Machine Data: Deriving Insight From Your Assets
Machine data is a rapidly growing part of the Big Data movement, which has become increasingly coveted by innovators and enterprises alike in recent years. Not least for its potential value in achieving competitive advantages. An enterprise IT environment contains a myriad of different physical and logical entities. Each, both comprised of and itself generating machine data in an array of differing formats. Some of which are natively unintelligible to the human eye.
Machine data is more than just system log files. Its ubiquitous nature includes everything from user transaction records and application behaviours, through to the configuration of an industrial system. Representing a rather powerful opportunity for organisations to derive important operational insights, which can identify anything from sophisticated security breaches and customer behaviours to the health or performance of key systems.
They way in which you leverage machine data will ultimately depend on the nature of your business. The possibilities though are far reaching. Infrastructure and operations management are where the use of machine data really started out. Its ability to be used in securing enterprise platforms has garnered a lot of popularity, particularly in terms of threat defence using statistical analysis for advance pattern detection. Application dependent companies are increasingly leveraging it to both deliver applications more rapidly and control the quality of service for end-users. More sophisticated users of machine data have also incorporated into their business analytics programmes. Leveraging it to gain insights into customer behaviours as well as building rich views on business processes and modelling the potential impact of any changes – before they are put into place. Internet connected devices are also a key application for machine data processing, enabling them to be monitored, controlled and actual usage analysed. Particularly in industrial and manufacturing environments.
There’s no doubt that machine data represents a substantial opportunity for companies to make informed decisions and run their businesses more efficiently. Though actually making use of it presents a real challenge. Machine data is high volume and high frequency, being generated every millisecond of every day by disparate devices in different locations. While ever-present, its heterogeneous composition is further exacerbated by the idiosyncrasies of any multi-vendor environment. Traditional data warehousing solutions simply aren’t sufficiently equipped to cope with such complexity – due much to their requirement for structured data and schemas. This instead necessitates a rather specialist toolset to make your machine data talk coherently. One which is capable of exploring massive amounts of machine data swiftly and intuitively, in its varying forms, with little manual configuration overhead.
While still an evolving ecosystem, visual analytic platforms for machine-generated data processing enables enterprises to derive insight from their assets – both in real time and using historical data. Machine data is ingested and stored in one location, with little configuration required. High velocity indexing and searching capabilities enables billions of events to be perused meaningfully, at speed. True value is derived from that ability to visualise data, in addition to creating custom dashboards and views. Pattern detection makes it easy for users of all skill levels to turn data into actual intelligence. Many of the leading tool sets also enable searches to be turned into real time alerts, which are monitored continually and set to trigger a service desk ticket on occurrence. Application and device specific apps super-charge data views and experience in differing ways, pertinent to the vendor’s technology at hand.
Selecting the right toolset is critical to deriving the most value from your machine assets. Not all platforms are created equally and each has its own strengths and weaknesses. As a big data specialist, we help enterprises select and implement the most appropriate platforms for their machine data analysis needs. Ensuring intelligence is truly achieved and the potential abyss of machine-generated data is negotiated successfully.
