally-savvy workforce? They can’t. Company culture must match customer culture.
According to the Intellyx paper, Machine Data: The Secret to Winning the Digital Experience Arms Race, “Unleashing machine data and using it to create business context by connecting the dots between business value and the technology that supports it will also have a broader effect. It will enable organizations to automate routine activities and then augment their internal experts to make them more productive and effective.”
To best enable your people based on their skill sets, first audit the data, processes, and business case studies to examine what already exists, such as data about customer interaction down to sensor data. In most cases, businesses don’t need to create new data sets or hire new employees with different capabilities. They just need to appropriately leverage the machine data and resources they already have to gather the right data at the right time to give employees the information they need to create more superior customer interactions.
However, it’s important to acknowledge the skills gap that exists today. Data scientists and cybersecurity experts, for example, are few and far between, so there should be a concerted effort made to invest in skills training for existing employees. Simultaneously, leaders should invest in tools that are easy to use, don’t require data scientist expertise, and can quickly yield business benefits.
Employees should be empowered to use machine data towards the shared business goal of improving overall customer experiences. At the same time, employers must be wary of falling into the newness trap – they should avoid creating new data sets and capabilities or making compromises on other business needs that don’t add value. Instead, they should focus on delivering easy to use platforms to employees to maximize their productivity.
According to Intellyx, “In most cases, it will not be a lack of either data or human capital that inhibits organizations from marching forward into their data-driven future. Instead, it will be their inability to appropriately leverage the machine data and resources they already have that will be their most significant barrier. Recognizing this gap, leading organizations are turning to technology companies, such as Devo, that specifically help them unleash the power of machine data and turn it into a strategic asset through business context. As we fully enter the digital era, the stakes in the digital experience arms race will continue to rise, while the challenges will only become greater. It will be those organizations that can best leverage machines — and machine data — to link the creation of business value to the technologies that support it that will win.”
Read the white paper to learn more about machine data from Intellyx.
By Chris O'Brien
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