Articles, DataOps and AI Ops

Putting Dollar Signs on your Data – How to Use Machine Data More Effectively

This text was guest-authored by Colin Fernandes, EMEA Product Marketing Director, at Sumo Logic.

Getting more out of your data seems like a simple thing to achieve – after all, you can gather data easily, you just want to apply it more effectively. Is that so hard? Achieving this in reality takes effort and perseverance. Doing this repeatedly is even harder, yet it’s here that the results can be extremely valuable – not just for developers or IT teams, but for the whole business.

Making data more useful for the businesses

The majority of companies today already create and store data from their IT systems – under the badge of “machine data” is included data from applications, IT infrastructure and cloud services, through to IoT device information and sensor data. Each of these streams of data has to be parsed and analyzed in order to deliver some value back to the business. This process – taking raw data, like logs, metrics and infrastructure reports and turning them into information that teams across the business can use – involves an awful lot of automation if you want to run at scale. However, this is essential if you want to be able to turn this data into continuous intelligence that you can use to make informed decisions.

For developers and IT operations teams, machine data tools can help ensure optimal application performance and identify security risks. However, machine data can and should be used outside the realm of IT. Typically, getting this in place has been difficult in the past – raw data has been difficult to parse, and reporting on IT performance has added limited value back to the wider business.

Today, the situation has changed. As more business processes rely on software, machine data has become a key indicator for how well the IT function is performing. In turn, this machine data analysis can become an indicator for how the business is performing against some of its objectives over time. Alongside this, business teams are starting to recognize the broader value of machine data. For example, roles like product managers, customer support and line of business executives can all machine data results within the analytics tools they work with every day.

Areas like customer experience are now dependent on online performance – according to research conducted by 451 Research earlier this year, one of the top three business goals for investment was around how machine data could be used to fix issues before they impact customer experience. Similarly, making product development more efficient based on data was in the top five most important business goals for investment.

Expanding data use across the business

So, we can see that this data is potentially useful for more than just IT or developers. But how can we make people aware that this data exists, and how can we package it for them to use?

There are three steps to this:

  • First is getting the right infrastructure for storing and analyzing data in place – due to the sheer volume of data involved, this will most likely be based on public cloud services;
  • Second is unifying the analytics process so that all those data sources can be kept in one place – this makes it easier to get that holistic approach in place;
  • Third is making it easier to get the right data for the right people – this involves providing data in a contextual and intuitive way, so that people from non-technical backgrounds can consume that content and access the insights that are available.

Bringing data together, analyzing it and laying it out in context should democratize machine data and make it available for more people to use. This approach should help all companies make more use of the data they create, turning it into more valuable insights that can be used to benefit everyone across the business.